Merge pull request #186 from CartoDB/develop

Release for 0.6.0

.travis.yml

@@ -1,4 +1,6 @@
 language: c
+dist: precise
+sudo: required
 
 env:
   global:
@@ -42,9 +44,9 @@ before_install:
   - sudo apt-get -y remove --purge postgis-2.2
   - sudo apt-get -y autoremove
 
-  - sudo apt-get -y install postgresql-9.5=9.5.2-3cdb2
-  - sudo apt-get -y install postgresql-server-dev-9.5=9.5.2-3cdb2
-  - sudo apt-get -y install postgresql-plpython-9.5=9.5.2-3cdb2
+  - sudo apt-get -y install postgresql-9.5=9.5.2-3cdb3
+  - sudo apt-get -y install postgresql-server-dev-9.5=9.5.2-3cdb3
+  - sudo apt-get -y install postgresql-plpython-9.5=9.5.2-3cdb3
   - sudo apt-get -y install postgresql-9.5-postgis-scripts=2.2.2.0-cdb2
   - sudo apt-get -y install postgresql-9.5-postgis-2.2=2.2.2.0-cdb2
 

Makefile.global

@@ -4,3 +4,5 @@ PACKAGE           = crankshaft
 EXTVERSION        = $(shell grep default_version $(SELF_DIR)/src/pg/$(EXTENSION).control | sed -e "s/default_version[[:space:]]*=[[:space:]]*'\([^']*\)'/\1/")
 RELEASE_VERSION  ?= $(EXTVERSION)
 SED               = sed
+PIP               = pip
+NOSETESTS         = nosetests

NEWS.md

@@ -1,3 +1,7 @@
+0.6.0 (2017-11-08)
+------------------
+* Adds new functions: `CDB_GWR` and `CDB_GWR_Predict`
+
 0.5.2 (2017-05-12)
 ------------------
 * Fixes missing comma for dict creation #172

doc/21_gwr.md

@@ -0,0 +1,128 @@
+## Regression
+
+### Predictive geographically weighted regression (GWR)
+
+Predictive GWR generates estimates of the dependent variable at locations where it has not been observed. It predicts these unknown values by first using the GWR model estimation analysis with known data values of the dependent and independent variables sampled from around the prediction location(s) to build a geographically weighted, spatially-varying regression model. It then uses this model and known values of the independent variables at the prediction locations to predict the value of the dependent variable where it is otherwise unknown.
+
+For predictive GWR to work, a dataset needs known independent variables, some known dependent variables, and some unknown dependent variables. The dataset also needs to have geometry data (e.g., point, lines, or polygons).
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| subquery | TEXT | SQL query that expose the data to be analyzed (e.g., `SELECT * FROM regression_inputs`). This query must have the geometry column name (see the optional `geom_col` for default), the id column name (see `id_col`), and the dependent (`dep_var`) and independent (`ind_vars`) column names. |
+| dep_var | TEXT | Name of the dependent variable in the regression model |
+| ind_vars | TEXT[] | Text array of independent variable column names used in the model to describe the dependent variable. |
+| bw (optional) | NUMERIC | Value of bandwidth. If `NULL` then select optimal (default). |
+| fixed (optional) | BOOLEAN | True for distance based kernel function and False (default) for adaptive (nearest neighbor) kernel function. Defaults to `False`. |
+| kernel (optional)| TEXT | Type of kernel function used to weight observations. One of `gaussian`, `bisquare` (default), or `exponential`. |
+
+
+#### Returns
+
+| Column Name | Type | Description |
+|-------------|------|-------------|
+| coeffs | JSON | JSON object with parameter estimates for each of the dependent variables. The keys of the JSON object are the dependent variables, with values corresponding to the parameter estimate. |
+| stand_errs | JSON | Standard errors for each of the dependent variables. The keys of the JSON object are the dependent variables, with values corresponding to the respective standard errors. |
+| t_vals | JSON | T-values for each of the dependent variables. The keys of the JSON object are the dependent variable names, with values corresponding to the respective t-value. |
+| predicted | NUMERIC | predicted value of y |
+| residuals | NUMERIC | residuals of the response |
+| r_squared | NUMERIC | R-squared for the parameter fit |
+| bandwidth | NUMERIC | bandwidth value consisting of either a distance or N nearest neighbors |
+| rowid | INTEGER | row id of the original row |
+
+
+#### Example Usage
+
+```sql
+SELECT
+  g.cartodb_id,
+  g.the_geom,
+  g.the_geom_webmercator,
+  (gwr.coeffs->>'pctblack')::numeric as coeff_pctblack,
+  (gwr.coeffs->>'pctrural')::numeric as coeff_pctrural,
+  (gwr.coeffs->>'pcteld')::numeric as coeff_pcteld,
+  (gwr.coeffs->>'pctpov')::numeric as coeff_pctpov,
+  gwr.residuals
+FROM cdb_crankshaft.CDB_GWR_Predict('select * from g_utm'::text,   
+  'pctbach'::text,
+  Array['pctblack', 'pctrural', 'pcteld', 'pctpov']) As gwr
+JOIN g_utm as g
+on g.cartodb_id = gwr.rowid
+```
+
+Note: See [PostgreSQL syntax for parsing JSON objects](https://www.postgresql.org/docs/9.5/static/functions-json.html).
+
+### Geographically weighted regression model estimation
+
+This analysis generates the model coefficients for a geographically weighted, spatially-varying regression. The model coefficients, along with their respective statistics, allow one to make inferences or describe a dependent variable based on a set of independent variables. Similar to traditional linear regression, GWR takes a linear combination of independent variables and a known dependent variable to estimate an optimal set of coefficients. The model coefficients are spatially varying (controlled by the `bandwidth` and `fixed` parameters), so that the model output is allowed to vary from geometry to geometry. This allows GWR to capture non-stationarity -- that is, how local processes vary over space. In contrast, coefficients obtained from estimating a traditional linear regression model assume that processes are constant over space.
+
+#### Arguments
+
+| Name | Type | Description |
+|------|------|-------------|
+| subquery | TEXT | SQL query that expose the data to be analyzed (e.g., `SELECT * FROM regression_inputs`). This query must have the geometry column name (see the optional `geom_col` for default), the id column name (see `id_col`), dependent and independent column names. |
+| dep_var | TEXT | name of the dependent variable in the regression model |
+| ind_vars | TEXT[] | Text array of independent variables used in the model to describe the dependent variable |
+| bw (optional) | NUMERIC | Value of bandwidth. If `NULL` then select optimal (default). |
+| fixed (optional) | BOOLEAN | True for distance based kernel function and False for adaptive (nearest neighbor) kernel function (default). Defaults to false. |
+| kernel | TEXT | Type of kernel function used to weight observations. One of `gaussian`, `bisquare` (default), or `exponential`. |
+
+
+#### Returns
+
+| Column Name | Type | Description |
+|-------------|------|-------------|
+| coeffs | JSON | JSON object with parameter estimates for each of the dependent variables. The keys of the JSON object are the dependent variables, with values corresponding to the parameter estimate. |
+| stand_errs | JSON | Standard errors for each of the dependent variables. The keys of the JSON object are the dependent variables, with values corresponding to the respective standard errors. |
+| t_vals | JSON | T-values for each of the dependent variables. The keys of the JSON object are the dependent variable names, with values corresponding to the respective t-value. |
+| predicted | NUMERIC | predicted value of y |
+| residuals | NUMERIC | residuals of the response |
+| r_squared | NUMERIC | R-squared for the parameter fit |
+| bandwidth | NUMERIC | bandwidth value consisting of either a distance or N nearest neighbors |
+| rowid | INTEGER | row id of the original row |
+
+
+#### Example Usage
+
+```sql
+SELECT
+  g.cartodb_id,
+  g.the_geom,
+  g.the_geom_webmercator,
+  (gwr.coeffs->>'pctblack')::numeric as coeff_pctblack,
+  (gwr.coeffs->>'pctrural')::numeric as coeff_pctrural,
+  (gwr.coeffs->>'pcteld')::numeric as coeff_pcteld,
+  (gwr.coeffs->>'pctpov')::numeric as coeff_pctpov,
+  gwr.residuals
+FROM cdb_crankshaft.CDB_GWR('select * from g_utm'::text, 'pctbach'::text, Array['pctblack', 'pctrural', 'pcteld', 'pctpov']) As gwr
+JOIN g_utm as g
+on g.cartodb_id = gwr.rowid
+```
+
+Note: See [PostgreSQL syntax for parsing JSON objects](https://www.postgresql.org/docs/9.5/static/functions-json.html).
+
+
+## Advanced reading
+
+*   Fotheringham, A. Stewart, Chris Brunsdon, and Martin Charlton. 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. John Wiley & Sons. <http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471496162.html>
+
+*   Brunsdon, Chris, A. Stewart Fotheringham, and Martin E. Charlton. 1996. "Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity." Geographical Analysis 28 (4): 281–98. <http://onlinelibrary.wiley.com/doi/10.1111/j.1538-4632.1996.tb00936.x/abstract>
+
+*   Brunsdon, Chris, Stewart Fotheringham, and Martin Charlton. 1998. "Geographically Weighted Regression." Journal of the Royal Statistical Society: Series D (The Statistician) 47 (3): 431–43. <http://onlinelibrary.wiley.com/doi/10.1111/1467-9884.00145/abstract>
+
+*   Fotheringham, A. S., M. E. Charlton, and C. Brunsdon. 1998. "Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis." Environment and Planning A 30 (11): 1905–27. doi:10.1068/a301905. <https://www.researchgate.net/publication/23538637_Geographically_Weighted_Regression_A_Natural_Evolution_Of_The_Expansion_Method_for_Spatial_Data_Analysis>
+
+### GWR for prediction
+
+*   Harris, P., A. S. Fotheringham, R. Crespo, and M. Charlton. 2010. "The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets." Mathematical Geosciences 42 (6): 657–80. doi:10.1007/s11004-010-9284-7. <https://www.researchgate.net/publication/225757830_The_Use_of_Geographically_Weighted_Regression_for_Spatial_Prediction_An_Evaluation_of_Models_Using_Simulated_Data_Sets>
+
+### GWR in application
+
+*   Cahill, Meagan, and Gordon Mulligan. 2007. "Using Geographically Weighted Regression to Explore Local Crime Patterns." Social Science Computer Review 25 (2): 174–93. doi:10.1177/0894439307298925. <http://isites.harvard.edu/fs/docs/icb.topic923297.files/174.pdf>
+
+*   Gilbert, Angela, and Jayajit Chakraborty. 2011. "Using Geographically Weighted Regression for Environmental Justice Analysis: Cumulative Cancer Risks from Air Toxics in Florida." Social Science Research 40 (1): 273–86. doi:10.1016/j.ssresearch.2010.08.006. <http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=2985&context=etd>
+
+*   Ali, Kamar, Mark D. Partridge, and M. Rose Olfert. 2007. "Can Geographically Weighted Regressions Improve Regional Analysis and Policy Making?" International Regional Science Review 30 (3): 300–329. doi:10.1177/0160017607301609. <https://www.researchgate.net/publication/249682503_Can_Geographically_Weighted_Regressions_Improve_Regional_Analysis_and_Policy_Making>
+
+*   Lu, Binbin, Martin Charlton, and A. Stewart Fotheringhama. 2011. "Geographically Weighted Regression Using a Non-Euclidean Distance Metric with a Study on London House Price Data." Procedia Environmental Sciences, Spatial Statistics 2011: Mapping Global Change, 7: 92–97. doi:10.1016/j.proenv.2011.07.017. <https://www.researchgate.net/publication/261960122_Geographically_weighted_regression_with_a_non-Euclidean_distance_metric_A_case_study_using_hedonic_house_price_data>

release/crankshaft.control

@@ -1,5 +1,5 @@
 comment = 'CartoDB Spatial Analysis extension'
-default_version = '0.5.2'
+default_version = '0.6.0'
 requires = 'plpythonu, postgis'
 superuser = true
 schema = cdb_crankshaft

release/python/0.6.0/crankshaft/crankshaft/__init__.py

@@ -0,0 +1,7 @@
+"""Import all modules"""
+import crankshaft.random_seeds
+import crankshaft.clustering
+import crankshaft.space_time_dynamics
+import crankshaft.segmentation
+import crankshaft.regression
+import analysis_data_provider

release/python/0.6.0/crankshaft/crankshaft/analysis_data_provider.py

@@ -0,0 +1,85 @@
+"""class for fetching data"""
+import plpy
+import pysal_utils as pu
+
+
+class AnalysisDataProvider:
+    def get_getis(self, w_type, params):
+        """fetch data for getis ord's g"""
+        try:
+            query = pu.construct_neighbor_query(w_type, params)
+            result = plpy.execute(query)
+            # if there are no neighbors, exit
+            if len(result) == 0:
+                return pu.empty_zipped_array(4)
+            else:
+                return result
+        except plpy.SPIError, err:
+            plpy.error('Analysis failed: %s' % err)
+
+    def get_markov(self, w_type, params):
+        """fetch data for spatial markov"""
+        try:
+            query = pu.construct_neighbor_query(w_type, params)
+            data = plpy.execute(query)
+
+            if len(data) == 0:
+                return pu.empty_zipped_array(4)
+
+            return data
+        except plpy.SPIError, err:
+            plpy.error('Analysis failed: %s' % err)
+
+    def get_moran(self, w_type, params):
+        """fetch data for moran's i analyses"""
+        try:
+            query = pu.construct_neighbor_query(w_type, params)
+            data = plpy.execute(query)
+
+            # if there are no neighbors, exit
+            if len(data) == 0:
+                return pu.empty_zipped_array(2)
+            return data
+        except plpy.SPIError, err:
+            plpy.error('Analysis failed: %s' % e)
+            return pu.empty_zipped_array(2)
+
+    def get_nonspatial_kmeans(self, query):
+        """fetch data for non-spatial kmeans"""
+        try:
+            data = plpy.execute(query)
+            return data
+        except plpy.SPIError, err:
+            plpy.error('Analysis failed: %s' % err)
+
+    def get_spatial_kmeans(self, params):
+        """fetch data for spatial kmeans"""
+        query = ("SELECT "
+                 "array_agg({id_col} ORDER BY {id_col}) as ids,"
+                 "array_agg(ST_X({geom_col}) ORDER BY {id_col}) As xs,"
+                 "array_agg(ST_Y({geom_col}) ORDER BY {id_col}) As ys "
+                 "FROM ({subquery}) As a "
+                 "WHERE {geom_col} IS NOT NULL").format(**params)
+        try:
+            data = plpy.execute(query)
+            return data
+        except plpy.SPIError, err:
+            plpy.error('Analysis failed: %s' % err)
+
+    def get_gwr(self, params):
+        """fetch data for gwr analysis"""
+        query = pu.gwr_query(params)
+        try:
+            query_result = plpy.execute(query)
+            return query_result
+        except plpy.SPIError, err:
+            plpy.error('Analysis failed: %s' % err)
+
+    def get_gwr_predict(self, params):
+        """fetch data for gwr predict"""
+        query = pu.gwr_predict_query(params)
+        try:
+            query_result = plpy.execute(query)
+            return query_result
+        except plpy.SPIError, err:
+            plpy.error('Analysis failed: %s' % err)

release/python/0.6.0/crankshaft/crankshaft/centers/median_center.py

@@ -0,0 +1,76 @@
+"""
+    Based on the Weiszfeld algorithm:
+      https://en.wikipedia.org/wiki/Geometric_median
+"""
+
+
+# import plpy
+import numpy as np
+from numpy.linalg import norm
+
+
+def median_center(tablename, geom_col, num_iters=50, tolerance=0.001):
+
+    query = '''
+        SELECT array_agg(ST_X({geom_col})) As x_coords,
+               array_agg(ST_Y({geom_col})) As y_coords
+          FROM {tablename}
+    '''.format(geom_col=geom_col, tablename=tablename)
+
+    try:
+        resp = plpy.execute(query)
+        data = np.vstack((resp['x_coords'][0],
+                          resp['y_coords'][0])).T
+
+        plpy.notice('coords: %s' % str(coords))
+    except Exception, err:
+        # plpy.error('Analysis failed: %s' % err)
+        print('No plpy')
+        data = np.array([[1.2 * np.random.random() + 10.,
+                          1.1 * (np.random.random() - 1.) + 3.]
+                        for i in range(1, 100)])
+
+    # initialize 'median center' to be the mean
+    coords_center_temp = data.mean(axis=0)
+
+    # plpy.notice('temp_center: %s' % str(coords_center_temp))
+    print('temp_center: %s' % str(coords_center_temp))
+
+    for i in range(0, num_iters):
+        old_coords_center = coords_center_temp.copy()
+        denom = denominator(coords_center_temp, data)
+        coords_center_temp = np.sum([data[j] * numerator(coords_center_temp,
+                                                         data[j])
+                                     for j in range(len(data))], axis=0)
+        coords_center_temp = coords_center_temp / denom
+
+        print("Pass #%d" % i)
+        print("max, min of data: %0.4f, %0.4f" % (data.max(), data.min()))
+        print('temp_center: %s' % str(coords_center_temp))
+        print("Change in center: %0.4f" % np.linalg.norm(old_coords_center -
+                                                         coords_center_temp))
+        print("Center coords: %s" % str(coords_center_temp))
+        print("Objective Function: %0.4f" % obj_func(coords_center_temp, data))
+
+    return coords_center_temp
+
+
+def obj_func(center_coords, data):
+    """
+
+    """
+    return np.linalg.norm(center_coords - data)
+
+
+def numerator(center_coords, data_i):
+    """
+
+    """
+    return np.reciprocal(np.linalg.norm(center_coords - data_i))
+
+
+def denominator(center_coords, data):
+    """
+
+    """
+    return np.reciprocal(np.linalg.norm(data - center_coords))

release/python/0.6.0/crankshaft/crankshaft/clustering/__init__.py

@@ -0,0 +1,4 @@
+"""Import all functions from for clustering"""
+from moran import *
+from kmeans import *
+from getis import *

release/python/0.6.0/crankshaft/crankshaft/clustering/getis.py

@@ -0,0 +1,50 @@
+"""
+Getis-Ord's G geostatistics (hotspot/coldspot analysis)
+"""
+
+import pysal as ps
+from collections import OrderedDict
+
+# crankshaft modules
+import crankshaft.pysal_utils as pu
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+
+# High level interface ---------------------------------------
+
+
+class Getis:
+    def __init__(self, data_provider=None):
+        if data_provider is None:
+            self.data_provider = AnalysisDataProvider()
+        else:
+            self.data_provider = data_provider
+
+    def getis_ord(self, subquery, attr,
+                  w_type, num_ngbrs, permutations, geom_col, id_col):
+        """
+        Getis-Ord's G*
+        Implementation building neighbors with a PostGIS database and PySAL's
+          Getis-Ord's G* hotspot/coldspot module.
+        Andy Eschbacher
+        """
+
+        # geometries with attributes that are null are ignored
+        # resulting in a collection of not as near neighbors if kNN is chosen
+
+        qvals = OrderedDict([("id_col", id_col),
+                             ("attr1", attr),
+                             ("geom_col", geom_col),
+                             ("subquery", subquery),
+                             ("num_ngbrs", num_ngbrs)])
+
+        result = self.data_provider.get_getis(w_type, qvals)
+        attr_vals = pu.get_attributes(result)
+
+        # build PySAL weight object
+        weight = pu.get_weight(result, w_type, num_ngbrs)
+
+        # calculate Getis-Ord's G* z- and p-values
+        getis = ps.esda.getisord.G_Local(attr_vals, weight,
+                                         star=True, permutations=permutations)
+
+        return zip(getis.z_sim, getis.p_sim, getis.p_z_sim, weight.id_order)

release/python/0.6.0/crankshaft/crankshaft/clustering/kmeans.py

@@ -0,0 +1,32 @@
+from sklearn.cluster import KMeans
+import numpy as np
+
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+
+
+class Kmeans:
+    def __init__(self, data_provider=None):
+        if data_provider is None:
+            self.data_provider = AnalysisDataProvider()
+        else:
+            self.data_provider = data_provider
+
+    def spatial(self, query, no_clusters, no_init=20):
+        """
+            find centers based on clusters of latitude/longitude pairs
+            query: SQL query that has a WGS84 geometry (the_geom)
+        """
+        params = {"subquery": query,
+                  "geom_col": "the_geom",
+                  "id_col": "cartodb_id"}
+
+        data = self.data_provider.get_spatial_kmeans(params)
+
+        # Unpack query response
+        xs = data[0]['xs']
+        ys = data[0]['ys']
+        ids = data[0]['ids']
+
+        km = KMeans(n_clusters=no_clusters, n_init=no_init)
+        labels = km.fit_predict(zip(xs, ys))
+        return zip(ids, labels)

release/python/0.6.0/crankshaft/crankshaft/clustering/moran.py

@@ -0,0 +1,208 @@
+"""
+Moran's I geostatistics (global clustering & outliers presence)
+"""
+
+# TODO: Fill in local neighbors which have null/NoneType values with the
+#       average of the their neighborhood
+
+import pysal as ps
+from collections import OrderedDict
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+
+# crankshaft module
+import crankshaft.pysal_utils as pu
+
+# High level interface ---------------------------------------
+
+
+class Moran:
+    def __init__(self, data_provider=None):
+        if data_provider is None:
+            self.data_provider = AnalysisDataProvider()
+        else:
+            self.data_provider = data_provider
+
+    def global_stat(self, subquery, attr_name,
+                    w_type, num_ngbrs, permutations, geom_col, id_col):
+        """
+        Moran's I (global)
+        Implementation building neighbors with a PostGIS database and Moran's I
+         core clusters with PySAL.
+        Andy Eschbacher
+        """
+        params = OrderedDict([("id_col", id_col),
+                              ("attr1", attr_name),
+                              ("geom_col", geom_col),
+                              ("subquery", subquery),
+                              ("num_ngbrs", num_ngbrs)])
+
+        result = self.data_provider.get_moran(w_type, params)
+
+        # collect attributes
+        attr_vals = pu.get_attributes(result)
+
+        # calculate weights
+        weight = pu.get_weight(result, w_type, num_ngbrs)
+
+        # calculate moran global
+        moran_global = ps.esda.moran.Moran(attr_vals, weight,
+                                           permutations=permutations)
+
+        return zip([moran_global.I], [moran_global.EI])
+
+    def local_stat(self, subquery, attr,
+                   w_type, num_ngbrs, permutations, geom_col, id_col):
+        """
+        Moran's I implementation for PL/Python
+        Andy Eschbacher
+        """
+
+        # geometries with attributes that are null are ignored
+        # resulting in a collection of not as near neighbors
+
+        params = OrderedDict([("id_col", id_col),
+                              ("attr1", attr),
+                              ("geom_col", geom_col),
+                              ("subquery", subquery),
+                              ("num_ngbrs", num_ngbrs)])
+
+        result = self.data_provider.get_moran(w_type, params)
+
+        attr_vals = pu.get_attributes(result)
+        weight = pu.get_weight(result, w_type, num_ngbrs)
+
+        # calculate LISA values
+        lisa = ps.esda.moran.Moran_Local(attr_vals, weight,
+                                         permutations=permutations)
+
+        # find quadrants for each geometry
+        quads = quad_position(lisa.q)
+
+        return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
+
+    def global_rate_stat(self, subquery, numerator, denominator,
+                         w_type, num_ngbrs, permutations, geom_col, id_col):
+        """
+        Moran's I Rate (global)
+        Andy Eschbacher
+        """
+        params = OrderedDict([("id_col", id_col),
+                              ("attr1", numerator),
+                              ("attr2", denominator),
+                              ("geom_col", geom_col),
+                              ("subquery", subquery),
+                              ("num_ngbrs", num_ngbrs)])
+
+        result = self.data_provider.get_moran(w_type, params)
+
+        # collect attributes
+        numer = pu.get_attributes(result, 1)
+        denom = pu.get_attributes(result, 2)
+
+        weight = pu.get_weight(result, w_type, num_ngbrs)
+
+        # calculate moran global rate
+        lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
+                                             permutations=permutations)
+
+        return zip([lisa_rate.I], [lisa_rate.EI])
+
+    def local_rate_stat(self, subquery, numerator, denominator,
+                        w_type, num_ngbrs, permutations, geom_col, id_col):
+        """
+            Moran's I Local Rate
+            Andy Eschbacher
+        """
+        # geometries with values that are null are ignored
+        # resulting in a collection of not as near neighbors
+
+        params = OrderedDict([("id_col", id_col),
+                              ("numerator", numerator),
+                              ("denominator", denominator),
+                              ("geom_col", geom_col),
+                              ("subquery", subquery),
+                              ("num_ngbrs", num_ngbrs)])
+
+        result = self.data_provider.get_moran(w_type, params)
+
+        # collect attributes
+        numer = pu.get_attributes(result, 1)
+        denom = pu.get_attributes(result, 2)
+
+        weight = pu.get_weight(result, w_type, num_ngbrs)
+
+        # calculate LISA values
+        lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
+                                              permutations=permutations)
+
+        # find quadrants for each geometry
+        quads = quad_position(lisa.q)
+
+        return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
+
+    def local_bivariate_stat(self, subquery, attr1, attr2,
+                             permutations, geom_col, id_col,
+                             w_type, num_ngbrs):
+        """
+            Moran's I (local) Bivariate (untested)
+        """
+
+        params = OrderedDict([("id_col", id_col),
+                              ("attr1", attr1),
+                              ("attr2", attr2),
+                              ("geom_col", geom_col),
+                              ("subquery", subquery),
+                              ("num_ngbrs", num_ngbrs)])
+
+        result = self.data_provider.get_moran(w_type, params)
+
+        # collect attributes
+        attr1_vals = pu.get_attributes(result, 1)
+        attr2_vals = pu.get_attributes(result, 2)
+
+        # create weights
+        weight = pu.get_weight(result, w_type, num_ngbrs)
+
+        # calculate LISA values
+        lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
+                                            permutations=permutations)
+
+        # find clustering of significance
+        lisa_sig = quad_position(lisa.q)
+
+        return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
+
+# Low level functions ----------------------------------------
+
+
+def map_quads(coord):
+    """
+        Map a quadrant number to Moran's I designation
+        HH=1, LH=2, LL=3, HL=4
+        Input:
+        @param coord (int): quadrant of a specific measurement
+        Output:
+            classification (one of 'HH', 'LH', 'LL', or 'HL')
+    """
+    if coord == 1:
+        return 'HH'
+    elif coord == 2:
+        return 'LH'
+    elif coord == 3:
+        return 'LL'
+    elif coord == 4:
+        return 'HL'
+    else:
+        return None
+
+
+def quad_position(quads):
+    """
+        Produce Moran's I classification based of n
+        Input:
+        @param quads ndarray: an array of quads classified by
+          1-4 (PySAL default)
+        Output:
+        @param list: an array of quads classied by 'HH', 'LL', etc.
+    """
+    return [map_quads(q) for q in quads]

release/python/0.6.0/crankshaft/crankshaft/pysal_utils/__init__.py

@@ -0,0 +1,2 @@
+"""Import all functions for pysal_utils"""
+from crankshaft.pysal_utils.pysal_utils import *

release/python/0.6.0/crankshaft/crankshaft/pysal_utils/pysal_utils.py

@@ -0,0 +1,270 @@
+"""
+    Utilities module for generic PySAL functionality, mainly centered on
+      translating queries into numpy arrays or PySAL weights objects
+"""
+
+import numpy as np
+import pysal as ps
+
+
+def construct_neighbor_query(w_type, query_vals):
+    """Return query (a string) used for finding neighbors
+        @param w_type text: type of neighbors to calculate ('knn' or 'queen')
+        @param query_vals dict: values used to construct the query
+    """
+
+    if w_type.lower() == 'knn':
+        return knn(query_vals)
+    else:
+        return queen(query_vals)
+
+
+# Build weight object
+def get_weight(query_res, w_type='knn', num_ngbrs=5):
+    """
+        Construct PySAL weight from return value of query
+        @param query_res dict-like: query results with attributes and neighbors
+    """
+    # if w_type.lower() == 'knn':
+    #     row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
+    #     weights = {x['id']: row_normed_weights for x in query_res}
+    # else:
+    #     weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors'])
+    #                         if len(x['neighbors']) > 0
+    #                         else [] for x in query_res}
+
+    neighbors = {x['id']: x['neighbors'] for x in query_res}
+    print 'len of neighbors: %d' % len(neighbors)
+
+    built_weight = ps.W(neighbors)
+    built_weight.transform = 'r'
+
+    return built_weight
+
+
+def query_attr_select(params, table_ref=True):
+    """
+        Create portion of SELECT statement for attributes inolved in query.
+        Defaults to order in the params
+        @param params: dict of information used in query (column names,
+                       table name, etc.)
+            Example:
+            OrderedDict([('numerator', 'price'),
+                         ('denominator', 'sq_meters'),
+                         ('subquery', 'SELECT * FROM interesting_data')])
+        Output:
+          "i.\"price\"::numeric As attr1, " \
+          "i.\"sq_meters\"::numeric As attr2, "
+    """
+
+    attr_string = ""
+    template = "\"%(col)s\"::numeric As attr%(alias_num)s, "
+
+    if table_ref:
+        template = "i." + template
+
+    if ('time_cols' in params) or ('ind_vars' in params):
+        # if markov or gwr analysis
+        attrs = (params['time_cols'] if 'time_cols' in params
+                 else params['ind_vars'])
+        if 'ind_vars' in params:
+            template = "array_agg(\"%(col)s\"::numeric) As attr%(alias_num)s, "
+
+        for idx, val in enumerate(attrs):
+            attr_string += template % {"col": val, "alias_num": idx + 1}
+    else:
+        # if moran's analysis
+        attrs = [k for k in params
+                 if k not in ('id_col', 'geom_col', 'subquery',
+                              'num_ngbrs', 'subquery')]
+
+        for idx, val in enumerate(attrs):
+            attr_string += template % {"col": params[val],
+                                       "alias_num": idx + 1}
+
+    return attr_string
+
+
+def query_attr_where(params, table_ref=True):
+    """
+      Construct where conditions when building neighbors query
+        Create portion of WHERE clauses for weeding out NULL-valued geometries
+        Input: dict of params:
+            {'subquery': ...,
+             'numerator': 'data1',
+             'denominator': 'data2',
+             '': ...}
+        Output:
+          'idx_replace."data1" IS NOT NULL AND idx_replace."data2" IS NOT NULL'
+        Input:
+        {'subquery': ...,
+         'time_cols': ['time1', 'time2', 'time3'],
+         'etc': ...}
+        Output: 'idx_replace."time1" IS NOT NULL AND idx_replace."time2" IS NOT
+          NULL AND idx_replace."time3" IS NOT NULL'
+    """
+    attr_string = []
+    template = "\"%s\" IS NOT NULL"
+    if table_ref:
+        template = "idx_replace." + template
+
+    if ('time_cols' in params) or ('ind_vars' in params):
+        # markov or gwr where clauses
+        attrs = (params['time_cols'] if 'time_cols' in params
+                 else params['ind_vars'])
+        # add values to template
+        for attr in attrs:
+            attr_string.append(template % attr)
+    else:
+        # moran where clauses
+
+        # get keys
+        attrs = [k for k in params
+                 if k not in ('id_col', 'geom_col', 'subquery',
+                              'num_ngbrs', 'subquery')]
+
+        # add values to template
+        for attr in attrs:
+            attr_string.append(template % params[attr])
+
+        if 'denominator' in attrs:
+            attr_string.append(
+              "idx_replace.\"%s\" <> 0" % params['denominator'])
+
+    out = " AND ".join(attr_string)
+
+    return out
+
+
+def knn(params):
+    """SQL query for k-nearest neighbors.
+        @param vars: dict of values to fill template
+    """
+
+    attr_select = query_attr_select(params, table_ref=True)
+    attr_where = query_attr_where(params, table_ref=True)
+
+    replacements = {"attr_select": attr_select,
+                    "attr_where_i": attr_where.replace("idx_replace", "i"),
+                    "attr_where_j": attr_where.replace("idx_replace", "j")}
+
+    query = "SELECT " \
+                "i.\"{id_col}\" As id, " \
+                "%(attr_select)s" \
+                "(SELECT ARRAY(SELECT j.\"{id_col}\" " \
+                              "FROM ({subquery}) As j " \
+                              "WHERE " \
+                                "i.\"{id_col}\" <> j.\"{id_col}\" AND " \
+                                "%(attr_where_j)s " \
+                              "ORDER BY " \
+                                "j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \
+                              "LIMIT {num_ngbrs})" \
+                ") As neighbors " \
+            "FROM ({subquery}) As i " \
+            "WHERE " \
+                "%(attr_where_i)s " \
+            "ORDER BY i.\"{id_col}\" ASC;" % replacements
+
+    return query.format(**params)
+
+
+# SQL query for finding queens neighbors (all contiguous polygons)
+def queen(params):
+    """SQL query for queen neighbors.
+        @param params dict: information to fill query
+    """
+    attr_select = query_attr_select(params)
+    attr_where = query_attr_where(params)
+
+    replacements = {"attr_select": attr_select,
+                    "attr_where_i": attr_where.replace("idx_replace", "i"),
+                    "attr_where_j": attr_where.replace("idx_replace", "j")}
+
+    query = "SELECT " \
+                "i.\"{id_col}\" As id, " \
+                "%(attr_select)s" \
+                "(SELECT ARRAY(SELECT j.\"{id_col}\" " \
+                 "FROM ({subquery}) As j " \
+                 "WHERE i.\"{id_col}\" <> j.\"{id_col}\" AND " \
+                       "ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \
+                       "%(attr_where_j)s)" \
+                ") As neighbors " \
+            "FROM ({subquery}) As i " \
+            "WHERE " \
+                "%(attr_where_i)s " \
+            "ORDER BY i.\"{id_col}\" ASC;" % replacements
+
+    return query.format(**params)
+
+
+def gwr_query(params):
+    """
+    GWR query
+    """
+
+    replacements = {"ind_vars_select": query_attr_select(params,
+                                                         table_ref=None),
+                    "ind_vars_where": query_attr_where(params,
+                                                       table_ref=None)}
+
+    query = '''
+      SELECT
+        array_agg(ST_X(ST_Centroid("{geom_col}"))) As x,
+        array_agg(ST_Y(ST_Centroid("{geom_col}"))) As y,
+        array_agg("{dep_var}") As dep_var,
+        %(ind_vars_select)s
+        array_agg("{id_col}") As rowid
+      FROM ({subquery}) As q
+      WHERE
+        "{dep_var}" IS NOT NULL AND
+        %(ind_vars_where)s
+        ''' % replacements
+
+    return query.format(**params).strip()
+
+
+def gwr_predict_query(params):
+    """
+    GWR query
+    """
+
+    replacements = {"ind_vars_select": query_attr_select(params,
+                                                         table_ref=None),
+                    "ind_vars_where": query_attr_where(params,
+                                                       table_ref=None)}
+
+    query = '''
+      SELECT
+        array_agg(ST_X(ST_Centroid({geom_col}))) As x,
+        array_agg(ST_Y(ST_Centroid({geom_col}))) As y,
+        array_agg({dep_var}) As dep_var,
+        %(ind_vars_select)s
+        array_agg({id_col}) As rowid
+      FROM ({subquery}) As q
+      WHERE
+        %(ind_vars_where)s
+        ''' % replacements
+
+    return query.format(**params).strip()
+# to add more weight methods open a ticket or pull request
+
+
+def get_attributes(query_res, attr_num=1):
+    """
+        @param query_res: query results with attributes and neighbors
+        @param attr_num: attribute number (1, 2, ...)
+    """
+    return np.array([x['attr' + str(attr_num)] for x in query_res],
+                    dtype=np.float)
+
+
+def empty_zipped_array(num_nones):
+    """
+        prepare return values for cases of empty weights objects (no neighbors)
+        Input:
+        @param num_nones int: number of columns (e.g., 4)
+        Output:
+        [(None, None, None, None)]
+    """
+
+    return [tuple([None] * num_nones)]

release/python/0.6.0/crankshaft/crankshaft/random_seeds.py

@@ -0,0 +1,11 @@
+"""Random seed generator used for non-deterministic functions in crankshaft"""
+import random
+import numpy
+
+def set_random_seeds(value):
+    """
+    Set the seeds of the RNGs (Random Number Generators)
+    used internally.
+    """
+    random.seed(value)
+    numpy.random.seed(value)

release/python/0.6.0/crankshaft/crankshaft/regression/__init__.py

@@ -0,0 +1,3 @@
+from crankshaft.regression.gwr import *
+from crankshaft.regression.glm import *
+from crankshaft.regression.gwr_cs import *

release/python/0.6.0/crankshaft/crankshaft/regression/glm/GLM_validate_estimation.ipynb

@@ -0,0 +1,444 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#Import GLM and pysal\n",
+    "import os\n",
+    "import numpy as np\n",
+    "os.chdir('/Users/toshan/dev/pysal/pysal/contrib/glm')\n",
+    "from glm import GLM\n",
+    "import pysal\n",
+    "import pandas as pd\n",
+    "import statsmodels.formula.api as smf\n",
+    "import statsmodels.api as sm\n",
+    "from family import Gaussian, Binomial, Poisson, QuasiPoisson\n",
+    "\n",
+    "from statsmodels.api import families"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#Prepare some test data - columbus example\n",
+    "db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')\n",
+    "y = np.array(db.by_col(\"HOVAL\"))\n",
+    "y = np.reshape(y, (49,1))\n",
+    "X = []\n",
+    "#X.append(np.ones(len(y)))\n",
+    "X.append(db.by_col(\"INC\"))\n",
+    "X.append(db.by_col(\"CRIME\"))\n",
+    "X = np.array(X).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 46.42818268]\n",
+      " [  0.62898397]\n",
+      " [ -0.48488854]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#First fit pysal OLS model\n",
+    "from pysal.spreg import ols\n",
+    "OLS = ols.OLS(y, X)\n",
+    "print OLS.betas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'family.Gaussian'>\n",
+      "<class 'family.Gaussian'>\n",
+      "<class 'family.Gaussian'>\n",
+      "[ 46.42818268   0.62898397  -0.48488854]\n",
+      "[ 46.42818268   0.62898397  -0.48488854]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Then fit Gaussian GLM\n",
+    "\n",
+    "#create Gaussian GLM model object\n",
+    "model = GLM(y, X, Gaussian())\n",
+    "model\n",
+    "\n",
+    "#Fit model to estimate coefficients and return GLMResults object\n",
+    "results = model.fit()\n",
+    "\n",
+    "#Check coefficients - R betas [46.4282, 0.6290, -0.4849]\n",
+    "print results.params\n",
+    "\n",
+    "# Gaussian GLM results from statsmodels\n",
+    "sm_model = smf.GLM(y, sm.add_constant(X), family=families.Gaussian())\n",
+    "sm_results = sm_model.fit()\n",
+    "print sm_results.params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 2\n",
+      "<class 'family.Gaussian'>\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "<class 'family.Gaussian'>\n",
+      "<class 'family.Gaussian'>\n",
+      "<class 'family.Gaussian'>\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print results.df_model, sm_results.df_model\n",
+    "print np.allclose(results.aic, sm_results.aic)\n",
+    "print np.allclose(results.bic, sm_results.bic)\n",
+    "print np.allclose(results.deviance, sm_results.deviance)\n",
+    "print np.allclose(results.df_model, sm_results.df_model)\n",
+    "print np.allclose(results.df_resid, sm_results.df_resid)\n",
+    "print np.allclose(results.llf, sm_results.llf)\n",
+    "print np.allclose(results.mu, sm_results.mu)\n",
+    "print np.allclose(results.n, sm_results.nobs)\n",
+    "print np.allclose(results.null, sm_results.null)\n",
+    "print np.allclose(results.null_deviance, sm_results.null_deviance)\n",
+    "print np.allclose(results.params, sm_results.params)\n",
+    "print np.allclose(results.pearson_chi2, sm_results.pearson_chi2)\n",
+    "print np.allclose(results.resid_anscombe, sm_results.resid_anscombe)\n",
+    "print np.allclose(results.resid_deviance, sm_results.resid_deviance)\n",
+    "print np.allclose(results.resid_pearson, sm_results.resid_pearson)\n",
+    "print np.allclose(results.resid_response, sm_results.resid_response)\n",
+    "print np.allclose(results.resid_working, sm_results.resid_working)\n",
+    "print np.allclose(results.scale, sm_results.scale)\n",
+    "print np.allclose(results.normalized_cov_params, sm_results.normalized_cov_params)\n",
+    "print np.allclose(results.cov_params(), sm_results.cov_params())\n",
+    "print np.allclose(results.bse, sm_results.bse)\n",
+    "print np.allclose(results.conf_int(), sm_results.conf_int())\n",
+    "print np.allclose(results.pvalues, sm_results.pvalues)\n",
+    "print np.allclose(results.tvalues, sm_results.tvalues)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'family.Poisson'>\n",
+      "<class 'family.Poisson'>\n",
+      "<class 'family.Poisson'>\n",
+      "[ 3.92159085  0.01183491 -0.01371397]\n",
+      "[ 3.92159085  0.01183491 -0.01371397]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Now fit a Poisson GLM \n",
+    "\n",
+    "poisson_y = np.round(y).astype(int)\n",
+    "\n",
+    "#create Poisson GLM model object\n",
+    "model = GLM(poisson_y, X, Poisson())\n",
+    "model\n",
+    "\n",
+    "#Fit model to estimate coefficients and return GLMResults object\n",
+    "results = model.fit()\n",
+    "\n",
+    "#Check coefficients - R betas [3.91926, 0.01198, -0.01371]\n",
+    "print results.params.T\n",
+    "\n",
+    "# Poisson GLM results from statsmodels\n",
+    "sm_results = smf.GLM(poisson_y, sm.add_constant(X), family=families.Poisson()).fit()\n",
+    "print sm_results.params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'family.Poisson'>\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "<class 'family.Poisson'>\n",
+      "<class 'family.Poisson'>\n",
+      "<class 'family.Poisson'>\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "[ 0.13049161  0.00511599  0.00193769] [ 0.13049161  0.00511599  0.00193769]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print np.allclose(results.aic, sm_results.aic)\n",
+    "print np.allclose(results.bic, sm_results.bic)\n",
+    "print np.allclose(results.deviance, sm_results.deviance)\n",
+    "print np.allclose(results.df_model, sm_results.df_model)\n",
+    "print np.allclose(results.df_resid, sm_results.df_resid)\n",
+    "print np.allclose(results.llf, sm_results.llf)\n",
+    "print np.allclose(results.mu, sm_results.mu)\n",
+    "print np.allclose(results.n, sm_results.nobs)\n",
+    "print np.allclose(results.null, sm_results.null)\n",
+    "print np.allclose(results.null_deviance, sm_results.null_deviance)\n",
+    "print np.allclose(results.params, sm_results.params)\n",
+    "print np.allclose(results.pearson_chi2, sm_results.pearson_chi2)\n",
+    "print np.allclose(results.resid_anscombe, sm_results.resid_anscombe)\n",
+    "print np.allclose(results.resid_deviance, sm_results.resid_deviance)\n",
+    "print np.allclose(results.resid_pearson, sm_results.resid_pearson)\n",
+    "print np.allclose(results.resid_response, sm_results.resid_response)\n",
+    "print np.allclose(results.resid_working, sm_results.resid_working)\n",
+    "print np.allclose(results.scale, sm_results.scale)\n",
+    "print np.allclose(results.normalized_cov_params, sm_results.normalized_cov_params)\n",
+    "print np.allclose(results.cov_params(), sm_results.cov_params())\n",
+    "print np.allclose(results.bse, sm_results.bse)\n",
+    "print np.allclose(results.conf_int(), sm_results.conf_int())\n",
+    "print np.allclose(results.pvalues, sm_results.pvalues)\n",
+    "print np.allclose(results.tvalues, sm_results.tvalues)\n",
+    "print results.bse, sm_results.bse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-5.33638276  0.0287754 ]\n",
+      "[-5.33638276  0.0287754 ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Now fit a binomial GLM\n",
+    "londonhp = pd.read_csv('/Users/toshan/projects/londonhp.csv')\n",
+    "#londonhp = pd.read_csv('/Users/qszhao/Dropbox/pysal/pysal/contrib/gwr/londonhp.csv')\n",
+    "y = londonhp['BATH2'].values\n",
+    "y = np.reshape(y, (316,1))\n",
+    "X = londonhp['FLOORSZ'].values\n",
+    "X = np.reshape(X, (316,1))\n",
+    "\n",
+    "#create logistic GLM model object\n",
+    "model = GLM(y, X, Binomial())\n",
+    "model\n",
+    "\n",
+    "#Fit model to estimate coefficients and return GLMResults object\n",
+    "results = model.fit()\n",
+    "\n",
+    "#Check coefficients - R betas [-5.33638, 0.02878]\n",
+    "print results.params.T\n",
+    "\n",
+    "# Logistic GLM results from statsmodels\n",
+    "sm_results = smf.GLM(y, sm.add_constant(X), family=families.Binomial()).fit()\n",
+    "print sm_results.params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 1\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print results.df_model, sm_results.df_model\n",
+    "print np.allclose(results.aic, sm_results.aic)\n",
+    "print np.allclose(results.bic, sm_results.bic)\n",
+    "print np.allclose(results.deviance, sm_results.deviance)\n",
+    "print np.allclose(results.df_model, sm_results.df_model)\n",
+    "print np.allclose(results.df_resid, sm_results.df_resid)\n",
+    "print np.allclose(results.llf, sm_results.llf)\n",
+    "print np.allclose(results.mu, sm_results.mu)\n",
+    "print np.allclose(results.n, sm_results.nobs)\n",
+    "print np.allclose(results.null, sm_results.null)\n",
+    "print np.allclose(results.null_deviance, sm_results.null_deviance)\n",
+    "print np.allclose(results.params, sm_results.params)\n",
+    "print np.allclose(results.pearson_chi2, sm_results.pearson_chi2)\n",
+    "print np.allclose(results.resid_anscombe, sm_results.resid_anscombe)\n",
+    "print np.allclose(results.resid_deviance, sm_results.resid_deviance)\n",
+    "print np.allclose(results.resid_pearson, sm_results.resid_pearson)\n",
+    "print np.allclose(results.resid_response, sm_results.resid_response)\n",
+    "print np.allclose(results.resid_working, sm_results.resid_working)\n",
+    "print np.allclose(results.scale, sm_results.scale)\n",
+    "print np.allclose(results.normalized_cov_params, sm_results.normalized_cov_params)\n",
+    "print np.allclose(results.cov_params(), sm_results.cov_params())\n",
+    "print np.allclose(results.bse, sm_results.bse)\n",
+    "print np.allclose(results.conf_int(), sm_results.conf_int())\n",
+    "print np.allclose(results.pvalues, sm_results.pvalues)\n",
+    "print np.allclose(results.tvalues, sm_results.tvalues)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'family.QuasiPoisson'>\n",
+      "<class 'family.QuasiPoisson'>\n",
+      "<class 'family.QuasiPoisson'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "#create QUasiPoisson GLM model object\n",
+    "model = GLM(poisson_y, X, QuasiPoisson())\n",
+    "model\n",
+    "\n",
+    "#Fit model to estimate coefficients and return GLMResults object\n",
+    "results = model.fit()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

release/python/0.6.0/crankshaft/crankshaft/regression/glm/__init__.py

@@ -0,0 +1,4 @@
+import glm
+import family
+import utils
+import iwls

release/python/0.6.0/crankshaft/crankshaft/regression/glm/base.py

@@ -0,0 +1,959 @@
+
+from __future__ import print_function
+import numpy as np
+from scipy import stats
+from utils import cache_readonly
+
+class Results(object):
+    """
+    Class to contain model results
+    Parameters
+    ----------
+    model : class instance
+        the previously specified model instance
+    params : array
+        parameter estimates from the fit model
+    """
+    def __init__(self, model, params, **kwd):
+        self.__dict__.update(kwd)
+        self.initialize(model, params, **kwd)
+        self._data_attr = []
+
+    def initialize(self, model, params, **kwd):
+        self.params = params
+        self.model = model
+        if hasattr(model, 'k_constant'):
+            self.k_constant = model.k_constant
+
+    def predict(self, exog=None, transform=True, *args, **kwargs):
+        """
+        Call self.model.predict with self.params as the first argument.
+        Parameters
+        ----------
+        exog : array-like, optional
+            The values for which you want to predict.
+        transform : bool, optional
+            If the model was fit via a formula, do you want to pass
+            exog through the formula. Default is True. E.g., if you fit
+            a model y ~ log(x1) + log(x2), and transform is True, then
+            you can pass a data structure that contains x1 and x2 in
+            their original form. Otherwise, you'd need to log the data
+            first.
+        args, kwargs :
+            Some models can take additional arguments or keywords, see the
+            predict method of the model for the details.
+        Returns
+        -------
+        prediction : ndarray or pandas.Series
+            See self.model.predict
+        """
+        if transform and hasattr(self.model, 'formula') and exog is not None:
+            from patsy import dmatrix
+            exog = dmatrix(self.model.data.design_info.builder,
+                           exog)
+
+        if exog is not None:
+            exog = np.asarray(exog)
+            if exog.ndim == 1 and (self.model.exog.ndim == 1 or
+                                   self.model.exog.shape[1] == 1):
+                exog = exog[:, None]
+            exog = np.atleast_2d(exog)  # needed in count model shape[1]
+
+        return self.model.predict(self.params, exog, *args, **kwargs)
+
+
+#TODO: public method?
+class LikelihoodModelResults(Results):
+    """
+    Class to contain results from likelihood models
+    Parameters
+    -----------
+    model : LikelihoodModel instance or subclass instance
+        LikelihoodModelResults holds a reference to the model that is fit.
+    params : 1d array_like
+        parameter estimates from estimated model
+    normalized_cov_params : 2d array
+       Normalized (before scaling) covariance of params. (dot(X.T,X))**-1
+    scale : float
+        For (some subset of models) scale will typically be the
+        mean square error from the estimated model (sigma^2)
+    Returns
+    -------
+    **Attributes**
+    mle_retvals : dict
+        Contains the values returned from the chosen optimization method if
+        full_output is True during the fit.  Available only if the model
+        is fit by maximum likelihood.  See notes below for the output from
+        the different methods.
+    mle_settings : dict
+        Contains the arguments passed to the chosen optimization method.
+        Available if the model is fit by maximum likelihood.  See
+        LikelihoodModel.fit for more information.
+    model : model instance
+        LikelihoodResults contains a reference to the model that is fit.
+    params : ndarray
+        The parameters estimated for the model.
+    scale : float
+        The scaling factor of the model given during instantiation.
+    tvalues : array
+        The t-values of the standard errors.
+    Notes
+    -----
+    The covariance of params is given by scale times normalized_cov_params.
+    Return values by solver if full_output is True during fit:
+        'newton'
+            fopt : float
+                The value of the (negative) loglikelihood at its
+                minimum.
+            iterations : int
+                Number of iterations performed.
+            score : ndarray
+                The score vector at the optimum.
+            Hessian : ndarray
+                The Hessian at the optimum.
+            warnflag : int
+                1 if maxiter is exceeded. 0 if successful convergence.
+            converged : bool
+                True: converged. False: did not converge.
+            allvecs : list
+                List of solutions at each iteration.
+        'nm'
+            fopt : float
+                The value of the (negative) loglikelihood at its
+                minimum.
+            iterations : int
+                Number of iterations performed.
+            warnflag : int
+                1: Maximum number of function evaluations made.
+                2: Maximum number of iterations reached.
+            converged : bool
+                True: converged. False: did not converge.
+            allvecs : list
+                List of solutions at each iteration.
+        'bfgs'
+            fopt : float
+                Value of the (negative) loglikelihood at its minimum.
+            gopt : float
+                Value of gradient at minimum, which should be near 0.
+            Hinv : ndarray
+                value of the inverse Hessian matrix at minimum.  Note
+                that this is just an approximation and will often be
+                different from the value of the analytic Hessian.
+            fcalls : int
+                Number of calls to loglike.
+            gcalls : int
+                Number of calls to gradient/score.
+            warnflag : int
+                1: Maximum number of iterations exceeded. 2: Gradient
+                and/or function calls are not changing.
+            converged : bool
+                True: converged.  False: did not converge.
+            allvecs : list
+                Results at each iteration.
+        'lbfgs'
+            fopt : float
+                Value of the (negative) loglikelihood at its minimum.
+            gopt : float
+                Value of gradient at minimum, which should be near 0.
+            fcalls : int
+                Number of calls to loglike.
+            warnflag : int
+                Warning flag:
+                - 0 if converged
+                - 1 if too many function evaluations or too many iterations
+                - 2 if stopped for another reason
+            converged : bool
+                True: converged.  False: did not converge.
+        'powell'
+            fopt : float
+                Value of the (negative) loglikelihood at its minimum.
+            direc : ndarray
+                Current direction set.
+            iterations : int
+                Number of iterations performed.
+            fcalls : int
+                Number of calls to loglike.
+            warnflag : int
+                1: Maximum number of function evaluations. 2: Maximum number
+                of iterations.
+            converged : bool
+                True : converged. False: did not converge.
+            allvecs : list
+                Results at each iteration.
+        'cg'
+            fopt : float
+                Value of the (negative) loglikelihood at its minimum.
+            fcalls : int
+                Number of calls to loglike.
+            gcalls : int
+                Number of calls to gradient/score.
+            warnflag : int
+                1: Maximum number of iterations exceeded. 2: Gradient and/
+                or function calls not changing.
+            converged : bool
+                True: converged. False: did not converge.
+            allvecs : list
+                Results at each iteration.
+        'ncg'
+            fopt : float
+                Value of the (negative) loglikelihood at its minimum.
+            fcalls : int
+                Number of calls to loglike.
+            gcalls : int
+                Number of calls to gradient/score.
+            hcalls : int
+                Number of calls to hessian.
+            warnflag : int
+                1: Maximum number of iterations exceeded.
+            converged : bool
+                True: converged. False: did not converge.
+            allvecs : list
+                Results at each iteration.
+        """
+
+    # by default we use normal distribution
+    # can be overwritten by instances or subclasses
+    use_t = False
+
+    def __init__(self, model, params, normalized_cov_params=None, scale=1.,
+                 **kwargs):
+        super(LikelihoodModelResults, self).__init__(model, params)
+        self.normalized_cov_params = normalized_cov_params
+        self.scale = scale
+
+        # robust covariance
+        # We put cov_type in kwargs so subclasses can decide in fit whether to
+        # use this generic implementation
+        if 'use_t' in kwargs:
+            use_t = kwargs['use_t']
+            if use_t is not None:
+                self.use_t = use_t
+        if 'cov_type' in kwargs:
+            cov_type = kwargs.get('cov_type', 'nonrobust')
+            cov_kwds = kwargs.get('cov_kwds', {})
+
+            if cov_type == 'nonrobust':
+                self.cov_type = 'nonrobust'
+                self.cov_kwds = {'description' : 'Standard Errors assume that the ' +
+                                 'covariance matrix of the errors is correctly ' +
+                                 'specified.'}
+            else:
+                from statsmodels.base.covtype import get_robustcov_results
+                if cov_kwds is None:
+                    cov_kwds = {}
+                use_t = self.use_t
+                # TODO: we shouldn't need use_t in get_robustcov_results
+                get_robustcov_results(self, cov_type=cov_type, use_self=True,
+                                           use_t=use_t, **cov_kwds)
+
+
+    def normalized_cov_params(self):
+        raise NotImplementedError
+
+
+    def _get_robustcov_results(self, cov_type='nonrobust', use_self=True,
+                                   use_t=None, **cov_kwds):
+        from statsmodels.base.covtype import get_robustcov_results
+        if cov_kwds is None:
+            cov_kwds = {}
+
+        if cov_type == 'nonrobust':
+            self.cov_type = 'nonrobust'
+            self.cov_kwds = {'description' : 'Standard Errors assume that the ' +
+                             'covariance matrix of the errors is correctly ' +
+                             'specified.'}
+        else:
+            # TODO: we shouldn't need use_t in get_robustcov_results
+            get_robustcov_results(self, cov_type=cov_type, use_self=True,
+                                       use_t=use_t, **cov_kwds)
+
+    @cache_readonly
+    def llf(self):
+        return self.model.loglike(self.params)
+
+    @cache_readonly
+    def bse(self):
+        return np.sqrt(np.diag(self.cov_params()))
+
+    @cache_readonly
+    def tvalues(self):
+        """
+        Return the t-statistic for a given parameter estimate.
+        """
+        return self.params / self.bse
+
+    @cache_readonly
+    def pvalues(self):
+        if self.use_t:
+            df_resid = getattr(self, 'df_resid_inference', self.df_resid)
+            return stats.t.sf(np.abs(self.tvalues), df_resid)*2
+        else:
+            return stats.norm.sf(np.abs(self.tvalues))*2
+
+
+    def cov_params(self, r_matrix=None, column=None, scale=None, cov_p=None,
+            other=None):
+        """
+        Returns the variance/covariance matrix.
+        The variance/covariance matrix can be of a linear contrast
+        of the estimates of params or all params multiplied by scale which
+        will usually be an estimate of sigma^2.  Scale is assumed to be
+        a scalar.
+        Parameters
+        ----------
+        r_matrix : array-like
+            Can be 1d, or 2d.  Can be used alone or with other.
+        column :  array-like, optional
+            Must be used on its own.  Can be 0d or 1d see below.
+        scale : float, optional
+            Can be specified or not.  Default is None, which means that
+            the scale argument is taken from the model.
+        other : array-like, optional
+            Can be used when r_matrix is specified.
+        Returns
+        -------
+        cov : ndarray
+            covariance matrix of the parameter estimates or of linear
+            combination of parameter estimates. See Notes.
+        Notes
+        -----
+        (The below are assumed to be in matrix notation.)
+        If no argument is specified returns the covariance matrix of a model
+        ``(scale)*(X.T X)^(-1)``
+        If contrast is specified it pre and post-multiplies as follows
+        ``(scale) * r_matrix (X.T X)^(-1) r_matrix.T``
+        If contrast and other are specified returns
+        ``(scale) * r_matrix (X.T X)^(-1) other.T``
+        If column is specified returns
+        ``(scale) * (X.T X)^(-1)[column,column]`` if column is 0d
+        OR
+        ``(scale) * (X.T X)^(-1)[column][:,column]`` if column is 1d
+        """
+        if (hasattr(self, 'mle_settings') and
+                self.mle_settings['optimizer'] in ['l1', 'l1_cvxopt_cp']):
+            dot_fun = nan_dot
+        else:
+            dot_fun = np.dot
+
+        if (cov_p is None and self.normalized_cov_params is None and
+            not hasattr(self, 'cov_params_default')):
+            raise ValueError('need covariance of parameters for computing '
+                             '(unnormalized) covariances')
+        if column is not None and (r_matrix is not None or other is not None):
+            raise ValueError('Column should be specified without other '
+                             'arguments.')
+        if other is not None and r_matrix is None:
+            raise ValueError('other can only be specified with r_matrix')
+
+        if cov_p is None:
+            if hasattr(self, 'cov_params_default'):
+                cov_p = self.cov_params_default
+            else:
+                if scale is None:
+                    scale = self.scale
+                cov_p = self.normalized_cov_params * scale
+
+        if column is not None:
+            column = np.asarray(column)
+            if column.shape == ():
+                return cov_p[column, column]
+            else:
+                #return cov_p[column][:, column]
+                return cov_p[column[:, None], column]
+        elif r_matrix is not None:
+            r_matrix = np.asarray(r_matrix)
+            if r_matrix.shape == ():
+                raise ValueError("r_matrix should be 1d or 2d")
+            if other is None:
+                other = r_matrix
+            else:
+                other = np.asarray(other)
+            tmp = dot_fun(r_matrix, dot_fun(cov_p, np.transpose(other)))
+            return tmp
+        else:  # if r_matrix is None and column is None:
+            return cov_p
+
+    #TODO: make sure this works as needed for GLMs
+    def t_test(self, r_matrix, cov_p=None, scale=None,
+               use_t=None):
+        """
+        Compute a t-test for a each linear hypothesis of the form Rb = q
+        Parameters
+        ----------
+        r_matrix : array-like, str, tuple
+            - array : If an array is given, a p x k 2d array or length k 1d
+              array specifying the linear restrictions. It is assumed
+              that the linear combination is equal to zero.
+            - str : The full hypotheses to test can be given as a string.
+              See the examples.
+            - tuple : A tuple of arrays in the form (R, q). If q is given,
+              can be either a scalar or a length p row vector.
+        cov_p : array-like, optional
+            An alternative estimate for the parameter covariance matrix.
+            If None is given, self.normalized_cov_params is used.
+        scale : float, optional
+            An optional `scale` to use.  Default is the scale specified
+            by the model fit.
+        use_t : bool, optional
+            If use_t is None, then the default of the model is used.
+            If use_t is True, then the p-values are based on the t
+            distribution.
+            If use_t is False, then the p-values are based on the normal
+            distribution.
+        Returns
+        -------
+        res : ContrastResults instance
+            The results for the test are attributes of this results instance.
+            The available results have the same elements as the parameter table
+            in `summary()`.
+        Examples
+        --------
+        >>> import numpy as np
+        >>> import statsmodels.api as sm
+        >>> data = sm.datasets.longley.load()
+        >>> data.exog = sm.add_constant(data.exog)
+        >>> results = sm.OLS(data.endog, data.exog).fit()
+        >>> r = np.zeros_like(results.params)
+        >>> r[5:] = [1,-1]
+        >>> print(r)
+        [ 0.  0.  0.  0.  0.  1. -1.]
+        r tests that the coefficients on the 5th and 6th independent
+        variable are the same.
+        >>> T_test = results.t_test(r)
+        >>> print(T_test)
+        <T contrast: effect=-1829.2025687192481, sd=455.39079425193762,
+        t=-4.0167754636411717, p=0.0015163772380899498, df_denom=9>
+        >>> T_test.effect
+        -1829.2025687192481
+        >>> T_test.sd
+        455.39079425193762
+        >>> T_test.tvalue
+        -4.0167754636411717
+        >>> T_test.pvalue
+        0.0015163772380899498
+        Alternatively, you can specify the hypothesis tests using a string
+        >>> from statsmodels.formula.api import ols
+        >>> dta = sm.datasets.longley.load_pandas().data
+        >>> formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR'
+        >>> results = ols(formula, dta).fit()
+        >>> hypotheses = 'GNPDEFL = GNP, UNEMP = 2, YEAR/1829 = 1'
+        >>> t_test = results.t_test(hypotheses)
+        >>> print(t_test)
+        See Also
+        ---------
+        tvalues : individual t statistics
+        f_test : for F tests
+        patsy.DesignInfo.linear_constraint
+        """
+        from patsy import DesignInfo
+        names = self.model.data.param_names
+        LC = DesignInfo(names).linear_constraint(r_matrix)
+        r_matrix, q_matrix = LC.coefs, LC.constants
+        num_ttests = r_matrix.shape[0]
+        num_params = r_matrix.shape[1]
+
+        if (cov_p is None and self.normalized_cov_params is None and
+            not hasattr(self, 'cov_params_default')):
+            raise ValueError('Need covariance of parameters for computing '
+                             'T statistics')
+        if num_params != self.params.shape[0]:
+            raise ValueError('r_matrix and params are not aligned')
+        if q_matrix is None:
+            q_matrix = np.zeros(num_ttests)
+        else:
+            q_matrix = np.asarray(q_matrix)
+            q_matrix = q_matrix.squeeze()
+        if q_matrix.size > 1:
+            if q_matrix.shape[0] != num_ttests:
+                raise ValueError("r_matrix and q_matrix must have the same "
+                                 "number of rows")
+
+        if use_t is None:
+            #switch to use_t false if undefined
+            use_t = (hasattr(self, 'use_t') and self.use_t)
+
+        _t = _sd = None
+
+        _effect = np.dot(r_matrix, self.params)
+        # nan_dot multiplies with the convention nan * 0 = 0
+
+        # Perform the test
+        if num_ttests > 1:
+            _sd = np.sqrt(np.diag(self.cov_params(
+                r_matrix=r_matrix, cov_p=cov_p)))
+        else:
+            _sd = np.sqrt(self.cov_params(r_matrix=r_matrix, cov_p=cov_p))
+        _t = (_effect - q_matrix) * recipr(_sd)
+
+        df_resid = getattr(self, 'df_resid_inference', self.df_resid)
+
+        if use_t:
+            return ContrastResults(effect=_effect, t=_t, sd=_sd,
+                                   df_denom=df_resid)
+        else:
+            return ContrastResults(effect=_effect, statistic=_t, sd=_sd,
+                                   df_denom=df_resid,
+                                   distribution='norm')
+
+    def f_test(self, r_matrix, cov_p=None, scale=1.0, invcov=None):
+        """
+        Compute the F-test for a joint linear hypothesis.
+        This is a special case of `wald_test` that always uses the F
+        distribution.
+        Parameters
+        ----------
+        r_matrix : array-like, str, or tuple
+            - array : An r x k array where r is the number of restrictions to
+              test and k is the number of regressors. It is assumed
+              that the linear combination is equal to zero.
+            - str : The full hypotheses to test can be given as a string.
+              See the examples.
+            - tuple : A tuple of arrays in the form (R, q), ``q`` can be
+              either a scalar or a length k row vector.
+        cov_p : array-like, optional
+            An alternative estimate for the parameter covariance matrix.
+            If None is given, self.normalized_cov_params is used.
+        scale : float, optional
+            Default is 1.0 for no scaling.
+        invcov : array-like, optional
+            A q x q array to specify an inverse covariance matrix based on a
+            restrictions matrix.
+        Returns
+        -------
+        res : ContrastResults instance
+            The results for the test are attributes of this results instance.
+        Examples
+        --------
+        >>> import numpy as np
+        >>> import statsmodels.api as sm
+        >>> data = sm.datasets.longley.load()
+        >>> data.exog = sm.add_constant(data.exog)
+        >>> results = sm.OLS(data.endog, data.exog).fit()
+        >>> A = np.identity(len(results.params))
+        >>> A = A[1:,:]
+        This tests that each coefficient is jointly statistically
+        significantly different from zero.
+        >>> print(results.f_test(A))
+        <F contrast: F=330.28533923463488, p=4.98403052872e-10,
+         df_denom=9, df_num=6>
+        Compare this to
+        >>> results.fvalue
+        330.2853392346658
+        >>> results.f_pvalue
+        4.98403096572e-10
+        >>> B = np.array(([0,0,1,-1,0,0,0],[0,0,0,0,0,1,-1]))
+        This tests that the coefficient on the 2nd and 3rd regressors are
+        equal and jointly that the coefficient on the 5th and 6th regressors
+        are equal.
+        >>> print(results.f_test(B))
+        <F contrast: F=9.740461873303655, p=0.00560528853174, df_denom=9,
+         df_num=2>
+        Alternatively, you can specify the hypothesis tests using a string
+        >>> from statsmodels.datasets import longley
+        >>> from statsmodels.formula.api import ols
+        >>> dta = longley.load_pandas().data
+        >>> formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR'
+        >>> results = ols(formula, dta).fit()
+        >>> hypotheses = '(GNPDEFL = GNP), (UNEMP = 2), (YEAR/1829 = 1)'
+        >>> f_test = results.f_test(hypotheses)
+        >>> print(f_test)
+        See Also
+        --------
+        statsmodels.stats.contrast.ContrastResults
+        wald_test
+        t_test
+        patsy.DesignInfo.linear_constraint
+        Notes
+        -----
+        The matrix `r_matrix` is assumed to be non-singular. More precisely,
+        r_matrix (pX pX.T) r_matrix.T
+        is assumed invertible. Here, pX is the generalized inverse of the
+        design matrix of the model. There can be problems in non-OLS models
+        where the rank of the covariance of the noise is not full.
+        """
+        res = self.wald_test(r_matrix, cov_p=cov_p, scale=scale,
+                             invcov=invcov, use_f=True)
+        return res
+
+    #TODO: untested for GLMs?
+    def wald_test(self, r_matrix, cov_p=None, scale=1.0, invcov=None,
+                  use_f=None):
+        """
+        Compute a Wald-test for a joint linear hypothesis.
+        Parameters
+        ----------
+        r_matrix : array-like, str, or tuple
+            - array : An r x k array where r is the number of restrictions to
+              test and k is the number of regressors. It is assumed that the
+              linear combination is equal to zero.
+            - str : The full hypotheses to test can be given as a string.
+              See the examples.
+            - tuple : A tuple of arrays in the form (R, q), ``q`` can be
+              either a scalar or a length p row vector.
+        cov_p : array-like, optional
+            An alternative estimate for the parameter covariance matrix.
+            If None is given, self.normalized_cov_params is used.
+        scale : float, optional
+            Default is 1.0 for no scaling.
+        invcov : array-like, optional
+            A q x q array to specify an inverse covariance matrix based on a
+            restrictions matrix.
+        use_f : bool
+            If True, then the F-distribution is used. If False, then the
+            asymptotic distribution, chisquare is used. If use_f is None, then
+            the F distribution is used if the model specifies that use_t is True.
+            The test statistic is proportionally adjusted for the distribution
+            by the number of constraints in the hypothesis.
+        Returns
+        -------
+        res : ContrastResults instance
+            The results for the test are attributes of this results instance.
+        See also
+        --------
+        statsmodels.stats.contrast.ContrastResults
+        f_test
+        t_test
+        patsy.DesignInfo.linear_constraint
+        Notes
+        -----
+        The matrix `r_matrix` is assumed to be non-singular. More precisely,
+        r_matrix (pX pX.T) r_matrix.T
+        is assumed invertible. Here, pX is the generalized inverse of the
+        design matrix of the model. There can be problems in non-OLS models
+        where the rank of the covariance of the noise is not full.
+        """
+        if use_f is None:
+            #switch to use_t false if undefined
+            use_f = (hasattr(self, 'use_t') and self.use_t)
+
+        from patsy import DesignInfo
+        names = self.model.data.param_names
+        LC = DesignInfo(names).linear_constraint(r_matrix)
+        r_matrix, q_matrix = LC.coefs, LC.constants
+
+        if (self.normalized_cov_params is None and cov_p is None and
+                invcov is None and not hasattr(self, 'cov_params_default')):
+            raise ValueError('need covariance of parameters for computing '
+                             'F statistics')
+
+        cparams = np.dot(r_matrix, self.params[:, None])
+        J = float(r_matrix.shape[0])  # number of restrictions
+        if q_matrix is None:
+            q_matrix = np.zeros(J)
+        else:
+            q_matrix = np.asarray(q_matrix)
+        if q_matrix.ndim == 1:
+            q_matrix = q_matrix[:, None]
+            if q_matrix.shape[0] != J:
+                raise ValueError("r_matrix and q_matrix must have the same "
+                                 "number of rows")
+        Rbq = cparams - q_matrix
+        if invcov is None:
+            cov_p = self.cov_params(r_matrix=r_matrix, cov_p=cov_p)
+            if np.isnan(cov_p).max():
+                raise ValueError("r_matrix performs f_test for using "
+                                 "dimensions that are asymptotically "
+                                 "non-normal")
+            invcov = np.linalg.inv(cov_p)
+
+        if (hasattr(self, 'mle_settings') and
+                self.mle_settings['optimizer'] in ['l1', 'l1_cvxopt_cp']):
+            F = nan_dot(nan_dot(Rbq.T, invcov), Rbq)
+        else:
+            F = np.dot(np.dot(Rbq.T, invcov), Rbq)
+
+        df_resid = getattr(self, 'df_resid_inference', self.df_resid)
+        if use_f:
+            F /= J
+            return ContrastResults(F=F, df_denom=df_resid,
+                                   df_num=invcov.shape[0])
+        else:
+            return ContrastResults(chi2=F, df_denom=J, statistic=F,
+                                   distribution='chi2', distargs=(J,))
+
+
+    def wald_test_terms(self, skip_single=False, extra_constraints=None,
+                   combine_terms=None):
+        """
+        Compute a sequence of Wald tests for terms over multiple columns
+        This computes joined Wald tests for the hypothesis that all
+        coefficients corresponding to a `term` are zero.
+        `Terms` are defined by the underlying formula or by string matching.
+        Parameters
+        ----------
+        skip_single : boolean
+            If true, then terms that consist only of a single column and,
+            therefore, refers only to a single parameter is skipped.
+            If false, then all terms are included.
+        extra_constraints : ndarray
+            not tested yet
+        combine_terms : None or list of strings
+            Each string in this list is matched to the name of the terms or
+            the name of the exogenous variables. All columns whose name
+            includes that string are combined in one joint test.
+        Returns
+        -------
+        test_result : result instance
+            The result instance contains `table` which is a pandas DataFrame
+            with the test results: test statistic, degrees of freedom and
+            pvalues.
+        Examples
+        --------
+        >>> res_ols = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)",
+                          data).fit()
+        >>> res_ols.wald_test_terms()
+        <class 'statsmodels.stats.contrast.WaldTestResults'>
+                                                  F                P>F  df constraint  df denom
+        Intercept                        279.754525  2.37985521351e-22              1        51
+        C(Duration, Sum)                   5.367071    0.0245738436636              1        51
+        C(Weight, Sum)                    12.432445  3.99943118767e-05              2        51
+        C(Duration, Sum):C(Weight, Sum)    0.176002      0.83912310946              2        51
+        >>> res_poi = Poisson.from_formula("Days ~ C(Weight) * C(Duration)",
+                                           data).fit(cov_type='HC0')
+        >>> wt = res_poi.wald_test_terms(skip_single=False,
+                                         combine_terms=['Duration', 'Weight'])
+        >>> print(wt)
+                                    chi2             P>chi2  df constraint
+        Intercept              15.695625  7.43960374424e-05              1
+        C(Weight)              16.132616  0.000313940174705              2
+        C(Duration)             1.009147     0.315107378931              1
+        C(Weight):C(Duration)   0.216694     0.897315972824              2
+        Duration               11.187849     0.010752286833              3
+        Weight                 30.263368  4.32586407145e-06              4
+        """
+        # lazy import
+        from collections import defaultdict
+
+        result = self
+        if extra_constraints is None:
+            extra_constraints = []
+        if combine_terms is None:
+            combine_terms = []
+        design_info = getattr(result.model.data.orig_exog, 'design_info', None)
+
+        if design_info is None and extra_constraints is None:
+            raise ValueError('no constraints, nothing to do')
+
+
+        identity = np.eye(len(result.params))
+        constraints = []
+        combined = defaultdict(list)
+        if design_info is not None:
+            for term in design_info.terms:
+                cols = design_info.slice(term)
+                name = term.name()
+                constraint_matrix = identity[cols]
+
+                # check if in combined
+                for cname in combine_terms:
+                    if cname in name:
+                        combined[cname].append(constraint_matrix)
+
+                k_constraint = constraint_matrix.shape[0]
+                if skip_single:
+                    if k_constraint == 1:
+                        continue
+
+                constraints.append((name, constraint_matrix))
+
+            combined_constraints = []
+            for cname in combine_terms:
+                combined_constraints.append((cname, np.vstack(combined[cname])))
+        else:
+            # check by exog/params names if there is no formula info
+            for col, name in enumerate(result.model.exog_names):
+                constraint_matrix = identity[col]
+
+                # check if in combined
+                for cname in combine_terms:
+                    if cname in name:
+                        combined[cname].append(constraint_matrix)
+
+                if skip_single:
+                    continue
+
+                constraints.append((name, constraint_matrix))
+
+            combined_constraints = []
+            for cname in combine_terms:
+                combined_constraints.append((cname, np.vstack(combined[cname])))
+
+        use_t = result.use_t
+        distribution = ['chi2', 'F'][use_t]
+
+        res_wald = []
+        index = []
+        for name, constraint in constraints + combined_constraints + extra_constraints:
+            wt = result.wald_test(constraint)
+            row = [wt.statistic.item(), wt.pvalue, constraint.shape[0]]
+            if use_t:
+                row.append(wt.df_denom)
+            res_wald.append(row)
+            index.append(name)
+
+        # distribution nerutral names
+        col_names = ['statistic', 'pvalue', 'df_constraint']
+        if use_t:
+            col_names.append('df_denom')
+        # TODO: maybe move DataFrame creation to results class
+        from pandas import DataFrame
+        table = DataFrame(res_wald, index=index, columns=col_names)
+        res = WaldTestResults(None, distribution, None, table=table)
+        # TODO: remove temp again, added for testing
+        res.temp = constraints + combined_constraints + extra_constraints
+        return res
+
+
+    def conf_int(self, alpha=.05, cols=None, method='default'):
+        """
+        Returns the confidence interval of the fitted parameters.
+        Parameters
+        ----------
+        alpha : float, optional
+            The significance level for the confidence interval.
+            ie., The default `alpha` = .05 returns a 95% confidence interval.
+        cols : array-like, optional
+            `cols` specifies which confidence intervals to return
+        method : string
+            Not Implemented Yet
+            Method to estimate the confidence_interval.
+            "Default" : uses self.bse which is based on inverse Hessian for MLE
+            "hjjh" :
+            "jac" :
+            "boot-bse"
+            "boot_quant"
+            "profile"
+        Returns
+        --------
+        conf_int : array
+            Each row contains [lower, upper] limits of the confidence interval
+            for the corresponding parameter. The first column contains all
+            lower, the second column contains all upper limits.
+        Examples
+        --------
+        >>> import statsmodels.api as sm
+        >>> data = sm.datasets.longley.load()
+        >>> data.exog = sm.add_constant(data.exog)
+        >>> results = sm.OLS(data.endog, data.exog).fit()
+        >>> results.conf_int()
+        array([[-5496529.48322745, -1467987.78596704],
+               [    -177.02903529,      207.15277984],
+               [      -0.1115811 ,        0.03994274],
+               [      -3.12506664,       -0.91539297],
+               [      -1.5179487 ,       -0.54850503],
+               [      -0.56251721,        0.460309  ],
+               [     798.7875153 ,     2859.51541392]])
+        >>> results.conf_int(cols=(2,3))
+        array([[-0.1115811 ,  0.03994274],
+               [-3.12506664, -0.91539297]])
+        Notes
+        -----
+        The confidence interval is based on the standard normal distribution.
+        Models wish to use a different distribution should overwrite this
+        method.
+        """
+        bse = self.bse
+
+        if self.use_t:
+            dist = stats.t
+            df_resid = getattr(self, 'df_resid_inference', self.df_resid)
+            q = dist.ppf(1 - alpha / 2, df_resid)
+        else:
+            dist = stats.norm
+            q = dist.ppf(1 - alpha / 2)
+
+        if cols is None:
+            lower = self.params - q * bse
+            upper = self.params + q * bse
+        else:
+            cols = np.asarray(cols)
+            lower = self.params[cols] - q * bse[cols]
+            upper = self.params[cols] + q * bse[cols]
+        return np.asarray(lzip(lower, upper))
+
+    def save(self, fname, remove_data=False):
+        '''
+        save a pickle of this instance
+        Parameters
+        ----------
+        fname : string or filehandle
+            fname can be a string to a file path or filename, or a filehandle.
+        remove_data : bool
+            If False (default), then the instance is pickled without changes.
+            If True, then all arrays with length nobs are set to None before
+            pickling. See the remove_data method.
+            In some cases not all arrays will be set to None.
+        Notes
+        -----
+        If remove_data is true and the model result does not implement a
+        remove_data method then this will raise an exception.
+        '''
+
+        from statsmodels.iolib.smpickle import save_pickle
+
+        if remove_data:
+            self.remove_data()
+
+        save_pickle(self, fname)
+
+    @classmethod
+    def load(cls, fname):
+        '''
+        load a pickle, (class method)
+        Parameters
+        ----------
+        fname : string or filehandle
+            fname can be a string to a file path or filename, or a filehandle.
+        Returns
+        -------
+        unpickled instance
+        '''
+
+        from statsmodels.iolib.smpickle import load_pickle
+        return load_pickle(fname)
+
+    def remove_data(self):
+        '''remove data arrays, all nobs arrays from result and model
+        This reduces the size of the instance, so it can be pickled with less
+        memory. Currently tested for use with predict from an unpickled
+        results and model instance.
+        .. warning:: Since data and some intermediate results have been removed
+           calculating new statistics that require them will raise exceptions.
+           The exception will occur the first time an attribute is accessed
+           that has been set to None.
+        Not fully tested for time series models, tsa, and might delete too much
+        for prediction or not all that would be possible.
+        The list of arrays to delete is maintained as an attribute of the
+        result and model instance, except for cached values. These lists could
+        be changed before calling remove_data.
+        '''
+        def wipe(obj, att):
+            #get to last element in attribute path
+            p = att.split('.')
+            att_ = p.pop(-1)
+            try:
+                obj_ = reduce(getattr, [obj] + p)
+
+                #print(repr(obj), repr(att))
+                #print(hasattr(obj_, att_))
+                if hasattr(obj_, att_):
+                    #print('removing3', att_)
+                    setattr(obj_, att_, None)
+            except AttributeError:
+                pass
+
+        model_attr = ['model.' + i for i in self.model._data_attr]
+        for att in self._data_attr + model_attr:
+            #print('removing', att)
+            wipe(self, att)
+
+        data_in_cache = getattr(self, 'data_in_cache', [])
+        data_in_cache += ['fittedvalues', 'resid', 'wresid']
+        for key in data_in_cache:
+            try:
+                self._cache[key] = None
+            except (AttributeError, KeyError):
+                pass
+
+def lzip(*args, **kwargs):
+    return list(zip(*args, **kwargs))

release/python/0.6.0/crankshaft/crankshaft/regression/glm/glm.py

@@ -0,0 +1,326 @@
+
+import numpy as np
+import numpy.linalg as la
+from pysal.spreg.utils import RegressionPropsY, spdot
+import pysal.spreg.user_output as USER
+from utils import cache_readonly
+from base import LikelihoodModelResults
+import family
+from iwls import iwls
+
+__all__ = ['GLM']
+
+class GLM(RegressionPropsY):
+    """
+    Generalised linear models. Can currently estimate Guassian, Poisson and
+    Logisitc regression coefficients. GLM object prepares model input and fit
+    method performs estimation which then returns a GLMResults object.
+
+    Parameters
+    ----------
+        y             : array
+                        n*1, dependent variable.
+        X             : array
+                        n*k, independent variable, exlcuding the constant.
+        family        : string
+                        Model type: 'Gaussian', 'Poisson', 'Binomial'
+
+    Attributes
+    ----------
+        y             : array
+                        n*1, dependent variable.
+        X             : array
+                        n*k, independent variable, including constant.
+        family        : string
+                        Model type: 'Gaussian', 'Poisson', 'logistic'
+        n             : integer
+                        Number of observations
+        k             : integer
+                        Number of independent variables
+        df_model      : float
+                        k-1, where k is the number of variables (including
+                        intercept)
+        df_residual   : float
+                        observations minus variables (n-k)
+        mean_y        : float
+                        Mean of y
+        std_y         : float
+                        Standard deviation of y
+        fit_params     : dict
+                        Parameters passed into fit method to define estimation
+                        routine.
+        normalized_cov_params   : array
+                                k*k, approximates [X.T*X]-1
+    """
+    def __init__(self, y, X, family=family.Gaussian(), constant=True):
+        """
+        Initialize class
+        """
+        self.n = USER.check_arrays(y, X)
+        USER.check_y(y, self.n)
+        self.y = y
+        if constant:
+            self.X = USER.check_constant(X)
+        else:
+            self.X = X
+        self.family = family
+        self.k = self.X.shape[1]
+        self.fit_params = {}
+
+    def fit(self, ini_betas=None, tol=1.0e-6, max_iter=200, solve='iwls'):
+        """
+        Method that fits a model with a particular estimation routine.
+
+        Parameters
+        ----------
+
+        ini_betas     : array
+                        k*1, initial coefficient values, including constant.
+                        Default is None, which calculates initial values during
+                        estimation.
+        tol:            float
+                        Tolerence for estimation convergence.
+        max_iter       : integer
+                        Maximum number of iterations if convergence not
+                        achieved.
+        solve         :string
+                       Technique to solve MLE equations.
+                       'iwls' = iteratively (re)weighted least squares (default)
+        """
+        self.fit_params['ini_betas'] = ini_betas
+        self.fit_params['tol'] = tol
+        self.fit_params['max_iter'] = max_iter
+        self.fit_params['solve']=solve
+        if solve.lower() == 'iwls':
+            params, predy, w, n_iter = iwls(self.y, self.X, self.family,
+                    ini_betas=ini_betas, tol=tol, max_iter=max_iter)
+            self.fit_params['n_iter'] = n_iter
+        return GLMResults(self, params.flatten(), predy, w)
+
+    @cache_readonly
+    def df_model(self):
+        return self.X.shape[1] - 1
+
+    @cache_readonly
+    def df_resid(self):
+        return self.n - self.df_model - 1
+
+class GLMResults(LikelihoodModelResults):
+    """
+    Results of estimated GLM and diagnostics.
+
+    Parameters
+    ----------
+        model         : GLM object
+                        Pointer to GLM object with estimation parameters.
+        params         : array
+                        k*1, estimared coefficients
+        mu         : array
+                        n*1, predicted y values.
+        w             : array
+                        n*1, final weight used for iwls
+
+    Attributes
+    ----------
+        model         : GLM Object
+                        Points to GLM object for which parameters have been
+                        estimated.
+        y             : array
+                        n*1, dependent variable.
+        x             : array
+                        n*k, independent variable, including constant.
+        family        : string
+                        Model type: 'Gaussian', 'Poisson', 'Logistic'
+        n             : integer
+                        Number of observations
+        k             : integer
+                        Number of independent variables
+        df_model      : float
+                        k-1, where k is the number of variables (including
+                        intercept)
+        df_residual   : float
+                        observations minus variables (n-k)
+        fit_params    : dict
+                        parameters passed into fit method to define estimation
+                        routine.
+        scale         : float
+                        sigma squared used for subsequent computations.
+        params         : array
+                        n*k, estimared beta coefficients
+        w             : array
+                        n*1, final weight values of x
+        mu            : array
+                        n*1, predicted value of y (i.e., fittedvalues)
+        cov_params    : array
+                        Variance covariance matrix (kxk) of betas which has been
+                        appropriately scaled by sigma-squared
+        bse           : array
+                        k*1, standard errors of betas
+        pvalues       : array
+                        k*1, two-tailed pvalues of parameters
+        tvalues       : array
+                        k*1, the tvalues of the standard errors
+        null          : array
+                        n*1, predicted values of y for null model
+        deviance      : float
+                        value of the deviance function evalued at params;
+                        see family.py for distribution-specific deviance
+        null_deviance : float
+                        value of the deviance function for the model fit with
+                        a constant as the only regressor
+        llf           : float
+                        value of the loglikelihood function evalued at params;
+                        see family.py for distribution-specific loglikelihoods
+        llnull       : float
+                        value of log-likelihood function evaluated at null
+        aic           : float 
+                        AIC
+        bic           : float 
+                        BIC
+        D2            : float
+                        percent deviance explained
+        adj_D2        : float
+                        adjusted percent deviance explained
+        pseudo_R2       : float
+                        McFadden's pseudo R2  (coefficient of determination) 
+        adj_pseudoR2    : float
+                        adjusted McFadden's pseudo R2
+        resid_response          : array
+                                  response residuals; defined as y-mu
+        resid_pearson           : array
+                                  Pearson residuals; defined as (y-mu)/sqrt(VAR(mu))
+                                  where VAR is the distribution specific variance
+                                  function; see family.py and varfuncs.py for more information.
+        resid_working           : array
+                                  Working residuals; the working residuals are defined as
+                                  resid_response/link'(mu); see links.py for the
+                                  derivatives of the link functions.
+
+        resid_anscombe          : array
+                                 Anscombe residuals; see family.py for 
+                                 distribution-specific Anscombe residuals.
+        
+        resid_deviance          : array
+                                 deviance residuals; see family.py for 
+                                 distribution-specific deviance residuals.
+
+        pearson_chi2            : float
+                                  chi-Squared statistic is defined as the sum 
+                                  of the squares of the Pearson residuals
+
+        normalized_cov_params   : array
+                                k*k, approximates [X.T*X]-1
+    """
+    def __init__(self, model, params, mu, w):
+        self.model = model
+        self.n = model.n
+        self.y = model.y.T.flatten()
+        self.X = model.X
+        self.k = model.k
+        self.family = model.family
+        self.fit_params = model.fit_params
+        self.params = params
+        self.w = w
+        self.mu = mu.flatten()
+        self._cache = {}
+    
+    @cache_readonly
+    def df_model(self):
+        return self.model.df_model
+
+    @cache_readonly
+    def df_resid(self):
+        return self.model.df_resid
+
+    @cache_readonly
+    def normalized_cov_params(self):
+        return la.inv(spdot(self.w.T, self.w))
+
+    @cache_readonly
+    def resid_response(self):
+        return (self.y-self.mu)
+
+    @cache_readonly
+    def resid_pearson(self):
+        return  ((self.y-self.mu) /
+                np.sqrt(self.family.variance(self.mu)))
+
+    @cache_readonly
+    def resid_working(self):
+        return (self.resid_response / self.family.link.deriv(self.mu))
+
+    @cache_readonly
+    def resid_anscombe(self):
+        return (self.family.resid_anscombe(self.y, self.mu))
+
+    @cache_readonly
+    def resid_deviance(self):
+        return (self.family.resid_dev(self.y, self.mu))
+
+    @cache_readonly
+    def pearson_chi2(self):
+        chisq = (self.y - self.mu)**2 / self.family.variance(self.mu)
+        chisqsum = np.sum(chisq)
+        return chisqsum
+
+    @cache_readonly
+    def null(self):
+        y = np.reshape(self.y, (-1,1))
+        model = self.model
+        X = np.ones((len(y), 1))
+        null_mod =  GLM(y, X, family=self.family, constant=False)
+        return null_mod.fit().mu
+   
+    @cache_readonly
+    def scale(self):
+        if isinstance(self.family, (family.Binomial, family.Poisson)):
+            return 1.
+        else:
+            return (((np.power(self.resid_response, 2) /
+                         self.family.variance(self.mu))).sum() /
+                        (self.df_resid))
+    @cache_readonly
+    def deviance(self):
+        return self.family.deviance(self.y, self.mu)
+
+    @cache_readonly
+    def null_deviance(self):
+        return self.family.deviance(self.y, self.null)
+   
+    @cache_readonly
+    def llnull(self):
+        return self.family.loglike(self.y, self.null, scale=self.scale)
+
+    @cache_readonly
+    def llf(self):
+        return self.family.loglike(self.y, self.mu, scale=self.scale)
+    
+    @cache_readonly
+    def aic(self):
+        if isinstance(self.family, family.QuasiPoisson):
+        	return np.nan
+        else:
+            return -2 * self.llf + 2*(self.df_model+1)
+
+    @cache_readonly
+    def bic(self):
+        return (self.deviance -
+                (self.model.n - self.df_model - 1) *
+                np.log(self.model.n))
+    
+    @cache_readonly
+    def D2(self):
+        return 1 - (self.deviance / self.null_deviance)
+
+    @cache_readonly
+    def adj_D2(self):
+        return 1.0 - (float(self.n) - 1.0)/(float(self.n) - float(self.k)) * (1.0-self.D2)
+    
+    @cache_readonly
+    def pseudoR2(self):
+        return 1 - (self.llf/self.llnull)
+    
+    @cache_readonly
+    def adj_pseudoR2(self):
+        return 1 - ((self.llf-self.k)/self.llnull)
+    

release/python/0.6.0/crankshaft/crankshaft/regression/glm/iwls.py

@@ -0,0 +1,84 @@
+import numpy as np
+import numpy.linalg as la
+from scipy import sparse as sp
+from scipy.sparse import linalg as spla
+from pysal.spreg.utils import spdot, spmultiply
+from family import Binomial, Poisson
+
+def _compute_betas(y, x):
+    """
+    compute MLE coefficients using iwls routine
+
+    Methods: p189, Iteratively (Re)weighted Least Squares (IWLS),
+    Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+    Geographically weighted regression: the analysis of spatially varying relationships.
+    """
+    xT = x.T
+    xtx = spdot(xT, x)
+    xtx_inv = la.inv(xtx)
+    xtx_inv = sp.csr_matrix(xtx_inv)
+    xTy = spdot(xT, y, array_out=False)
+    betas = spdot(xtx_inv, xTy)
+    return betas
+
+def _compute_betas_gwr(y, x, wi):
+    """
+    compute MLE coefficients using iwls routine
+
+    Methods: p189, Iteratively (Re)weighted Least Squares (IWLS),
+    Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+    Geographically weighted regression: the analysis of spatially varying relationships.
+    """
+    xT = (x * wi).T
+    xtx = np.dot(xT, x)
+    xtx_inv = la.inv(xtx)
+    xtx_inv_xt = np.dot(xtx_inv, xT)
+    betas = np.dot(xtx_inv_xt, y)
+    return betas, xtx_inv_xt
+
+def iwls(y, x, family, offset=1.0, ini_betas=None, tol=1.0e-8, max_iter=200, wi=None):
+    """
+    Iteratively re-weighted least squares estimation routine
+    """
+    n_iter = 0
+    diff = 1.0e6
+    if ini_betas is None:
+        betas = np.zeros((x.shape[1], 1), np.float)
+    else:
+        betas = ini_betas
+    if isinstance(family, Binomial):
+        y = family.link._clean(y)
+    if isinstance(family, Poisson):
+    	y_off = y/offset
+        y_off = family.starting_mu(y_off)
+        v = family.predict(y_off)
+        mu = family.starting_mu(y)
+    else:
+        mu = family.starting_mu(y)
+        v = family.predict(mu)
+    
+    while diff > tol and n_iter < max_iter:
+    	n_iter += 1
+        w = family.weights(mu)
+        z = v + (family.link.deriv(mu)*(y-mu))
+        w = np.sqrt(w)
+        if type(x) != np.ndarray:
+        	w = sp.csr_matrix(w)
+        	z = sp.csr_matrix(z)
+        wx = spmultiply(x, w, array_out=False)
+        wz = spmultiply(z, w, array_out=False)
+        if wi is None:
+            n_betas = _compute_betas(wz, wx)
+        else:
+            n_betas, xtx_inv_xt = _compute_betas_gwr(wz, wx, wi)
+        v = spdot(x, n_betas)
+        mu  = family.fitted(v)
+        if isinstance(family, Poisson):
+            mu = mu * offset
+        diff = min(abs(n_betas-betas))
+        betas = n_betas
+        
+    if wi is None:
+        return betas, mu, wx, n_iter
+    else:
+        return betas, mu, v, w, z, xtx_inv_xt, n_iter

release/python/0.6.0/crankshaft/crankshaft/regression/glm/links.py

@@ -0,0 +1,953 @@
+'''
+Defines the link functions to be used with GLM and GEE families.
+'''
+
+import numpy as np
+import scipy.stats
+FLOAT_EPS = np.finfo(float).eps
+
+
+class Link(object):
+    """
+    A generic link function for one-parameter exponential family.
+
+    `Link` does nothing, but lays out the methods expected of any subclass.
+    """
+
+    def __call__(self, p):
+        """
+        Return the value of the link function.  This is just a placeholder.
+
+        Parameters
+        ----------
+        p : array-like
+            Probabilities
+
+        Returns
+        -------
+        g(p) : array-like
+            The value of the link function g(p) = z
+        """
+        return NotImplementedError
+
+    def inverse(self, z):
+        """
+        Inverse of the link function.  Just a placeholder.
+
+        Parameters
+        ----------
+        z : array-like
+            `z` is usually the linear predictor of the transformed variable
+            in the IRLS algorithm for GLM.
+
+        Returns
+        -------
+        g^(-1)(z) : array
+            The value of the inverse of the link function g^(-1)(z) = p
+
+
+        """
+        return NotImplementedError
+
+    def deriv(self, p):
+        """
+        Derivative of the link function g'(p).  Just a placeholder.
+
+        Parameters
+        ----------
+        p : array-like
+
+        Returns
+        -------
+        g'(p) : array
+            The value of the derivative of the link function g'(p)
+        """
+        return NotImplementedError
+
+    def deriv2(self, p):
+        """Second derivative of the link function g''(p)
+
+        implemented through numerical differentiation
+        """
+        from statsmodels.tools.numdiff import approx_fprime_cs
+        # TODO: workaround proplem with numdiff for 1d
+        return np.diag(approx_fprime_cs(p, self.deriv))
+
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse link function g^(-1)(z).
+
+        Notes
+        -----
+        This reference implementation gives the correct result but is
+        inefficient, so it can be overriden in subclasses.
+
+        Parameters
+        ----------
+        z : array-like
+            `z` is usually the linear predictor for a GLM or GEE model.
+
+        Returns
+        -------
+        g'^(-1)(z) : array
+            The value of the derivative of the inverse of the link function
+
+        """
+        return 1 / self.deriv(self.inverse(z))
+
+
+class Logit(Link):
+    """
+    The logit transform
+
+    Notes
+    -----
+    call and derivative use a private method _clean to make trim p by
+    machine epsilon so that p is in (0,1)
+
+    Alias of Logit:
+    logit = Logit()
+    """
+
+    def _clean(self, p):
+        """
+        Clip logistic values to range (eps, 1-eps)
+
+        Parameters
+        -----------
+        p : array-like
+            Probabilities
+
+        Returns
+        --------
+        pclip : array
+            Clipped probabilities
+        """
+        return np.clip(p, FLOAT_EPS, 1. - FLOAT_EPS)
+
+    def __call__(self, p):
+        """
+        The logit transform
+
+        Parameters
+        ----------
+        p : array-like
+            Probabilities
+
+        Returns
+        -------
+        z : array
+            Logit transform of `p`
+
+        Notes
+        -----
+        g(p) = log(p / (1 - p))
+        """
+        p = self._clean(p)
+        return np.log(p / (1. - p))
+
+    def inverse(self, z):
+        """
+        Inverse of the logit transform
+
+        Parameters
+        ----------
+        z : array-like
+            The value of the logit transform at `p`
+
+        Returns
+        -------
+        p : array
+            Probabilities
+
+        Notes
+        -----
+        g^(-1)(z) = exp(z)/(1+exp(z))
+        """
+        z = np.asarray(z)
+        t = np.exp(-z)
+        return 1. / (1. + t)
+
+    def deriv(self, p):
+
+        """
+        Derivative of the logit transform
+
+        Parameters
+        ----------
+        p: array-like
+            Probabilities
+
+        Returns
+        -------
+        g'(p) : array
+            Value of the derivative of logit transform at `p`
+
+        Notes
+        -----
+        g'(p) = 1 / (p * (1 - p))
+
+        Alias for `Logit`:
+        logit = Logit()
+        """
+        p = self._clean(p)
+        return 1. / (p * (1 - p))
+
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse of the logit transform
+
+        Parameters
+        ----------
+        z : array-like
+            `z` is usually the linear predictor for a GLM or GEE model.
+
+        Returns
+        -------
+        g'^(-1)(z) : array
+            The value of the derivative of the inverse of the logit function
+
+        """
+        t = np.exp(z)
+        return t/(1 + t)**2
+
+
+    def deriv2(self, p):
+        """
+        Second derivative of the logit function.
+
+        Parameters
+        ----------
+        p : array-like
+            probabilities
+
+        Returns
+        -------
+        g''(z) : array
+            The value of the second derivative of the logit function
+        """
+        v = p * (1 - p)
+        return (2*p - 1) / v**2
+
+class logit(Logit):
+    pass
+
+
+class Power(Link):
+    """
+    The power transform
+
+    Parameters
+    ----------
+    power : float
+        The exponent of the power transform
+
+    Notes
+    -----
+    Aliases of Power:
+    inverse = Power(power=-1)
+    sqrt = Power(power=.5)
+    inverse_squared = Power(power=-2.)
+    identity = Power(power=1.)
+    """
+
+    def __init__(self, power=1.):
+        self.power = power
+
+    def __call__(self, p):
+        """
+        Power transform link function
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        z : array-like
+            Power transform of x
+
+        Notes
+        -----
+        g(p) = x**self.power
+        """
+
+        z = np.power(p, self.power)
+        return z
+
+    def inverse(self, z):
+        """
+        Inverse of the power transform link function
+
+        Parameters
+        ----------
+        `z` : array-like
+            Value of the transformed mean parameters at `p`
+
+        Returns
+        -------
+        `p` : array
+            Mean parameters
+
+        Notes
+        -----
+        g^(-1)(z`) = `z`**(1/`power`)
+        """
+
+        p = np.power(z, 1. / self.power)
+        return p
+
+    def deriv(self, p):
+        """
+        Derivative of the power transform
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        --------
+        g'(p) : array
+            Derivative of power transform of `p`
+
+        Notes
+        -----
+        g'(`p`) = `power` * `p`**(`power` - 1)
+        """
+        return self.power * np.power(p, self.power - 1)
+
+    def deriv2(self, p):
+        """
+        Second derivative of the power transform
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        --------
+        g''(p) : array
+            Second derivative of the power transform of `p`
+
+        Notes
+        -----
+        g''(`p`) = `power` * (`power` - 1) * `p`**(`power` - 2)
+        """
+        return self.power * (self.power - 1) * np.power(p, self.power - 2)
+
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse of the power transform
+
+        Parameters
+        ----------
+        z : array-like
+            `z` is usually the linear predictor for a GLM or GEE model.
+
+        Returns
+        -------
+        g^(-1)'(z) : array
+            The value of the derivative of the inverse of the power transform
+        function
+        """
+        return np.power(z, (1 - self.power)/self.power) / self.power
+
+
+class inverse_power(Power):
+    """
+    The inverse transform
+
+    Notes
+    -----
+    g(p) = 1/p
+
+    Alias of statsmodels.family.links.Power(power=-1.)
+    """
+    def __init__(self):
+        super(inverse_power, self).__init__(power=-1.)
+
+
+class sqrt(Power):
+    """
+    The square-root transform
+
+    Notes
+    -----
+    g(`p`) = sqrt(`p`)
+
+    Alias of statsmodels.family.links.Power(power=.5)
+    """
+    def __init__(self):
+        super(sqrt, self).__init__(power=.5)
+
+
+class inverse_squared(Power):
+    """
+    The inverse squared transform
+
+    Notes
+    -----
+    g(`p`) = 1/(`p`\ \*\*2)
+
+    Alias of statsmodels.family.links.Power(power=2.)
+    """
+    def __init__(self):
+        super(inverse_squared, self).__init__(power=-2.)
+
+
+class identity(Power):
+    """
+    The identity transform
+
+    Notes
+    -----
+    g(`p`) = `p`
+
+    Alias of statsmodels.family.links.Power(power=1.)
+    """
+    def __init__(self):
+        super(identity, self).__init__(power=1.)
+
+
+class Log(Link):
+    """
+    The log transform
+
+    Notes
+    -----
+    call and derivative call a private method _clean to trim the data by
+    machine epsilon so that p is in (0,1). log is an alias of Log.
+    """
+
+    def _clean(self, x):
+        return np.clip(x, FLOAT_EPS, np.inf)
+
+    def __call__(self, p, **extra):
+        """
+        Log transform link function
+
+        Parameters
+        ----------
+        x : array-like
+            Mean parameters
+
+        Returns
+        -------
+        z : array
+            log(x)
+
+        Notes
+        -----
+        g(p) = log(p)
+        """
+        x = self._clean(p)
+        return np.log(x)
+
+    def inverse(self, z):
+        """
+        Inverse of log transform link function
+
+        Parameters
+        ----------
+        z : array
+            The inverse of the link function at `p`
+
+        Returns
+        -------
+        p : array
+            The mean probabilities given the value of the inverse `z`
+
+        Notes
+        -----
+        g^{-1}(z) = exp(z)
+        """
+        return np.exp(z)
+
+    def deriv(self, p):
+        """
+        Derivative of log transform link function
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        g'(p) : array
+            derivative of log transform of x
+
+        Notes
+        -----
+        g'(x) = 1/x
+        """
+        p = self._clean(p)
+        return 1. / p
+
+    def deriv2(self, p):
+        """
+        Second derivative of the log transform link function
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        g''(p) : array
+            Second derivative of log transform of x
+
+        Notes
+        -----
+        g''(x) = -1/x^2
+        """
+        p = self._clean(p)
+        return -1. / p**2
+
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse of the log transform link function
+
+        Parameters
+        ----------
+        z : array
+            The inverse of the link function at `p`
+
+        Returns
+        -------
+        g^(-1)'(z) : array
+            The value of the derivative of the inverse of the log function,
+            the exponential function
+        """
+        return np.exp(z)
+
+
+class log(Log):
+    """
+    The log transform
+
+    Notes
+    -----
+    log is a an alias of Log.
+    """
+    pass
+
+
+# TODO: the CDFLink is untested
+class CDFLink(Logit):
+    """
+    The use the CDF of a scipy.stats distribution
+
+    CDFLink is a subclass of logit in order to use its _clean method
+    for the link and its derivative.
+
+    Parameters
+    ----------
+    dbn : scipy.stats distribution
+        Default is dbn=scipy.stats.norm
+
+    Notes
+    -----
+    The CDF link is untested.
+    """
+
+    def __init__(self, dbn=scipy.stats.norm):
+        self.dbn = dbn
+
+    def __call__(self, p):
+        """
+        CDF link function
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        z : array
+            (ppf) inverse of CDF transform of p
+
+        Notes
+        -----
+        g(`p`) = `dbn`.ppf(`p`)
+        """
+        p = self._clean(p)
+        return self.dbn.ppf(p)
+
+    def inverse(self, z):
+        """
+        The inverse of the CDF link
+
+        Parameters
+        ----------
+        z : array-like
+            The value of the inverse of the link function at `p`
+
+        Returns
+        -------
+        p : array
+            Mean probabilities.  The value of the inverse of CDF link of `z`
+
+        Notes
+        -----
+        g^(-1)(`z`) = `dbn`.cdf(`z`)
+        """
+        return self.dbn.cdf(z)
+
+    def deriv(self, p):
+        """
+        Derivative of CDF link
+
+        Parameters
+        ----------
+        p : array-like
+            mean parameters
+
+        Returns
+        -------
+        g'(p) : array
+            The derivative of CDF transform at `p`
+
+        Notes
+        -----
+        g'(`p`) = 1./ `dbn`.pdf(`dbn`.ppf(`p`))
+        """
+        p = self._clean(p)
+        return 1. / self.dbn.pdf(self.dbn.ppf(p))
+
+    def deriv2(self, p):
+        """
+        Second derivative of the link function g''(p)
+
+        implemented through numerical differentiation
+        """
+        from statsmodels.tools.numdiff import approx_fprime
+        p = np.atleast_1d(p)
+        # Note: special function for norm.ppf does not support complex
+        return np.diag(approx_fprime(p, self.deriv, centered=True))
+
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse of the CDF transformation link function
+
+        Parameters
+        ----------
+        z : array
+            The inverse of the link function at `p`
+
+        Returns
+        -------
+        g^(-1)'(z) : array
+            The value of the derivative of the inverse of the logit function
+        """
+        return 1/self.deriv(self.inverse(z))
+
+
+class probit(CDFLink):
+    """
+    The probit (standard normal CDF) transform
+
+    Notes
+    --------
+    g(p) = scipy.stats.norm.ppf(p)
+
+    probit is an alias of CDFLink.
+    """
+    pass
+
+
+class cauchy(CDFLink):
+    """
+    The Cauchy (standard Cauchy CDF) transform
+
+    Notes
+    -----
+    g(p) = scipy.stats.cauchy.ppf(p)
+
+    cauchy is an alias of CDFLink with dbn=scipy.stats.cauchy
+    """
+
+    def __init__(self):
+        super(cauchy, self).__init__(dbn=scipy.stats.cauchy)
+
+    def deriv2(self, p):
+        """
+        Second derivative of the Cauchy link function.
+
+        Parameters
+        ----------
+        p: array-like
+            Probabilities
+
+        Returns
+        -------
+        g''(p) : array
+            Value of the second derivative of Cauchy link function at `p`
+        """
+        a = np.pi * (p - 0.5)
+        d2 = 2 * np.pi**2 * np.sin(a) / np.cos(a)**3
+        return d2
+
+class CLogLog(Logit):
+    """
+    The complementary log-log transform
+
+    CLogLog inherits from Logit in order to have access to its _clean method
+    for the link and its derivative.
+
+    Notes
+    -----
+    CLogLog is untested.
+    """
+    def __call__(self, p):
+        """
+        C-Log-Log transform link function
+
+        Parameters
+        ----------
+        p : array
+            Mean parameters
+
+        Returns
+        -------
+        z : array
+            The CLogLog transform of `p`
+
+        Notes
+        -----
+        g(p) = log(-log(1-p))
+        """
+        p = self._clean(p)
+        return np.log(-np.log(1 - p))
+
+    def inverse(self, z):
+        """
+        Inverse of C-Log-Log transform link function
+
+
+        Parameters
+        ----------
+        z : array-like
+            The value of the inverse of the CLogLog link function at `p`
+
+        Returns
+        -------
+        p : array
+            Mean parameters
+
+        Notes
+        -----
+        g^(-1)(`z`) = 1-exp(-exp(`z`))
+        """
+        return 1 - np.exp(-np.exp(z))
+
+    def deriv(self, p):
+        """
+        Derivative of C-Log-Log transform link function
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        g'(p) : array
+            The derivative of the CLogLog transform link function
+
+        Notes
+        -----
+        g'(p) = - 1 / ((p-1)*log(1-p))
+        """
+        p = self._clean(p)
+        return 1. / ((p - 1) * (np.log(1 - p)))
+
+    def deriv2(self, p):
+        """
+        Second derivative of the C-Log-Log ink function
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        g''(p) : array
+            The second derivative of the CLogLog link function
+        """
+        p = self._clean(p)
+        fl = np.log(1 - p)
+        d2 = -1 / ((1 - p)**2 * fl)
+        d2 *= 1 + 1 / fl
+        return d2
+
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse of the C-Log-Log transform link function
+
+        Parameters
+        ----------
+        z : array-like
+            The value of the inverse of the CLogLog link function at `p`
+
+        Returns
+        -------
+        g^(-1)'(z) : array
+            The derivative of the inverse of the CLogLog link function
+        """
+        return np.exp(z - np.exp(z))
+
+
+class cloglog(CLogLog):
+    """
+    The CLogLog transform link function.
+
+    Notes
+    -----
+    g(`p`) = log(-log(1-`p`))
+
+    cloglog is an alias for CLogLog
+    cloglog = CLogLog()
+    """
+    pass
+
+
+class NegativeBinomial(object):
+    '''
+    The negative binomial link function
+
+    Parameters
+    ----------
+    alpha : float, optional
+        Alpha is the ancillary parameter of the Negative Binomial link
+        function. It is assumed to be nonstochastic.  The default value is 1.
+        Permissible values are usually assumed to be in (.01, 2).
+    '''
+
+    def __init__(self, alpha=1.):
+        self.alpha = alpha
+
+    def _clean(self, x):
+        return np.clip(x, FLOAT_EPS, np.inf)
+
+    def __call__(self, p):
+        '''
+        Negative Binomial transform link function
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        z : array
+            The negative binomial transform of `p`
+
+        Notes
+        -----
+        g(p) = log(p/(p + 1/alpha))
+        '''
+        p = self._clean(p)
+        return np.log(p/(p + 1/self.alpha))
+
+    def inverse(self, z):
+        '''
+        Inverse of the negative binomial transform
+
+        Parameters
+        -----------
+        z : array-like
+            The value of the inverse of the negative binomial link at `p`.
+
+        Returns
+        -------
+        p : array
+            Mean parameters
+
+        Notes
+        -----
+        g^(-1)(z) = exp(z)/(alpha*(1-exp(z)))
+        '''
+        return -1/(self.alpha * (1 - np.exp(-z)))
+
+    def deriv(self, p):
+        '''
+        Derivative of the negative binomial transform
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        g'(p) : array
+            The derivative of the negative binomial transform link function
+
+        Notes
+        -----
+        g'(x) = 1/(x+alpha*x^2)
+        '''
+        return 1/(p + self.alpha * p**2)
+
+    def deriv2(self,p):
+        '''
+        Second derivative of the negative binomial link function.
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        g''(p) : array
+            The second derivative of the negative binomial transform link
+            function
+
+        Notes
+        -----
+        g''(x) = -(1+2*alpha*x)/(x+alpha*x^2)^2
+        '''
+        numer = -(1 + 2 * self.alpha * p)
+        denom = (p + self.alpha * p**2)**2
+        return numer / denom
+
+    def inverse_deriv(self, z):
+        '''
+        Derivative of the inverse of the negative binomial transform
+
+        Parameters
+        -----------
+        z : array-like
+            Usually the linear predictor for a GLM or GEE model
+
+        Returns
+        -------
+        g^(-1)'(z) : array
+            The value of the derivative of the inverse of the negative
+            binomial link
+        '''
+        t = np.exp(z)
+        return t / (self.alpha * (1-t)**2)
+
+
+class nbinom(NegativeBinomial):
+    """
+    The negative binomial link function.
+
+    Notes
+    -----
+    g(p) = log(p/(p + 1/alpha))
+
+    nbinom is an alias of NegativeBinomial.
+    nbinom = NegativeBinomial(alpha=1.)
+    """
+    pass

release/python/0.6.0/crankshaft/crankshaft/regression/glm/tests/test_glm.py

@@ -0,0 +1,993 @@
+"""
+Tests for generalized linear models. Majority of code either directly borrowed
+or closely adapted from statsmodels package. Model results verfiied using glm
+function in R and GLM function in statsmodels.
+"""
+
+__author__ = 'Taylor Oshan tayoshan@gmail.com'
+
+from pysal.contrib.glm.glm import GLM
+from pysal.contrib.glm.family import Gaussian, Poisson, Binomial, QuasiPoisson
+import numpy as np
+import pysal
+import unittest
+import math
+
+
+class TestGaussian(unittest.TestCase):
+    """
+    Tests for Poisson GLM
+    """
+
+    def setUp(self):
+        db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+        y = np.array(db.by_col("HOVAL"))
+        self.y = np.reshape(y, (49,1))
+        X = []
+        X.append(db.by_col("INC"))
+        X.append(db.by_col("CRIME"))
+        self.X = np.array(X).T
+
+    def testIWLS(self):
+        model = GLM(self.y, self.X, family=Gaussian())
+        results = model.fit()
+        self.assertEqual(results.n, 49)
+        self.assertEqual(results.df_model, 2)
+        self.assertEqual(results.df_resid, 46)
+        self.assertEqual(results.aic, 408.73548964604873)
+        self.assertEqual(results.bic, 10467.991340493107)
+        self.assertEqual(results.deviance, 10647.015074206196)
+        self.assertEqual(results.llf, -201.36774482302437)
+        self.assertEqual(results.null_deviance, 16367.794631703124)
+        self.assertEqual(results.scale, 231.45684943926514)
+        np.testing.assert_allclose(results.params, [ 46.42818268,   0.62898397,
+            -0.48488854])
+        np.testing.assert_allclose(results.bse, [ 13.19175703,   0.53591045,
+            0.18267291])
+        np.testing.assert_allclose(results.cov_params(), 
+                [[  1.74022453e+02,  -6.52060364e+00,  -2.15109867e+00],
+                [ -6.52060364e+00,   2.87200008e-01,   6.80956787e-02],
+                [ -2.15109867e+00,   6.80956787e-02,   3.33693910e-02]])
+        np.testing.assert_allclose(results.tvalues, [ 3.51948437,  1.17367365,
+            -2.65440864])
+        np.testing.assert_allclose(results.pvalues, [ 0.00043239,  0.24052577,
+            0.00794475], atol=1.0e-8)
+        np.testing.assert_allclose(results.conf_int(), 
+                [[ 20.57281401,  72.28355135],
+                [ -0.42138121,   1.67934915],
+                [ -0.84292086,  -0.12685622]])
+        np.testing.assert_allclose(results.normalized_cov_params, 
+                [[  7.51857004e-01,  -2.81720055e-02,  -9.29373521e-03],
+                [ -2.81720055e-02,   1.24083607e-03,   2.94204638e-04],
+                [ -9.29373521e-03,   2.94204638e-04,   1.44171110e-04]])
+        np.testing.assert_allclose(results.mu, 
+                [ 51.08752105,  50.66601521,  41.61367567,  33.53969014,
+                28.90638232,  43.87074227,  51.64910882,  34.92671563,
+                42.69267622,  38.49449134,  20.92815471,  25.25228436,
+                29.78223486,  25.02403635,  29.07959539,  24.63352275,
+                34.71372149,  33.40443052,  27.29864225,  65.86219802,
+                33.69854751,  37.44976435,  50.01304928,  36.81219959,
+                22.02674837,  31.64775955,  27.63563294,  23.7697291 ,
+                22.43119725,  21.76987089,  48.51169321,  49.05891819,
+                32.31656426,  44.20550354,  35.49244888,  51.27811308,
+                36.55047181,  27.37048914,  48.78812922,  57.31744163,
+                51.22914162,  54.70515578,  37.06622277,  44.5075759 ,
+                41.24328983,  49.93821824,  44.85644299,  40.93838609,  47.32045464])
+        self.assertEqual(results.pearson_chi2, 10647.015074206196)
+        np.testing.assert_allclose(results.resid_response, 
+                [ 29.37948195,  -6.09901421, -15.26367567,  -0.33968914,
+                -5.68138232, -15.12074227,  23.35089118,   2.19828437,
+                9.90732178,  57.90551066,  -1.22815371,  -5.35228436,
+                11.91776614,  17.87596565, -11.07959539,  -5.83352375,
+                7.03627851,  26.59556948,   3.30135775,  15.40479998,
+                -13.72354751,  -6.99976335,  -2.28004728,  16.38780141,
+                -4.12674837, -11.34776055,   6.46436506,  -0.9197291 ,
+                10.06880275,   0.73012911, -16.71169421,  -8.75891919,
+                -8.71656426, -15.75550254,  -8.49244888, -14.97811408,
+                6.74952719,  -4.67048814,  -9.18813122,   4.63255937,
+                -9.12914362, -10.37215578, -11.36622177, -11.0075759 ,
+                -13.51028983,  26.16177976,  -2.35644299, -14.13838709, -11.52045564])
+        np.testing.assert_allclose(results.resid_working, 
+                [ 29.37948195,  -6.09901421, -15.26367567,  -0.33968914,
+                -5.68138232, -15.12074227,  23.35089118,   2.19828437,
+                9.90732178,  57.90551066,  -1.22815371,  -5.35228436,
+                11.91776614,  17.87596565, -11.07959539,  -5.83352375,
+                7.03627851,  26.59556948,   3.30135775,  15.40479998,
+                -13.72354751,  -6.99976335,  -2.28004728,  16.38780141,
+                -4.12674837, -11.34776055,   6.46436506,  -0.9197291 ,
+                10.06880275,   0.73012911, -16.71169421,  -8.75891919,
+                -8.71656426, -15.75550254,  -8.49244888, -14.97811408,
+                6.74952719,  -4.67048814,  -9.18813122,   4.63255937,
+                -9.12914362, -10.37215578, -11.36622177, -11.0075759 ,
+                -13.51028983,  26.16177976,  -2.35644299, -14.13838709, -11.52045564])
+        np.testing.assert_allclose(results.resid_pearson, 
+                [ 29.37948195,  -6.09901421, -15.26367567,  -0.33968914,
+                -5.68138232, -15.12074227,  23.35089118,   2.19828437,
+                9.90732178,  57.90551066,  -1.22815371,  -5.35228436,
+                11.91776614,  17.87596565, -11.07959539,  -5.83352375,
+                7.03627851,  26.59556948,   3.30135775,  15.40479998,
+                -13.72354751,  -6.99976335,  -2.28004728,  16.38780141,
+                -4.12674837, -11.34776055,   6.46436506,  -0.9197291 ,
+                10.06880275,   0.73012911, -16.71169421,  -8.75891919,
+                -8.71656426, -15.75550254,  -8.49244888, -14.97811408,
+                6.74952719,  -4.67048814,  -9.18813122,   4.63255937,
+                -9.12914362, -10.37215578, -11.36622177, -11.0075759 ,
+                -13.51028983,  26.16177976,  -2.35644299, -14.13838709, -11.52045564])
+        np.testing.assert_allclose(results.resid_anscombe, 
+                [ 29.37948195,  -6.09901421, -15.26367567,  -0.33968914,
+                -5.68138232, -15.12074227,  23.35089118,   2.19828437,
+                9.90732178,  57.90551066,  -1.22815371,  -5.35228436,
+                11.91776614,  17.87596565, -11.07959539,  -5.83352375,
+                7.03627851,  26.59556948,   3.30135775,  15.40479998,
+                -13.72354751,  -6.99976335,  -2.28004728,  16.38780141,
+                -4.12674837, -11.34776055,   6.46436506,  -0.9197291 ,
+                10.06880275,   0.73012911, -16.71169421,  -8.75891919,
+                -8.71656426, -15.75550254,  -8.49244888, -14.97811408,
+                6.74952719,  -4.67048814,  -9.18813122,   4.63255937,
+                -9.12914362, -10.37215578, -11.36622177, -11.0075759 ,
+                -13.51028983,  26.16177976,  -2.35644299, -14.13838709, -11.52045564])
+        np.testing.assert_allclose(results.resid_deviance, 
+                [ 29.37948195,  -6.09901421, -15.26367567,  -0.33968914,
+                -5.68138232, -15.12074227,  23.35089118,   2.19828437,
+                9.90732178,  57.90551066,  -1.22815371,  -5.35228436,
+                11.91776614,  17.87596565, -11.07959539,  -5.83352375,
+                7.03627851,  26.59556948,   3.30135775,  15.40479998,
+                -13.72354751,  -6.99976335,  -2.28004728,  16.38780141,
+                -4.12674837, -11.34776055,   6.46436506,  -0.9197291 ,
+                10.06880275,   0.73012911, -16.71169421,  -8.75891919,
+                -8.71656426, -15.75550254,  -8.49244888, -14.97811408,
+                6.74952719,  -4.67048814,  -9.18813122,   4.63255937,
+                -9.12914362, -10.37215578, -11.36622177, -11.0075759 ,
+                -13.51028983,  26.16177976,  -2.35644299, -14.13838709, -11.52045564])
+        np.testing.assert_allclose(results.null, 
+                [ 38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,  38.43622447])
+        self.assertAlmostEqual(results.D2, .349514377851)
+        self.assertAlmostEqual(results.adj_D2, 0.32123239427957673)
+
+class TestPoisson(unittest.TestCase):
+
+    def setUp(self):
+        db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+        y = np.array(db.by_col("HOVAL"))
+        y = np.reshape(y, (49,1))
+        self.y = np.round(y).astype(int)
+        X = []
+        X.append(db.by_col("INC"))
+        X.append(db.by_col("CRIME"))
+        self.X = np.array(X).T
+
+    def testIWLS(self):
+        model = GLM(self.y, self.X, family=Poisson())
+        results = model.fit()
+        self.assertEqual(results.n, 49)
+        self.assertEqual(results.df_model, 2)
+        self.assertEqual(results.df_resid, 46)
+        self.assertAlmostEqual(results.aic, 500.85184179938756)
+        self.assertAlmostEqual(results.bic, 51.436404535087661)
+        self.assertAlmostEqual(results.deviance, 230.46013824817649)
+        self.assertAlmostEqual(results.llf, -247.42592089969378)
+        self.assertAlmostEqual(results.null_deviance, 376.97293610347361)
+        self.assertEqual(results.scale, 1.0)
+        np.testing.assert_allclose(results.params, [ 3.92159085,  0.01183491,
+            -0.01371397], atol=1.0e-8)
+        np.testing.assert_allclose(results.bse, [ 0.13049161,  0.00511599,
+            0.00193769], atol=1.0e-8)
+        np.testing.assert_allclose(results.cov_params(), 
+                [[  1.70280610e-02,  -6.18628383e-04,  -2.21386966e-04],
+                [ -6.18628383e-04,   2.61733917e-05,   6.77496445e-06],
+                [ -2.21386966e-04,   6.77496445e-06,   3.75463502e-06]])
+        np.testing.assert_allclose(results.tvalues, [ 30.0524361 ,   2.31331634,
+            -7.07748998])
+        np.testing.assert_allclose(results.pvalues, [  2.02901657e-198,
+            2.07052532e-002,   1.46788805e-012])
+        np.testing.assert_allclose(results.conf_int(), 
+                [[  3.66583199e+00,   4.17734972e+00],
+                [  1.80774841e-03,   2.18620753e-02],
+                [ -1.75117666e-02,  -9.91616901e-03]])
+        np.testing.assert_allclose(results.normalized_cov_params, 
+                [[  1.70280610e-02,  -6.18628383e-04,  -2.21386966e-04],
+                [ -6.18628383e-04,   2.61733917e-05,   6.77496445e-06],
+                [ -2.21386966e-04,   6.77496445e-06,   3.75463502e-06]])
+        np.testing.assert_allclose(results.mu, 
+                [ 51.26831574,  50.15022766,  40.06142973,  34.13799739,
+                28.76119226,  42.6836241 ,  55.64593703,  34.08277997,
+                40.90389582,  37.19727958,  23.47459217,  26.12384057,
+                29.78303507,  25.96888223,  29.14073823,  26.04369592,
+                34.18996367,  32.28924005,  27.42284396,  72.69207879,
+                33.05316347,  36.52276972,  49.2551479 ,  35.33439632,
+                24.07252457,  31.67153709,  27.81699478,  25.38021219,
+                24.31759259,  23.13586161,  48.40724678,  48.57969818,
+                31.92596006,  43.3679231 ,  34.32925819,  51.78908089,
+                34.49778584,  27.56236198,  48.34273194,  57.50829097,
+                50.66038226,  54.68701352,  35.77103116,  43.21886784,
+                40.07615759,  49.98658004,  43.13352883,  40.28520774,  46.28910294])
+        self.assertAlmostEqual(results.pearson_chi2, 264.62262932090221)
+        np.testing.assert_allclose(results.resid_response, 
+                [ 28.73168426,  -5.15022766, -14.06142973,  -1.13799739,
+                -5.76119226, -13.6836241 ,  19.35406297,   2.91722003,
+                12.09610418,  58.80272042,  -3.47459217,  -6.12384057,
+                12.21696493,  17.03111777, -11.14073823,  -7.04369592,
+                7.81003633,  27.71075995,   3.57715604,   8.30792121,
+                -13.05316347,  -6.52276972,  -1.2551479 ,  17.66560368,
+                -6.07252457, -11.67153709,   6.18300522,  -2.38021219,
+                7.68240741,  -1.13586161, -16.40724678,  -8.57969818,
+                -7.92596006, -15.3679231 ,  -7.32925819, -15.78908089,
+                8.50221416,  -4.56236198,  -8.34273194,   4.49170903,
+                -8.66038226, -10.68701352,  -9.77103116,  -9.21886784,
+                -12.07615759,  26.01341996,  -1.13352883, -13.28520774, -10.28910294])
+        np.testing.assert_allclose(results.resid_working, 
+                [ 1473.02506034,  -258.28508941,  -563.32097891,   -38.84895192,
+                -165.69875817,  -584.06666725,  1076.97496919,    99.42696848,
+                494.77778514,  2187.30123163,   -81.56463405,  -159.97823479,
+                363.858295  ,   442.27909165,  -324.64933645,  -183.44387481,
+                267.02485844,   894.75938   ,    98.09579187,   603.9200634 ,
+                -431.44834594,  -238.2296165 ,   -61.82249568,   624.20344168,
+                -146.18099686,  -369.65551968,   171.99262399,   -60.41029031,
+                186.81765356,   -26.27913713,  -794.22964417,  -416.79914795,
+                -253.04388425,  -666.47490701,  -251.6079969 ,  -817.70198717,
+                293.30756327,  -125.74947222,  -403.31045369,   258.31051005,
+                -438.73827602,  -584.440853  ,  -349.51985996,  -398.42903071,
+                -483.96599444,  1300.32189904,   -48.89309853,  -535.19735391,
+                -476.27334527])
+        np.testing.assert_allclose(results.resid_pearson, 
+                [ 4.01269878, -0.72726045, -2.221602  , -0.19477008, -1.07425881,
+                -2.09445239,  2.59451042,  0.49969118,  1.89131202,  9.64143836,
+                -0.71714142, -1.19813392,  2.23861212,  3.34207756, -2.0637814 ,
+                -1.3802231 ,  1.33568403,  4.87662684,  0.68309584,  0.97442591,
+                -2.27043598, -1.07931992, -0.17884182,  2.97186889, -1.23768025,
+                -2.07392709,  1.1723155 , -0.47246327,  1.55789092, -0.23614708,
+                -2.35819937, -1.23096188, -1.40274877, -2.33362391, -1.25091503,
+                -2.19400568,  1.44755952, -0.8690235 , -1.19989348,  0.59230634,
+                -1.21675413, -1.44515442, -1.63370888, -1.40229988, -1.90759306,
+                3.67934693, -0.17259375, -2.09312684, -1.51230062])
+        np.testing.assert_allclose(results.resid_anscombe, 
+                [ 3.70889134, -0.74031295, -2.37729865, -0.19586855, -1.11374751,
+                -2.22611959,  2.46352013,  0.49282126,  1.80857757,  8.06444452,
+                -0.73610811, -1.25061371,  2.10820431,  3.05467547, -2.22437611,
+                -1.45136173,  1.28939698,  4.35942058,  0.66904552,  0.95674923,
+                -2.45438937, -1.11429881, -0.17961012,  2.76715848, -1.29658591,
+                -2.22816691,  1.13269136, -0.48017382,  1.48562248, -0.23812278,
+                -2.51664399, -1.2703721 , -1.4683091 , -2.49907536, -1.30026484,
+                -2.32398309,  1.39380683, -0.89495368, -1.23735395,  0.58485202,
+                -1.25435224, -1.4968484 , -1.71888038, -1.45756652, -2.01906267,
+                3.41729922, -0.17335867, -2.22921828, -1.57470549])
+        np.testing.assert_allclose(results.resid_deviance, 
+                [ 3.70529668, -0.74027329, -2.37536322, -0.19586751, -1.11349765,
+                -2.22466106,  2.46246446,  0.4928057 ,  1.80799655,  8.02696525,
+                -0.73602255, -1.25021555,  2.10699958,  3.05084608, -2.22214376,
+                -1.45072221,  1.28913747,  4.35106213,  0.6689982 ,  0.95669662,
+                -2.45171913, -1.11410444, -0.17960956,  2.76494217, -1.29609865,
+                -2.22612429,  1.13247453, -0.48015254,  1.48508549, -0.23812   ,
+                -2.51476072, -1.27015583, -1.46777697, -2.49699318, -1.29992892,
+                -2.32263069,  1.39348459, -0.89482132, -1.23715363,  0.58483655,
+                -1.25415329, -1.49653039, -1.7181055 , -1.45719072, -2.01791949,
+                3.41437156, -0.1733581 , -2.22765605, -1.57426046])
+        np.testing.assert_allclose(results.null, 
+                [ 38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,  38.42857143])
+        self.assertAlmostEqual(results.D2, .388656011675)
+        self.assertAlmostEqual(results.adj_D2, 0.36207583826952761)#.375648692774)
+
+    def testQuasi(self):
+        model = GLM(self.y, self.X, family=QuasiPoisson())
+        results = model.fit()
+        self.assertEqual(results.n, 49)
+        self.assertEqual(results.df_model, 2)
+        self.assertEqual(results.df_resid, 46)
+        self.assertTrue(math.isnan(results.aic))
+        self.assertAlmostEqual(results.bic, 51.436404535087661)
+        self.assertAlmostEqual(results.deviance, 230.46013824817649)
+        self.assertTrue(math.isnan(results.llf))
+        self.assertAlmostEqual(results.null_deviance, 376.97293610347361)
+        self.assertAlmostEqual(results.scale, 5.7526658548022223)
+        np.testing.assert_allclose(results.params, [ 3.92159085,  0.01183491,
+            -0.01371397], atol=1.0e-8)
+        np.testing.assert_allclose(results.bse, [ 0.31298042,  0.01227057,
+            0.00464749], atol=1.0e-8)
+        np.testing.assert_allclose(results.cov_params(), 
+                [[  9.79567451e-02,  -3.55876238e-03,  -1.27356524e-03],
+                [ -3.55876238e-03,   1.50566777e-04,   3.89741067e-05],
+                [ -1.27356524e-03,   3.89741067e-05,   2.15991606e-05]])
+        np.testing.assert_allclose(results.tvalues, [ 12.52982796,   0.96449604,
+            -2.95083339])
+        np.testing.assert_allclose(results.pvalues, [  5.12737770e-36,
+            3.34797291e-01,   3.16917819e-03])
+        np.testing.assert_allclose(results.conf_int(), 
+                [[ 3.3081605 ,  4.53502121],
+                [-0.01221495,  0.03588478],
+                [-0.02282288, -0.00460506]], atol=1.0e-8)
+        np.testing.assert_allclose(results.normalized_cov_params, 
+                [[  1.70280610e-02,  -6.18628383e-04,  -2.21386966e-04],
+                [ -6.18628383e-04,   2.61733917e-05,   6.77496445e-06],
+                [ -2.21386966e-04,   6.77496445e-06,   3.75463502e-06]])
+        np.testing.assert_allclose(results.mu, 
+                [ 51.26831574,  50.15022766,  40.06142973,  34.13799739,
+                28.76119226,  42.6836241 ,  55.64593703,  34.08277997,
+                40.90389582,  37.19727958,  23.47459217,  26.12384057,
+                29.78303507,  25.96888223,  29.14073823,  26.04369592,
+                34.18996367,  32.28924005,  27.42284396,  72.69207879,
+                33.05316347,  36.52276972,  49.2551479 ,  35.33439632,
+                24.07252457,  31.67153709,  27.81699478,  25.38021219,
+                24.31759259,  23.13586161,  48.40724678,  48.57969818,
+                31.92596006,  43.3679231 ,  34.32925819,  51.78908089,
+                34.49778584,  27.56236198,  48.34273194,  57.50829097,
+                50.66038226,  54.68701352,  35.77103116,  43.21886784,
+                40.07615759,  49.98658004,  43.13352883,  40.28520774,  46.28910294])
+        self.assertAlmostEqual(results.pearson_chi2, 264.62262932090221)
+        np.testing.assert_allclose(results.resid_response, 
+                [ 28.73168426,  -5.15022766, -14.06142973,  -1.13799739,
+                -5.76119226, -13.6836241 ,  19.35406297,   2.91722003,
+                12.09610418,  58.80272042,  -3.47459217,  -6.12384057,
+                12.21696493,  17.03111777, -11.14073823,  -7.04369592,
+                7.81003633,  27.71075995,   3.57715604,   8.30792121,
+                -13.05316347,  -6.52276972,  -1.2551479 ,  17.66560368,
+                -6.07252457, -11.67153709,   6.18300522,  -2.38021219,
+                7.68240741,  -1.13586161, -16.40724678,  -8.57969818,
+                -7.92596006, -15.3679231 ,  -7.32925819, -15.78908089,
+                8.50221416,  -4.56236198,  -8.34273194,   4.49170903,
+                -8.66038226, -10.68701352,  -9.77103116,  -9.21886784,
+                -12.07615759,  26.01341996,  -1.13352883, -13.28520774, -10.28910294])
+        np.testing.assert_allclose(results.resid_working, 
+                [ 1473.02506034,  -258.28508941,  -563.32097891,   -38.84895192,
+                -165.69875817,  -584.06666725,  1076.97496919,    99.42696848,
+                494.77778514,  2187.30123163,   -81.56463405,  -159.97823479,
+                363.858295  ,   442.27909165,  -324.64933645,  -183.44387481,
+                267.02485844,   894.75938   ,    98.09579187,   603.9200634 ,
+                -431.44834594,  -238.2296165 ,   -61.82249568,   624.20344168,
+                -146.18099686,  -369.65551968,   171.99262399,   -60.41029031,
+                186.81765356,   -26.27913713,  -794.22964417,  -416.79914795,
+                -253.04388425,  -666.47490701,  -251.6079969 ,  -817.70198717,
+                293.30756327,  -125.74947222,  -403.31045369,   258.31051005,
+                -438.73827602,  -584.440853  ,  -349.51985996,  -398.42903071,
+                -483.96599444,  1300.32189904,   -48.89309853,  -535.19735391,
+                -476.27334527])
+        np.testing.assert_allclose(results.resid_pearson, 
+                [ 4.01269878, -0.72726045, -2.221602  , -0.19477008, -1.07425881,
+                -2.09445239,  2.59451042,  0.49969118,  1.89131202,  9.64143836,
+                -0.71714142, -1.19813392,  2.23861212,  3.34207756, -2.0637814 ,
+                -1.3802231 ,  1.33568403,  4.87662684,  0.68309584,  0.97442591,
+                -2.27043598, -1.07931992, -0.17884182,  2.97186889, -1.23768025,
+                -2.07392709,  1.1723155 , -0.47246327,  1.55789092, -0.23614708,
+                -2.35819937, -1.23096188, -1.40274877, -2.33362391, -1.25091503,
+                -2.19400568,  1.44755952, -0.8690235 , -1.19989348,  0.59230634,
+                -1.21675413, -1.44515442, -1.63370888, -1.40229988, -1.90759306,
+                3.67934693, -0.17259375, -2.09312684, -1.51230062])
+        np.testing.assert_allclose(results.resid_anscombe, 
+                [ 3.70889134, -0.74031295, -2.37729865, -0.19586855, -1.11374751,
+                -2.22611959,  2.46352013,  0.49282126,  1.80857757,  8.06444452,
+                -0.73610811, -1.25061371,  2.10820431,  3.05467547, -2.22437611,
+                -1.45136173,  1.28939698,  4.35942058,  0.66904552,  0.95674923,
+                -2.45438937, -1.11429881, -0.17961012,  2.76715848, -1.29658591,
+                -2.22816691,  1.13269136, -0.48017382,  1.48562248, -0.23812278,
+                -2.51664399, -1.2703721 , -1.4683091 , -2.49907536, -1.30026484,
+                -2.32398309,  1.39380683, -0.89495368, -1.23735395,  0.58485202,
+                -1.25435224, -1.4968484 , -1.71888038, -1.45756652, -2.01906267,
+                3.41729922, -0.17335867, -2.22921828, -1.57470549])
+        np.testing.assert_allclose(results.resid_deviance, 
+                [ 3.70529668, -0.74027329, -2.37536322, -0.19586751, -1.11349765,
+                -2.22466106,  2.46246446,  0.4928057 ,  1.80799655,  8.02696525,
+                -0.73602255, -1.25021555,  2.10699958,  3.05084608, -2.22214376,
+                -1.45072221,  1.28913747,  4.35106213,  0.6689982 ,  0.95669662,
+                -2.45171913, -1.11410444, -0.17960956,  2.76494217, -1.29609865,
+                -2.22612429,  1.13247453, -0.48015254,  1.48508549, -0.23812   ,
+                -2.51476072, -1.27015583, -1.46777697, -2.49699318, -1.29992892,
+                -2.32263069,  1.39348459, -0.89482132, -1.23715363,  0.58483655,
+                -1.25415329, -1.49653039, -1.7181055 , -1.45719072, -2.01791949,
+                3.41437156, -0.1733581 , -2.22765605, -1.57426046])
+        np.testing.assert_allclose(results.null, 
+                [ 38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,  38.42857143])
+        self.assertAlmostEqual(results.D2, .388656011675)
+        self.assertAlmostEqual(results.adj_D2, 0.36207583826952761)
+
+class TestBinomial(unittest.TestCase):
+
+    def setUp(self):
+        #London house price data
+        #y: 'BATH2'
+        y = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+            0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
+            0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
+       	self.y = y.reshape((316,1))
+        #X: 'FLOORSZ'
+       	X = np.array([ 77,  75,  64,  95, 107, 100,  81, 151,  98, 260, 171, 161,  91,
+            80,  50,  85,  52,  69,  60,  84, 155,  97,  69, 126,  90,  43,
+            51,  41, 140,  80,  52,  86,  66,  60,  40, 155, 138,  97, 115,
+            148, 206,  60,  53,  96,  88, 160,  31,  43, 154,  60, 131,  60,
+            46,  61, 125, 150,  76,  92,  96, 100, 105,  72,  48,  41,  72,
+            65,  60,  65,  98,  33, 144, 111,  91, 108,  38,  48,  95,  63,
+            98, 129, 108,  51, 131,  66,  48, 127,  76,  68,  52,  64,  57,
+            121,  67,  76, 112,  96,  90,  53,  93,  64,  97,  58,  44, 157,
+            53,  70,  71, 167,  47,  70,  96,  77,  75,  71,  67,  47,  71,
+            90,  69,  64,  65,  95,  60,  60,  65,  54, 121, 105,  50,  85,
+            69,  69,  62,  65,  93,  93,  70,  62, 155,  68, 117,  80,  80,
+            75,  98, 114,  86,  70,  50,  51, 163, 124,  59,  95,  51,  63,
+            85,  53,  46, 102, 114,  83,  47,  40,  63, 123, 100,  63, 110,
+            79,  98,  99, 120,  52,  48,  37,  81,  30,  88,  50,  35, 116,
+            67,  45,  80,  86, 109,  59,  75,  60,  71, 141, 121,  50, 168,
+            90,  51, 133,  75, 133, 127,  37,  68, 105,  61, 123, 151, 110,
+            77, 220,  94,  77,  70, 100,  98, 126,  55, 105,  60, 176, 104,
+            68,  62,  70,  48, 102,  80,  97,  66,  80, 102, 160,  55,  60,
+            71, 125,  85,  85, 190, 137,  48,  41,  42,  51,  57,  60, 114,
+            88,  84, 108,  66,  85,  42,  98,  90, 127, 100,  55,  76,  82,
+            63,  80,  71,  76, 121, 109,  92, 160, 109, 185, 100,  90,  90,
+            86,  88,  95, 116, 135,  61,  74,  60, 235,  76,  66, 100,  49,
+            50,  37, 100,  88,  90,  52,  95,  81,  79,  96,  75,  91,  86,
+            83, 180, 108,  80,  96,  49, 117, 117,  86,  46,  66,  95,  57,
+            120, 137,  68, 240])
+        self.X = X.reshape((316,1))
+
+    def testIWLS(self):
+        model = GLM(self.y, self.X, family=Binomial())
+        results = model.fit()
+        self.assertEqual(results.n, 316)
+        self.assertEqual(results.df_model, 1)
+        self.assertEqual(results.df_resid, 314)
+        self.assertEqual(results.aic, 155.19347530342466)
+        self.assertEqual(results.bic, -1656.1095797628657)
+        self.assertEqual(results.deviance, 151.19347530342466)
+        self.assertEqual(results.llf, -75.596737651712331)
+        self.assertEqual(results.null_deviance, 189.16038985881212)
+        self.assertEqual(results.scale, 1.0)
+        np.testing.assert_allclose(results.params, [-5.33638276,  0.0287754 ])
+        np.testing.assert_allclose(results.bse, [ 0.64499904,  0.00518312],
+                atol=1.0e-8)
+        np.testing.assert_allclose(results.cov_params(), 
+            [[  4.16023762e-01,  -3.14338457e-03],
+            [ -3.14338457e-03,   2.68646833e-05]])
+        np.testing.assert_allclose(results.tvalues, [-8.27347396,  5.55175826])
+        np.testing.assert_allclose(results.pvalues, [  1.30111233e-16,
+            2.82810512e-08])
+        np.testing.assert_allclose(results.conf_int(), 
+            [[-6.60055765, -4.07220787],
+            [ 0.01861668,  0.03893412]], atol=1.0e-8)
+        np.testing.assert_allclose(results.normalized_cov_params, 
+            [[  4.16023762e-01,  -3.14338457e-03],
+            [ -3.14338457e-03,   2.68646833e-05]])
+        np.testing.assert_allclose(results.mu, 
+            [ 0.04226237,  0.03999333,  0.02946178,  0.0689636 ,  0.09471181,
+            0.07879431,  0.04717464,  0.27065598,  0.07471691,  0.89522144,
+            0.39752487,  0.33102718,  0.06192993,  0.04589793,  0.01988679,
+            0.0526265 ,  0.02104007,  0.03386636,  0.02634295,  0.05121018,
+            0.29396682,  0.07275173,  0.03386636,  0.15307528,  0.06027915,
+            0.01631789,  0.02045547,  0.01541937,  0.2128508 ,  0.04589793,
+            0.02104007,  0.05407977,  0.0311527 ,  0.02634295,  0.01498855,
+            0.29396682,  0.20336776,  0.07275173,  0.11637537,  0.25395607,
+            0.64367488,  0.02634295,  0.02164101,  0.07083428,  0.05710047,
+            0.32468619,  0.01160845,  0.01631789,  0.28803008,  0.02634295,
+            0.17267234,  0.02634295,  0.01776301,  0.02709115,  0.14938186,
+            0.26501331,  0.04111287,  0.06362285,  0.07083428,  0.07879431,
+            0.08989109,  0.03680743,  0.0187955 ,  0.01541937,  0.03680743,
+            0.03029581,  0.02634295,  0.03029581,  0.07471691,  0.01228768,
+            0.23277197,  0.10505173,  0.06192993,  0.09720799,  0.01416217,
+            0.0187955 ,  0.0689636 ,  0.02865003,  0.07471691,  0.16460503,
+            0.09720799,  0.02045547,  0.17267234,  0.0311527 ,  0.0187955 ,
+            0.15684317,  0.04111287,  0.03293737,  0.02104007,  0.02946178,
+            0.02421701,  0.1353385 ,  0.03203302,  0.04111287,  0.10778798,
+            0.07083428,  0.06027915,  0.02164101,  0.06535882,  0.02946178,
+            0.07275173,  0.02490638,  0.01678627,  0.30605146,  0.02164101,
+            0.03482061,  0.03580075,  0.37030921,  0.0182721 ,  0.03482061,
+            0.07083428,  0.04226237,  0.03999333,  0.03580075,  0.03203302,
+            0.0182721 ,  0.03580075,  0.06027915,  0.03386636,  0.02946178,
+            0.03029581,  0.0689636 ,  0.02634295,  0.02634295,  0.03029581,
+            0.02225873,  0.1353385 ,  0.08989109,  0.01988679,  0.0526265 ,
+            0.03386636,  0.03386636,  0.02786   ,  0.03029581,  0.06535882,
+            0.06535882,  0.03482061,  0.02786   ,  0.29396682,  0.03293737,
+            0.12242534,  0.04589793,  0.04589793,  0.03999333,  0.07471691,
+            0.11344884,  0.05407977,  0.03482061,  0.01988679,  0.02045547,
+            0.34389327,  0.14576223,  0.02561486,  0.0689636 ,  0.02045547,
+            0.02865003,  0.0526265 ,  0.02164101,  0.01776301,  0.08307425,
+            0.11344884,  0.04982997,  0.0182721 ,  0.01498855,  0.02865003,
+            0.14221564,  0.07879431,  0.02865003,  0.10237696,  0.04465416,
+            0.07471691,  0.07673078,  0.13200634,  0.02104007,  0.0187955 ,
+            0.01376599,  0.04717464,  0.01128289,  0.05710047,  0.01988679,
+            0.01300612,  0.11936722,  0.03203302,  0.01726786,  0.04589793,
+            0.05407977,  0.09976271,  0.02561486,  0.03999333,  0.02634295,
+            0.03580075,  0.21771181,  0.1353385 ,  0.01988679,  0.37704374,
+            0.06027915,  0.02045547,  0.18104935,  0.03999333,  0.18104935,
+            0.15684317,  0.01376599,  0.03293737,  0.08989109,  0.02709115,
+            0.14221564,  0.27065598,  0.10237696,  0.04226237,  0.72991785,
+            0.06713876,  0.04226237,  0.03482061,  0.07879431,  0.07471691,
+            0.15307528,  0.02289366,  0.08989109,  0.02634295,  0.43243779,
+            0.08756457,  0.03293737,  0.02786   ,  0.03482061,  0.0187955 ,
+            0.08307425,  0.04589793,  0.07275173,  0.0311527 ,  0.04589793,
+            0.08307425,  0.32468619,  0.02289366,  0.02634295,  0.03580075,
+            0.14938186,  0.0526265 ,  0.0526265 ,  0.53268924,  0.19874565,
+            0.0187955 ,  0.01541937,  0.01586237,  0.02045547,  0.02421701,
+            0.02634295,  0.11344884,  0.05710047,  0.05121018,  0.09720799,
+            0.0311527 ,  0.0526265 ,  0.01586237,  0.07471691,  0.06027915,
+            0.15684317,  0.07879431,  0.02289366,  0.04111287,  0.04848506,
+            0.02865003,  0.04589793,  0.03580075,  0.04111287,  0.1353385 ,
+            0.09976271,  0.06362285,  0.32468619,  0.09976271,  0.49676673,
+            0.07879431,  0.06027915,  0.06027915,  0.05407977,  0.05710047,
+            0.0689636 ,  0.11936722,  0.18973955,  0.02709115,  0.03890304,
+            0.02634295,  0.80625182,  0.04111287,  0.0311527 ,  0.07879431,
+            0.0193336 ,  0.01988679,  0.01376599,  0.07879431,  0.05710047,
+            0.06027915,  0.02104007,  0.0689636 ,  0.04717464,  0.04465416,
+            0.07083428,  0.03999333,  0.06192993,  0.05407977,  0.04982997,
+            0.46087756,  0.09720799,  0.04589793,  0.07083428,  0.0193336 ,
+            0.12242534,  0.12242534,  0.05407977,  0.01776301,  0.0311527 ,
+            0.0689636 ,  0.02421701,  0.13200634,  0.19874565,  0.03293737,
+            0.82774282], atol=1.0e-8)
+        self.assertAlmostEqual(results.pearson_chi2, 271.21110541713801)
+        np.testing.assert_allclose(results.resid_response, 
+            [-0.04226237, -0.03999333, -0.02946178, -0.0689636 , -0.09471181,
+            -0.07879431, -0.04717464, -0.27065598, -0.07471691,  0.10477856,
+            -0.39752487,  0.66897282, -0.06192993, -0.04589793, -0.01988679,
+            -0.0526265 , -0.02104007, -0.03386636, -0.02634295, -0.05121018,
+            -0.29396682,  0.92724827, -0.03386636, -0.15307528, -0.06027915,
+            -0.01631789, -0.02045547, -0.01541937, -0.2128508 , -0.04589793,
+            -0.02104007, -0.05407977, -0.0311527 , -0.02634295, -0.01498855,
+            -0.29396682,  0.79663224, -0.07275173, -0.11637537,  0.74604393,
+            -0.64367488, -0.02634295, -0.02164101, -0.07083428, -0.05710047,
+            -0.32468619, -0.01160845, -0.01631789, -0.28803008, -0.02634295,
+            -0.17267234, -0.02634295, -0.01776301, -0.02709115,  0.85061814,
+            0.73498669, -0.04111287, -0.06362285, -0.07083428, -0.07879431,
+            0.91010891, -0.03680743, -0.0187955 , -0.01541937, -0.03680743,
+            -0.03029581, -0.02634295, -0.03029581, -0.07471691, -0.01228768,
+            0.76722803, -0.10505173, -0.06192993, -0.09720799, -0.01416217,
+            -0.0187955 , -0.0689636 , -0.02865003, -0.07471691, -0.16460503,
+            -0.09720799, -0.02045547,  0.82732766, -0.0311527 , -0.0187955 ,
+            -0.15684317, -0.04111287, -0.03293737, -0.02104007, -0.02946178,
+            -0.02421701, -0.1353385 , -0.03203302, -0.04111287, -0.10778798,
+            -0.07083428, -0.06027915, -0.02164101, -0.06535882, -0.02946178,
+            -0.07275173, -0.02490638, -0.01678627, -0.30605146, -0.02164101,
+            -0.03482061, -0.03580075,  0.62969079, -0.0182721 , -0.03482061,
+            -0.07083428, -0.04226237, -0.03999333, -0.03580075, -0.03203302,
+            -0.0182721 , -0.03580075, -0.06027915, -0.03386636, -0.02946178,
+            -0.03029581, -0.0689636 , -0.02634295, -0.02634295, -0.03029581,
+            -0.02225873, -0.1353385 , -0.08989109, -0.01988679, -0.0526265 ,
+            -0.03386636, -0.03386636, -0.02786   , -0.03029581, -0.06535882,
+            -0.06535882, -0.03482061, -0.02786   , -0.29396682, -0.03293737,
+            -0.12242534, -0.04589793, -0.04589793, -0.03999333, -0.07471691,
+            -0.11344884, -0.05407977, -0.03482061, -0.01988679, -0.02045547,
+            0.65610673,  0.85423777, -0.02561486, -0.0689636 , -0.02045547,
+            -0.02865003, -0.0526265 , -0.02164101, -0.01776301, -0.08307425,
+            -0.11344884, -0.04982997, -0.0182721 , -0.01498855, -0.02865003,
+            -0.14221564, -0.07879431, -0.02865003, -0.10237696, -0.04465416,
+            -0.07471691, -0.07673078, -0.13200634, -0.02104007, -0.0187955 ,
+            -0.01376599, -0.04717464, -0.01128289,  0.94289953, -0.01988679,
+            -0.01300612, -0.11936722, -0.03203302, -0.01726786, -0.04589793,
+            -0.05407977, -0.09976271, -0.02561486, -0.03999333, -0.02634295,
+            -0.03580075, -0.21771181,  0.8646615 , -0.01988679,  0.62295626,
+            -0.06027915, -0.02045547, -0.18104935,  0.96000667, -0.18104935,
+            -0.15684317, -0.01376599, -0.03293737, -0.08989109, -0.02709115,
+            -0.14221564,  0.72934402, -0.10237696, -0.04226237, -0.72991785,
+            -0.06713876, -0.04226237, -0.03482061, -0.07879431, -0.07471691,
+            -0.15307528,  0.97710634,  0.91010891, -0.02634295, -0.43243779,
+            -0.08756457, -0.03293737, -0.02786   , -0.03482061, -0.0187955 ,
+             0.91692575, -0.04589793, -0.07275173, -0.0311527 , -0.04589793,
+            -0.08307425,  0.67531381, -0.02289366, -0.02634295, -0.03580075,
+            -0.14938186, -0.0526265 , -0.0526265 ,  0.46731076, -0.19874565,
+            -0.0187955 , -0.01541937, -0.01586237, -0.02045547, -0.02421701,
+            -0.02634295, -0.11344884, -0.05710047, -0.05121018, -0.09720799,
+            0.9688473 , -0.0526265 , -0.01586237, -0.07471691, -0.06027915,
+            -0.15684317, -0.07879431, -0.02289366, -0.04111287, -0.04848506,
+            -0.02865003, -0.04589793, -0.03580075, -0.04111287, -0.1353385 ,
+            -0.09976271, -0.06362285,  0.67531381, -0.09976271, -0.49676673,
+            -0.07879431, -0.06027915, -0.06027915, -0.05407977, -0.05710047,
+            -0.0689636 , -0.11936722, -0.18973955, -0.02709115, -0.03890304,
+            -0.02634295,  0.19374818, -0.04111287, -0.0311527 , -0.07879431,
+            -0.0193336 , -0.01988679, -0.01376599, -0.07879431,  0.94289953,
+            -0.06027915, -0.02104007, -0.0689636 , -0.04717464, -0.04465416,
+            0.92916572, -0.03999333, -0.06192993, -0.05407977, -0.04982997,
+            -0.46087756, -0.09720799, -0.04589793, -0.07083428, -0.0193336 ,
+            -0.12242534, -0.12242534, -0.05407977, -0.01776301, -0.0311527 ,
+            -0.0689636 , -0.02421701, -0.13200634, -0.19874565, -0.03293737,
+            -0.82774282], atol=1.0e-8)
+        np.testing.assert_allclose(results.resid_working, 
+            [ -1.71062283e-03,  -1.53549840e-03,  -8.42423701e-04,
+            -4.42798906e-03,  -8.12073047e-03,  -5.71934606e-03,
+            -2.12046213e-03,  -5.34278480e-02,  -5.16550074e-03,
+            9.82823035e-03,  -9.52067472e-02,   1.48142818e-01,
+            -3.59779501e-03,  -2.00993083e-03,  -3.87619325e-04,
+            -2.62379729e-03,  -4.33370579e-04,  -1.10808799e-03,
+            -6.75670103e-04,  -2.48818484e-03,  -6.10129090e-02,
+            6.25511612e-02,  -1.10808799e-03,  -1.98451739e-02,
+            -3.41454749e-03,  -2.61928659e-04,  -4.09867263e-04,
+            -2.34090923e-04,  -3.56621577e-02,  -2.00993083e-03,
+            -4.33370579e-04,  -2.76645832e-03,  -9.40257152e-04,
+            -6.75670103e-04,  -2.21289369e-04,  -6.10129090e-02,
+            1.29061842e-01,  -4.90775251e-03,  -1.19671283e-02,
+            1.41347263e-01,  -1.47631680e-01,  -6.75670103e-04,
+            -4.58198217e-04,  -4.66208406e-03,  -3.07429001e-03,
+            -7.11923401e-02,  -1.33191898e-04,  -2.61928659e-04,
+            -5.90659690e-02,  -6.75670103e-04,  -2.46673839e-02,
+            -6.75670103e-04,  -3.09919962e-04,  -7.14047519e-04,
+            1.08085429e-01,   1.43161630e-01,  -1.62077632e-03,
+            -3.79032977e-03,  -4.66208406e-03,  -5.71934606e-03,
+            7.44566288e-02,  -1.30492035e-03,  -3.46630910e-04,
+            -2.34090923e-04,  -1.30492035e-03,  -8.90029618e-04,
+            -6.75670103e-04,  -8.90029618e-04,  -5.16550074e-03,
+            -1.49131762e-04,   1.37018624e-01,  -9.87652847e-03,
+            -3.59779501e-03,  -8.53083698e-03,  -1.97726627e-04,
+            -3.46630910e-04,  -4.42798906e-03,  -7.97307494e-04,
+            -5.16550074e-03,  -2.26348718e-02,  -8.53083698e-03,
+            -4.09867263e-04,   1.18189219e-01,  -9.40257152e-04,
+            -3.46630910e-04,  -2.07414715e-02,  -1.62077632e-03,
+            -1.04913757e-03,  -4.33370579e-04,  -8.42423701e-04,
+            -5.72261321e-04,  -1.58375811e-02,  -9.93244730e-04,
+            -1.62077632e-03,  -1.03659408e-02,  -4.66208406e-03,
+            -3.41454749e-03,  -4.58198217e-04,  -3.99257703e-03,
+            -8.42423701e-04,  -4.90775251e-03,  -6.04877746e-04,
+            -2.77048947e-04,  -6.50004229e-02,  -4.58198217e-04,
+            -1.17025566e-03,  -1.23580799e-03,   1.46831486e-01,
+            -3.27769165e-04,  -1.17025566e-03,  -4.66208406e-03,
+            -1.71062283e-03,  -1.53549840e-03,  -1.23580799e-03,
+            -9.93244730e-04,  -3.27769165e-04,  -1.23580799e-03,
+            -3.41454749e-03,  -1.10808799e-03,  -8.42423701e-04,
+            -8.90029618e-04,  -4.42798906e-03,  -6.75670103e-04,
+            -6.75670103e-04,  -8.90029618e-04,  -4.84422741e-04,
+            -1.58375811e-02,  -7.35405096e-03,  -3.87619325e-04,
+            -2.62379729e-03,  -1.10808799e-03,  -1.10808799e-03,
+            -7.54555329e-04,  -8.90029618e-04,  -3.99257703e-03,
+            -3.99257703e-03,  -1.17025566e-03,  -7.54555329e-04,
+            -6.10129090e-02,  -1.04913757e-03,  -1.31530576e-02,
+            -2.00993083e-03,  -2.00993083e-03,  -1.53549840e-03,
+            -5.16550074e-03,  -1.14104800e-02,  -2.76645832e-03,
+            -1.17025566e-03,  -3.87619325e-04,  -4.09867263e-04,
+            1.48037813e-01,   1.06365931e-01,  -6.39314594e-04,
+            -4.42798906e-03,  -4.09867263e-04,  -7.97307494e-04,
+            -2.62379729e-03,  -4.58198217e-04,  -3.09919962e-04,
+            -6.32800839e-03,  -1.14104800e-02,  -2.35929680e-03,
+            -3.27769165e-04,  -2.21289369e-04,  -7.97307494e-04,
+            -1.73489362e-02,  -5.71934606e-03,  -7.97307494e-04,
+            -9.40802551e-03,  -1.90495384e-03,  -5.16550074e-03,
+            -5.43585191e-03,  -1.51253748e-02,  -4.33370579e-04,
+            -3.46630910e-04,  -1.86893696e-04,  -2.12046213e-03,
+            -1.25867293e-04,   5.07657192e-02,  -3.87619325e-04,
+            -1.66959104e-04,  -1.25477263e-02,  -9.93244730e-04,
+            -2.93030065e-04,  -2.00993083e-03,  -2.76645832e-03,
+            -8.95970087e-03,  -6.39314594e-04,  -1.53549840e-03,
+            -6.75670103e-04,  -1.23580799e-03,  -3.70792339e-02,
+            1.01184411e-01,  -3.87619325e-04,   1.46321062e-01,
+            -3.41454749e-03,  -4.09867263e-04,  -2.68442736e-02,
+            3.68583645e-02,  -2.68442736e-02,  -2.07414715e-02,
+            -1.86893696e-04,  -1.04913757e-03,  -7.35405096e-03,
+            -7.14047519e-04,  -1.73489362e-02,   1.43973473e-01,
+            -9.40802551e-03,  -1.71062283e-03,  -1.43894386e-01,
+            -4.20497779e-03,  -1.71062283e-03,  -1.17025566e-03,
+            -5.71934606e-03,  -5.16550074e-03,  -1.98451739e-02,
+            2.18574168e-02,   7.44566288e-02,  -6.75670103e-04,
+            -1.06135519e-01,  -6.99614755e-03,  -1.04913757e-03,
+            -7.54555329e-04,  -1.17025566e-03,  -3.46630910e-04,
+            6.98449121e-02,  -2.00993083e-03,  -4.90775251e-03,
+            -9.40257152e-04,  -2.00993083e-03,  -6.32800839e-03,
+            1.48072729e-01,  -5.12120512e-04,  -6.75670103e-04,
+            -1.23580799e-03,  -1.89814939e-02,  -2.62379729e-03,
+            -2.62379729e-03,   1.16328328e-01,  -3.16494123e-02,
+            -3.46630910e-04,  -2.34090923e-04,  -2.47623705e-04,
+            -4.09867263e-04,  -5.72261321e-04,  -6.75670103e-04,
+            -1.14104800e-02,  -3.07429001e-03,  -2.48818484e-03,
+            -8.53083698e-03,   2.92419496e-02,  -2.62379729e-03,
+            -2.47623705e-04,  -5.16550074e-03,  -3.41454749e-03,
+            -2.07414715e-02,  -5.71934606e-03,  -5.12120512e-04,
+            -1.62077632e-03,  -2.23682205e-03,  -7.97307494e-04,
+            -2.00993083e-03,  -1.23580799e-03,  -1.62077632e-03,
+            -1.58375811e-02,  -8.95970087e-03,  -3.79032977e-03,
+            1.48072729e-01,  -8.95970087e-03,  -1.24186489e-01,
+            -5.71934606e-03,  -3.41454749e-03,  -3.41454749e-03,
+            -2.76645832e-03,  -3.07429001e-03,  -4.42798906e-03,
+            -1.25477263e-02,  -2.91702648e-02,  -7.14047519e-04,
+            -1.45456868e-03,  -6.75670103e-04,   3.02653681e-02,
+            -1.62077632e-03,  -9.40257152e-04,  -5.71934606e-03,
+            -3.66561274e-04,  -3.87619325e-04,  -1.86893696e-04,
+            -5.71934606e-03,   5.07657192e-02,  -3.41454749e-03,
+            -4.33370579e-04,  -4.42798906e-03,  -2.12046213e-03,
+            -1.90495384e-03,   6.11546973e-02,  -1.53549840e-03,
+            -3.59779501e-03,  -2.76645832e-03,  -2.35929680e-03,
+            -1.14513988e-01,  -8.53083698e-03,  -2.00993083e-03,
+            -4.66208406e-03,  -3.66561274e-04,  -1.31530576e-02,
+            -1.31530576e-02,  -2.76645832e-03,  -3.09919962e-04,
+            -9.40257152e-04,  -4.42798906e-03,  -5.72261321e-04,
+            -1.51253748e-02,  -3.16494123e-02,  -1.04913757e-03,
+            -1.18023417e-01])
+        np.testing.assert_allclose(results.resid_pearson, 
+            [-0.21006498, -0.20410641, -0.17423009, -0.27216147, -0.3234511 ,
+            -0.29246179, -0.22250903, -0.60917574, -0.28416602,  0.3421141 ,
+            -0.81229277,  1.42158361, -0.25694055, -0.21933056, -0.142444  ,
+            -0.23569027, -0.14660243, -0.18722578, -0.16448609, -0.2323235 ,
+            -0.64526275,  3.57006696, -0.18722578, -0.42513819, -0.25327023,
+            -0.12879668, -0.14450826, -0.12514332, -0.5200069 , -0.21933056,
+            -0.14660243, -0.23910582, -0.17931646, -0.16448609, -0.12335569,
+            -0.64526275,  1.97919183, -0.28010679, -0.36290807,  1.71396874,
+            -1.3440334 , -0.16448609, -0.14872695, -0.27610555, -0.24608613,
+            -0.69339243, -0.1083734 , -0.12879668, -0.63604537, -0.16448609,
+            -0.45684893, -0.16448609, -0.13447767, -0.16686977,  2.3862634 ,
+            1.66535145, -0.20706426, -0.26066405, -0.27610555, -0.29246179,
+            3.18191348, -0.19548397, -0.13840353, -0.12514332, -0.19548397,
+            -0.17675498, -0.16448609, -0.17675498, -0.28416602, -0.11153719,
+            1.81550268, -0.34261205, -0.25694055, -0.32813846, -0.11985666,
+            -0.13840353, -0.27216147, -0.17174127, -0.28416602, -0.44389026,
+            -0.32813846, -0.14450826,  2.18890738, -0.17931646, -0.13840353,
+            -0.43129917, -0.20706426, -0.18455132, -0.14660243, -0.17423009,
+            -0.1575374 , -0.39562855, -0.18191506, -0.20706426, -0.34757708,
+            -0.27610555, -0.25327023, -0.14872695, -0.26444152, -0.17423009,
+            -0.28010679, -0.15982038, -0.13066317, -0.66410018, -0.14872695,
+            -0.189939  , -0.19269154,  1.30401147, -0.13642648, -0.189939  ,
+            -0.27610555, -0.21006498, -0.20410641, -0.19269154, -0.18191506,
+            -0.13642648, -0.19269154, -0.25327023, -0.18722578, -0.17423009,
+            -0.17675498, -0.27216147, -0.16448609, -0.16448609, -0.17675498,
+            -0.15088226, -0.39562855, -0.3142763 , -0.142444  , -0.23569027,
+            -0.18722578, -0.18722578, -0.169288  , -0.17675498, -0.26444152,
+            -0.26444152, -0.189939  , -0.169288  , -0.64526275, -0.18455132,
+            -0.3735026 , -0.21933056, -0.21933056, -0.20410641, -0.28416602,
+            -0.35772404, -0.23910582, -0.189939  , -0.142444  , -0.14450826,
+            1.38125991,  2.42084442, -0.16213645, -0.27216147, -0.14450826,
+            -0.17174127, -0.23569027, -0.14872695, -0.13447767, -0.30099975,
+            -0.35772404, -0.22900483, -0.13642648, -0.12335569, -0.17174127,
+            -0.4071783 , -0.29246179, -0.17174127, -0.33771794, -0.21619749,
+            -0.28416602, -0.28828407, -0.38997712, -0.14660243, -0.13840353,
+            -0.11814455, -0.22250903, -0.10682532,  4.06361781, -0.142444  ,
+            -0.11479334, -0.36816723, -0.18191506, -0.1325567 , -0.21933056,
+            -0.23910582, -0.33289374, -0.16213645, -0.20410641, -0.16448609,
+            -0.19269154, -0.52754269,  2.52762346, -0.142444  ,  1.28538406,
+            -0.25327023, -0.14450826, -0.47018591,  4.89940505, -0.47018591,
+            -0.43129917, -0.11814455, -0.18455132, -0.3142763 , -0.16686977,
+            -0.4071783 ,  1.64156241, -0.33771794, -0.21006498, -1.6439517 ,
+            -0.26827373, -0.21006498, -0.189939  , -0.29246179, -0.28416602,
+            -0.42513819,  6.53301013,  3.18191348, -0.16448609, -0.87288109,
+            -0.30978696, -0.18455132, -0.169288  , -0.189939  , -0.13840353,
+             3.32226189, -0.21933056, -0.28010679, -0.17931646, -0.21933056,
+            -0.30099975,  1.44218477, -0.1530688 , -0.16448609, -0.19269154,
+            -0.41906522, -0.23569027, -0.23569027,  0.93662539, -0.4980393 ,
+            -0.13840353, -0.12514332, -0.12695686, -0.14450826, -0.1575374 ,
+            -0.16448609, -0.35772404, -0.24608613, -0.2323235 , -0.32813846,
+            5.57673284, -0.23569027, -0.12695686, -0.28416602, -0.25327023,
+            -0.43129917, -0.29246179, -0.1530688 , -0.20706426, -0.22573357,
+            -0.17174127, -0.21933056, -0.19269154, -0.20706426, -0.39562855,
+            -0.33289374, -0.26066405,  1.44218477, -0.33289374, -0.99355423,
+            -0.29246179, -0.25327023, -0.25327023, -0.23910582, -0.24608613,
+            -0.27216147, -0.36816723, -0.48391225, -0.16686977, -0.20119082,
+            -0.16448609,  0.49021146, -0.20706426, -0.17931646, -0.29246179,
+            -0.14040923, -0.142444  , -0.11814455, -0.29246179,  4.06361781,
+            -0.25327023, -0.14660243, -0.27216147, -0.22250903, -0.21619749,
+            3.6218033 , -0.20410641, -0.25694055, -0.23910582, -0.22900483,
+            -0.92458976, -0.32813846, -0.21933056, -0.27610555, -0.14040923,
+            -0.3735026 , -0.3735026 , -0.23910582, -0.13447767, -0.17931646,
+            -0.27216147, -0.1575374 , -0.38997712, -0.4980393 , -0.18455132,
+            -2.19209332])
+        np.testing.assert_allclose(results.resid_anscombe, 
+            [-0.31237627, -0.3036605 , -0.25978208, -0.40240831, -0.47552289,
+            -0.43149255, -0.33053793, -0.85617194, -0.41962951,  0.50181328,
+            -1.0954382 ,  1.66940149, -0.38048321, -0.3259044 , -0.21280762,
+            -0.34971301, -0.21896842, -0.27890356, -0.2454118 , -0.34482158,
+            -0.90063409,  2.80452413, -0.27890356, -0.61652596, -0.37518169,
+            -0.19255932, -0.2158664 , -0.18713159, -0.74270558, -0.3259044 ,
+            -0.21896842, -0.35467084, -0.2672722 , -0.2454118 , -0.18447466,
+            -0.90063409,  2.05763941, -0.41381347, -0.53089521,  1.88552083,
+            -1.60654218, -0.2454118 , -0.22211425, -0.40807333, -0.3647888 ,
+            -0.95861559, -0.16218047, -0.19255932, -0.88935802, -0.2454118 ,
+            -0.65930821, -0.2454118 , -0.20099345, -0.24892975,  2.28774016,
+            1.85167195, -0.30798858, -0.38585584, -0.40807333, -0.43149255,
+            2.65398426, -0.2910267 , -0.20681747, -0.18713159, -0.2910267 ,
+            -0.26350118, -0.2454118 , -0.26350118, -0.41962951, -0.16689207,
+             1.95381191, -0.50251231, -0.38048321, -0.48214234, -0.17927213,
+            -0.20681747, -0.40240831, -0.25611424, -0.41962951, -0.64189694,
+            -0.48214234, -0.2158664 ,  2.18071204, -0.2672722 , -0.20681747,
+            -0.62488429, -0.30798858, -0.27497271, -0.21896842, -0.25978208,
+            -0.23514749, -0.57618899, -0.27109582, -0.30798858, -0.50947546,
+            -0.40807333, -0.37518169, -0.22211425, -0.39130036, -0.25978208,
+            -0.41381347, -0.2385213 , -0.19533116, -0.92350689, -0.22211425,
+            -0.28288904, -0.28692985,  1.5730846 , -0.20388497, -0.28288904,
+            -0.40807333, -0.31237627, -0.3036605 , -0.28692985, -0.27109582,
+            -0.20388497, -0.28692985, -0.37518169, -0.27890356, -0.25978208,
+            -0.26350118, -0.40240831, -0.2454118 , -0.2454118 , -0.26350118,
+            -0.22530448, -0.57618899, -0.46253505, -0.21280762, -0.34971301,
+            -0.27890356, -0.27890356, -0.25249702, -0.26350118, -0.39130036,
+            -0.39130036, -0.28288904, -0.25249702, -0.90063409, -0.27497271,
+            -0.5456246 , -0.3259044 , -0.3259044 , -0.3036605 , -0.41962951,
+            -0.52366614, -0.35467084, -0.28288904, -0.21280762, -0.2158664 ,
+            1.63703418,  2.30570989, -0.24194253, -0.40240831, -0.2158664 ,
+            -0.25611424, -0.34971301, -0.22211425, -0.20099345, -0.44366892,
+            -0.52366614, -0.33999576, -0.20388497, -0.18447466, -0.25611424,
+            -0.59203547, -0.43149255, -0.25611424, -0.49563627, -0.32133344,
+            -0.41962951, -0.42552227, -0.56840788, -0.21896842, -0.20681747,
+            -0.17672552, -0.33053793, -0.15987433,  2.9768074 , -0.21280762,
+            -0.17173916, -0.53821445, -0.27109582, -0.19814236, -0.3259044 ,
+            -0.35467084, -0.48884654, -0.24194253, -0.3036605 , -0.2454118 ,
+            -0.28692985, -0.75249089,  2.35983933, -0.21280762,  1.55726719,
+            -0.37518169, -0.2158664 , -0.67712261,  3.23165236, -0.67712261,
+            -0.62488429, -0.17672552, -0.27497271, -0.46253505, -0.24892975,
+            -0.59203547,  1.83482464, -0.49563627, -0.31237627, -1.83652534,
+            -0.39681759, -0.31237627, -0.28288904, -0.43149255, -0.41962951,
+            -0.61652596,  3.63983609,  2.65398426, -0.2454118 , -1.16171662,
+            -0.45616505, -0.27497271, -0.25249702, -0.28288904, -0.20681747,
+            2.71015945, -0.3259044 , -0.41381347, -0.2672722 , -0.3259044 ,
+            -0.44366892,  1.68567947, -0.22853969, -0.2454118 , -0.28692985,
+            -0.60826548, -0.34971301, -0.34971301,  1.2290223 , -0.71397735,
+            -0.20681747, -0.18713159, -0.1898263 , -0.2158664 , -0.23514749,
+            -0.2454118 , -0.52366614, -0.3647888 , -0.34482158, -0.48214234,
+            3.41271513, -0.34971301, -0.1898263 , -0.41962951, -0.37518169,
+            -0.62488429, -0.43149255, -0.22853969, -0.30798858, -0.3352348 ,
+            -0.25611424, -0.3259044 , -0.28692985, -0.30798858, -0.57618899,
+            -0.48884654, -0.38585584,  1.68567947, -0.48884654, -1.28709718,
+            -0.43149255, -0.37518169, -0.37518169, -0.35467084, -0.3647888 ,
+            -0.40240831, -0.53821445, -0.69534436, -0.24892975, -0.29939131,
+            -0.2454118 ,  0.70366797, -0.30798858, -0.2672722 , -0.43149255,
+            -0.2097915 , -0.21280762, -0.17672552, -0.43149255,  2.9768074 ,
+            -0.37518169, -0.21896842, -0.40240831, -0.33053793, -0.32133344,
+            2.82351017, -0.3036605 , -0.38048321, -0.35467084, -0.33999576,
+            -1.21650102, -0.48214234, -0.3259044 , -0.40807333, -0.2097915 ,
+            -0.5456246 , -0.5456246 , -0.35467084, -0.20099345, -0.2672722 ,
+            -0.40240831, -0.23514749, -0.56840788, -0.71397735, -0.27497271,
+            -2.18250381])
+        np.testing.assert_allclose(results.resid_deviance, 
+            [-0.29387552, -0.2857098 , -0.24455876, -0.37803944, -0.44609851,
+            -0.40514674, -0.31088148, -0.79449324, -0.39409528,  0.47049798,
+            -1.00668653,  1.48698001, -0.35757692, -0.30654405, -0.20043547,
+            -0.32882173, -0.20622595, -0.26249995, -0.23106769, -0.32424676,
+            -0.83437766,  2.28941155, -0.26249995, -0.57644334, -0.35262564,
+            -0.18139734, -0.20331052, -0.17629229, -0.69186337, -0.30654405,
+            -0.20622595, -0.33345774, -0.251588  , -0.23106769, -0.17379306,
+            -0.83437766,  1.78479093, -0.38867448, -0.4974393 ,  1.65565332,
+            -1.43660134, -0.23106769, -0.20918228, -0.38332275, -0.34291558,
+            -0.88609006, -0.15281596, -0.18139734, -0.82428104, -0.23106769,
+            -0.61571821, -0.23106769, -0.18932865, -0.234371  ,  1.94999969,
+            1.62970871, -0.2897651 , -0.36259328, -0.38332275, -0.40514674,
+            2.19506559, -0.27386827, -0.19480442, -0.17629229, -0.27386827,
+            -0.24804925, -0.23106769, -0.24804925, -0.39409528, -0.15725009,
+            1.7074519 , -0.47114617, -0.35757692, -0.4522457 , -0.16889886,
+            -0.19480442, -0.37803944, -0.24111595, -0.39409528, -0.59975102,
+            -0.4522457 , -0.20331052,  1.87422489, -0.251588  , -0.19480442,
+            -0.5841272 , -0.2897651 , -0.25881274, -0.20622595, -0.24455876,
+            -0.22142749, -0.53929061, -0.25517563, -0.2897651 , -0.47760126,
+            -0.38332275, -0.35262564, -0.20918228, -0.36767536, -0.24455876,
+            -0.38867448, -0.2245965 , -0.18400413, -0.85481866, -0.20918228,
+            -0.26623785, -0.27002708,  1.40955093, -0.19204738, -0.26623785,
+            -0.38332275, -0.29387552, -0.2857098 , -0.27002708, -0.25517563,
+            -0.19204738, -0.27002708, -0.35262564, -0.26249995, -0.24455876,
+            -0.24804925, -0.37803944, -0.23106769, -0.23106769, -0.24804925,
+            -0.21218006, -0.53929061, -0.43402996, -0.20043547, -0.32882173,
+            -0.26249995, -0.26249995, -0.23772023, -0.24804925, -0.36767536,
+            -0.36767536, -0.26623785, -0.23772023, -0.83437766, -0.25881274,
+            -0.51106408, -0.30654405, -0.30654405, -0.2857098 , -0.39409528,
+            -0.49074728, -0.33345774, -0.26623785, -0.20043547, -0.20331052,
+            1.46111186,  1.96253843, -0.22780971, -0.37803944, -0.20331052,
+            -0.24111595, -0.32882173, -0.20918228, -0.18932865, -0.41648237,
+            -0.49074728, -0.31973217, -0.19204738, -0.17379306, -0.24111595,
+            -0.55389988, -0.40514674, -0.24111595, -0.46476893, -0.30226435,
+            -0.39409528, -0.39958581, -0.53211065, -0.20622595, -0.19480442,
+            -0.16650295, -0.31088148, -0.15064545,  2.39288231, -0.20043547,
+            -0.16181126, -0.5042114 , -0.25517563, -0.18664773, -0.30654405,
+            -0.33345774, -0.45846897, -0.22780971, -0.2857098 , -0.23106769,
+            -0.27002708, -0.7007597 ,  1.99998811, -0.20043547,  1.39670618,
+            -0.35262564, -0.20331052, -0.63203077,  2.53733821, -0.63203077,
+            -0.5841272 , -0.16650295, -0.25881274, -0.43402996, -0.234371  ,
+            -0.55389988,  1.61672923, -0.46476893, -0.29387552, -1.61804148,
+            -0.37282386, -0.29387552, -0.26623785, -0.40514674, -0.39409528,
+            -0.57644334,  2.74841605,  2.19506559, -0.23106769, -1.06433539,
+            -0.42810736, -0.25881274, -0.23772023, -0.26623785, -0.19480442,
+            2.23070414, -0.30654405, -0.38867448, -0.251588  , -0.30654405,
+            -0.41648237,  1.49993075, -0.21521982, -0.23106769, -0.27002708,
+            -0.5688444 , -0.32882173, -0.32882173,  1.12233423, -0.66569789,
+            -0.19480442, -0.17629229, -0.17882689, -0.20331052, -0.22142749,
+            -0.23106769, -0.49074728, -0.34291558, -0.32424676, -0.4522457 ,
+            2.63395309, -0.32882173, -0.17882689, -0.39409528, -0.35262564,
+            -0.5841272 , -0.40514674, -0.21521982, -0.2897651 , -0.3152773 ,
+            -0.24111595, -0.30654405, -0.27002708, -0.2897651 , -0.53929061,
+            -0.45846897, -0.36259328,  1.49993075, -0.45846897, -1.17192274,
+            -0.40514674, -0.35262564, -0.35262564, -0.33345774, -0.34291558,
+            -0.37803944, -0.5042114 , -0.64869028, -0.234371  , -0.28170899,
+            -0.23106769,  0.65629132, -0.2897651 , -0.251588  , -0.40514674,
+            -0.19760028, -0.20043547, -0.16650295, -0.40514674,  2.39288231,
+            -0.35262564, -0.20622595, -0.37803944, -0.31088148, -0.30226435,
+            2.30104857, -0.2857098 , -0.35757692, -0.33345774, -0.31973217,
+            -1.11158678, -0.4522457 , -0.30654405, -0.38332275, -0.19760028,
+            -0.51106408, -0.51106408, -0.33345774, -0.18932865, -0.251588  ,
+            -0.37803944, -0.22142749, -0.53211065, -0.66569789, -0.25881274,
+            -1.87550882])
+        np.testing.assert_allclose(results.null, 
+            [ 0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759])
+        self.assertAlmostEqual(results.D2, .200712816165)
+        self.assertAlmostEqual(results.adj_D2, 0.19816731557930456)
+
+
+
+if __name__ == '__main__':
+	unittest.main()

release/python/0.6.0/crankshaft/crankshaft/regression/glm/utils.py

@@ -0,0 +1,350 @@
+
+from __future__ import absolute_import, print_function
+import numpy as np
+import warnings
+
+
+def _bit_length_26(x):
+    if x == 0:
+        return 0
+    elif x == 1:
+        return 1
+    else:
+        return len(bin(x)) - 2
+
+
+try:
+    from scipy.lib._version import NumpyVersion
+except ImportError:
+    import re
+    string_types = basestring 
+
+    class NumpyVersion():
+        """Parse and compare numpy version strings.
+        Numpy has the following versioning scheme (numbers given are examples; they
+        can be >9) in principle):
+        - Released version: '1.8.0', '1.8.1', etc.
+        - Alpha: '1.8.0a1', '1.8.0a2', etc.
+        - Beta: '1.8.0b1', '1.8.0b2', etc.
+        - Release candidates: '1.8.0rc1', '1.8.0rc2', etc.
+        - Development versions: '1.8.0.dev-f1234afa' (git commit hash appended)
+        - Development versions after a1: '1.8.0a1.dev-f1234afa',
+                                        '1.8.0b2.dev-f1234afa',
+                                        '1.8.1rc1.dev-f1234afa', etc.
+        - Development versions (no git hash available): '1.8.0.dev-Unknown'
+        Comparing needs to be done against a valid version string or other
+        `NumpyVersion` instance.
+        Parameters
+        ----------
+        vstring : str
+            Numpy version string (``np.__version__``).
+        Notes
+        -----
+        All dev versions of the same (pre-)release compare equal.
+        Examples
+        --------
+        >>> from scipy.lib._version import NumpyVersion
+        >>> if NumpyVersion(np.__version__) < '1.7.0':
+        ...     print('skip')
+        skip
+        >>> NumpyVersion('1.7')  # raises ValueError, add ".0"
+        """
+
+        def __init__(self, vstring):
+            self.vstring = vstring
+            ver_main = re.match(r'\d[.]\d+[.]\d+', vstring)
+            if not ver_main:
+                raise ValueError("Not a valid numpy version string")
+
+            self.version = ver_main.group()
+            self.major, self.minor, self.bugfix = [int(x) for x in
+                                                   self.version.split('.')]
+            if len(vstring) == ver_main.end():
+                self.pre_release = 'final'
+            else:
+                alpha = re.match(r'a\d', vstring[ver_main.end():])
+                beta = re.match(r'b\d', vstring[ver_main.end():])
+                rc = re.match(r'rc\d', vstring[ver_main.end():])
+                pre_rel = [m for m in [alpha, beta, rc] if m is not None]
+                if pre_rel:
+                    self.pre_release = pre_rel[0].group()
+                else:
+                    self.pre_release = ''
+
+            self.is_devversion = bool(re.search(r'.dev-', vstring))
+
+        def _compare_version(self, other):
+            """Compare major.minor.bugfix"""
+            if self.major == other.major:
+                if self.minor == other.minor:
+                    if self.bugfix == other.bugfix:
+                        vercmp = 0
+                    elif self.bugfix > other.bugfix:
+                        vercmp = 1
+                    else:
+                        vercmp = -1
+                elif self.minor > other.minor:
+                    vercmp = 1
+                else:
+                    vercmp = -1
+            elif self.major > other.major:
+                vercmp = 1
+            else:
+                vercmp = -1
+
+            return vercmp
+
+        def _compare_pre_release(self, other):
+            """Compare alpha/beta/rc/final."""
+            if self.pre_release == other.pre_release:
+                vercmp = 0
+            elif self.pre_release == 'final':
+                vercmp = 1
+            elif other.pre_release == 'final':
+                vercmp = -1
+            elif self.pre_release > other.pre_release:
+                vercmp = 1
+            else:
+                vercmp = -1
+
+            return vercmp
+
+        def _compare(self, other):
+            if not isinstance(other, (string_types, NumpyVersion)):
+                raise ValueError("Invalid object to compare with NumpyVersion.")
+
+            if isinstance(other, string_types):
+                other = NumpyVersion(other)
+
+            vercmp = self._compare_version(other)
+            if vercmp == 0:
+                # Same x.y.z version, check for alpha/beta/rc
+                vercmp = self._compare_pre_release(other)
+                if vercmp == 0:
+                    # Same version and same pre-release, check if dev version
+                    if self.is_devversion is other.is_devversion:
+                        vercmp = 0
+                    elif self.is_devversion:
+                        vercmp = -1
+                    else:
+                        vercmp = 1
+
+            return vercmp
+
+        def __lt__(self, other):
+            return self._compare(other) < 0
+
+        def __le__(self, other):
+            return self._compare(other) <= 0
+
+        def __eq__(self, other):
+            return self._compare(other) == 0
+
+        def __ne__(self, other):
+            return self._compare(other) != 0
+
+        def __gt__(self, other):
+            return self._compare(other) > 0
+
+        def __ge__(self, other):
+            return self._compare(other) >= 0
+
+        def __repr(self):
+            return "NumpyVersion(%s)" % self.vstring
+
+
+def _next_regular(target):
+    """
+    Find the next regular number greater than or equal to target.
+    Regular numbers are composites of the prime factors 2, 3, and 5.
+    Also known as 5-smooth numbers or Hamming numbers, these are the optimal
+    size for inputs to FFTPACK.
+    Target must be a positive integer.
+    """
+    if target <= 6:
+        return target
+
+    # Quickly check if it's already a power of 2
+    if not (target & (target - 1)):
+        return target
+
+    match = float('inf')  # Anything found will be smaller
+    p5 = 1
+    while p5 < target:
+        p35 = p5
+        while p35 < target:
+            # Ceiling integer division, avoiding conversion to float
+            # (quotient = ceil(target / p35))
+            quotient = -(-target // p35)
+            # Quickly find next power of 2 >= quotient
+            try:
+                p2 = 2 ** ((quotient - 1).bit_length())
+            except AttributeError:
+                # Fallback for Python <2.7
+                p2 = 2 ** _bit_length_26(quotient - 1)
+
+            N = p2 * p35
+            if N == target:
+                return N
+            elif N < match:
+                match = N
+            p35 *= 3
+            if p35 == target:
+                return p35
+        if p35 < match:
+            match = p35
+        p5 *= 5
+        if p5 == target:
+            return p5
+    if p5 < match:
+        match = p5
+    return match
+if NumpyVersion(np.__version__) >= '1.7.1':
+    np_matrix_rank = np.linalg.matrix_rank
+else:
+    def np_matrix_rank(M, tol=None):
+        """
+        Return matrix rank of array using SVD method
+        Rank of the array is the number of SVD singular values of the array that are
+        greater than `tol`.
+        Parameters
+        ----------
+        M : {(M,), (M, N)} array_like
+            array of <=2 dimensions
+        tol : {None, float}, optional
+        threshold below which SVD values are considered zero. If `tol` is
+        None, and ``S`` is an array with singular values for `M`, and
+        ``eps`` is the epsilon value for datatype of ``S``, then `tol` is
+        set to ``S.max() * max(M.shape) * eps``.
+        Notes
+        -----
+        The default threshold to detect rank deficiency is a test on the magnitude
+        of the singular values of `M`.  By default, we identify singular values less
+        than ``S.max() * max(M.shape) * eps`` as indicating rank deficiency (with
+        the symbols defined above). This is the algorithm MATLAB uses [1].  It also
+        appears in *Numerical recipes* in the discussion of SVD solutions for linear
+        least squares [2].
+        This default threshold is designed to detect rank deficiency accounting for
+        the numerical errors of the SVD computation.  Imagine that there is a column
+        in `M` that is an exact (in floating point) linear combination of other
+        columns in `M`. Computing the SVD on `M` will not produce a singular value
+        exactly equal to 0 in general: any difference of the smallest SVD value from
+        0 will be caused by numerical imprecision in the calculation of the SVD.
+        Our threshold for small SVD values takes this numerical imprecision into
+        account, and the default threshold will detect such numerical rank
+        deficiency.  The threshold may declare a matrix `M` rank deficient even if
+        the linear combination of some columns of `M` is not exactly equal to
+        another column of `M` but only numerically very close to another column of
+        `M`.
+        We chose our default threshold because it is in wide use.  Other thresholds
+        are possible.  For example, elsewhere in the 2007 edition of *Numerical
+        recipes* there is an alternative threshold of ``S.max() *
+        np.finfo(M.dtype).eps / 2. * np.sqrt(m + n + 1.)``. The authors describe
+        this threshold as being based on "expected roundoff error" (p 71).
+        The thresholds above deal with floating point roundoff error in the
+        calculation of the SVD.  However, you may have more information about the
+        sources of error in `M` that would make you consider other tolerance values
+        to detect *effective* rank deficiency.  The most useful measure of the
+        tolerance depends on the operations you intend to use on your matrix.  For
+        example, if your data come from uncertain measurements with uncertainties
+        greater than floating point epsilon, choosing a tolerance near that
+        uncertainty may be preferable.  The tolerance may be absolute if the
+        uncertainties are absolute rather than relative.
+        References
+        ----------
+        .. [1] MATLAB reference documention, "Rank"
+            http://www.mathworks.com/help/techdoc/ref/rank.html
+        .. [2] W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery,
+            "Numerical Recipes (3rd edition)", Cambridge University Press, 2007,
+            page 795.
+        Examples
+        --------
+        >>> from numpy.linalg import matrix_rank
+        >>> matrix_rank(np.eye(4)) # Full rank matrix
+        4
+        >>> I=np.eye(4); I[-1,-1] = 0. # rank deficient matrix
+        >>> matrix_rank(I)
+        3
+        >>> matrix_rank(np.ones((4,))) # 1 dimension - rank 1 unless all 0
+        1
+        >>> matrix_rank(np.zeros((4,)))
+        0
+        """
+        M = np.asarray(M)
+        if M.ndim > 2:
+            raise TypeError('array should have 2 or fewer dimensions')
+        if M.ndim < 2:
+            return int(not all(M == 0))
+        S = np.linalg.svd(M, compute_uv=False)
+        if tol is None:
+            tol = S.max() * max(M.shape) * np.finfo(S.dtype).eps
+        return np.sum(S > tol)
+
+
+
+class CacheWriteWarning(UserWarning):
+    pass
+
+class CachedAttribute(object):
+
+    def __init__(self, func, cachename=None, resetlist=None):
+        self.fget = func
+        self.name = func.__name__
+        self.cachename = cachename or '_cache'
+        self.resetlist = resetlist or ()
+
+    def __get__(self, obj, type=None):
+        if obj is None:
+            return self.fget
+        # Get the cache or set a default one if needed
+        _cachename = self.cachename
+        _cache = getattr(obj, _cachename, None)
+        if _cache is None:
+            setattr(obj, _cachename, resettable_cache())
+            _cache = getattr(obj, _cachename)
+        # Get the name of the attribute to set and cache
+        name = self.name
+        _cachedval = _cache.get(name, None)
+        # print("[_cachedval=%s]" % _cachedval)
+        if _cachedval is None:
+            # Call the "fget" function
+            _cachedval = self.fget(obj)
+            # Set the attribute in obj
+            # print("Setting %s in cache to %s" % (name, _cachedval))
+            try:
+                _cache[name] = _cachedval
+            except KeyError:
+                setattr(_cache, name, _cachedval)
+            # Update the reset list if needed (and possible)
+            resetlist = self.resetlist
+            if resetlist is not ():
+                try:
+                    _cache._resetdict[name] = self.resetlist
+                except AttributeError:
+                    pass
+        # else:
+        # print("Reading %s from cache (%s)" % (name, _cachedval))
+        return _cachedval
+
+    def __set__(self, obj, value):
+        errmsg = "The attribute '%s' cannot be overwritten" % self.name
+        warnings.warn(errmsg, CacheWriteWarning)
+
+
+class _cache_readonly(object):
+    """
+    Decorator for CachedAttribute
+    """
+
+    def __init__(self, cachename=None, resetlist=None):
+        self.func = None
+        self.cachename = cachename
+        self.resetlist = resetlist or None
+
+    def __call__(self, func):
+        return CachedAttribute(func,
+                               cachename=self.cachename,
+                               resetlist=self.resetlist)
+cache_readonly = _cache_readonly()
+
+

release/python/0.6.0/crankshaft/crankshaft/regression/glm/varfuncs.py

@@ -0,0 +1,284 @@
+"""
+Variance functions for use with the link functions in statsmodels.family.links
+"""
+
+__docformat__ = 'restructuredtext'
+
+import numpy as np
+FLOAT_EPS = np.finfo(float).eps
+
+class VarianceFunction(object):
+    """
+    Relates the variance of a random variable to its mean. Defaults to 1.
+
+    Methods
+    -------
+    call
+        Returns an array of ones that is the same shape as `mu`
+
+    Notes
+    -----
+    After a variance function is initialized, its call method can be used.
+
+    Alias for VarianceFunction:
+    constant = VarianceFunction()
+
+    See also
+    --------
+    statsmodels.family.family
+    """
+
+    def __call__(self, mu):
+        """
+        Default variance function
+
+        Parameters
+        -----------
+        mu : array-like
+            mean parameters
+
+        Returns
+        -------
+        v : array
+            ones(mu.shape)
+        """
+        mu = np.asarray(mu)
+        return np.ones(mu.shape, np.float64)
+
+
+    def deriv(self, mu):
+        """
+        Derivative of the variance function v'(mu)
+        """
+        from statsmodels.tools.numdiff import approx_fprime_cs
+        # TODO: diag workaround proplem with numdiff for 1d
+        return np.diag(approx_fprime_cs(mu, self))
+
+
+constant = VarianceFunction()
+constant.__doc__ = """
+The call method of constant returns a constant variance, i.e., a vector of ones.
+
+constant is an alias of VarianceFunction()
+"""
+
+class Power(object):
+    """
+    Power variance function
+
+    Parameters
+    ----------
+    power : float
+        exponent used in power variance function
+
+    Methods
+    -------
+    call
+        Returns the power variance
+
+    Formulas
+    --------
+    V(mu) = numpy.fabs(mu)**power
+
+    Notes
+    -----
+    Aliases for Power:
+    mu = Power()
+    mu_squared = Power(power=2)
+    mu_cubed = Power(power=3)
+    """
+
+    def __init__(self, power=1.):
+        self.power = power
+
+    def __call__(self, mu):
+        """
+        Power variance function
+
+        Parameters
+        ----------
+        mu : array-like
+            mean parameters
+
+        Returns
+        -------
+        variance : array
+            numpy.fabs(mu)**self.power
+        """
+        return np.power(np.fabs(mu), self.power)
+
+
+    def deriv(self, mu):
+        """
+        Derivative of the variance function v'(mu)
+        """
+        from statsmodels.tools.numdiff import approx_fprime_cs, approx_fprime
+        #return approx_fprime_cs(mu, self)  # TODO fix breaks in `fabs
+        # TODO: diag is workaround problem with numdiff for 1d
+        return np.diag(approx_fprime(mu, self))
+
+
+mu = Power()
+mu.__doc__ = """
+Returns np.fabs(mu)
+
+Notes
+-----
+This is an alias of Power()
+"""
+mu_squared = Power(power=2)
+mu_squared.__doc__ = """
+Returns np.fabs(mu)**2
+
+Notes
+-----
+This is an alias of statsmodels.family.links.Power(power=2)
+"""
+mu_cubed = Power(power=3)
+mu_cubed.__doc__ = """
+Returns np.fabs(mu)**3
+
+Notes
+-----
+This is an alias of statsmodels.family.links.Power(power=3)
+"""
+
+class Binomial(object):
+    """
+    Binomial variance function
+
+    Parameters
+    ----------
+    n : int, optional
+        The number of trials for a binomial variable.  The default is 1 for
+        p in (0,1)
+
+    Methods
+    -------
+    call
+        Returns the binomial variance
+
+    Formulas
+    --------
+    V(mu) = p * (1 - p) * n
+
+    where p = mu / n
+
+    Notes
+    -----
+    Alias for Binomial:
+    binary = Binomial()
+
+    A private method _clean trims the data by machine epsilon so that p is
+    in (0,1)
+    """
+
+    def __init__(self, n=1):
+        self.n = n
+
+    def _clean(self, p):
+        return np.clip(p, FLOAT_EPS, 1 - FLOAT_EPS)
+
+    def __call__(self, mu):
+        """
+        Binomial variance function
+
+        Parameters
+        -----------
+        mu : array-like
+            mean parameters
+
+        Returns
+        -------
+        variance : array
+           variance = mu/n * (1 - mu/n) * self.n
+        """
+        p = self._clean(mu / self.n)
+        return p * (1 - p) * self.n
+
+    #TODO: inherit from super
+    def deriv(self, mu):
+        """
+        Derivative of the variance function v'(mu)
+        """
+        from statsmodels.tools.numdiff import approx_fprime_cs, approx_fprime
+        # TODO: diag workaround proplem with numdiff for 1d
+        return np.diag(approx_fprime_cs(mu, self))
+
+
+binary = Binomial()
+binary.__doc__ = """
+The binomial variance function for n = 1
+
+Notes
+-----
+This is an alias of Binomial(n=1)
+"""
+
+class NegativeBinomial(object):
+    '''
+    Negative binomial variance function
+
+    Parameters
+    ----------
+    alpha : float
+        The ancillary parameter for the negative binomial variance function.
+        `alpha` is assumed to be nonstochastic.  The default is 1.
+
+    Methods
+    -------
+    call
+        Returns the negative binomial variance
+
+    Formulas
+    --------
+    V(mu) = mu + alpha*mu**2
+
+    Notes
+    -----
+    Alias for NegativeBinomial:
+    nbinom = NegativeBinomial()
+
+    A private method _clean trims the data by machine epsilon so that p is
+    in (0,inf)
+    '''
+
+    def __init__(self, alpha=1.):
+        self.alpha = alpha
+
+    def _clean(self, p):
+        return np.clip(p, FLOAT_EPS, np.inf)
+
+    def __call__(self, mu):
+        """
+        Negative binomial variance function
+
+        Parameters
+        ----------
+        mu : array-like
+            mean parameters
+
+        Returns
+        -------
+        variance : array
+            variance = mu + alpha*mu**2
+        """
+        p = self._clean(mu)
+        return p + self.alpha*p**2
+
+    def deriv(self, mu):
+        """
+        Derivative of the negative binomial variance function.
+        """
+
+        p = self._clean(mu)
+        return 1 + 2 * self.alpha * p
+
+nbinom = NegativeBinomial()
+nbinom.__doc__ = """
+Negative Binomial variance function.
+
+Notes
+-----
+This is an alias of NegativeBinomial(alpha=1.)
+"""

release/python/0.6.0/crankshaft/crankshaft/regression/gwr/__init__.py

@@ -0,0 +1 @@
+from base import *

release/python/0.6.0/crankshaft/crankshaft/regression/gwr/base/__init__.py

@@ -0,0 +1,4 @@
+import gwr
+import sel_bw
+import diagnostics
+import kernels

release/python/0.6.0/crankshaft/crankshaft/regression/gwr/base/diagnostics.py

@@ -0,0 +1,81 @@
+"""
+Diagnostics for estimated gwr modesl
+"""
+__author__ = "Taylor Oshan tayoshan@gmail.com"
+
+import numpy as np
+from crankshaft.regression.glm.family import Gaussian, Poisson, Binomial
+
+def get_AICc(gwr):
+    """
+    Get AICc value
+    
+    Gaussian: p61, (2.33), Fotheringham, Brunsdon and Charlton (2002)
+    
+    GWGLM: AICc=AIC+2k(k+1)/(n-k-1), Nakaya et al. (2005): p2704, (36)
+
+    """
+    n = gwr.n
+    k = gwr.tr_S
+    if isinstance(gwr.family, Gaussian):
+        aicc = -2.0*gwr.llf + 2.0*n*(k + 1.0)/(n-k-2.0)  
+    elif isinstance(gwr.family, (Poisson, Binomial)):
+        aicc = get_AIC(gwr) + 2.0 * k * (k+1.0) / (n - k - 1.0) 
+    return aicc
+
+def get_AIC(gwr):
+    """
+    Get AIC calue
+
+    Gaussian: p96, (4.22), Fotheringham, Brunsdon and Charlton (2002)
+
+    GWGLM:  AIC(G)=D(G) + 2K(G), where D and K denote the deviance and the effective
+    number of parameters in the model with bandwidth G, respectively.
+    
+    """   
+    k = gwr.tr_S
+    #deviance = -2*log-likelihood
+    y = gwr.y
+    mu = gwr.mu
+    if isinstance(gwr.family, Gaussian):
+        aic = -2.0 * gwr.llf + 2.0 * (k+1)
+    elif isinstance(gwr.family, (Poisson, Binomial)):
+        aic = np.sum(gwr.family.resid_dev(y, mu)**2) + 2.0 * k
+    return aic 
+
+def get_BIC(gwr):
+    """
+    Get BIC value
+
+    Gaussian: p61 (2.34), Fotheringham, Brunsdon and Charlton (2002)
+    BIC = -2log(L)+klog(n)
+
+    GWGLM: BIC = dev + tr_S * log(n)
+
+    """
+    n = gwr.n      # (scalar) number of observations
+    k = gwr.tr_S  
+    y = gwr.y
+    mu = gwr.mu
+    if isinstance(gwr.family, Gaussian):
+        bic = -2.0 * gwr.llf + (k+1) * np.log(n) 
+    elif isinstance(gwr.family, (Poisson, Binomial)):
+        bic = np.sum(gwr.family.resid_dev(y, mu)**2) + k * np.log(n)
+    return bic
+
+def get_CV(gwr):
+    """
+    Get CV value
+
+    Gaussian only
+
+    Methods: p60, (2.31) or p212 (9.4)
+    Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+    Geographically weighted regression: the analysis of spatially varying relationships.
+    Modification: sum of residual squared is divided by n according to GWR4 results
+
+    """
+    aa = gwr.resid_response.reshape((-1,1))/(1.0-gwr.influ)
+    cv = np.sum(aa**2)/gwr.n
+    return cv
+

release/python/0.6.0/crankshaft/crankshaft/regression/gwr/base/kernels.py

@@ -0,0 +1,120 @@
+# GWR kernel function specifications
+
+__author__ = "Taylor Oshan tayoshan@gmail.com" 
+
+#from pysal.weights.Distance import Kernel 
+import scipy
+from scipy.spatial.kdtree import KDTree
+import numpy as np
+
+#adaptive specifications should be parameterized with nn-1 to match original gwr
+#implementation. That is, pysal counts self neighbors with knn automatically.
+
+def fix_gauss(coords, bw, points=None):
+    w = _Kernel(coords, function='gwr_gaussian', bandwidth=bw,
+            truncate=False, points=points)
+    return w.kernel
+
+def adapt_gauss(coords, nn, points=None):
+    w = _Kernel(coords, fixed=False, k=nn-1, function='gwr_gaussian',
+            truncate=False, points=points)
+    return w.kernel
+
+def fix_bisquare(coords, bw, points=None):
+    w = _Kernel(coords, function='bisquare', bandwidth=bw, points=points)
+    return w.kernel
+
+def adapt_bisquare(coords, nn, points=None):
+    w = _Kernel(coords, fixed=False, k=nn-1, function='bisquare', points=points)
+    return w.kernel
+
+def fix_exp(coords, bw, points=None):
+    w = _Kernel(coords, function='exponential', bandwidth=bw,
+            truncate=False, points=points)
+    return w.kernel
+
+def adapt_exp(coords, nn, points=None):
+    w = _Kernel(coords, fixed=False, k=nn-1, function='exponential',
+            truncate=False, points=points)
+    return w.kernel
+
+from scipy.spatial.distance import cdist
+
+class _Kernel(object):
+    """
+
+    """
+    def __init__(self, data, bandwidth=None, fixed=True, k=None,
+                 function='triangular', eps=1.0000001, ids=None, truncate=True, 
+                 points=None): #Added truncate flag
+        if issubclass(type(data), scipy.spatial.KDTree):
+            self.data = data.data
+            data = self.data
+        else:
+            self.data = data
+        if k is not None:
+            self.k = int(k) + 1
+        else:
+            self.k = k
+        if points is None:
+            self.dmat = cdist(self.data, self.data)
+        else:
+            self.points = points
+            self.dmat = cdist(self.points, self.data)
+        self.function = function.lower()
+        self.fixed = fixed
+        self.eps = eps
+        self.trunc = truncate
+        if bandwidth:
+            try:
+                bandwidth = np.array(bandwidth)
+                bandwidth.shape = (len(bandwidth), 1)
+            except:
+                bandwidth = np.ones((len(data), 1), 'float') * bandwidth
+            self.bandwidth = bandwidth
+        else:
+            self._set_bw()
+        self.kernel = self._kernel_funcs(self.dmat/self.bandwidth)
+        
+        if self.trunc:
+            mask = np.repeat(self.bandwidth, len(self.data), axis=1)
+            self.kernel[(self.dmat >= mask)] = 0
+
+    def _set_bw(self):
+        if self.k is not None:
+            dmat = np.sort(self.dmat)[:,:self.k]
+        else:
+            dmat = self.dmat
+        if self.fixed:
+            # use max knn distance as bandwidth
+            bandwidth = dmat.max() * self.eps
+            n = len(self.data)
+            self.bandwidth = np.ones((n, 1), 'float') * bandwidth
+        else:
+            # use local max knn distance
+            self.bandwidth = dmat.max(axis=1) * self.eps
+            self.bandwidth.shape = (self.bandwidth.size, 1)
+            
+
+    def _kernel_funcs(self, zs):
+        # functions follow Anselin and Rey (2010) table 5.4
+        if self.function == 'triangular':
+            return 1 - zs 
+        elif self.function == 'uniform':
+            return np.ones(zi.shape) * 0.5 
+        elif self.function == 'quadratic':
+            return (3. / 4) * (1 - zs ** 2) 
+        elif self.function == 'quartic':
+            return (15. / 16) * (1 - zs ** 2) ** 2 
+        elif self.function == 'gaussian':
+            c = np.pi * 2
+            c = c ** (-0.5)
+            return c * np.exp(-(zs ** 2) / 2.)
+        elif self.function == 'gwr_gaussian':
+            return np.exp(-0.5*(zs)**2)
+        elif self.function == 'bisquare':
+            return (1-(zs)**2)**2
+        elif self.function =='exponential':
+            return np.exp(-zs)
+        else:
+            print('Unsupported kernel function', self.function)

release/python/0.6.0/crankshaft/crankshaft/regression/gwr/base/search.py

@@ -0,0 +1,208 @@
+#Bandwidth optimization methods
+
+__author__ = "Taylor Oshan"
+
+import numpy as np
+
+def golden_section(a, c, delta, function, tol, max_iter, int_score=False):
+    """
+    Golden section search routine
+    Method: p212, 9.6.4
+    Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+    Geographically weighted regression: the analysis of spatially varying relationships.
+
+    Parameters
+    ----------
+    a               : float
+                      initial max search section value
+    b               : float
+                      initial min search section value
+    delta           : float
+                      constant used to determine width of search sections
+    function        : function
+                      obejective function to be evaluated at different section
+                      values
+    int_score       : boolean
+                      False for float score, True for integer score
+    tol             : float
+                      tolerance used to determine convergence
+    max_iter        : integer
+                      maximum iterations if no convergence to tolerance
+
+    Returns
+    -------
+    opt_val         : float
+                      optimal value
+    opt_score       : kernel
+                      optimal score
+    output          : list of tuples
+                      searching history
+    """
+    b = a + delta * np.abs(c-a)
+    d = c - delta * np.abs(c-a)
+    score = 0.0
+    diff = 1.0e9
+    iters  = 0
+    output = []
+    while np.abs(diff) > tol and iters < max_iter:
+        iters += 1
+        if int_score:
+        	b = np.round(b)
+        	d = np.round(d)
+
+        score_a = function(a)
+        score_b = function(b)
+        score_c = function(c)
+        score_d = function(d)
+        
+        if score_b <= score_d:
+            opt_val = b
+            opt_score = score_b
+            c = d
+            d = b
+            b = a + delta * np.abs(c-a)
+            #if int_score:
+                #b = np.round(b)
+        else:
+            opt_val = d
+            opt_score = score_d
+            a = b
+            b = d
+            d = c - delta * np.abs(c-a)
+            #if int_score:
+                #d = np.round(b)
+        
+        #if int_score:
+        #	opt_val = np.round(opt_val)
+        output.append((opt_val, opt_score))
+        diff = score_b - score_d
+        score = opt_score
+    return np.round(opt_val, 2), opt_score, output
+
+def equal_interval(l_bound, u_bound, interval, function, int_score=False):
+    """
+    Interval search, using interval as stepsize
+
+    Parameters
+    ----------
+    l_bound         : float
+                      initial min search section value
+    u_bound         : float
+                      initial max search section value
+    interval        : float
+                      constant used to determine width of search sections
+    function        : function
+                      obejective function to be evaluated at different section
+                      values
+    int_score       : boolean
+                      False for float score, True for integer score
+
+    Returns
+    -------
+    opt_val         : float
+                      optimal value
+    opt_score       : kernel
+                      optimal score
+    output          : list of tuples
+                      searching history
+    """
+    a = l_bound
+    c = u_bound
+    b = a + interval
+    if int_score:
+        a = np.round(a,0)
+        c = np.round(c,0)
+        b = np.round(b,0)
+    
+    output = []
+
+    score_a = function(a)
+    score_c = function(c)
+
+    output.append((a,score_a))
+    output.append((c,score_c))
+
+    if score_a < score_c:
+        opt_val = a
+        opt_score = score_a
+    else:
+        opt_val = c
+        opt_score = score_c
+
+    while b < c:
+        score_b = function(b)
+
+        output.append((b,score_b))
+
+        if score_b < opt_score:
+            opt_val = b
+            opt_score = score_b
+        b = b + interval
+
+    return opt_val, opt_score, output
+
+
+def flexible_bw(init, y, X, n, k, family, tol, max_iter, rss_score,
+        gwr_func, bw_func, sel_func):
+    if init:
+        bw = sel_func(bw_func(y, X))
+        print bw
+        optim_model = gwr_func(y, X, bw)
+        err = optim_model.resid_response.reshape((-1,1))
+        est = optim_model.params
+    else:
+        model = GLM(y, X, family=self.family, constant=False).fit()
+        err = model.resid_response.reshape((-1,1))
+        est = np.repeat(model.params.T, n, axis=0)
+        
+    
+    XB = np.multiply(est, X)
+    if rss_score:
+        rss = np.sum((err)**2)
+    iters = 0
+    scores = []
+    delta = 1e6
+    BWs = []
+    VALs = []
+
+    while delta > tol and iters < max_iter:
+        iters += 1
+        new_XB = np.zeros_like(X)
+        bws = []
+        vals = []
+        ests = np.zeros_like(X)
+        f_XB = XB.copy()
+        f_err = err.copy()
+        for i in range(k):
+            temp_y = XB[:,i].reshape((-1,1))
+            temp_y = temp_y + err
+            temp_X = X[:,i].reshape((-1,1))
+            bw_class = bw_func(temp_y, temp_X)
+            bw = sel_func(bw_class)
+            optim_model = gwr_func(temp_y, temp_X, bw)
+            err = optim_model.resid_response.reshape((-1,1))
+            est = optim_model.params.reshape((-1,))
+
+            new_XB[:,i] = np.multiply(est, temp_X.reshape((-1,)))
+            bws.append(bw)
+            ests[:,i] = est
+            vals.append(bw_class.bw[1])
+            
+        predy = np.sum(np.multiply(ests, X), axis=1).reshape((-1,1))
+        num = np.sum((new_XB - XB)**2)/n
+        den = np.sum(np.sum(new_XB, axis=1)**2)
+        score = (num/den)**0.5
+        XB = new_XB
+            
+        if rss_score:
+            new_rss = np.sum((y - predy)**2)
+            score = np.abs((new_rss - rss)/new_rss)
+            rss = new_rss
+        print score
+        scores.append(score)
+        delta = score
+        BWs.append(bws) 
+        VALs.append(vals)
+
+    opt_bws = BWs[-1]
+    return opt_bws, np.array(BWs), np.array(VALs), np.array(scores), f_XB, f_err

release/python/0.6.0/crankshaft/crankshaft/regression/gwr/base/sel_bw.py

@@ -0,0 +1,286 @@
+# GWR Bandwidth selection class
+
+#Thinking about removing the search method and just having optimization begin in
+#class __init__
+
+#x_glob and offset parameters dont yet do anything; former is for semiparametric
+#GWR and later is for offset variable for Poisson model
+
+__author__ = "Taylor Oshan Tayoshan@gmail.com"
+
+from kernels import *
+from search import golden_section, equal_interval, flexible_bw
+from gwr import GWR
+from crankshaft.regression.glm.family import Gaussian, Poisson, Binomial
+import pysal.spreg.user_output as USER
+from diagnostics import get_AICc, get_AIC, get_BIC, get_CV
+from scipy.spatial.distance import pdist, squareform
+from pysal.common import KDTree
+import numpy as np
+
+kernels = {1: fix_gauss, 2: adapt_gauss, 3: fix_bisquare, 4:
+        adapt_bisquare, 5: fix_exp, 6:adapt_exp}
+getDiag = {'AICc': get_AICc,'AIC':get_AIC, 'BIC': get_BIC, 'CV': get_CV}
+
+class Sel_BW(object):
+    """
+    Select bandwidth for kernel
+
+    Methods: p211 - p213, bandwidth selection
+    Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+    Geographically weighted regression: the analysis of spatially varying relationships.
+
+    Parameters
+    ----------
+    y              : array
+                     n*1, dependent variable.
+    x_glob         : array
+                     n*k1, fixed independent variable.
+    x_loc          : array
+                     n*k2, local independent variable, including constant.
+    coords         : list of tuples
+                     (x,y) of points used in bandwidth selection
+    family         : string
+                     GWR model type: 'Gaussian', 'logistic, 'Poisson''
+    offset         : array
+                     n*1, offset variable for Poisson model
+    kernel         : string
+                     kernel function: 'gaussian', 'bisquare', 'exponetial'
+    fixed          : boolean
+                     True for fixed bandwidth and False for adaptive (NN)
+    fb             : True for flexible (mutliple covaraite-specific) bandwidths
+                     False for a traditional (same for  all covariates)
+                     bandwdith; defualt is False.
+    constant       : boolean
+                     True to include intercept (default) in model and False to exclude
+                     intercept.
+
+
+    Attributes
+    ----------
+    y              : array
+                     n*1, dependent variable.
+    x_glob         : array
+                     n*k1, fixed independent variable.
+    x_loc          : array
+                     n*k2, local independent variable, including constant.
+    coords         : list of tuples
+                     (x,y) of points used in bandwidth selection
+    family         : string
+                     GWR model type: 'Gaussian', 'logistic, 'Poisson''
+    kernel         : string
+                     type of kernel used and wether fixed or adaptive
+    criterion      : string
+                     bw selection criterion: 'AICc', 'AIC', 'BIC', 'CV'
+    search         : string
+                     bw search method: 'golden', 'interval'
+    bw_min         : float
+                     min value used in bandwidth search
+    bw_max         : float
+                     max value used in bandwidth search
+    interval       : float
+                     interval increment used in interval search
+    tol            : float
+                     tolerance used to determine convergence
+    max_iter       : integer
+                     max interations if no convergence to tol
+    fb             : True for flexible (mutliple covaraite-specific) bandwidths
+                     False for a traditional (same for  all covariates)
+                     bandwdith; defualt is False.
+    constant       : boolean
+                     True to include intercept (default) in model and False to exclude
+                     intercept.
+    """
+    def __init__(self, coords, y, x_loc, x_glob=None, family=Gaussian(),
+            offset=None, kernel='bisquare', fixed=False, fb=False, constant=True):
+        self.coords = coords
+        self.y = y
+        self.x_loc = x_loc
+        if x_glob is not None:
+            self.x_glob = x_glob
+        else:
+            self.x_glob = []
+        self.family=family
+        self.fixed = fixed
+        self.kernel = kernel
+        if offset is None:
+        	self.offset = np.ones((len(y), 1))
+        else:
+            self.offset = offset * 1.0
+        self.fb = fb
+        self.constant = constant
+
+    def search(self, search='golden_section', criterion='AICc', bw_min=0.0, 
+            bw_max=0.0, interval=0.0, tol=1.0e-6, max_iter=200, init_fb=True,
+            tol_fb=1.0e-5, rss_score=False, max_iter_fb=200):
+        """
+        Parameters
+        ----------
+        criterion      : string
+                         bw selection criterion: 'AICc', 'AIC', 'BIC', 'CV'
+        search         : string
+                         bw search method: 'golden', 'interval'
+        bw_min         : float
+                         min value used in bandwidth search
+        bw_max         : float
+                         max value used in bandwidth search
+        interval       : float
+                         interval increment used in interval search
+        tol            : float
+                         tolerance used to determine convergence
+        max_iter       : integer
+                         max iterations if no convergence to tol
+        init_fb        : True to initialize flexible bandwidth search with
+                         esitmates from a traditional GWR and False to
+                         initialize flexible bandwidth search with global
+                         regression estimates
+        tol_fb         : convergence tolerence for the flexible bandwidth
+                         backfitting algorithm; a larger tolerance may stop the
+                         algorith faster though it may result in a less optimal
+                         model
+        max_iter_fb    : max iterations if no convergence to tol for flexible
+                         bandwidth backfittign algorithm
+        rss_score      : True to use the residual sum of sqaures to evaluate
+                         each iteration of the flexible bandwidth backfitting
+                         routine and False to use a smooth function; default is
+                         False
+
+        Returns
+        -------
+        bw             : scalar or array
+                         optimal bandwidth value or values; returns scalar for
+                         fb=False and array for fb=True; ordering of bandwidths
+                         matches the ordering of the covariates (columns) of the
+                         designs matrix, X
+        """     
+        self.search = search
+        self.criterion = criterion
+        self.bw_min = bw_min
+        self.bw_max = bw_max
+        self.interval = interval
+        self.tol = tol
+        self.max_iter = max_iter
+        self.init_fb = init_fb
+        self.tol_fb = tol_fb
+        self.rss_score = rss_score
+        self.max_iter_fb = max_iter_fb
+        
+
+        if self.fixed:
+            if self.kernel == 'gaussian':
+                ktype = 1
+            elif self.kernel == 'bisquare':
+                ktype = 3
+            elif self.kernel == 'exponential':
+                ktype = 5
+            else:
+                raise TypeError('Unsupported kernel function ', self.kernel)
+        else:
+            if self.kernel == 'gaussian':
+            	ktype = 2
+            elif self.kernel == 'bisquare':
+                ktype = 4
+            elif self.kernel == 'exponential':
+                ktype = 6
+            else:
+                raise TypeError('Unsupported kernel function ', self.kernel)
+
+        function = lambda bw: getDiag[criterion](
+                GWR(self.coords, self.y, self.x_loc, bw, family=self.family,
+                    kernel=self.kernel, fixed=self.fixed, offset=self.offset).fit())
+        
+        if ktype % 2 == 0:
+            int_score = True
+        else:
+            int_score = False
+        self.int_score = int_score
+        
+        if self.fb:
+            self._fbw()
+            print self.bw[1]
+            self.XB = self.bw[4]
+            self.err = self.bw[5]
+        else:
+            self._bw()
+
+        return self.bw[0]
+
+    def _bw(self):
+        gwr_func = lambda bw: getDiag[self.criterion](
+                GWR(self.coords, self.y, self.x_loc, bw, family=self.family,
+                    kernel=self.kernel, fixed=self.fixed, constant=self.constant).fit())
+        if self.search == 'golden_section':
+            a,c = self._init_section(self.x_glob, self.x_loc, self.coords,
+                    self.constant)
+            delta = 0.38197 #1 - (np.sqrt(5.0)-1.0)/2.0
+            self.bw = golden_section(a, c, delta, gwr_func, self.tol, 
+                    self.max_iter, self.int_score)
+        elif self.search == 'interval':
+            self.bw = equal_interval(self.bw_min, self.bw_max, self.interval,
+                    gwr_func, self.int_score)
+        else:
+            raise TypeError('Unsupported computational search method ', search)
+   
+    def _fbw(self):
+        y = self.y
+        if self.constant:
+        	X = USER.check_constant(self.x_loc)
+        else:
+            X = self.x_loc
+        n, k = X.shape
+        family = self.family
+        offset = self.offset
+        kernel = self.kernel
+        fixed = self.fixed
+        coords = self.coords
+        search = self.search
+        criterion = self.criterion
+        bw_min = self.bw_min
+        bw_max = self.bw_max
+        interval = self.interval
+        tol = self.tol
+        max_iter = self.max_iter
+        gwr_func = lambda y, X, bw: GWR(coords, y, X, bw, family=family, 
+                kernel=kernel, fixed=fixed, offset=offset, constant=False).fit()
+        bw_func = lambda y, X: Sel_BW(coords, y, X, x_glob=[], family=family,
+                kernel=kernel, fixed=fixed, offset=offset, constant=False)
+        sel_func = lambda bw_func: bw_func.search(search=search, 
+                        criterion=criterion, bw_min=bw_min, bw_max=bw_max, 
+                        interval=interval, tol=tol, max_iter=max_iter)
+        self.bw = flexible_bw(self.init_fb, y, X, n, k, family, self.tol_fb,
+               self.max_iter_fb, self.rss_score, gwr_func, bw_func, sel_func)
+
+
+
+    def _init_section(self, x_glob, x_loc, coords, constant):
+        if len(x_glob) > 0:
+            n_glob = x_glob.shape[1]
+        else:
+            n_glob = 0
+        if len(x_loc) > 0:
+            n_loc = x_loc.shape[1]
+        else:
+            n_loc = 0
+        if constant:
+            n_vars = n_glob + n_loc + 1
+        else:
+            n_vars = n_glob + n_loc
+        n = np.array(coords).shape[0]
+
+        if self.int_score:
+            a = 40 + 2 * n_vars
+            c = n
+        else:
+            nn = 40 + 2 * n_vars
+            sq_dists = squareform(pdist(coords))
+            sort_dists = np.sort(sq_dists, axis=1)
+            min_dists = sort_dists[:,nn-1]
+            max_dists = sort_dists[:,-1]
+            a = np.min(min_dists)/2.0
+            c = np.max(max_dists)/2.0
+      
+        if a < self.bw_min:
+            a = self.bw_min
+        if c > self.bw_max and self.bw_max > 0:
+            c = self.bw_max
+        return a, c

release/python/0.6.0/crankshaft/crankshaft/regression/gwr/base/tests/test_gwr.py

@@ -0,0 +1,853 @@
+"""
+GWR is tested against results from GWR4
+"""
+
+import unittest
+import pickle as pk
+from crankshaft.regression.gwr.gwr import GWR, FBGWR
+from crankshaft.regression.gwr.sel_bw import Sel_BW
+from crankshaft.regression.gwr.diagnostics import get_AICc, get_AIC, get_BIC, get_CV
+from crankshaft.regression.glm.family import Gaussian, Poisson, Binomial
+import numpy as np
+import pysal
+
+class TestGWRGaussian(unittest.TestCase):
+    def setUp(self):
+        data = pysal.open(pysal.examples.get_path('GData_utm.csv'))
+        self.coords = zip(data.by_col('X'), data.by_col('Y'))
+        self.y = np.array(data.by_col('PctBach')).reshape((-1,1))
+        rural  = np.array(data.by_col('PctRural')).reshape((-1,1))
+        pov = np.array(data.by_col('PctPov')).reshape((-1,1)) 
+        black = np.array(data.by_col('PctBlack')).reshape((-1,1))
+        self.X = np.hstack([rural, pov, black])
+        self.BS_F = pysal.open(pysal.examples.get_path('georgia_BS_F_listwise.csv'))
+        self.BS_NN = pysal.open(pysal.examples.get_path('georgia_BS_NN_listwise.csv'))
+        self.GS_F = pysal.open(pysal.examples.get_path('georgia_GS_F_listwise.csv'))
+        self.GS_NN = pysal.open(pysal.examples.get_path('georgia_GS_NN_listwise.csv'))
+        self.FB = pk.load(open(pysal.examples.get_path('FB.p'), 'r'))
+        self.XB = pk.load(open(pysal.examples.get_path('XB.p'), 'r'))
+        self.err = pk.load(open(pysal.examples.get_path('err.p'), 'r'))
+
+    def test_BS_F(self):
+        est_Int = self.BS_F.by_col(' est_Intercept')
+        se_Int = self.BS_F.by_col(' se_Intercept')
+        t_Int = self.BS_F.by_col(' t_Intercept')
+        est_rural = self.BS_F.by_col(' est_PctRural')
+        se_rural = self.BS_F.by_col(' se_PctRural')
+        t_rural = self.BS_F.by_col(' t_PctRural')
+        est_pov = self.BS_F.by_col(' est_PctPov')
+        se_pov = self.BS_F.by_col(' se_PctPov')
+        t_pov = self.BS_F.by_col(' t_PctPov')
+        est_black = self.BS_F.by_col(' est_PctBlack')
+        se_black = self.BS_F.by_col(' se_PctBlack')
+        t_black = self.BS_F.by_col(' t_PctBlack')
+        yhat = self.BS_F.by_col(' yhat')
+        res = np.array(self.BS_F.by_col(' residual'))
+        std_res = np.array(self.BS_F.by_col(' std_residual')).reshape((-1,1))
+        localR2 = np.array(self.BS_F.by_col(' localR2')).reshape((-1,1))
+        inf = np.array(self.BS_F.by_col(' influence')).reshape((-1,1))
+        cooksD = np.array(self.BS_F.by_col(' CooksD')).reshape((-1,1))
+        
+        model = GWR(self.coords, self.y, self.X, bw=209267.689, fixed=True)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        CV = get_CV(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 894.0)
+        self.assertAlmostEquals(np.floor(AIC), 890.0)
+        self.assertAlmostEquals(np.floor(BIC), 944.0)
+        self.assertAlmostEquals(np.round(CV,2), 18.25)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
+        np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04)
+        np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04)
+        np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04)
+        np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04)
+        np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04)
+        np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04)
+        np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02)
+        np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02)
+        np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05)
+        np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04)
+        np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04)
+        np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05)
+        np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04)
+        np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00)
+
+    def test_BS_NN(self):
+        est_Int = self.BS_NN.by_col(' est_Intercept')
+        se_Int = self.BS_NN.by_col(' se_Intercept')
+        t_Int = self.BS_NN.by_col(' t_Intercept')
+        est_rural = self.BS_NN.by_col(' est_PctRural')
+        se_rural = self.BS_NN.by_col(' se_PctRural')
+        t_rural = self.BS_NN.by_col(' t_PctRural')
+        est_pov = self.BS_NN.by_col(' est_PctPov')
+        se_pov = self.BS_NN.by_col(' se_PctPov')
+        t_pov = self.BS_NN.by_col(' t_PctPov')
+        est_black = self.BS_NN.by_col(' est_PctBlack')
+        se_black = self.BS_NN.by_col(' se_PctBlack')
+        t_black = self.BS_NN.by_col(' t_PctBlack')
+        yhat = self.BS_NN.by_col(' yhat')
+        res = np.array(self.BS_NN.by_col(' residual'))
+        std_res = np.array(self.BS_NN.by_col(' std_residual')).reshape((-1,1))
+        localR2 = np.array(self.BS_NN.by_col(' localR2')).reshape((-1,1))
+        inf = np.array(self.BS_NN.by_col(' influence')).reshape((-1,1))
+        cooksD = np.array(self.BS_NN.by_col(' CooksD')).reshape((-1,1))
+
+        model = GWR(self.coords, self.y, self.X, bw=90.000, fixed=False)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        CV = get_CV(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 896.0)
+        self.assertAlmostEquals(np.floor(AIC), 892.0)
+        self.assertAlmostEquals(np.floor(BIC), 941.0)
+        self.assertAlmostEquals(np.around(CV, 2), 19.19)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
+        np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04)
+        np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04)
+        np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04)
+        np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04)
+        np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04)
+        np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04)
+        np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02)
+        np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02)
+        np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05)
+        np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04)
+        np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04)
+        np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05)
+        np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04)
+        np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00)
+
+    def test_GS_F(self):
+        est_Int = self.GS_F.by_col(' est_Intercept')
+        se_Int = self.GS_F.by_col(' se_Intercept')
+        t_Int = self.GS_F.by_col(' t_Intercept')
+        est_rural = self.GS_F.by_col(' est_PctRural')
+        se_rural = self.GS_F.by_col(' se_PctRural')
+        t_rural = self.GS_F.by_col(' t_PctRural')
+        est_pov = self.GS_F.by_col(' est_PctPov')
+        se_pov = self.GS_F.by_col(' se_PctPov')
+        t_pov = self.GS_F.by_col(' t_PctPov')
+        est_black = self.GS_F.by_col(' est_PctBlack')
+        se_black = self.GS_F.by_col(' se_PctBlack')
+        t_black = self.GS_F.by_col(' t_PctBlack')
+        yhat = self.GS_F.by_col(' yhat')
+        res = np.array(self.GS_F.by_col(' residual'))
+        std_res = np.array(self.GS_F.by_col(' std_residual')).reshape((-1,1))
+        localR2 = np.array(self.GS_F.by_col(' localR2')).reshape((-1,1))
+        inf = np.array(self.GS_F.by_col(' influence')).reshape((-1,1))
+        cooksD = np.array(self.GS_F.by_col(' CooksD')).reshape((-1,1))
+        
+        model = GWR(self.coords, self.y, self.X, bw=87308.298,
+                kernel='gaussian', fixed=True)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        CV = get_CV(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 895.0)
+        self.assertAlmostEquals(np.floor(AIC), 890.0)
+        self.assertAlmostEquals(np.floor(BIC), 943.0)
+        self.assertAlmostEquals(np.around(CV, 2), 18.21)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
+        np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04)
+        np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04)
+        np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04)
+        np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04)
+        np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04)
+        np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04)
+        np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02)
+        np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02)
+        np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05)
+        np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04)
+        np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04)
+        np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05)
+        np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04)
+        np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00)
+        
+    def test_GS_NN(self):
+        est_Int = self.GS_NN.by_col(' est_Intercept')
+        se_Int = self.GS_NN.by_col(' se_Intercept')
+        t_Int = self.GS_NN.by_col(' t_Intercept')
+        est_rural = self.GS_NN.by_col(' est_PctRural')
+        se_rural = self.GS_NN.by_col(' se_PctRural')
+        t_rural = self.GS_NN.by_col(' t_PctRural')
+        est_pov = self.GS_NN.by_col(' est_PctPov')
+        se_pov = self.GS_NN.by_col(' se_PctPov')
+        t_pov = self.GS_NN.by_col(' t_PctPov')
+        est_black = self.GS_NN.by_col(' est_PctBlack')
+        se_black = self.GS_NN.by_col(' se_PctBlack')
+        t_black = self.GS_NN.by_col(' t_PctBlack')
+        yhat = self.GS_NN.by_col(' yhat')
+        res = np.array(self.GS_NN.by_col(' residual'))
+        std_res = np.array(self.GS_NN.by_col(' std_residual')).reshape((-1,1))
+        localR2 = np.array(self.GS_NN.by_col(' localR2')).reshape((-1,1))
+        inf = np.array(self.GS_NN.by_col(' influence')).reshape((-1,1))
+        cooksD = np.array(self.GS_NN.by_col(' CooksD')).reshape((-1,1))
+
+        model = GWR(self.coords, self.y, self.X, bw=49.000,
+                kernel='gaussian', fixed=False)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        CV = get_CV(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc),  896)
+        self.assertAlmostEquals(np.floor(AIC), 894.0)
+        self.assertAlmostEquals(np.floor(BIC), 922.0)
+        self.assertAlmostEquals(np.around(CV, 2), 17.91)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
+        np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04)
+        np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04)
+        np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04)
+        np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04)
+        np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04)
+        np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04)
+        np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02)
+        np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02)
+        np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05)
+        np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04)
+        np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04)
+        np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05)
+        np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04)
+        np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00)
+    
+    def test_FBGWR(self):
+        model = FBGWR(self.coords, self.y, self.X, [157.0, 65.0, 52.0],
+                XB=self.XB, err=self.err, constant=False)
+        rslt = model.fit()
+
+        np.testing.assert_allclose(rslt.predy, self.FB['predy'], atol=1e-07)
+        np.testing.assert_allclose(rslt.params, self.FB['params'], atol=1e-07)
+        np.testing.assert_allclose(rslt.resid_response, self.FB['u'], atol=1e-05)
+        np.testing.assert_almost_equal(rslt.resid_ss, 6339.3497144025841)
+
+    def test_Prediction(self):
+        coords =np.array(self.coords)
+        index = np.arange(len(self.y))
+        #train = index[0:-10]
+        test = index[-10:]
+
+        #y_train = self.y[train]
+        #X_train = self.X[train]
+        #coords_train = list(coords[train])
+
+        #y_test = self.y[test]
+        X_test = self.X[test]
+        coords_test = list(coords[test])
+
+
+        model = GWR(self.coords, self.y, self.X, 93, family=Gaussian(),
+                fixed=False, kernel='bisquare')
+        results = model.predict(coords_test, X_test)
+        
+        params = np.array([22.77198, -0.10254,    -0.215093,   -0.01405,
+            19.10531,    -0.094177,   -0.232529,   0.071913,
+            19.743421,   -0.080447,   -0.30893,    0.083206,
+            17.505759,   -0.078919,   -0.187955,   0.051719,
+            27.747402,   -0.165335,   -0.208553,   0.004067,
+            26.210627,   -0.138398,   -0.360514,   0.072199,
+            18.034833,   -0.077047,   -0.260556,   0.084319,
+            28.452802,   -0.163408,   -0.14097,    -0.063076,
+            22.353095,   -0.103046,   -0.226654,   0.002992,
+            18.220508,   -0.074034,   -0.309812,   0.108636]).reshape((10,4))
+        np.testing.assert_allclose(params, results.params, rtol=1e-03)
+
+        bse = np.array([2.080166,    0.021462,    0.102954,    0.049627,
+            2.536355,    0.022111,    0.123857,    0.051917,
+            1.967813,    0.019716,    0.102562,    0.054918,
+            2.463219,    0.021745,    0.110297,    0.044189,
+            1.556056,    0.019513,    0.12764,     0.040315,
+            1.664108,    0.020114,    0.131208,    0.041613,
+            2.5835,      0.021481,    0.113158,    0.047243,
+            1.709483,    0.019752,    0.116944,    0.043636,
+            1.958233,    0.020947,    0.09974,     0.049821,
+            2.276849,    0.020122,    0.107867,    0.047842]).reshape((10,4))
+        np.testing.assert_allclose(bse, results.bse, rtol=1e-03)
+
+        tvalues = np.array([10.947193,   -4.777659,   -2.089223,   -0.283103,
+            7.532584,    -4.259179,   -1.877395,   1.385161,
+            10.033179,   -4.080362,   -3.012133,   1.515096,
+            7.106862,    -3.629311,   -1.704079,   1.17042,
+            17.831878,   -8.473156,   -1.633924,   0.100891,
+            15.750552,   -6.880725,   -2.74765,    1.734978,
+            6.980774,    -3.586757,   -2.302575,   1.784818,
+            16.644095,   -8.273001,   -1.205451,   -1.445501,
+            11.414933,   -4.919384,   -2.272458,   0.060064,
+            8.00251, -3.679274,   -2.872176,   2.270738]).reshape((10,4))
+        np.testing.assert_allclose(tvalues, results.tvalues, rtol=1e-03)
+
+        localR2 = np.array([[ 0.53068693],
+                            [ 0.59582647],
+                            [ 0.59700925],
+                            [ 0.45769954],
+                            [ 0.54634509],
+                            [ 0.5494828 ],
+                            [ 0.55159604],
+                            [ 0.55634237],
+                            [ 0.53903842],
+                            [ 0.55884954]])
+        np.testing.assert_allclose(localR2, results.localR2, rtol=1e-05)
+
+class TestGWRPoisson(unittest.TestCase):
+    def setUp(self):
+        data = pysal.open(pysal.examples.get_path('Tokyomortality.csv'), mode='Ur')
+        self.coords = zip(data.by_col('X_CENTROID'), data.by_col('Y_CENTROID'))
+        self.y = np.array(data.by_col('db2564')).reshape((-1,1))
+        self.off = np.array(data.by_col('eb2564')).reshape((-1,1))
+        OCC  = np.array(data.by_col('OCC_TEC')).reshape((-1,1))
+        OWN = np.array(data.by_col('OWNH')).reshape((-1,1)) 
+        POP = np.array(data.by_col('POP65')).reshape((-1,1))
+        UNEMP = np.array(data.by_col('UNEMP')).reshape((-1,1))
+        self.X = np.hstack([OCC,OWN,POP,UNEMP])
+        self.BS_F = pysal.open(pysal.examples.get_path('tokyo_BS_F_listwise.csv'))
+        self.BS_NN = pysal.open(pysal.examples.get_path('tokyo_BS_NN_listwise.csv'))
+        self.GS_F = pysal.open(pysal.examples.get_path('tokyo_GS_F_listwise.csv'))
+        self.GS_NN = pysal.open(pysal.examples.get_path('tokyo_GS_NN_listwise.csv'))
+        self.BS_NN_OFF = pysal.open(pysal.examples.get_path('tokyo_BS_NN_OFF_listwise.csv'))
+
+    def test_BS_F(self):
+        est_Int = self.BS_F.by_col(' est_Intercept')
+        se_Int = self.BS_F.by_col(' se_Intercept')
+        t_Int = self.BS_F.by_col(' t_Intercept')
+        est_OCC = self.BS_F.by_col(' est_OCC_TEC')
+        se_OCC = self.BS_F.by_col(' se_OCC_TEC')
+        t_OCC = self.BS_F.by_col(' t_OCC_TEC')
+        est_OWN = self.BS_F.by_col(' est_OWNH')
+        se_OWN = self.BS_F.by_col(' se_OWNH')
+        t_OWN = self.BS_F.by_col(' t_OWNH')
+        est_POP = self.BS_F.by_col(' est_POP65')
+        se_POP = self.BS_F.by_col(' se_POP65')
+        t_POP = self.BS_F.by_col(' t_POP65')
+        est_UNEMP = self.BS_F.by_col(' est_UNEMP')
+        se_UNEMP = self.BS_F.by_col(' se_UNEMP')
+        t_UNEMP = self.BS_F.by_col(' t_UNEMP')
+        yhat = self.BS_F.by_col(' yhat')
+        pdev = np.array(self.BS_F.by_col(' localpdev')).reshape((-1,1))
+        
+        model = GWR(self.coords, self.y, self.X, bw=26029.625, family=Poisson(), 
+                kernel='bisquare', fixed=True)
+        rslt = model.fit()
+
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 13294.0)
+        self.assertAlmostEquals(np.floor(AIC), 13247.0)
+        self.assertAlmostEquals(np.floor(BIC), 13485.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-05)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-03)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-03)
+        np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-04)
+        np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02)
+        np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02)
+        np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04)
+        np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-03)
+        np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-03)
+        np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-04)
+        np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02)
+        np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02)
+        np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04)
+        np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02)
+        np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05)
+        np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+
+    def test_BS_NN(self):
+        est_Int = self.BS_NN.by_col(' est_Intercept')
+        se_Int = self.BS_NN.by_col(' se_Intercept')
+        t_Int = self.BS_NN.by_col(' t_Intercept')
+        est_OCC = self.BS_NN.by_col(' est_OCC_TEC')
+        se_OCC = self.BS_NN.by_col(' se_OCC_TEC')
+        t_OCC = self.BS_NN.by_col(' t_OCC_TEC')
+        est_OWN = self.BS_NN.by_col(' est_OWNH')
+        se_OWN = self.BS_NN.by_col(' se_OWNH')
+        t_OWN = self.BS_NN.by_col(' t_OWNH')
+        est_POP = self.BS_NN.by_col(' est_POP65')
+        se_POP = self.BS_NN.by_col(' se_POP65')
+        t_POP = self.BS_NN.by_col(' t_POP65')
+        est_UNEMP = self.BS_NN.by_col(' est_UNEMP')
+        se_UNEMP = self.BS_NN.by_col(' se_UNEMP')
+        t_UNEMP = self.BS_NN.by_col(' t_UNEMP')
+        yhat = self.BS_NN.by_col(' yhat')
+        pdev = np.array(self.BS_NN.by_col(' localpdev')).reshape((-1,1))
+
+        model = GWR(self.coords, self.y, self.X, bw=50, family=Poisson(), 
+                kernel='bisquare', fixed=False)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 13285)
+        self.assertAlmostEquals(np.floor(AIC), 13259.0)
+        self.assertAlmostEquals(np.floor(BIC), 13442.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02)
+        np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03)
+        np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02)
+        np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02)
+        np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04)
+        np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02)
+        np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02)
+        np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-03)
+        np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02)
+        np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02)
+        np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04)
+        np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02)
+        np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04)
+        np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+    
+    def test_BS_NN_Offset(self):
+        est_Int = self.BS_NN_OFF.by_col(' est_Intercept')
+        se_Int = self.BS_NN_OFF.by_col(' se_Intercept')
+        t_Int = self.BS_NN_OFF.by_col(' t_Intercept')
+        est_OCC = self.BS_NN_OFF.by_col(' est_OCC_TEC')
+        se_OCC = self.BS_NN_OFF.by_col(' se_OCC_TEC')
+        t_OCC = self.BS_NN_OFF.by_col(' t_OCC_TEC')
+        est_OWN = self.BS_NN_OFF.by_col(' est_OWNH')
+        se_OWN = self.BS_NN_OFF.by_col(' se_OWNH')
+        t_OWN = self.BS_NN_OFF.by_col(' t_OWNH')
+        est_POP = self.BS_NN_OFF.by_col(' est_POP65')
+        se_POP = self.BS_NN_OFF.by_col(' se_POP65')
+        t_POP = self.BS_NN_OFF.by_col(' t_POP65')
+        est_UNEMP = self.BS_NN_OFF.by_col(' est_UNEMP')
+        se_UNEMP = self.BS_NN_OFF.by_col(' se_UNEMP')
+        t_UNEMP = self.BS_NN_OFF.by_col(' t_UNEMP')
+        yhat = self.BS_NN_OFF.by_col(' yhat')
+        pdev = np.array(self.BS_NN_OFF.by_col(' localpdev')).reshape((-1,1))
+
+        model = GWR(self.coords, self.y, self.X, bw=100, offset=self.off, family=Poisson(), 
+                kernel='bisquare', fixed=False)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 367.0)
+        self.assertAlmostEquals(np.floor(AIC), 361.0)
+        self.assertAlmostEquals(np.floor(BIC), 451.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-02,
+                atol=1e-02)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02, atol=1e-02)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-01,
+                atol=1e-02)
+        np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03,
+                atol=1e-02)
+        np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02, atol=1e-02)
+        np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-01,
+                atol=1e-02)
+        np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04,
+                atol=1e-02)
+        np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02, atol=1e-02)
+        np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-01,
+                atol=1e-02)
+        np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-03,
+                atol=1e-02)
+        np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02, atol=1e-02)
+        np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-01,
+                atol=1e-02)
+        np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04,
+                atol=1e-02)
+        np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02,
+                atol=1e-02)
+        np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-01,
+                atol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-03, atol=1e-02)
+        np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-04, atol=1e-02)
+
+    def test_GS_F(self):
+        est_Int = self.GS_F.by_col(' est_Intercept')
+        se_Int = self.GS_F.by_col(' se_Intercept')
+        t_Int = self.GS_F.by_col(' t_Intercept')
+        est_OCC = self.GS_F.by_col(' est_OCC_TEC')
+        se_OCC = self.GS_F.by_col(' se_OCC_TEC')
+        t_OCC = self.GS_F.by_col(' t_OCC_TEC')
+        est_OWN = self.GS_F.by_col(' est_OWNH')
+        se_OWN = self.GS_F.by_col(' se_OWNH')
+        t_OWN = self.GS_F.by_col(' t_OWNH')
+        est_POP = self.GS_F.by_col(' est_POP65')
+        se_POP = self.GS_F.by_col(' se_POP65')
+        t_POP = self.GS_F.by_col(' t_POP65')
+        est_UNEMP = self.GS_F.by_col(' est_UNEMP')
+        se_UNEMP = self.GS_F.by_col(' se_UNEMP')
+        t_UNEMP = self.GS_F.by_col(' t_UNEMP')
+        yhat = self.GS_F.by_col(' yhat')
+        pdev = np.array(self.GS_F.by_col(' localpdev')).reshape((-1,1))
+        
+        model = GWR(self.coords, self.y, self.X, bw=8764.474, family=Poisson(), 
+                kernel='gaussian', fixed=True)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 11283.0)
+        self.assertAlmostEquals(np.floor(AIC), 11211.0)
+        self.assertAlmostEquals(np.floor(BIC), 11497.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-03)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02)
+        np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03)
+        np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02)
+        np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02)
+        np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-03)
+        np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02)
+        np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02)
+        np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-02)
+        np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02)
+        np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02)
+        np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-02)
+        np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02)
+        np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04)
+        np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+    def test_GS_NN(self):
+        est_Int = self.GS_NN.by_col(' est_Intercept')
+        se_Int = self.GS_NN.by_col(' se_Intercept')
+        t_Int = self.GS_NN.by_col(' t_Intercept')
+        est_OCC = self.GS_NN.by_col(' est_OCC_TEC')
+        se_OCC = self.GS_NN.by_col(' se_OCC_TEC')
+        t_OCC = self.GS_NN.by_col(' t_OCC_TEC')
+        est_OWN = self.GS_NN.by_col(' est_OWNH')
+        se_OWN = self.GS_NN.by_col(' se_OWNH')
+        t_OWN = self.GS_NN.by_col(' t_OWNH')
+        est_POP = self.GS_NN.by_col(' est_POP65')
+        se_POP = self.GS_NN.by_col(' se_POP65')
+        t_POP = self.GS_NN.by_col(' t_POP65')
+        est_UNEMP = self.GS_NN.by_col(' est_UNEMP')
+        se_UNEMP = self.GS_NN.by_col(' se_UNEMP')
+        t_UNEMP = self.GS_NN.by_col(' t_UNEMP')
+        yhat = self.GS_NN.by_col(' yhat')
+        pdev = np.array(self.GS_NN.by_col(' localpdev')).reshape((-1,1))
+        
+        model = GWR(self.coords, self.y, self.X, bw=50, family=Poisson(), 
+                kernel='gaussian', fixed=False)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 21070.0)
+        self.assertAlmostEquals(np.floor(AIC), 21069.0)
+        self.assertAlmostEquals(np.floor(BIC), 21111.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02)
+        np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03)
+        np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02)
+        np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02)
+        np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04)
+        np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02)
+        np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02)
+        np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-02)
+        np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02)
+        np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02)
+        np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-02)
+        np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02)
+        np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04)
+        np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+class TestGWRBinomial(unittest.TestCase):
+    def setUp(self):
+        data = pysal.open(pysal.examples.get_path('landslides.csv'))
+        self.coords = zip(data.by_col('X'), data.by_col('Y'))
+        self.y = np.array(data.by_col('Landslid')).reshape((-1,1))
+        ELEV  = np.array(data.by_col('Elev')).reshape((-1,1))
+        SLOPE = np.array(data.by_col('Slope')).reshape((-1,1)) 
+        SIN = np.array(data.by_col('SinAspct')).reshape((-1,1))
+        COS = np.array(data.by_col('CosAspct')).reshape((-1,1))
+        SOUTH = np.array(data.by_col('AbsSouth')).reshape((-1,1))
+        DIST = np.array(data.by_col('DistStrm')).reshape((-1,1))
+        self.X = np.hstack([ELEV, SLOPE, SIN, COS, SOUTH, DIST])
+        self.BS_F = pysal.open(pysal.examples.get_path('clearwater_BS_F_listwise.csv'))
+        self.BS_NN = pysal.open(pysal.examples.get_path('clearwater_BS_NN_listwise.csv'))
+        self.GS_F = pysal.open(pysal.examples.get_path('clearwater_GS_F_listwise.csv'))
+        self.GS_NN = pysal.open(pysal.examples.get_path('clearwater_GS_NN_listwise.csv'))
+
+    def test_BS_F(self):
+        est_Int = self.BS_F.by_col(' est_Intercept')
+        se_Int = self.BS_F.by_col(' se_Intercept')
+        t_Int = self.BS_F.by_col(' t_Intercept')
+        est_elev = self.BS_F.by_col(' est_Elev')
+        se_elev = self.BS_F.by_col(' se_Elev')
+        t_elev = self.BS_F.by_col(' t_Elev')
+        est_slope = self.BS_F.by_col(' est_Slope')
+        se_slope = self.BS_F.by_col(' se_Slope')
+        t_slope = self.BS_F.by_col(' t_Slope')
+        est_sin = self.BS_F.by_col(' est_SinAspct')
+        se_sin = self.BS_F.by_col(' se_SinAspct')
+        t_sin = self.BS_F.by_col(' t_SinAspct')
+        est_cos = self.BS_F.by_col(' est_CosAspct')
+        se_cos = self.BS_F.by_col(' se_CosAspct')
+        t_cos = self.BS_F.by_col(' t_CosAspct')
+        est_south = self.BS_F.by_col(' est_AbsSouth')
+        se_south = self.BS_F.by_col(' se_AbsSouth')
+        t_south = self.BS_F.by_col(' t_AbsSouth')
+        est_strm = self.BS_F.by_col(' est_DistStrm')
+        se_strm = self.BS_F.by_col(' se_DistStrm')
+        t_strm = self.BS_F.by_col(' t_DistStrm') 
+        yhat = self.BS_F.by_col(' yhat')
+        pdev = np.array(self.BS_F.by_col(' localpdev')).reshape((-1,1))
+
+        model = GWR(self.coords, self.y, self.X, bw=19642.170, family=Binomial(), 
+                kernel='bisquare', fixed=True)
+        rslt = model.fit()
+
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 275.0)
+        self.assertAlmostEquals(np.floor(AIC), 271.0)
+        self.assertAlmostEquals(np.floor(BIC), 349.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
+        np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00)
+        np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00)
+        np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00)
+        np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00)
+        np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00)
+        np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00)
+        np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01)
+        np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01)
+        np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01)
+        np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01)
+        np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01)
+        np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01)
+        np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01)
+        np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01)
+        np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01)
+        np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02)
+        np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01)
+        np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01)
+        #This test fails - likely due to compound rounding errors
+        #Has been tested using statsmodels.family calculations and
+        #code from Jing's python version, which both yield the same
+        #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+    
+    def test_BS_NN(self):
+        est_Int = self.BS_NN.by_col(' est_Intercept')
+        se_Int = self.BS_NN.by_col(' se_Intercept')
+        t_Int = self.BS_NN.by_col(' t_Intercept')
+        est_elev = self.BS_NN.by_col(' est_Elev')
+        se_elev = self.BS_NN.by_col(' se_Elev')
+        t_elev = self.BS_NN.by_col(' t_Elev')
+        est_slope = self.BS_NN.by_col(' est_Slope')
+        se_slope = self.BS_NN.by_col(' se_Slope')
+        t_slope = self.BS_NN.by_col(' t_Slope')
+        est_sin = self.BS_NN.by_col(' est_SinAspct')
+        se_sin = self.BS_NN.by_col(' se_SinAspct')
+        t_sin = self.BS_NN.by_col(' t_SinAspct')
+        est_cos = self.BS_NN.by_col(' est_CosAspct')
+        se_cos = self.BS_NN.by_col(' se_CosAspct')
+        t_cos = self.BS_NN.by_col(' t_CosAspct')
+        est_south = self.BS_NN.by_col(' est_AbsSouth')
+        se_south = self.BS_NN.by_col(' se_AbsSouth')
+        t_south = self.BS_NN.by_col(' t_AbsSouth')
+        est_strm = self.BS_NN.by_col(' est_DistStrm')
+        se_strm = self.BS_NN.by_col(' se_DistStrm')
+        t_strm = self.BS_NN.by_col(' t_DistStrm') 
+        yhat = self.BS_NN.by_col(' yhat')
+        pdev = self.BS_NN.by_col(' localpdev')
+        
+        model = GWR(self.coords, self.y, self.X, bw=158, family=Binomial(), 
+                kernel='bisquare', fixed=False)
+        rslt = model.fit()
+
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 277.0)
+        self.assertAlmostEquals(np.floor(AIC), 271.0)
+        self.assertAlmostEquals(np.floor(BIC), 358.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
+        np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00)
+        np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00)
+        np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00)
+        np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00)
+        np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00)
+        np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00)
+        np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01)
+        np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01)
+        np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01)
+        np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01)
+        np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01)
+        np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01)
+        np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01)
+        np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01)
+        np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01)
+        np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e03)
+        np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01)
+        np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e03)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01)
+        #This test fails - likely due to compound rounding errors
+        #Has been tested using statsmodels.family calculations and
+        #code from Jing's python version, which both yield the same
+        #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+    def test_GS_F(self):
+        est_Int = self.GS_F.by_col(' est_Intercept')
+        se_Int = self.GS_F.by_col(' se_Intercept')
+        t_Int = self.GS_F.by_col(' t_Intercept')
+        est_elev = self.GS_F.by_col(' est_Elev')
+        se_elev = self.GS_F.by_col(' se_Elev')
+        t_elev = self.GS_F.by_col(' t_Elev')
+        est_slope = self.GS_F.by_col(' est_Slope')
+        se_slope = self.GS_F.by_col(' se_Slope')
+        t_slope = self.GS_F.by_col(' t_Slope')
+        est_sin = self.GS_F.by_col(' est_SinAspct')
+        se_sin = self.GS_F.by_col(' se_SinAspct')
+        t_sin = self.GS_F.by_col(' t_SinAspct')
+        est_cos = self.GS_F.by_col(' est_CosAspct')
+        se_cos = self.GS_F.by_col(' se_CosAspct')
+        t_cos = self.GS_F.by_col(' t_CosAspct')
+        est_south = self.GS_F.by_col(' est_AbsSouth')
+        se_south = self.GS_F.by_col(' se_AbsSouth')
+        t_south = self.GS_F.by_col(' t_AbsSouth')
+        est_strm = self.GS_F.by_col(' est_DistStrm')
+        se_strm = self.GS_F.by_col(' se_DistStrm')
+        t_strm = self.GS_F.by_col(' t_DistStrm') 
+        yhat = self.GS_F.by_col(' yhat')
+        pdev = self.GS_F.by_col(' localpdev')
+
+        model = GWR(self.coords, self.y, self.X, bw=8929.061, family=Binomial(), 
+                kernel='gaussian', fixed=True)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 276.0)
+        self.assertAlmostEquals(np.floor(AIC), 272.0)
+        self.assertAlmostEquals(np.floor(BIC), 341.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
+        np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00)
+        np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00)
+        np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00)
+        np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00)
+        np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00)
+        np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00)
+        np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01)
+        np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01)
+        np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01)
+        np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01)
+        np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01)
+        np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01)
+        np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01)
+        np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01)
+        np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01)
+        np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02)
+        np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01)
+        np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01)
+        #This test fails - likely due to compound rounding errors
+        #Has been tested using statsmodels.family calculations and
+        #code from Jing's python version, which both yield the same
+        #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+    def test_GS_NN(self):
+        est_Int = self.GS_NN.by_col(' est_Intercept')
+        se_Int = self.GS_NN.by_col(' se_Intercept')
+        t_Int = self.GS_NN.by_col(' t_Intercept')
+        est_elev = self.GS_NN.by_col(' est_Elev')
+        se_elev = self.GS_NN.by_col(' se_Elev')
+        t_elev = self.GS_NN.by_col(' t_Elev')
+        est_slope = self.GS_NN.by_col(' est_Slope')
+        se_slope = self.GS_NN.by_col(' se_Slope')
+        t_slope = self.GS_NN.by_col(' t_Slope')
+        est_sin = self.GS_NN.by_col(' est_SinAspct')
+        se_sin = self.GS_NN.by_col(' se_SinAspct')
+        t_sin = self.GS_NN.by_col(' t_SinAspct')
+        est_cos = self.GS_NN.by_col(' est_CosAspct')
+        se_cos = self.GS_NN.by_col(' se_CosAspct')
+        t_cos = self.GS_NN.by_col(' t_CosAspct')
+        est_south = self.GS_NN.by_col(' est_AbsSouth')
+        se_south = self.GS_NN.by_col(' se_AbsSouth')
+        t_south = self.GS_NN.by_col(' t_AbsSouth')
+        est_strm = self.GS_NN.by_col(' est_DistStrm')
+        se_strm = self.GS_NN.by_col(' se_DistStrm')
+        t_strm = self.GS_NN.by_col(' t_DistStrm') 
+        yhat = self.GS_NN.by_col(' yhat')
+        pdev = self.GS_NN.by_col(' localpdev')
+        
+        model = GWR(self.coords, self.y, self.X, bw=64, family=Binomial(), 
+                kernel='gaussian', fixed=False)
+        rslt = model.fit()
+
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 276.0)
+        self.assertAlmostEquals(np.floor(AIC), 273.0)
+        self.assertAlmostEquals(np.floor(BIC), 331.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
+        np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00)
+        np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00)
+        np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00)
+        np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00)
+        np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00)
+        np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00)
+        np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01)
+        np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01)
+        np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01)
+        np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01)
+        np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01)
+        np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01)
+        np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01)
+        np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01)
+        np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01)
+        np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02)
+        np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01)
+        np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-00)
+        #This test fails - likely due to compound rounding errors
+        #Has been tested using statsmodels.family calculations and
+        #code from Jing's python version, which both yield the same
+        #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+if __name__ == '__main__':
+	unittest.main()

release/python/0.6.0/crankshaft/crankshaft/regression/gwr/base/tests/test_kernels.py

@@ -0,0 +1,84 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.contrib.gwr.kernels import *
+
+PEGP = pysal.examples.get_path
+
+class TestKernels(unittest.TestCase):
+    def setUp(self):
+        np.random.seed(1234)
+        x = np.arange(1,6)
+        y = np.arange(5,0, -1)
+        np.random.shuffle(x)
+        np.random.shuffle(y)
+        self.coords = np.array(zip(x, y))
+        self.fix_gauss_kern = np.array([
+        [ 1.        ,  0.38889556,  0.48567179,  0.48567179,  0.89483932],
+        [ 0.38889556,  1.        ,  0.89483932,  0.64118039,  0.48567179],
+        [ 0.48567179,  0.89483932,  1.        ,  0.89483932,  0.48567179],
+        [ 0.48567179,  0.64118039,  0.89483932,  1.        ,  0.38889556],
+        [ 0.89483932,  0.48567179,  0.48567179,  0.38889556,  1.        ]])
+        self.adapt_gauss_kern = np.array([
+        [ 1.        ,  0.52004183,  0.60653072,  0.60653072,  0.92596109],
+        [ 0.34559083,  1.        ,  0.88249692,  0.60653072,  0.44374738],
+        [ 0.03877423,  0.60653072,  1.        ,  0.60653072,  0.03877423],
+        [ 0.44374738,  0.60653072,  0.88249692,  1.        ,  0.34559083],
+        [ 0.92596109,  0.60653072,  0.60653072,  0.52004183,  1.        ]])
+        self.fix_bisquare_kern = np.array([
+        [ 1.        ,  0.        ,  0.        ,  0.        ,  0.60493827],
+        [ 0.        ,  1.        ,  0.60493827,  0.01234568,  0.        ],
+        [ 0.        ,  0.60493827,  1.        ,  0.60493827,  0.        ],
+        [ 0.        ,  0.01234568,  0.60493827,  1.        ,  0.        ],
+        [ 0.60493827,  0.        ,  0.        ,  0.        ,  1.        ]])
+        self.adapt_bisquare_kern = np.array([
+        [  1.00000000e+00,   0.00000000e+00,   0.00000000e+00,
+           3.99999881e-14,   7.15976383e-01],
+        [  0.00000000e+00,   1.00000000e+00,   5.62500075e-01,
+           3.99999881e-14,   0.00000000e+00],
+        [  0.00000000e+00,   3.99999881e-14,   1.00000000e+00,
+           3.99999881e-14,   0.00000000e+00],
+        [  0.00000000e+00,   3.99999881e-14,   5.62500075e-01,
+           1.00000000e+00,   0.00000000e+00],
+        [  7.15976383e-01,   0.00000000e+00,   3.99999881e-14,
+           0.00000000e+00,   1.00000000e+00]])
+        self.fix_exp_kern = np.array([
+        [ 1.        ,  0.2529993 ,  0.30063739,  0.30063739,  0.62412506],
+        [ 0.2529993 ,  1.        ,  0.62412506,  0.38953209,  0.30063739],
+        [ 0.30063739,  0.62412506,  1.        ,  0.62412506,  0.30063739],
+        [ 0.30063739,  0.38953209,  0.62412506,  1.        ,  0.2529993 ],
+        [ 0.62412506,  0.30063739,  0.30063739,  0.2529993 ,  1.        ]])
+        self.adapt_exp_kern = np.array([
+        [ 1.        ,  0.31868771,  0.36787948,  0.36787948,  0.67554721],
+        [ 0.23276223,  1.        ,  0.60653069,  0.36787948,  0.27949951],
+        [ 0.07811997,  0.36787948,  1.        ,  0.36787948,  0.07811997],
+        [ 0.27949951,  0.36787948,  0.60653069,  1.        ,  0.23276223],
+        [ 0.67554721,  0.36787948,  0.36787948,  0.31868771,  1.        ]])
+
+    def test_fix_gauss(self):
+        kern = fix_gauss(self.coords, 3)
+        np.testing.assert_allclose(kern, self.fix_gauss_kern)
+
+    def test_adapt_gauss(self):
+        kern = adapt_gauss(self.coords, 3)
+        np.testing.assert_allclose(kern, self.adapt_gauss_kern)
+
+    def test_fix_biqsquare(self):
+        kern = fix_bisquare(self.coords, 3)
+        np.testing.assert_allclose(kern, self.fix_bisquare_kern,
+                atol=1e-01)
+
+    def test_adapt_bisqaure(self):
+        kern = adapt_bisquare(self.coords, 3)
+        np.testing.assert_allclose(kern, self.adapt_bisquare_kern, atol=1e-012)
+    
+    def test_fix_exp(self):
+        kern = fix_exp(self.coords, 3)
+        np.testing.assert_allclose(kern, self.fix_exp_kern)
+
+    def test_adapt_exp(self):
+        kern = adapt_exp(self.coords, 3)
+        np.testing.assert_allclose(kern, self.adapt_exp_kern)
+
+if __name__ == '__main__':
+    unittest.main()

release/python/0.6.0/crankshaft/crankshaft/regression/gwr/base/tests/test_sel_bw.py

@@ -0,0 +1,139 @@
+
+"""
+GWR is tested against results from GWR4
+"""
+
+import unittest
+import pickle as pk
+from pysal.contrib.glm.family import Gaussian, Poisson, Binomial
+from pysal.contrib.gwr.sel_bw import Sel_BW
+import numpy as np
+import pysal
+
+class TestSelBW(unittest.TestCase):
+    def setUp(self):
+        data = pysal.open(pysal.examples.get_path('GData_utm.csv'))
+        self.coords = zip(data.by_col('X'), data.by_col('Y'))
+        self.y = np.array(data.by_col('PctBach')).reshape((-1,1))
+        rural  = np.array(data.by_col('PctRural')).reshape((-1,1))
+        pov = np.array(data.by_col('PctPov')).reshape((-1,1)) 
+        black = np.array(data.by_col('PctBlack')).reshape((-1,1))
+        self.X = np.hstack([rural, pov, black])
+        self.XB = pk.load(open(pysal.examples.get_path('XB.p'), 'r'))
+        self.err = pk.load(open(pysal.examples.get_path('err.p'), 'r'))
+  
+    def test_golden_fixed_AICc(self):
+        bw1 = 211027.34
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare',
+                fixed=True).search(criterion='AICc')
+        self.assertAlmostEqual(bw1, bw2)
+
+    def test_golden_adapt_AICc(self):
+        bw1 = 93.0
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare',
+                fixed=False).search(criterion='AICc')
+        self.assertAlmostEqual(bw1, bw2)
+
+    def test_golden_fixed_AIC(self):
+        bw1 = 76169.15
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=True).search(criterion='AIC')
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_golden_adapt_AIC(self):
+        bw1 = 50.0
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=False).search(criterion='AIC')
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_golden_fixed_BIC(self):
+        bw1 = 279451.43
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=True).search(criterion='BIC')
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_golden_adapt_BIC(self):
+        bw1 = 62.0
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=False).search(criterion='BIC')
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_golden_fixed_CV(self):
+        bw1 = 130406.67
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=True).search(criterion='CV')
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_golden_adapt_CV(self):
+        bw1 = 68.0
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=False).search(criterion='CV')
+        self.assertAlmostEqual(bw1, bw2)
+   
+    def test_interval_fixed_AICc(self):
+        bw1 = 211025.0#211027.00
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare',
+                fixed=True).search(criterion='AICc', search='interval', bw_min=211001.,
+                        bw_max=211035.0, interval=2)
+        self.assertAlmostEqual(bw1, bw2)
+
+    def test_interval_adapt_AICc(self):
+        bw1 = 93.0
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare',
+                fixed=False).search(criterion='AICc', search='interval',
+                        bw_min=90.0, bw_max=95.0, interval=1)
+        self.assertAlmostEqual(bw1, bw2)
+
+    def test_interval_fixed_AIC(self):
+        bw1 = 76175.0#76169.00
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=True).search(criterion='AIC', search='interval',
+                        bw_min=76161.0, bw_max=76175.0, interval=1)
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_interval_adapt_AIC(self):
+        bw1 = 40.0#50.0
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=False).search(criterion='AIC', search='interval', bw_min=40.0,
+                        bw_max=60.0, interval=2)
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_interval_fixed_BIC(self):
+        bw1 = 279461.0#279451.00
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=True).search(criterion='BIC', search='interval', bw_min=279441.0,
+                        bw_max=279461.0, interval=2)
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_interval_adapt_BIC(self):
+        bw1 = 62.0
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=False).search(criterion='BIC', search='interval',
+                        bw_min=52.0, bw_max=72.0, interval=2)
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_interval_fixed_CV(self):
+        bw1 = 130400.0#130406.00
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=True).search(criterion='CV', search='interval', bw_min=130400.0,
+                        bw_max=130410.0, interval=1)
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_interval_adapt_CV(self):
+        bw1 = 62.0#68.0
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=False).search(criterion='CV', search='interval', bw_min=60.0,
+                        bw_max=76.0 , interval=2)
+        self.assertAlmostEqual(bw1, bw2)
+
+    def test_FBGWR_AIC(self):
+        bw1 = [157.0, 65.0, 52.0]
+        sel = Sel_BW(self.coords, self.y, self.X, fb=True, kernel='bisquare',
+                constant=False)
+        bw2 = sel.search(tol_fb=1e-03)
+        np.testing.assert_allclose(bw1, bw2)
+        np.testing.assert_allclose(sel.XB, self.XB, atol=1e-05)
+        np.testing.assert_allclose(sel.err, self.err, atol=1e-05)
+
+if __name__ == '__main__':
+	unittest.main()

release/python/0.6.0/crankshaft/crankshaft/regression/gwr_cs.py

@@ -0,0 +1,202 @@
+"""
+    Geographically weighted regression
+"""
+import numpy as np
+from gwr.base.gwr import GWR as PySAL_GWR
+from gwr.base.sel_bw import Sel_BW
+import json
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+import plpy
+
+
+class GWR:
+    def __init__(self, data_provider=None):
+        if data_provider:
+            self.data_provider = data_provider
+        else:
+            self.data_provider = AnalysisDataProvider()
+
+    def gwr(self, subquery, dep_var, ind_vars,
+            bw=None, fixed=False, kernel='bisquare',
+            geom_col='the_geom', id_col='cartodb_id'):
+        """
+            subquery: 'select * from demographics'
+            dep_var: 'pctbachelor'
+            ind_vars: ['intercept', 'pctpov', 'pctrural', 'pctblack']
+            bw: value of bandwidth, if None then select optimal
+            fixed: False (kNN) or True ('distance')
+            kernel: 'bisquare' (default), or 'exponential', 'gaussian'
+        """
+
+        params = {'geom_col': geom_col,
+                  'id_col': id_col,
+                  'subquery': subquery,
+                  'dep_var': dep_var,
+                  'ind_vars': ind_vars}
+
+        # get data from data provider
+        query_result = self.data_provider.get_gwr(params)
+
+        # exit if data to analyze is empty
+        if len(query_result) == 0:
+            plpy.error('No data passed to analysis or independent variables '
+                       'are all null-valued')
+
+        # unique ids and variable names list
+        rowid = np.array(query_result[0]['rowid'], dtype=np.int)
+
+        # x, y are centroids of input geometries
+        x = np.array(query_result[0]['x'], dtype=np.float)
+        y = np.array(query_result[0]['y'], dtype=np.float)
+        coords = zip(x, y)
+
+        # extract dependent variable
+        Y = np.array(query_result[0]['dep_var'], dtype=np.float).reshape((-1, 1))
+
+        n = Y.shape[0]
+        k = len(ind_vars)
+        X = np.zeros((n, k))
+
+        # extract query result
+        for attr in range(0, k):
+            attr_name = 'attr' + str(attr + 1)
+            X[:, attr] = np.array(
+              query_result[0][attr_name], dtype=np.float).flatten()
+
+        # add intercept variable name
+        ind_vars.insert(0, 'intercept')
+
+        # calculate bandwidth if none is supplied
+        if bw is None:
+            bw = Sel_BW(coords, Y, X,
+                        fixed=fixed, kernel=kernel).search()
+        model = PySAL_GWR(coords, Y, X, bw,
+                          fixed=fixed, kernel=kernel).fit()
+
+        # containers for outputs
+        coeffs = []
+        stand_errs = []
+        t_vals = []
+        filtered_t_vals = []
+
+        # extracted model information
+        c_alpha = model.adj_alpha
+        filtered_t = model.filter_tvals(c_alpha[1])
+        predicted = model.predy.flatten()
+        residuals = model.resid_response
+        r_squared = model.localR2.flatten()
+        bw = np.repeat(float(bw), n)
+
+        # create lists of json objs for model outputs
+        for idx in xrange(n):
+            coeffs.append(json.dumps({var: model.params[idx, k]
+                                      for k, var in enumerate(ind_vars)}))
+            stand_errs.append(json.dumps({var: model.bse[idx, k]
+                                          for k, var in enumerate(ind_vars)}))
+            t_vals.append(json.dumps({var: model.tvalues[idx, k]
+                                      for k, var in enumerate(ind_vars)}))
+            filtered_t_vals.append(
+                    json.dumps({var: filtered_t[idx, k]
+                                for k, var in enumerate(ind_vars)}))
+
+        return zip(coeffs, stand_errs, t_vals, filtered_t_vals,
+                   predicted, residuals, r_squared, bw, rowid)
+
+    def gwr_predict(self, subquery, dep_var, ind_vars,
+                    bw=None, fixed=False, kernel='bisquare',
+                    geom_col='the_geom', id_col='cartodb_id'):
+        """
+        subquery: 'select * from demographics'
+        dep_var: 'pctbachelor'
+        ind_vars: ['intercept', 'pctpov', 'pctrural', 'pctblack']
+        bw: value of bandwidth, if None then select optimal
+        fixed: False (kNN) or True ('distance')
+        kernel: 'bisquare' (default), or 'exponential', 'gaussian'
+        """
+
+        params = {'geom_col': geom_col,
+                  'id_col': id_col,
+                  'subquery': subquery,
+                  'dep_var': dep_var,
+                  'ind_vars': ind_vars}
+
+        # get data from data provider
+        query_result = self.data_provider.get_gwr_predict(params)
+
+        # exit if data to analyze is empty
+        if len(query_result) == 0:
+            plpy.error('No data passed to analysis or independent variables '
+                       'are all null-valued')
+
+        # unique ids and variable names list
+        rowid = np.array(query_result[0]['rowid'], dtype=np.int)
+
+        x = np.array(query_result[0]['x'], dtype=np.float)
+        y = np.array(query_result[0]['y'], dtype=np.float)
+        coords = np.array(zip(x, y), dtype=np.float)
+
+        # extract dependent variable
+        Y = np.array(query_result[0]['dep_var']).reshape((-1, 1))
+
+        n = Y.shape[0]
+        k = len(ind_vars)
+        X = np.empty((n, k), dtype=np.float)
+
+        for attr in range(0, k):
+            attr_name = 'attr' + str(attr + 1)
+            X[:, attr] = np.array(
+              query_result[0][attr_name], dtype=np.float).flatten()
+
+        # add intercept variable name
+        ind_vars.insert(0, 'intercept')
+
+        # split data into "training" and "test" for predictions
+        # create index to split based on null y values
+        train = np.where(Y != np.array(None))[0]
+        test = np.where(Y == np.array(None))[0]
+
+        # report error if there is no data to predict
+        if len(test) < 1:
+            plpy.error('No rows flagged for prediction: verify that rows '
+                       'denoting prediction locations have a dependent '
+                       'variable value of `null`')
+
+        # split dependent variable (only need training which is non-Null's)
+        Y_train = Y[train].reshape((-1, 1))
+        Y_train = Y_train.astype(np.float)
+
+        # split coords
+        coords_train = coords[train]
+        coords_test = coords[test]
+
+        # split explanatory variables
+        X_train = X[train]
+        X_test = X[test]
+
+        # calculate bandwidth if none is supplied
+        if bw is None:
+            bw = Sel_BW(coords_train, Y_train, X_train,
+                        fixed=fixed, kernel=kernel).search()
+
+        # estimate model and predict at new locations
+        model = PySAL_GWR(coords_train, Y_train, X_train,
+                          bw, fixed=fixed,
+                          kernel=kernel).predict(coords_test, X_test)
+
+        coeffs = []
+        stand_errs = []
+        t_vals = []
+        r_squared = model.localR2.flatten()
+        predicted = model.predy.flatten()
+
+        m = len(model.predy)
+        for idx in xrange(m):
+            coeffs.append(json.dumps({var: model.params[idx, k]
+                                      for k, var in enumerate(ind_vars)}))
+            stand_errs.append(json.dumps({var: model.bse[idx, k]
+                                          for k, var in enumerate(ind_vars)}))
+            t_vals.append(json.dumps({var: model.tvalues[idx, k]
+                                      for k, var in enumerate(ind_vars)}))
+
+        return zip(coeffs, stand_errs, t_vals,
+                   r_squared, predicted, rowid[test])

release/python/0.6.0/crankshaft/crankshaft/segmentation/__init__.py

@@ -0,0 +1 @@
+from segmentation import * 

release/python/0.6.0/crankshaft/crankshaft/segmentation/segmentation.py

@@ -0,0 +1,176 @@
+"""
+Segmentation creation and prediction
+"""
+
+import sklearn
+import numpy as np
+import plpy
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn import metrics
+from sklearn.cross_validation import train_test_split
+
+# Lower level functions
+#----------------------
+
+def replace_nan_with_mean(array):
+    """
+        Input:
+            @param array: an array of floats which may have null-valued entries
+        Output:
+            array with nans filled in with the mean of the dataset
+    """
+    # returns an array of rows and column indices
+    indices = np.where(np.isnan(array))
+
+    # iterate through entries which have nan values
+    for row, col in zip(*indices):
+            array[row, col] = np.mean(array[~np.isnan(array[:, col]), col])
+
+    return array
+
+def get_data(variable, feature_columns, query):
+    """
+        Fetch data from the database, clean, and package into
+          numpy arrays
+        Input:
+            @param variable: name of the target variable
+            @param feature_columns: list of column names
+            @param query: subquery that data is pulled from for the packaging
+        Output:
+            prepared data, packaged into NumPy arrays
+    """
+
+    columns = ','.join(['array_agg("{col}") As "{col}"'.format(col=col) for col in feature_columns])
+
+    try:
+        data = plpy.execute('''SELECT array_agg("{variable}") As target, {columns} FROM ({query}) As a'''.format(
+            variable=variable,
+            columns=columns,
+            query=query))
+    except Exception, e:
+        plpy.error('Failed to access data to build segmentation model: %s' % e)
+
+    # extract target data from plpy object
+    target = np.array(data[0]['target'])
+
+    # put n feature data arrays into an n x m array of arrays
+    features = np.column_stack([np.array(data[0][col], dtype=float) for col in feature_columns])
+
+    return replace_nan_with_mean(target), replace_nan_with_mean(features)
+
+# High level interface
+# --------------------
+
+def create_and_predict_segment_agg(target, features, target_features, target_ids, model_parameters):
+    """
+    Version of create_and_predict_segment that works on arrays that come stright form the SQL calling
+    the function.
+
+        Input:
+            @param target: The 1D array of lenth NSamples containing the target variable we want the model to predict
+            @param features: Thw 2D array of size NSamples * NFeatures that form the imput to the model
+            @param target_ids: A 1D array of target_ids that will be used to associate the results of the prediction with the rows which they come from
+            @param model_parameters: A dictionary containing parameters for the model.
+    """
+
+    clean_target = replace_nan_with_mean(target)
+    clean_features = replace_nan_with_mean(features)
+    target_features = replace_nan_with_mean(target_features)
+
+    model, accuracy = train_model(clean_target, clean_features, model_parameters, 0.2)
+    prediction = model.predict(target_features)
+    accuracy_array = [accuracy]*prediction.shape[0]
+    return zip(target_ids, prediction, np.full(prediction.shape, accuracy_array))
+
+
+
+def create_and_predict_segment(query, variable, target_query, model_params):
+    """
+    generate a segment with machine learning
+    Stuart Lynn
+    """
+
+    ## fetch column names
+    try:
+        columns = plpy.execute('SELECT * FROM ({query}) As a LIMIT 1  '.format(query=query))[0].keys()
+    except Exception, e:
+        plpy.error('Failed to build segmentation model: %s' % e)
+
+    ## extract column names to be used in building the segmentation model
+    feature_columns = set(columns) - set([variable, 'cartodb_id', 'the_geom', 'the_geom_webmercator'])
+    ## get data from database
+    target, features = get_data(variable, feature_columns, query)
+
+    model, accuracy = train_model(target, features, model_params, 0.2)
+    cartodb_ids, result = predict_segment(model, feature_columns, target_query)
+    accuracy_array = [accuracy]*result.shape[0]
+    return zip(cartodb_ids, result, accuracy_array)
+
+
+def train_model(target, features, model_params, test_split):
+    """
+        Train the Gradient Boosting model on the provided data and calculate the accuracy of the model
+        Input:
+            @param target: 1D Array of the variable that the model is to be trianed to predict
+            @param features: 2D Array NSamples * NFeatures to use in trining the model
+            @param model_params: A dictionary of model parameters, the full specification can be found on the
+                scikit learn page for [GradientBoostingRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html)
+            @parma test_split: The fraction of the data to be withheld for testing the model / calculating the accuray
+    """
+    features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split)
+    model = GradientBoostingRegressor(**model_params)
+    model.fit(features_train, target_train)
+    accuracy = calculate_model_accuracy(model, features, target)
+    return model, accuracy
+
+def calculate_model_accuracy(model, features, target):
+    """
+        Calculate the mean squared error of the model prediction
+        Input:
+            @param model: model trained from input features
+            @param features: features to make a prediction from
+            @param target: target to compare prediction to
+        Output:
+            mean squared error of the model prection compared to the target
+    """
+    prediction = model.predict(features)
+    return metrics.mean_squared_error(prediction, target)
+
+def predict_segment(model, features, target_query):
+    """
+    Use the provided model to predict the values for the new feature set
+        Input:
+            @param model: The pretrained model
+            @features: A list of features to use in the model prediction (list of column names)
+            @target_query: The query to run to obtain the data to predict on and the cartdb_ids associated with it.
+    """
+
+    batch_size = 1000
+    joined_features = ','.join(['"{0}"::numeric'.format(a) for a in features])
+
+    try:
+        cursor = plpy.cursor('SELECT Array[{joined_features}] As features FROM ({target_query}) As a'.format(
+            joined_features=joined_features,
+            target_query=target_query))
+    except Exception, e:
+        plpy.error('Failed to build segmentation model: %s' % e)
+
+    results = []
+
+    while True:
+        rows = cursor.fetch(batch_size)
+        if not rows:
+            break
+        batch = np.row_stack([np.array(row['features'], dtype=float) for row in rows])
+
+        #Need to fix this. Should be global mean. This will cause weird effects
+        batch = replace_nan_with_mean(batch)
+        prediction = model.predict(batch)
+        results.append(prediction)
+
+    try:
+        cartodb_ids = plpy.execute('''SELECT array_agg(cartodb_id ORDER BY cartodb_id) As cartodb_ids FROM ({0}) As a'''.format(target_query))[0]['cartodb_ids']
+    except Exception, e:
+        plpy.error('Failed to build segmentation model: %s' % e)
+
+    return cartodb_ids, np.concatenate(results)

release/python/0.6.0/crankshaft/crankshaft/space_time_dynamics/__init__.py

@@ -0,0 +1,2 @@
+"""Import all functions from clustering libraries."""
+from markov import *

release/python/0.6.0/crankshaft/crankshaft/space_time_dynamics/markov.py

@@ -0,0 +1,194 @@
+"""
+Spatial dynamics measurements using Spatial Markov
+"""
+
+# TODO: remove all plpy dependencies
+
+import numpy as np
+import pysal as ps
+import plpy
+import crankshaft.pysal_utils as pu
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+
+
+class Markov:
+    def __init__(self, data_provider=None):
+        if data_provider is None:
+            self.data_provider = AnalysisDataProvider()
+        else:
+            self.data_provider = data_provider
+
+    def spatial_trend(self, subquery, time_cols, num_classes=7,
+                      w_type='knn', num_ngbrs=5, permutations=0,
+                      geom_col='the_geom', id_col='cartodb_id'):
+        """
+            Predict the trends of a unit based on:
+            1. history of its transitions to different classes (e.g., 1st
+               quantile -> 2nd quantile)
+            2. average class of its neighbors
+
+            Inputs:
+            @param subquery string: e.g., SELECT the_geom, cartodb_id,
+              interesting_time_column FROM table_name
+            @param time_cols list of strings: list of strings of column names
+            @param num_classes (optional): number of classes to break
+              distribution of values into. Currently uses quantile bins.
+            @param w_type string (optional): weight type ('knn' or 'queen')
+            @param num_ngbrs int (optional): number of neighbors (if knn type)
+            @param permutations int (optional): number of permutations for test
+              stats
+            @param geom_col string (optional): name of column which contains
+              the geometries
+            @param id_col string (optional): name of column which has the ids
+              of the table
+
+            Outputs:
+            @param trend_up float: probablity that a geom will move to a higher
+              class
+            @param trend_down float: probablity that a geom will move to a
+              lower class
+            @param trend float: (trend_up - trend_down) / trend_static
+            @param volatility float: a measure of the volatility based on
+              probability stddev(prob array)
+        """
+
+        if len(time_cols) < 2:
+            plpy.error('More than one time column needs to be passed')
+
+        params = {"id_col": id_col,
+                  "time_cols": time_cols,
+                  "geom_col": geom_col,
+                  "subquery": subquery,
+                  "num_ngbrs": num_ngbrs}
+
+        query_result = self.data_provider.get_markov(w_type, params)
+
+        # build weight
+        weights = pu.get_weight(query_result, w_type)
+        weights.transform = 'r'
+
+        # prep time data
+        t_data = get_time_data(query_result, time_cols)
+
+        sp_markov_result = ps.Spatial_Markov(t_data,
+                                             weights,
+                                             k=num_classes,
+                                             fixed=False,
+                                             permutations=permutations)
+
+        # get lag classes
+        lag_classes = ps.Quantiles(
+            ps.lag_spatial(weights, t_data[:, -1]),
+            k=num_classes).yb
+
+        # look up probablity distribution for each unit according to class and
+        #  lag class
+        prob_dist = get_prob_dist(sp_markov_result.P,
+                                  lag_classes,
+                                  sp_markov_result.classes[:, -1])
+
+        # find the ups and down and overall distribution of each cell
+        trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist, sp_markov_result.classes[:, -1])
+
+        # output the results
+        return zip(trend, trend_up, trend_down, volatility, weights.id_order)
+
+
+
+def get_time_data(markov_data, time_cols):
+    """
+        Extract the time columns and bin appropriately
+    """
+    num_attrs = len(time_cols)
+    return np.array([[x['attr' + str(i)] for x in markov_data]
+                     for i in range(1, num_attrs+1)], dtype=float).transpose()
+
+
+# not currently used
+def rebin_data(time_data, num_time_per_bin):
+    """
+        Convert an n x l matrix into an (n/m) x l matrix where the values are
+         reduced (averaged) for the intervening states:
+          1 2 3 4    1.5 3.5
+          5 6 7 8 -> 5.5 7.5
+          9 8 7 6    8.5 6.5
+          5 4 3 2    4.5 2.5
+
+          if m = 2, the 4 x 4 matrix is transformed to a 2 x 4 matrix.
+
+        This process effectively resamples the data at a longer time span n
+         units longer than the input data.
+        For cases when there is a remainder (remainder(5/3) = 2), the remaining
+         two columns are binned together as the last time period, while the
+         first three are binned together for the first period.
+
+        Input:
+          @param time_data n x l  ndarray: measurements of an attribute at
+           different time intervals
+          @param num_time_per_bin int: number of columns to average into a new
+           column
+        Output:
+          ceil(n / m) x l ndarray of resampled time series
+    """
+
+    if time_data.shape[1] % num_time_per_bin == 0:
+        # if fit is perfect, then use it
+        n_max = time_data.shape[1] / num_time_per_bin
+    else:
+        # fit remainders into an additional column
+        n_max = time_data.shape[1] / num_time_per_bin + 1
+
+    return np.array(
+      [time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
+       for i in range(n_max)]).T
+
+
+def get_prob_dist(transition_matrix, lag_indices, unit_indices):
+    """
+        Given an array of transition matrices, look up the probability
+        associated with the arrangements passed
+
+        Input:
+        @param transition_matrix ndarray[k,k,k]:
+        @param lag_indices ndarray:
+        @param unit_indices ndarray:
+
+        Output:
+        Array of probability distributions
+    """
+
+    return np.array([transition_matrix[(lag_indices[i], unit_indices[i])]
+                     for i in range(len(lag_indices))])
+
+
+def get_prob_stats(prob_dist, unit_indices):
+    """
+        get the statistics of the probability distributions
+
+        Outputs:
+            @param trend_up ndarray(float): sum of probabilities for upward
+               movement (relative to the unit index of that prob)
+            @param trend_down ndarray(float): sum of probabilities for downward
+               movement (relative to the unit index of that prob)
+            @param trend ndarray(float): difference of upward and downward
+               movements
+    """
+
+    num_elements = len(unit_indices)
+    trend_up = np.empty(num_elements, dtype=float)
+    trend_down = np.empty(num_elements, dtype=float)
+    trend = np.empty(num_elements, dtype=float)
+
+    for i in range(num_elements):
+        trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum()
+        trend_down[i] = prob_dist[i, :unit_indices[i]].sum()
+        if prob_dist[i, unit_indices[i]] > 0.0:
+            trend[i] = (trend_up[i] - trend_down[i]) / (
+              prob_dist[i, unit_indices[i]])
+        else:
+            trend[i] = None
+
+    # calculate volatility of distribution
+    volatility = prob_dist.std(axis=1)
+
+    return trend_up, trend_down, trend, volatility

release/python/0.6.0/crankshaft/requirements.txt

@@ -0,0 +1,5 @@
+joblib==0.8.3
+numpy==1.6.1
+scipy==0.14.0
+pysal==1.11.2
+scikit-learn==0.14.1

release/python/0.6.0/crankshaft/setup.py

@@ -0,0 +1,49 @@
+
+"""
+CartoDB Spatial Analysis Python Library
+See:
+https://github.com/CartoDB/crankshaft
+"""
+
+from setuptools import setup, find_packages
+
+setup(
+    name='crankshaft',
+
+    version='0.6.0',
+
+    description='CartoDB Spatial Analysis Python Library',
+
+    url='https://github.com/CartoDB/crankshaft',
+
+    author='Data Services Team - CartoDB',
+    author_email='dataservices@cartodb.com',
+
+    license='MIT',
+
+    classifiers=[
+        'Development Status :: 3 - Alpha',
+        'Intended Audience :: Mapping comunity',
+        'Topic :: Maps :: Mapping Tools',
+        'License :: OSI Approved :: MIT License',
+        'Programming Language :: Python :: 2.7',
+    ],
+
+    keywords='maps mapping tools spatial analysis geostatistics',
+
+    packages=find_packages(exclude=['contrib', 'docs', 'tests']),
+
+    extras_require={
+        'dev': ['unittest'],
+        'test': ['unittest', 'nose', 'mock'],
+    },
+
+    # The choice of component versions is dictated by what's
+    # provisioned in the production servers.
+    # IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation.
+    install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
+
+    requires=['pysal', 'numpy', 'sklearn'],
+
+    test_suite='test'
+)

release/python/0.6.0/crankshaft/test/fixtures/getis.json

@@ -0,0 +1 @@
+[[0.004793783909323601, 0.17999999999999999, 0.49808756424021061], [-1.0701189472090842, 0.079000000000000001, 0.14228288580832316], [-0.67867750971877305, 0.42099999999999999, 0.24867110969448558], [-0.67407386707620487, 0.246, 0.25013217644612995], [-0.79495689068870035, 0.33200000000000002, 0.21331928959090596], [-0.49279481022182703, 0.058999999999999997, 0.31107878905057329], [-0.38075627530057132, 0.28399999999999997, 0.35169205342069643], [-0.86710921611314895, 0.23699999999999999, 0.19294108571294855], [-0.78618647240956485, 0.050000000000000003, 0.2158791250244505], [-0.76108527223116984, 0.064000000000000001, 0.22330306830813684], [-0.13340753531942209, 0.247, 0.44693554317763651], [-0.57584545722033043, 0.48999999999999999, 0.28235982246156488], [-0.78882694661192831, 0.433, 0.2151065788731219], [-0.38769767950046219, 0.375, 0.34911988661484239], [-0.56057819488052207, 0.41399999999999998, 0.28754255985169652], [-0.41354017495644935, 0.45500000000000002, 0.339605447117173], [-0.23993577722243081, 0.49099999999999999, 0.40519002230969337], [-0.1389080156677496, 0.40400000000000003, 0.44476141839645233], [-0.25485737510500855, 0.376, 0.39941662953554224], [-0.71218610582902353, 0.17399999999999999, 0.23817476979886087], [-0.54533105995872144, 0.13700000000000001, 0.2927629228714812], [-0.39547917847510977, 0.033000000000000002, 0.34624464252424236], [-0.43052658996257548, 0.35399999999999998, 0.33340631435564982], [-0.37296719193774736, 0.40300000000000002, 0.35458643102865428], [-0.66482612169465694, 0.31900000000000001, 0.25308085650392698], [-0.13772133540823422, 0.34699999999999998, 0.44523032843016275], [-0.6765304487868502, 0.20999999999999999, 0.24935196033890672], [-0.64518763494323472, 0.32200000000000001, 0.25940279912025543], [-0.5078622084312413, 0.41099999999999998, 0.30577498972600159], [-0.12652006733772059, 0.42899999999999999, 0.44966013262301163], [-0.32691133022814595, 0.498, 0.37186747562269029], [0.25533848511500978, 0.42399999999999999, 0.39923083899077472], [2.7045138116476508, 0.0050000000000000001, 0.0034202212972238577], [-0.1551614486076057, 0.44400000000000001, 0.43834701985429037], [1.9524487722567723, 0.012999999999999999, 0.025442473674991528], [-1.2055816465306763, 0.017000000000000001, 0.11398941970467646], [3.478472976017831, 0.002, 0.00025213964072468009], [-1.4621715757903719, 0.002, 0.071847099325659136], [-0.84010307600180256, 0.085000000000000006, 0.20042529779230778], [5.7097646237318243, 0.0030000000000000001, 5.6566262784940591e-09], [1.5082367956567375, 0.065000000000000002, 0.065746966514827365], [-0.58337270103430816, 0.44, 0.27982121546450034], [-0.083271860457022437, 0.45100000000000001, 0.46681768733385554], [-0.46872337815000953, 0.34599999999999997, 0.31963368715684204], [0.18490279849545319, 0.23799999999999999, 0.42665263797981101], [3.470424529947997, 0.012, 0.00025981817437825683], [-0.99942612137154796, 0.032000000000000001, 0.15879415560388499], [-1.3650387953594485, 0.034000000000000002, 0.08612042845912049], [1.8617160516432014, 0.081000000000000003, 0.03132156240215267], [1.1321188945775384, 0.11600000000000001, 0.12879222611766061], [0.064116686050580601, 0.27300000000000002, 0.4744386578180424], [-0.42032194540259099, 0.29999999999999999, 0.33712514016213468], [-0.79581215423980922, 0.123, 0.21307061309098785], [-0.42792753720906046, 0.45600000000000002, 0.33435193892883741], [-1.0629378527428395, 0.051999999999999998, 0.14390506780140866], [-0.54164761752225477, 0.33700000000000002, 0.29403064095211839], [1.0934778886820793, 0.13700000000000001, 0.13709201601893539], [-0.094068785378413719, 0.38200000000000001, 0.46252725802998929], [0.13482026574801856, 0.36799999999999999, 0.44637699118865737], [-0.13976995315653129, 0.34699999999999998, 0.44442087706276601], [-0.051047663924746682, 0.32000000000000001, 0.47964376985626245], [-0.21468297736730158, 0.41699999999999998, 0.41500724761906527], [-0.20873154637330626, 0.38800000000000001, 0.41732890604390893], [-0.32427876152583485, 0.49199999999999999, 0.37286349875557478], [-0.65254842943280977, 0.374, 0.25702372075306734], [-0.48611858196118796, 0.23300000000000001, 0.31344154643990074], [-0.14482354344529477, 0.32600000000000001, 0.44242509660469886], [-0.51052030974200002, 0.439, 0.30484349480873729], [0.56814382285283538, 0.14999999999999999, 0.28496865660103166], [0.58680919931668207, 0.161, 0.27866592887231878], [0.013390357044409013, 0.25800000000000001, 0.49465818005865647], [-0.19050728887961568, 0.41399999999999998, 0.4244558160399462], [-0.60531777422216049, 0.35199999999999998, 0.2724839368239631], [1.0899331115425805, 0.127, 0.13787130480311838], [0.17015055382651084, 0.36899999999999999, 0.43244586845546418], [-0.21738337124409801, 0.40600000000000003, 0.41395479459421991], [1.0329303331079593, 0.079000000000000001, 0.15081825117169467], [1.0218317101096221, 0.104, 0.15343027913308094]]

release/python/0.6.0/crankshaft/test/fixtures/kmeans.json

@@ -0,0 +1 @@
+[{"xs": [9.917239463463458, 9.042767302696836, 10.798929825304187, 8.763751051762995, 11.383882954810852, 11.018206993460897, 8.939526075734316, 9.636159342565252, 10.136336896960058, 11.480610059427342, 12.115011910725082, 9.173267848893428, 10.239300931201738, 8.00012512174072, 8.979962292282131, 9.318376124429575, 10.82259513754284, 10.391747171927115, 10.04904588886165, 9.96007160443463, -0.78825626804569, -0.3511819898577426, -1.2796410003764271, -0.3977049391203402, 2.4792311265774667, 1.3670311632092624, 1.2963504112955613, 2.0404844103073025, -1.6439708506073223, 0.39122885445645805, 1.026031821452462, -0.04044477160482201, -0.7442346929085072, -0.34687120826243034, -0.23420359971379054, -0.5919629143336708, -0.202903054395391, -0.1893399644841902, 1.9331834251176807, -0.12321054392851609], "ys": [8.735627063679981, 9.857615954045011, 10.81439096759407, 10.586727233537191, 9.232919976568622, 11.54281262696508, 8.392787912674466, 9.355119689665944, 9.22380703532752, 10.542142541823122, 10.111980619367035, 10.760836265570738, 8.819773453269804, 10.25325722424816, 9.802077905695608, 8.955420161552611, 9.833801181904477, 10.491684241001613, 12.076108669877556, 11.74289693140474, -0.5685725015474191, -0.5715728344759778, -0.20180907868635137, 0.38431336480089595, -0.3402202083684184, -2.4652736827783586, 0.08295159401756182, 0.8503818775816505, 0.6488691600321166, 0.5794762568230527, -0.6770063922144103, -0.6557616416449478, -1.2834289177624947, 0.1096318195532717, -0.38986922166834853, -1.6224497706950238, 0.09429787743230483, 0.4005097316394031, -0.508002811195673, -1.2473463371366507], "ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]}]

release/python/0.6.0/crankshaft/test/fixtures/markov.json

@@ -0,0 +1 @@
+[[0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 0], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 1], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 2], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 3], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 4], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 5], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 6], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 7], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 8], [0.19047619047619049, 0.16, 0.0, 0.32594478059941379, 9], [-0.23529411764705882, 0.0, 0.19047619047619047, 0.31356338348865387, 10], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 11], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 12], [0.027777777777777783, 0.11111111111111112, 0.088888888888888892, 0.30339641183779581, 13], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 14], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 15], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 16], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 17], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 18], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 19], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 20], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 21], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 22], [-0.16666666666666663, 0.18181818181818182, 0.27272727272727271, 0.20246415864836445, 23], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 24], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 25], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 26], [-0.043478260869565216, 0.0, 0.041666666666666664, 0.37950991789118999, 27], [0.22222222222222221, 0.18181818181818182, 0.0, 0.31701083225750354, 28], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 29], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 30], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 31], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 32], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 33], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 34], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 35], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 36], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 37], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 38], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 39], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 40], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 41], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 42], [0.0, 0.0, 0.0, 0.40000000000000002, 43], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 44], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 45], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 46], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 47]]

release/python/0.6.0/crankshaft/test/fixtures/moran.json

@@ -0,0 +1,52 @@
+[[0.9319096128346788, "HH"],
+[-1.135787401862846, "HL"],
+[0.11732030672508517, "LL"],
+[0.6152779669180425, "LL"],
+[-0.14657336660125297, "LH"],
+[0.6967858120189607, "LL"],
+[0.07949310115714454, "HH"],
+[0.4703198759258987, "HH"],
+[0.4421125200498064, "HH"],
+[0.5724288737143592, "LL"],
+[0.8970743435692062, "LL"],
+[0.18327334401918674, "LL"],
+[-0.01466729201304962, "HL"],
+[0.3481559372544409, "LL"],
+[0.06547094736902978, "LL"],
+[0.15482141569329988, "HH"],
+[0.4373841193538136, "HH"],
+[0.15971286468915544, "LL"],
+[1.0543588860308968, "HH"],
+[1.7372866900020818, "HH"],
+[1.091998586053999, "LL"],
+[0.1171572584252222, "HH"],
+[0.08438455015300014, "LL"],
+[0.06547094736902978, "LL"],
+[0.15482141569329985, "HH"],
+[1.1627044812890683, "HH"],
+[0.06547094736902978, "LL"],
+[0.795275137550483, "HH"],
+[0.18562939195219, "LL"],
+[0.3010757406693439, "LL"],
+[2.8205795942839376, "HH"],
+[0.11259190602909264, "LL"],
+[-0.07116352791516614, "HL"],
+[-0.09945240794119009, "LH"],
+[0.18562939195219, "LL"],
+[0.1832733440191868, "LL"],
+[-0.39054253768447705, "HL"],
+[-0.1672071289487642, "HL"],
+[0.3337669247916343, "HH"],
+[0.2584386102554792, "HH"],
+[-0.19733845476322634, "HL"],
+[-0.9379282899805409, "LH"],
+[-0.028770969951095866, "LH"],
+[0.051367269430983485, "LL"],
+[-0.2172548045913472, "LH"],
+[0.05136726943098351, "LL"],
+[0.04191046803899837, "LL"],
+[0.7482357030403517, "HH"],
+[-0.014585767863118111, "LH"],
+[0.5410013139159929, "HH"],
+[1.0223932668429925, "LL"],
+[1.4179402898927476, "LL"]]

release/python/0.6.0/crankshaft/test/fixtures/neighbors.json

@@ -0,0 +1,54 @@
+[
+    {"neighbors": [48, 26, 20, 9, 31], "id": 1, "value": 0.5},
+    {"neighbors": [30, 16, 46, 3, 4], "id": 2, "value": 0.7},
+    {"neighbors": [46, 30, 2, 12, 16], "id": 3, "value": 0.2},
+    {"neighbors": [18, 30, 23, 2, 52], "id": 4, "value": 0.1},
+    {"neighbors": [47, 40, 45, 37, 28], "id": 5, "value": 0.3},
+    {"neighbors": [10, 21, 41, 14, 37], "id": 6, "value": 0.05},
+    {"neighbors": [8, 17, 43, 25, 12], "id": 7, "value": 0.4},
+    {"neighbors": [17, 25, 43, 22, 7], "id": 8, "value": 0.7},
+    {"neighbors": [39, 34, 1, 26, 48], "id": 9, "value": 0.5},
+    {"neighbors": [6, 37, 5, 45, 49], "id": 10, "value": 0.04},
+    {"neighbors": [51, 41, 29, 21, 14], "id": 11, "value": 0.08},
+    {"neighbors": [44, 46, 43, 50, 3], "id": 12, "value": 0.2},
+    {"neighbors": [45, 23, 14, 28, 18], "id": 13, "value": 0.4},
+    {"neighbors": [41, 29, 13, 23, 6], "id": 14, "value": 0.2},
+    {"neighbors": [36, 27, 32, 33, 24], "id": 15, "value": 0.3},
+    {"neighbors": [19, 2, 46, 44, 28], "id": 16, "value": 0.4},
+    {"neighbors": [8, 25, 43, 7, 22], "id": 17, "value": 0.6},
+    {"neighbors": [23, 4, 29, 14, 13], "id": 18, "value": 0.3},
+    {"neighbors": [42, 16, 28, 26, 40], "id": 19, "value": 0.7},
+    {"neighbors": [1, 48, 31, 26, 42], "id": 20, "value": 0.8},
+    {"neighbors": [41, 6, 11, 14, 10], "id": 21, "value": 0.1},
+    {"neighbors": [25, 50, 43, 31, 44], "id": 22, "value": 0.4},
+    {"neighbors": [18, 13, 14, 4, 2], "id": 23, "value": 0.1},
+    {"neighbors": [33, 49, 34, 47, 27], "id": 24, "value": 0.3},
+    {"neighbors": [43, 8, 22, 17, 50], "id": 25, "value": 0.4},
+    {"neighbors": [1, 42, 20, 31, 48], "id": 26, "value": 0.6},
+    {"neighbors": [32, 15, 36, 33, 24], "id": 27, "value": 0.3},
+    {"neighbors": [40, 45, 19, 5, 13], "id": 28, "value": 0.8},
+    {"neighbors": [11, 51, 41, 14, 18], "id": 29, "value": 0.3},
+    {"neighbors": [2, 3, 4, 46, 18], "id": 30, "value": 0.1},
+    {"neighbors": [20, 26, 1, 50, 48], "id": 31, "value": 0.9},
+    {"neighbors": [27, 36, 15, 49, 24], "id": 32, "value": 0.3},
+    {"neighbors": [24, 27, 49, 34, 32], "id": 33, "value": 0.4},
+    {"neighbors": [47, 9, 39, 40, 24], "id": 34, "value": 0.3},
+    {"neighbors": [38, 51, 11, 21, 41], "id": 35, "value": 0.3},
+    {"neighbors": [15, 32, 27, 49, 33], "id": 36, "value": 0.2},
+    {"neighbors": [49, 10, 5, 47, 24], "id": 37, "value": 0.5},
+    {"neighbors": [35, 21, 51, 11, 41], "id": 38, "value": 0.4},
+    {"neighbors": [9, 34, 48, 1, 47], "id": 39, "value": 0.6},
+    {"neighbors": [28, 47, 5, 9, 34], "id": 40, "value": 0.5},
+    {"neighbors": [11, 14, 29, 21, 6], "id": 41, "value": 0.4},
+    {"neighbors": [26, 19, 1, 9, 31], "id": 42, "value": 0.2},
+    {"neighbors": [25, 12, 8, 22, 44], "id": 43, "value": 0.3},
+    {"neighbors": [12, 50, 46, 16, 43], "id": 44, "value": 0.2},
+    {"neighbors": [28, 13, 5, 40, 19], "id": 45, "value": 0.3},
+    {"neighbors": [3, 12, 44, 2, 16], "id": 46, "value": 0.2},
+    {"neighbors": [34, 40, 5, 49, 24], "id": 47, "value": 0.3},
+    {"neighbors": [1, 20, 26, 9, 39], "id": 48, "value": 0.5},
+    {"neighbors": [24, 37, 47, 5, 33], "id": 49, "value": 0.2},
+    {"neighbors": [44, 22, 31, 42, 26], "id": 50, "value": 0.6},
+    {"neighbors": [11, 29, 41, 14, 21], "id": 51, "value": 0.01},
+    {"neighbors": [4, 18, 29, 51, 23], "id": 52, "value": 0.01}
+  ]

release/python/0.6.0/crankshaft/test/helper.py

@@ -0,0 +1,13 @@
+import unittest
+
+from mock_plpy import MockPlPy
+plpy = MockPlPy()
+
+import sys
+sys.modules['plpy'] = plpy
+
+import os
+
+def fixture_file(name):
+    dir = os.path.dirname(os.path.realpath(__file__))
+    return os.path.join(dir, 'fixtures', name)

release/python/0.6.0/crankshaft/test/mock_plpy.py

@@ -0,0 +1,54 @@
+import re
+
+
+class MockCursor:
+    def __init__(self, data):
+        self.cursor_pos = 0
+        self.data = data
+
+    def fetch(self, batch_size):
+        batch = self.data[self.cursor_pos:self.cursor_pos + batch_size]
+        self.cursor_pos += batch_size
+        return batch
+
+
+class MockPlPy:
+    def __init__(self):
+        self._reset()
+
+    def _reset(self):
+        self.infos = []
+        self.notices = []
+        self.debugs = []
+        self.logs = []
+        self.warnings = []
+        self.errors = []
+        self.fatals = []
+        self.executes = []
+        self.results = []
+        self.prepares = []
+        self.results = []
+
+    def _define_result(self, query, result):
+        pattern = re.compile(query, re.IGNORECASE | re.MULTILINE)
+        self.results.append([pattern, result])
+
+    def notice(self, msg):
+        self.notices.append(msg)
+
+    def debug(self, msg):
+        self.notices.append(msg)
+
+    def info(self, msg):
+        self.infos.append(msg)
+
+    def cursor(self, query):
+        data = self.execute(query)
+        return MockCursor(data)
+
+    # TODO: additional arguments
+    def execute(self, query):
+        for result in self.results:
+            if result[0].match(query):
+                return result[1]
+        return []

release/python/0.6.0/crankshaft/test/test_clustering_getis.py

@@ -0,0 +1,78 @@
+import unittest
+import numpy as np
+
+from helper import fixture_file
+
+from crankshaft.clustering import Getis
+import crankshaft.pysal_utils as pu
+from crankshaft import random_seeds
+import json
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+
+# Fixture files produced as follows
+#
+# import pysal as ps
+# import numpy as np
+# import random
+#
+# # setup variables
+# f = ps.open(ps.examples.get_path("stl_hom.dbf"))
+# y = np.array(f.by_col['HR8893'])
+# w_queen = ps.queen_from_shapefile(ps.examples.get_path("stl_hom.shp"))
+#
+# out_queen = [{"id": index + 1,
+#               "neighbors": [x+1 for x in w_queen.neighbors[index]],
+#               "value": val} for index, val in enumerate(y)]
+#
+# with open('neighbors_queen_getis.json', 'w') as f:
+#     f.write(str(out_queen))
+#
+# random.seed(1234)
+# np.random.seed(1234)
+# lgstar_queen = ps.esda.getisord.G_Local(y, w_queen, star=True,
+#                                         permutations=999)
+#
+# with open('getis_queen.json', 'w') as f:
+#     f.write(str(zip(lgstar_queen.z_sim,
+#                     lgstar_queen.p_sim, lgstar_queen.p_z_sim)))
+
+
+class FakeDataProvider(AnalysisDataProvider):
+    def __init__(self, mock_data):
+        self.mock_result = mock_data
+
+    def get_getis(self, w_type, param):
+        return self.mock_result
+
+
+class GetisTest(unittest.TestCase):
+    """Testing class for Getis-Ord's G* funtion
+       This test replicates the work done in PySAL documentation:
+          https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/autocorrelation.html#local-g-and-g
+    """
+
+    def setUp(self):
+        # load raw data for analysis
+        self.neighbors_data = json.loads(
+          open(fixture_file('neighbors_getis.json')).read())
+
+        # load pre-computed/known values
+        self.getis_data = json.loads(
+          open(fixture_file('getis.json')).read())
+
+    def test_getis_ord(self):
+        """Test Getis-Ord's G*"""
+        data = [{'id': d['id'],
+                 'attr1': d['value'],
+                 'neighbors': d['neighbors']} for d in self.neighbors_data]
+
+        random_seeds.set_random_seeds(1234)
+        getis = Getis(FakeDataProvider(data))
+
+        result = getis.getis_ord('subquery', 'value',
+                                 'queen', None, 999, 'the_geom',
+                                 'cartodb_id')
+        result = [(row[0], row[1]) for row in result]
+        expected = np.array(self.getis_data)[:, 0:2]
+        for ([res_z, res_p], [exp_z, exp_p]) in zip(result, expected):
+            self.assertAlmostEqual(res_z, exp_z, delta=1e-2)

release/python/0.6.0/crankshaft/test/test_clustering_kmeans.py

@@ -0,0 +1,56 @@
+import unittest
+import numpy as np
+
+
+# from mock_plpy import MockPlPy
+# plpy = MockPlPy()
+#
+# import sys
+# sys.modules['plpy'] = plpy
+from helper import fixture_file
+from crankshaft.clustering import Kmeans
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+import crankshaft.clustering as cc
+
+from crankshaft import random_seeds
+import json
+from collections import OrderedDict
+
+
+class FakeDataProvider(AnalysisDataProvider):
+    def __init__(self, mocked_result):
+        self.mocked_result = mocked_result
+
+    def get_spatial_kmeans(self, query):
+        return self.mocked_result
+
+    def get_nonspatial_kmeans(self, query, standarize):
+        return self.mocked_result
+
+
+class KMeansTest(unittest.TestCase):
+    """Testing class for k-means spatial"""
+
+    def setUp(self):
+        self.cluster_data = json.loads(
+          open(fixture_file('kmeans.json')).read())
+        self.params = {"subquery": "select * from table",
+                       "no_clusters": "10"}
+
+    def test_kmeans(self):
+        """
+        """
+        data = [{'xs': d['xs'],
+                 'ys': d['ys'],
+                 'ids': d['ids']} for d in self.cluster_data]
+
+        random_seeds.set_random_seeds(1234)
+        kmeans = Kmeans(FakeDataProvider(data))
+        clusters = kmeans.spatial('subquery', 2)
+        labels = [a[1] for a in clusters]
+        c1 = [a for a in clusters if a[1] == 0]
+        c2 = [a for a in clusters if a[1] == 1]
+
+        self.assertEqual(len(np.unique(labels)), 2)
+        self.assertEqual(len(c1), 20)
+        self.assertEqual(len(c2), 20)

release/python/0.6.0/crankshaft/test/test_clustering_moran.py

@@ -0,0 +1,112 @@
+import unittest
+import numpy as np
+
+from helper import fixture_file
+from crankshaft.clustering import Moran
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+import crankshaft.pysal_utils as pu
+from crankshaft import random_seeds
+import json
+from collections import OrderedDict
+
+
+class FakeDataProvider(AnalysisDataProvider):
+    def __init__(self, mock_data):
+        self.mock_result = mock_data
+
+    def get_moran(self, w_type, params):
+        return self.mock_result
+
+
+class MoranTest(unittest.TestCase):
+    """Testing class for Moran's I functions"""
+
+    def setUp(self):
+        self.params = {"id_col": "cartodb_id",
+                       "attr1": "andy",
+                       "attr2": "jay_z",
+                       "subquery": "SELECT * FROM a_list",
+                       "geom_col": "the_geom",
+                       "num_ngbrs": 321}
+        self.params_markov = {"id_col": "cartodb_id",
+                              "time_cols": ["_2013_dec", "_2014_jan",
+                                            "_2014_feb"],
+                              "subquery": "SELECT * FROM a_list",
+                              "geom_col": "the_geom",
+                              "num_ngbrs": 321}
+        self.neighbors_data = json.loads(
+          open(fixture_file('neighbors.json')).read())
+        self.moran_data = json.loads(
+          open(fixture_file('moran.json')).read())
+
+    def test_map_quads(self):
+        """Test map_quads"""
+        from crankshaft.clustering import map_quads
+        self.assertEqual(map_quads(1), 'HH')
+        self.assertEqual(map_quads(2), 'LH')
+        self.assertEqual(map_quads(3), 'LL')
+        self.assertEqual(map_quads(4), 'HL')
+        self.assertEqual(map_quads(33), None)
+        self.assertEqual(map_quads('andy'), None)
+
+    def test_quad_position(self):
+        """Test lisa_sig_vals"""
+        from crankshaft.clustering import quad_position
+
+        quads = np.array([1, 2, 3, 4], np.int)
+
+        ans = np.array(['HH', 'LH', 'LL', 'HL'])
+        test_ans = quad_position(quads)
+
+        self.assertTrue((test_ans == ans).all())
+
+    def test_local_stat(self):
+        """Test Moran's I local"""
+        data = [OrderedDict([('id', d['id']),
+                             ('attr1', d['value']),
+                             ('neighbors', d['neighbors'])])
+                for d in self.neighbors_data]
+
+        moran = Moran(FakeDataProvider(data))
+        random_seeds.set_random_seeds(1234)
+        result = moran.local_stat('subquery', 'value',
+                                  'knn', 5, 99, 'the_geom', 'cartodb_id')
+        result = [(row[0], row[1]) for row in result]
+        zipped_values = zip(result, self.moran_data)
+
+        for ([res_val, res_quad], [exp_val, exp_quad]) in zipped_values:
+            self.assertAlmostEqual(res_val, exp_val)
+            self.assertEqual(res_quad, exp_quad)
+
+    def test_moran_local_rate(self):
+        """Test Moran's I rate"""
+        data = [{'id': d['id'],
+                 'attr1': d['value'],
+                 'attr2': 1,
+                 'neighbors': d['neighbors']} for d in self.neighbors_data]
+
+        random_seeds.set_random_seeds(1234)
+        moran = Moran(FakeDataProvider(data))
+        result = moran.local_rate_stat('subquery', 'numerator', 'denominator',
+                                       'knn', 5, 99, 'the_geom', 'cartodb_id')
+        result = [(row[0], row[1]) for row in result]
+
+        zipped_values = zip(result, self.moran_data)
+
+        for ([res_val, res_quad], [exp_val, exp_quad]) in zipped_values:
+            self.assertAlmostEqual(res_val, exp_val)
+
+    def test_moran(self):
+        """Test Moran's I global"""
+        data = [{'id': d['id'],
+                 'attr1': d['value'],
+                 'neighbors': d['neighbors']} for d in self.neighbors_data]
+        random_seeds.set_random_seeds(1235)
+        moran = Moran(FakeDataProvider(data))
+        result = moran.global_stat('table', 'value',
+                                   'knn', 5, 99, 'the_geom',
+                                   'cartodb_id')
+
+        result_moran = result[0][0]
+        expected_moran = np.array([row[0] for row in self.moran_data]).mean()
+        self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)

release/python/0.6.0/crankshaft/test/test_pysal_utils.py

@@ -0,0 +1,160 @@
+import unittest
+
+import crankshaft.pysal_utils as pu
+from crankshaft import random_seeds
+from collections import OrderedDict
+
+
+class PysalUtilsTest(unittest.TestCase):
+    """Testing class for utility functions related to PySAL integrations"""
+
+    def setUp(self):
+        self.params1 = OrderedDict([("id_col", "cartodb_id"),
+                                    ("attr1", "andy"),
+                                    ("attr2", "jay_z"),
+                                    ("subquery", "SELECT * FROM a_list"),
+                                    ("geom_col", "the_geom"),
+                                    ("num_ngbrs", 321)])
+
+        self.params2 = OrderedDict([("id_col", "cartodb_id"),
+                                    ("numerator", "price"),
+                                    ("denominator", "sq_meters"),
+                                    ("subquery", "SELECT * FROM pecan"),
+                                    ("geom_col", "the_geom"),
+                                    ("num_ngbrs", 321)])
+
+        self.params3 = OrderedDict([("id_col", "cartodb_id"),
+                                    ("numerator", "sq_meters"),
+                                    ("denominator", "price"),
+                                    ("subquery", "SELECT * FROM pecan"),
+                                    ("geom_col", "the_geom"),
+                                    ("num_ngbrs", 321)])
+
+        self.params_array = {"id_col": "cartodb_id",
+                             "time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
+                             "subquery": "SELECT * FROM a_list",
+                             "geom_col": "the_geom",
+                             "num_ngbrs": 321}
+
+    def test_query_attr_select(self):
+        """Test query_attr_select"""
+
+        ans1 = ("i.\"andy\"::numeric As attr1, "
+                "i.\"jay_z\"::numeric As attr2, ")
+
+        ans2 = ("i.\"price\"::numeric As attr1, "
+                "i.\"sq_meters\"::numeric As attr2, ")
+
+        ans3 = ("i.\"sq_meters\"::numeric As attr1, "
+                "i.\"price\"::numeric As attr2, ")
+
+        ans_array = ("i.\"_2013_dec\"::numeric As attr1, "
+                     "i.\"_2014_jan\"::numeric As attr2, "
+                     "i.\"_2014_feb\"::numeric As attr3, ")
+
+        self.assertEqual(pu.query_attr_select(self.params1), ans1)
+        self.assertEqual(pu.query_attr_select(self.params2), ans2)
+        self.assertEqual(pu.query_attr_select(self.params3), ans3)
+        self.assertEqual(pu.query_attr_select(self.params_array), ans_array)
+
+    def test_query_attr_where(self):
+        """Test pu.query_attr_where"""
+
+        ans1 = ("idx_replace.\"andy\" IS NOT NULL AND "
+                "idx_replace.\"jay_z\" IS NOT NULL")
+
+        ans_array = ("idx_replace.\"_2013_dec\" IS NOT NULL AND "
+                     "idx_replace.\"_2014_jan\" IS NOT NULL AND "
+                     "idx_replace.\"_2014_feb\" IS NOT NULL")
+
+        self.assertEqual(pu.query_attr_where(self.params1), ans1)
+        self.assertEqual(pu.query_attr_where(self.params_array), ans_array)
+
+    def test_knn(self):
+        """Test knn neighbors constructor"""
+
+        ans1 = "SELECT i.\"cartodb_id\" As id, " \
+                     "i.\"andy\"::numeric As attr1, " \
+                     "i.\"jay_z\"::numeric As attr2, " \
+                     "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
+                                   "FROM (SELECT * FROM a_list) As j " \
+                                   "WHERE " \
+                                    "i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
+                                    "j.\"andy\" IS NOT NULL AND " \
+                                    "j.\"jay_z\" IS NOT NULL " \
+                                   "ORDER BY " \
+                                    "j.\"the_geom\" <-> i.\"the_geom\" ASC " \
+                      "LIMIT 321)) As neighbors " \
+              "FROM (SELECT * FROM a_list) As i " \
+              "WHERE i.\"andy\" IS NOT NULL AND " \
+                    "i.\"jay_z\" IS NOT NULL " \
+              "ORDER BY i.\"cartodb_id\" ASC;"
+
+        ans_array = "SELECT i.\"cartodb_id\" As id, " \
+              "i.\"_2013_dec\"::numeric As attr1, " \
+              "i.\"_2014_jan\"::numeric As attr2, " \
+              "i.\"_2014_feb\"::numeric As attr3, " \
+              "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
+                            "FROM (SELECT * FROM a_list) As j " \
+                            "WHERE i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
+                                  "j.\"_2013_dec\" IS NOT NULL AND " \
+                                  "j.\"_2014_jan\" IS NOT NULL AND " \
+                                  "j.\"_2014_feb\" IS NOT NULL " \
+                            "ORDER BY j.\"the_geom\" <-> i.\"the_geom\" ASC " \
+                            "LIMIT 321)) As neighbors " \
+              "FROM (SELECT * FROM a_list) As i " \
+              "WHERE i.\"_2013_dec\" IS NOT NULL AND " \
+                    "i.\"_2014_jan\" IS NOT NULL AND " \
+                    "i.\"_2014_feb\" IS NOT NULL "\
+              "ORDER BY i.\"cartodb_id\" ASC;"
+
+        self.assertEqual(pu.knn(self.params1), ans1)
+        self.assertEqual(pu.knn(self.params_array), ans_array)
+
+    def test_queen(self):
+        """Test queen neighbors constructor"""
+
+        ans1 = "SELECT i.\"cartodb_id\" As id, " \
+                     "i.\"andy\"::numeric As attr1, " \
+                     "i.\"jay_z\"::numeric As attr2, " \
+                     "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
+                                   "FROM (SELECT * FROM a_list) As j " \
+                                   "WHERE " \
+                                   "i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
+                                   "ST_Touches(i.\"the_geom\", " \
+                                              "j.\"the_geom\") AND " \
+                                   "j.\"andy\" IS NOT NULL AND " \
+                                   "j.\"jay_z\" IS NOT NULL)" \
+                                  ") As neighbors " \
+              "FROM (SELECT * FROM a_list) As i " \
+              "WHERE i.\"andy\" IS NOT NULL AND " \
+                    "i.\"jay_z\" IS NOT NULL " \
+              "ORDER BY i.\"cartodb_id\" ASC;"
+
+        self.assertEqual(pu.queen(self.params1), ans1)
+
+    def test_construct_neighbor_query(self):
+        """Test construct_neighbor_query"""
+
+        # Compare to raw knn query
+        self.assertEqual(pu.construct_neighbor_query('knn', self.params1),
+                         pu.knn(self.params1))
+
+    def test_get_attributes(self):
+        """Test get_attributes"""
+
+        ## need to add tests
+
+        self.assertEqual(True, True)
+
+    def test_get_weight(self):
+        """Test get_weight"""
+
+        self.assertEqual(True, True)
+
+    def test_empty_zipped_array(self):
+        """Test empty_zipped_array"""
+        ans2 = [(None, None)]
+        ans4 = [(None, None, None, None)]
+        self.assertEqual(pu.empty_zipped_array(2), ans2)
+        self.assertEqual(pu.empty_zipped_array(4), ans4)

release/python/0.6.0/crankshaft/test/test_regression_gwr.py

@@ -0,0 +1,130 @@
+import unittest
+import json
+import numpy as np
+
+
+from crankshaft import random_seeds
+from helper import fixture_file
+from crankshaft.regression import GWR
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+
+
+class FakeDataProvider(AnalysisDataProvider):
+    def __init__(self, mocked_result):
+        self.mocked_result = mocked_result
+
+    def get_gwr(self, params):
+        return self.mocked_result
+
+    def get_gwr_predict(self, params):
+        return self.mocked_result
+
+
+class GWRTest(unittest.TestCase):
+    """Testing class for geographically weighted regression (gwr)"""
+
+    def setUp(self):
+        """
+            fixture packed from canonical GWR georgia dataset using the
+            following query:
+                SELECT array_agg(x) As x,
+                       array_agg(y) As y,
+                       array_agg(pctbach) As dep_var,
+                       array_agg(pctrural) As attr1,
+                       array_agg(pctpov) As attr2,
+                       array_agg(pctblack) As attr3,
+                       array_agg(areakey) As rowid
+                FROM g_utm
+                WHERE pctbach is not NULL AND
+                      pctrural IS NOT NULL AND
+                      pctpov IS NOT NULL AND
+                      pctblack IS NOT NULL
+        """
+        import copy
+        # data packed from https://github.com/TaylorOshan/pysal/blob/1d6af33bda46b1d623f70912c56155064463383f/pysal/examples/georgia/GData_utm.csv
+        self.data = json.loads(
+              open(fixture_file('gwr_packed_data.json')).read())
+
+        # data packed from https://github.com/TaylorOshan/pysal/blob/a44c5541e2e0d10a99ff05edc1b7f81b70f5a82f/pysal/examples/georgia/georgia_BS_NN_listwise.csv
+        self.knowns = json.loads(
+              open(fixture_file('gwr_packed_knowns.json')).read())
+
+        # data for GWR prediction
+        self.data_predict = copy.deepcopy(self.data)
+        self.ids_of_unknowns = [13083, 13009, 13281, 13115, 13247, 13169]
+        self.idx_ids_of_unknowns = [self.data_predict[0]['rowid'].index(idx)
+                                    for idx in self.ids_of_unknowns]
+
+        for idx in self.idx_ids_of_unknowns:
+            self.data_predict[0]['dep_var'][idx] = None
+
+        self.predicted_knowns = {13009: 10.879,
+                                 13083: 4.5259,
+                                 13115: 9.4022,
+                                 13169: 6.0793,
+                                 13247: 8.1608,
+                                 13281: 13.886}
+
+        # params, with ind_vars in same ordering as query above
+        self.params = {'subquery': 'select * from table',
+                       'dep_var': 'pctbach',
+                       'ind_vars': ['pctrural', 'pctpov', 'pctblack'],
+                       'bw': 90.000,
+                       'fixed': False,
+                       'geom_col': 'the_geom',
+                       'id_col': 'areakey'}
+
+    def test_gwr(self):
+        """
+        """
+        gwr = GWR(FakeDataProvider(self.data))
+        gwr_resp = gwr.gwr(self.params['subquery'],
+                           self.params['dep_var'],
+                           self.params['ind_vars'],
+                           bw=self.params['bw'],
+                           fixed=self.params['fixed'])
+
+        # unpack response
+        coeffs, stand_errs, t_vals, t_vals_filtered, predicteds, \
+            residuals, r_squareds, bws, rowids = zip(*gwr_resp)
+
+        # prepare for comparision
+        coeff_known_pctpov = self.knowns['est_pctpov']
+        tval_known_pctblack = self.knowns['t_pctrural']
+        pctpov_se = self.knowns['se_pctpov']
+        ids = self.knowns['area_key']
+        resp_idx = None
+
+        # test pctpov coefficient estimates
+        for idx, val in enumerate(coeff_known_pctpov):
+            resp_idx = rowids.index(ids[idx])
+            self.assertAlmostEquals(val,
+                                    json.loads(coeffs[resp_idx])['pctpov'],
+                                    places=4)
+        # test pctrural tvals
+        for idx, val in enumerate(tval_known_pctblack):
+            resp_idx = rowids.index(ids[idx])
+            self.assertAlmostEquals(val,
+                                    json.loads(t_vals[resp_idx])['pctrural'],
+                                    places=4)
+
+    def test_gwr_predict(self):
+        """Testing for GWR_Predict"""
+        gwr = GWR(FakeDataProvider(self.data_predict))
+        gwr_resp = gwr.gwr_predict(self.params['subquery'],
+                                   self.params['dep_var'],
+                                   self.params['ind_vars'],
+                                   bw=self.params['bw'],
+                                   fixed=self.params['fixed'])
+
+        # unpack response
+        coeffs, stand_errs, t_vals, \
+            r_squareds, predicteds, rowid = zip(*gwr_resp)
+        threshold = 0.01
+
+        for i, idx in enumerate(self.idx_ids_of_unknowns):
+
+            known_val = self.predicted_knowns[rowid[i]]
+            predicted_val = predicteds[i]
+            test_val = abs(known_val - predicted_val) / known_val
+            self.assertTrue(test_val < threshold)

release/python/0.6.0/crankshaft/test/test_segmentation.py

@@ -0,0 +1,64 @@
+import unittest
+import numpy as np
+from helper import plpy, fixture_file
+import crankshaft.segmentation as segmentation
+import json
+
+class SegmentationTest(unittest.TestCase):
+    """Testing class for Moran's I functions"""
+
+    def setUp(self):
+        plpy._reset()
+
+    def generate_random_data(self,n_samples,random_state,  row_type=False):
+        x1 = random_state.uniform(size=n_samples)
+        x2 = random_state.uniform(size=n_samples)
+        x3 = random_state.randint(0, 4, size=n_samples)
+
+        y = x1+x2*x2+x3
+        cartodb_id  = range(len(x1))
+
+        if row_type:
+            return [ {'features': vals} for vals in zip(x1,x2,x3)], y
+        else:
+            return  [dict( zip(['x1','x2','x3','target', 'cartodb_id'],[x1,x2,x3,y,cartodb_id]))]
+
+    def test_replace_nan_with_mean(self):
+        test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan])
+
+    def test_create_and_predict_segment(self):
+        n_samples = 1000
+
+        random_state_train = np.random.RandomState(13)
+        random_state_test = np.random.RandomState(134)
+        training_data = self.generate_random_data(n_samples, random_state_train)
+        test_data, test_y = self.generate_random_data(n_samples, random_state_test, row_type=True)
+
+
+        ids =  [{'cartodb_ids': range(len(test_data))}]
+        rows =  [{'x1': 0,'x2':0,'x3':0,'y':0,'cartodb_id':0}]
+
+        plpy._define_result('select \* from  \(select \* from training\) a  limit 1',rows)
+        plpy._define_result('.*from \(select \* from training\) as a' ,training_data)
+        plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',ids)
+        plpy._define_result('.*select \* from test.*' ,test_data)
+
+        model_parameters =  {'n_estimators': 1200,
+                             'max_depth': 3,
+                             'subsample' : 0.5,
+                             'learning_rate': 0.01,
+                             'min_samples_leaf': 1}
+
+        result = segmentation.create_and_predict_segment(
+                'select * from training',
+                'target',
+                'select * from test',
+                model_parameters)
+
+        prediction = [r[1] for r in result]
+
+        accuracy =np.sqrt(np.mean( np.square( np.array(prediction) - np.array(test_y))))
+
+        self.assertEqual(len(result),len(test_data))
+        self.assertTrue( result[0][2] < 0.01)
+        self.assertTrue( accuracy < 0.5*np.mean(test_y)  )

release/python/0.6.0/crankshaft/test/test_space_time_dynamics.py

@@ -0,0 +1,349 @@
+import unittest
+import numpy as np
+
+import unittest
+
+
+from helper import fixture_file
+
+from crankshaft.space_time_dynamics import Markov
+import crankshaft.space_time_dynamics as std
+from crankshaft import random_seeds
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+import json
+
+
+class FakeDataProvider(AnalysisDataProvider):
+    def __init__(self, data):
+        self.mock_result = data
+
+    def get_markov(self, w_type, params):
+        return self.mock_result
+
+
+class SpaceTimeTests(unittest.TestCase):
+    """Testing class for Markov Functions."""
+
+    def setUp(self):
+        self.params = {"id_col": "cartodb_id",
+                       "time_cols": ['dec_2013', 'jan_2014', 'feb_2014'],
+                       "subquery": "SELECT * FROM a_list",
+                       "geom_col": "the_geom",
+                       "num_ngbrs": 321}
+        self.neighbors_data = json.loads(
+          open(fixture_file('neighbors_markov.json')).read())
+        self.markov_data = json.loads(open(fixture_file('markov.json')).read())
+
+        self.time_data = np.array([i * np.ones(10, dtype=float)
+                                   for i in range(10)]).T
+
+        self.transition_matrix = np.array([
+                [[0.96341463, 0.0304878, 0.00609756, 0., 0.],
+                 [0.06040268, 0.83221477, 0.10738255, 0., 0.],
+                 [0., 0.14, 0.74, 0.12, 0.],
+                 [0., 0.03571429, 0.32142857, 0.57142857, 0.07142857],
+                 [0., 0., 0., 0.16666667, 0.83333333]],
+                [[0.79831933, 0.16806723, 0.03361345, 0., 0.],
+                 [0.0754717, 0.88207547, 0.04245283, 0., 0.],
+                 [0.00537634, 0.06989247, 0.8655914, 0.05913978, 0.],
+                 [0., 0., 0.06372549, 0.90196078, 0.03431373],
+                 [0., 0., 0., 0.19444444, 0.80555556]],
+                [[0.84693878, 0.15306122, 0., 0., 0.],
+                 [0.08133971, 0.78947368, 0.1291866, 0., 0.],
+                 [0.00518135, 0.0984456, 0.79274611, 0.0984456, 0.00518135],
+                 [0., 0., 0.09411765, 0.87058824, 0.03529412],
+                 [0., 0., 0., 0.10204082, 0.89795918]],
+                [[0.8852459, 0.09836066, 0., 0.01639344, 0.],
+                 [0.03875969, 0.81395349, 0.13953488, 0., 0.00775194],
+                 [0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505],
+                 [0., 0.02339181, 0.12865497, 0.75438596, 0.09356725],
+                 [0., 0., 0., 0.09661836, 0.90338164]],
+                [[0.33333333, 0.66666667, 0., 0., 0.],
+                 [0.0483871, 0.77419355, 0.16129032, 0.01612903, 0.],
+                 [0.01149425, 0.16091954, 0.74712644, 0.08045977, 0.],
+                 [0., 0.01036269, 0.06217617, 0.89637306, 0.03108808],
+                 [0., 0., 0., 0.02352941, 0.97647059]]]
+                 )
+
+    def test_spatial_markov(self):
+        """Test Spatial Markov."""
+        data = [{'id': d['id'],
+                 'attr1': d['y1995'],
+                 'attr2': d['y1996'],
+                 'attr3': d['y1997'],
+                 'attr4': d['y1998'],
+                 'attr5': d['y1999'],
+                 'attr6': d['y2000'],
+                 'attr7': d['y2001'],
+                 'attr8': d['y2002'],
+                 'attr9': d['y2003'],
+                 'attr10': d['y2004'],
+                 'attr11': d['y2005'],
+                 'attr12': d['y2006'],
+                 'attr13': d['y2007'],
+                 'attr14': d['y2008'],
+                 'attr15': d['y2009'],
+                 'neighbors': d['neighbors']} for d in self.neighbors_data]
+        # print(str(data[0]))
+        markov = Markov(FakeDataProvider(data))
+        random_seeds.set_random_seeds(1234)
+
+        result = markov.spatial_trend('subquery',
+                                      ['y1995', 'y1996', 'y1997', 'y1998',
+                                       'y1999', 'y2000', 'y2001', 'y2002',
+                                       'y2003', 'y2004', 'y2005', 'y2006',
+                                       'y2007', 'y2008', 'y2009'],
+                                      5, 'knn', 5, 0, 'the_geom',
+                                      'cartodb_id')
+
+        self.assertTrue(result is not None)
+        result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
+        print result[0]
+        expected = self.markov_data
+        for ([res_trend, res_up, res_down, res_vol, res_id],
+             [exp_trend, exp_up, exp_down, exp_vol, exp_id]
+             ) in zip(result, expected):
+            self.assertAlmostEqual(res_trend, exp_trend)
+
+    def test_get_time_data(self):
+        """Test get_time_data"""
+        data = [{'attr1': d['y1995'],
+                 'attr2': d['y1996'],
+                 'attr3': d['y1997'],
+                 'attr4': d['y1998'],
+                 'attr5': d['y1999'],
+                 'attr6': d['y2000'],
+                 'attr7': d['y2001'],
+                 'attr8': d['y2002'],
+                 'attr9': d['y2003'],
+                 'attr10': d['y2004'],
+                 'attr11': d['y2005'],
+                 'attr12': d['y2006'],
+                 'attr13': d['y2007'],
+                 'attr14': d['y2008'],
+                 'attr15': d['y2009']} for d in self.neighbors_data]
+
+        result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998',
+                                          'y1999', 'y2000', 'y2001', 'y2002',
+                                          'y2003', 'y2004', 'y2005', 'y2006',
+                                          'y2007', 'y2008', 'y2009'])
+
+        # expected was prepared from PySAL example:
+        # f = ps.open(ps.examples.get_path("usjoin.csv"))
+        # pci = np.array([f.by_col[str(y)]
+        #                 for y in range(1995, 2010)]).transpose()
+        # rpci = pci / (pci.mean(axis = 0))
+
+        expected = np.array(
+          [[0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154,
+            0.83271652,  0.83786314, 0.85012593, 0.85509656, 0.86416612,
+            0.87119375, 0.86302631,  0.86148267, 0.86252252, 0.86746356],
+           [0.9188951,  0.91757931, 0.92333258, 0.92517289, 0.92552388,
+            0.90746978,  0.89830489, 0.89431991, 0.88924794, 0.89815176,
+            0.91832091, 0.91706054,  0.90139505, 0.87897455, 0.86216858],
+           [0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522,
+            0.78964559,  0.80584442, 0.8084998,  0.82258551, 0.82668196,
+            0.82373724, 0.81814804,  0.83675961, 0.83574199, 0.84647177],
+           [1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841,
+            1.14506948,  1.12151133, 1.11160697, 1.10888621, 1.11399806,
+            1.12168029, 1.13164797,  1.12958508, 1.11371818, 1.09936775],
+           [1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025,
+            1.16898201,  1.17212488, 1.14752303, 1.11843284, 1.11024964,
+            1.11943471, 1.11736468,  1.10863242, 1.09642516, 1.07762337],
+           [1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684,
+            1.44184737,  1.44782832, 1.41978227, 1.39092208, 1.4059372,
+            1.40788646, 1.44052766,  1.45241216, 1.43306098, 1.4174431],
+           [1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149,
+            1.10888138,  1.11856629, 1.13062931, 1.11944984, 1.12446239,
+            1.11671008, 1.10880034,  1.08401709, 1.06959206, 1.07875225],
+           [1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545,
+            0.99854316,  0.9880258,  0.99669587, 0.99327676, 1.01400905,
+            1.03176742, 1.040511,  1.01749645, 0.9936394,  0.98279746],
+           [0.98996986, 1.00143564, 0.99491,  1.00188408, 1.00455845,
+            0.99127006,  0.97925917, 0.9683482,  0.95335147, 0.93694787,
+            0.94308213, 0.92232874,  0.91284091, 0.89689833, 0.88928858],
+           [0.87418391, 0.86416601, 0.84425695, 0.8404494,  0.83903044,
+            0.8578708,  0.86036185, 0.86107306, 0.8500772,  0.86981998,
+            0.86837929, 0.87204141,  0.86633032, 0.84946077, 0.83287146],
+           [1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624,
+            1.14450183,  1.12349752, 1.12596664, 1.12213996, 1.1119989,
+            1.10257792, 1.10491258,  1.11059842, 1.10509795, 1.10020097],
+           [0.97282463, 0.96700147, 0.96252588, 0.9653878,  0.96057687,
+            0.95831051,  0.94480909, 0.94804195, 0.95430286, 0.94103989,
+            0.92122519, 0.91010201,  0.89280392, 0.89298243, 0.89165385],
+           [0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647,
+            0.9480927,  0.93539182, 0.95388718, 0.94597005, 0.96918424,
+            0.94781281, 0.93466815,  0.94281559, 0.96520315, 0.96715441],
+           [0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897,
+            0.98687073,  0.99237486, 0.98209969, 0.9877653,  0.97399471,
+            0.96910087, 0.98416665,  0.98423613, 0.99823861, 0.99545704],
+           [0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012,
+            0.86191535,  0.84981451, 0.85472102, 0.84564835, 0.83998883,
+            0.83478547, 0.82803648,  0.8198736,  0.82265395, 0.8399404],
+           [0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136,
+            0.82785597,  0.86008789, 0.86776298, 0.86720209, 0.8676334,
+            0.89179317, 0.94202108,  0.9422231,  0.93902708, 0.94479184],
+           [0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238,
+            0.90906632,  0.92693339, 0.93695966, 0.94242697, 0.94338265,
+            0.91981796, 0.91108804,  0.90543476, 0.91737138, 0.94793657],
+           [1.1977611,  1.18222564, 1.18439158, 1.18267865, 1.19286723,
+            1.20172869,  1.21328691, 1.22624778, 1.22397075, 1.23857042,
+            1.24419893, 1.23929384,  1.23418676, 1.23626739, 1.26754398],
+           [1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667,
+            1.34790023,  1.34399863, 1.32575181, 1.30795492, 1.30544841,
+            1.30303302, 1.32107766,  1.32936244, 1.33001241, 1.33288462],
+           [1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093,
+            1.05059016,  1.03405057, 1.02747623, 1.03162734, 0.9961416,
+            0.97356208, 0.94241549,  0.92754547, 0.92549227, 0.92138102],
+           [1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264,
+            1.13889622,  1.12442212, 1.13367018, 1.13982256, 1.14029944,
+            1.11979401, 1.10905389,  1.10577769, 1.11166825, 1.09985155],
+           [0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284,
+            0.74480073,  0.76098396, 0.76156903, 0.76651952, 0.76533288,
+            0.78205934, 0.76842416,  0.77487118, 0.77768683, 0.78801192],
+           [0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803,
+            0.97370819,  0.96419154, 0.97209861, 0.97441313, 0.96356162,
+            0.94745352, 0.93965462,  0.93069645, 0.94020973, 0.94358232],
+           [0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801,
+            0.80071489,  0.83358256, 0.83451613, 0.85175032, 0.85954307,
+            0.86790024, 0.87170334,  0.87863799, 0.87497981, 0.87888675],
+           [0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619,
+            0.98733195,  0.99644997, 0.99669587, 1.02559097, 1.01116651,
+            0.99988024, 0.97906749,  0.99323123, 1.00204939, 0.99602148],
+           [1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683,
+            1.08312397,  1.05192626, 1.04230892, 1.05577278, 1.08569751,
+            1.12443486, 1.08891079,  1.08603695, 1.05997314, 1.02160943],
+           [1.11368269, 1.1057147,  1.11893431, 1.13778669, 1.1432272,
+            1.18257029,  1.16226243, 1.16009196, 1.14467789, 1.14820235,
+            1.12386598, 1.12680236,  1.12357937, 1.1159258,  1.12570828],
+           [1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667,
+            1.31210239,  1.29989156, 1.29203193, 1.27183516, 1.26830786,
+            1.2617743,  1.28656675,  1.29734097, 1.29390205, 1.29345446],
+           [0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864,
+            0.78772975,  0.82848011, 0.8259679,  0.82435705, 0.83108634,
+            0.84373784, 0.83891093,  0.84349247, 0.85637272, 0.86539395],
+           [1.23450087, 1.2426022,  1.23537935, 1.23581293, 1.24522626,
+            1.2256767,  1.21126648, 1.19377804, 1.18355337, 1.19674434,
+            1.21536573, 1.23653297,  1.27962009, 1.27968392, 1.25907738],
+           [0.9769662,  0.97400719, 0.98035944, 0.97581531, 0.95543282,
+            0.96480308,  0.94686376, 0.93679073, 0.92540049, 0.92988835,
+            0.93442917, 0.92100464,  0.91475304, 0.90249622, 0.9021363],
+           [0.84986886, 0.8986851,  0.84295997, 0.87280534, 0.85659368,
+            0.88937573,  0.894401, 0.90448993, 0.95495898, 0.92698333,
+            0.94745352, 0.92562488,  0.96635366, 1.02520312, 1.0394296],
+           [1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073,
+            1.00759019,  0.99192968, 0.99747298, 0.99550759, 0.97583768,
+            0.9610168,  0.94779638,  0.93759089, 0.93353431, 0.94121705],
+           [0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613,
+            0.83434854,  0.85813595, 0.84667961, 0.84374558, 0.85951183,
+            0.87194227, 0.89455097,  0.88283929, 0.90349491, 0.90600675],
+           [1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086,
+            1.00581626,  0.98850522, 0.99291168, 0.98983209, 0.97511924,
+            0.96134615, 0.96382634,  0.95011401, 0.9434686,  0.94637765],
+           [1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857,
+            1.04800023,  1.03024941, 1.04200483, 1.0402554,  1.03296979,
+            1.02191682, 1.02476275,  1.02347523, 1.02517684, 1.04359571],
+           [1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043,
+            1.0531801,  1.07452771, 1.09383478, 1.1052447,  1.10322136,
+            1.09167939, 1.08772756,  1.08859544, 1.09177338, 1.1096083],
+           [0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809,
+            0.86287327,  0.85169796, 0.85411285, 0.84886336, 0.84517414,
+            0.84843858, 0.84488343,  0.83374329, 0.82812044, 0.82878599],
+           [0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286,
+            0.92652175,  0.94278865, 0.93682452, 0.98655146, 0.992237,
+            0.9798497,  0.93869677,  0.96947771, 1.00362626, 0.98102351],
+           [0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967,
+            0.93092109,  0.92662519, 0.93412152, 0.93501274, 0.92879506,
+            0.92110542, 0.91035556,  0.90430364, 0.89994694, 0.90073864],
+           [0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824,
+            0.98882205,  0.97662234, 0.95601578, 0.94905385, 0.94934888,
+            0.97152609, 0.97163004,  0.9700702,  0.97158948, 0.95884908],
+           [0.83980439, 0.84726737, 0.85747,  0.85467221, 0.8556751,
+            0.84818516,  0.85265681, 0.84502402, 0.82645665, 0.81743586,
+            0.83550406, 0.83338919,  0.83511679, 0.82136617, 0.80921874],
+           [0.95118156, 0.9466212,  0.94688098, 0.9508583,  0.9512441,
+            0.95440787,  0.96364363, 0.96804412, 0.97136214, 0.97583768,
+            0.95571724, 0.96895368,  0.97001634, 0.97082733, 0.98782366],
+           [1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249,
+            1.10558188,  1.1214086,  1.12292577, 1.13021031, 1.13342735,
+            1.14686068, 1.14502975,  1.14474747, 1.14084037, 1.16142926],
+           [1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863,
+            1.11856702,  1.09764283, 1.08815849, 1.08044313, 1.09278827,
+            1.07003204, 1.08398066,  1.09831768, 1.09298232, 1.09176125],
+           [0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744,
+            0.77751194,  0.79902974, 0.81437881, 0.80788828, 0.79603865,
+            0.78966436, 0.79949807,  0.80172182, 0.82168155, 0.85587911],
+           [1.0052447,  1.00007696, 1.00475899, 1.00613942, 1.00639561,
+            1.00162979,  0.99860739, 1.00814981, 1.00574316, 0.99030032,
+            0.97682565, 0.97292596,  0.96519561, 0.96173403, 0.95890284],
+           [0.95808419, 0.9382568,  0.9654441,  0.95561201, 0.96987289,
+            0.96608031,  0.99727185, 1.00781194, 1.03484236, 1.05333619,
+            1.0983263,  1.1704974,  1.17025154, 1.18730553, 1.14242645]])
+
+        self.assertTrue(np.allclose(result, expected))
+        self.assertTrue(type(result) == type(expected))
+        self.assertTrue(result.shape == expected.shape)
+
+    def test_rebin_data(self):
+        """Test rebin_data"""
+        # sample in double the time (even case since 10 % 2 = 0):
+        #   (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2
+        # = 0.5,     2.5,     4.5,     6.5,     8.5
+        ans_even = np.array([(i + 0.5) * np.ones(10, dtype=float)
+                             for i in range(0, 10, 2)]).T
+
+        self.assertTrue(
+          np.array_equal(std.rebin_data(self.time_data, 2), ans_even))
+
+        # sample in triple the time (uneven since 10 % 3 = 1):
+        #   (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1
+        # = 1,         4,         7,         9
+        ans_odd = np.array([i * np.ones(10, dtype=float)
+                            for i in (1, 4, 7, 9)]).T
+        self.assertTrue(
+          np.array_equal(std.rebin_data(self.time_data, 3), ans_odd))
+
+    def test_get_prob_dist(self):
+        """Test get_prob_dist"""
+        lag_indices = np.array([1, 2, 3, 4])
+        unit_indices = np.array([1, 3, 2, 4])
+        answer = np.array([
+            [0.0754717, 0.88207547, 0.04245283, 0., 0.],
+            [0., 0., 0.09411765, 0.87058824, 0.03529412],
+            [0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505],
+            [0., 0., 0., 0.02352941, 0.97647059]
+        ])
+        result = std.get_prob_dist(self.transition_matrix,
+                                   lag_indices, unit_indices)
+
+        self.assertTrue(np.array_equal(result, answer))
+
+    def test_get_prob_stats(self):
+        """Test get_prob_stats"""
+
+        probs = np.array([
+            [0.0754717, 0.88207547, 0.04245283, 0., 0.],
+            [0., 0., 0.09411765, 0.87058824, 0.03529412],
+            [0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505],
+            [0., 0., 0., 0.02352941, 0.97647059]
+        ])
+        unit_indices = np.array([1, 3, 2, 4])
+        answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.])
+        answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941])
+        answer_trend = np.array([-0.03301887 / 0.88207547,
+                                 -0.05882353 / 0.87058824,
+                                 0.02475248 / 0.77722772,
+                                 -0.02352941 / 0.97647059])
+        answer_volatility = np.array([0.34221495,  0.33705421,
+                                      0.29226542,  0.38834223])
+
+        result = std.get_prob_stats(probs, unit_indices)
+        result_up = result[0]
+        result_down = result[1]
+        result_trend = result[2]
+        result_volatility = result[3]
+
+        self.assertTrue(np.allclose(result_up, answer_up))
+        self.assertTrue(np.allclose(result_down, answer_down))
+        self.assertTrue(np.allclose(result_trend, answer_trend))
+        self.assertTrue(np.allclose(result_volatility, answer_volatility))

src/pg/Makefile

@@ -25,7 +25,7 @@ $(DATA): $(SOURCES_DATA)
 	$(SED) $(REPLACEMENTS) $(SOURCES_DATA_DIR)/*.sql > $@
 
 TEST_DIR = test
-REGRESS = $(notdir $(basename $(wildcard $(TEST_DIR)/sql/*test.sql)))
+REGRESS = $(sort $(notdir $(basename $(wildcard $(TEST_DIR)/sql/*test.sql))))
 REGRESS_OPTS = --inputdir='$(TEST_DIR)' --outputdir='$(TEST_DIR)'
 
 PG_CONFIG = pg_config

src/pg/crankshaft.control

@@ -1,5 +1,5 @@
 comment = 'CartoDB Spatial Analysis extension'
-default_version = '0.5.2'
+default_version = '0.6.0'
 requires = 'plpythonu, postgis'
 superuser = true
 schema = cdb_crankshaft

src/pg/sql/21_gwr.sql

@@ -0,0 +1,36 @@
+CREATE OR REPLACE FUNCTION
+CDB_GWR(subquery text, dep_var text, ind_vars text[],
+        bw numeric default null, fixed boolean default False,
+        kernel text default 'bisquare', geom_col text default 'the_geom',
+        id_col text default 'cartodb_id')
+RETURNS table(coeffs JSON, stand_errs JSON, t_vals JSON,
+              filtered_t_vals JSON, predicted numeric,
+              residuals numeric, r_squared numeric, bandwidth numeric,
+              rowid bigint)
+AS $$
+
+from crankshaft.regression import GWR
+
+gwr = GWR()
+
+return gwr.gwr(subquery, dep_var, ind_vars, bw, fixed, kernel, geom_col, id_col)
+
+$$ LANGUAGE plpythonu;
+
+
+CREATE OR REPLACE FUNCTION
+CDB_GWR_Predict(subquery text, dep_var text, ind_vars text[],
+                bw numeric default null, fixed boolean default False,
+                kernel text default 'bisquare',
+                geom_col text default 'the_geom',
+                id_col text default 'cartodb_id')
+RETURNS table(coeffs JSON, stand_errs JSON, t_vals JSON,
+              r_squared numeric, predicted numeric, rowid bigint)
+AS $$
+
+from crankshaft.regression import GWR
+gwr = GWR()
+
+return gwr.gwr_predict(subquery, dep_var, ind_vars, bw, fixed, kernel, geom_col, id_col)
+
+$$ LANGUAGE plpythonu;

src/pg/test/expected/01_install_test.out

@@ -1,6 +1,6 @@
 -- Install dependencies
 CREATE EXTENSION plpythonu;
-CREATE EXTENSION postgis VERSION '2.2.2';
+CREATE EXTENSION postgis;
 -- Create role publicuser if it does not exist
 DO
 $$

src/pg/test/expected/21_gwr_test.out

@@ -0,0 +1,12 @@
+-- test of Geographically Weighted Regression (GWR)
+SET client_min_messages TO WARNING;
+\set ECHO none
+rowid|coeff_pctrural|std_errs_pctrural|t_vals_pctrural|predicted|residuals|r_squared|bandwidth
+13001|-0.0852|0.0220|-3.8678|8.8071|-0.6071|0.5218|90.0
+13027|-0.0719|0.0221|-3.2506|9.9673|-0.8673|0.5443|90.0
+13027|-0.0719|0.0221|-3.2506|9.9673|-0.8673|0.5443|90.0
+13039|-0.0959|0.0241|-3.9755|13.4802|0.0198|0.6269|90.0
+13231|-0.1383|0.0181|-7.6634|8.5520|0.7480|0.6337|90.0
+13293|-0.1207|0.0184|-6.5553|12.9930|-3.9930|0.6446|90.0
+13321|-0.0720|0.0204|-3.5337|8.2738|-1.9738|0.5573|90.0
+(7 rows)

src/pg/test/fixtures/gwr_georgia.sql

@@ -0,0 +1,199 @@
+SET client_min_messages TO WARNING;
+\set ECHO none
+--
+-- PostgreSQL database dump
+--
+-- Data from:
+--  https://github.com/TaylorOshan/pysal/blob/1d6af33bda46b1d623f70912c56155064463383f/pysal/examples/georgia/GData_utm.csv
+
+CREATE TABLE g_utm_testing (
+  cartodb_id bigint,
+  the_geom geometry(Geometry, 2239),
+  pctblack numeric,
+  pctpov numeric,
+  pctbach numeric,
+  pctrural numeric,
+  x numeric,
+  y numeric,
+  areakey int
+);
+
+COPY g_utm_testing (cartodb_id, the_geom, pctblack, pctpov, pctbach, pctrural, x, y, areakey) FROM stdin;
+122	0101000020BF080000CDCCCCCC2AEB2A410000000043F74A41	34.4500000000000028	27.3000000000000007	8.59999999999999964	72.5999999999999943	882069.400000000023	3534470	13271
+9	0101000020BF0800009A999999786823410000000080684D41	0.349999999999999978	14.5999999999999996	8	96.5	635964.300000000047	3854592	13083
+30	0101000020BF0800009A9999990ACF294100000080E5174D41	9.89000000000000057	16.5	9.5	87.2000000000000028	845701.300000000047	3813323	13119
+121	0101000020BF08000000000000B67D2F4100000080AA6F4B41	14.0299999999999994	12.6999999999999993	7.59999999999999964	89.0999999999999943	1031899	3596117	13103
+139	0101000020BF080000CDCCCCCCFF4B2B410000008038A54A41	25.4600000000000009	22.5	11.0999999999999996	64.5999999999999943	894463.900000000023	3492465	13069
+78	0101000020BF08000066666666FB632741000000802BF44B41	34.0300000000000011	16.3000000000000007	10	63.6000000000000014	766461.699999999953	3663959	13171
+103	0101000020BF0800009A999999BEB62741000000809A594B41	58.7199999999999989	29.1999999999999993	10.0999999999999996	65.5999999999999943	777055.300000000047	3584821	13193
+104	0101000020BF0800009A999999E52C27410000008039874B41	43.2100000000000009	29.5	7.09999999999999964	100	759410.800000000047	3608179	13269
+160	0101000020BF0800009A999999A3DC2541000000004D544A41	27.4800000000000004	22.1000000000000014	8.19999999999999929	100	716369.800000000047	3451034	13201
+99	0101000020BF08000033333333DCC3294100000080D56E4B41	22.3599999999999994	18.3000000000000007	10.3000000000000007	57.8999999999999986	844270.099999999977	3595691	13023
+16	0101000020BF08000033333333C4532741000000004B164D41	0.28999999999999998	12.8000000000000007	8.59999999999999964	100	764386.099999999977	3812502	13085
+34	0101000020BF080000CDCCCCCCFE622441000000000FB94C41	14.3000000000000007	16.3000000000000007	6.79999999999999982	66.5	668031.400000000023	3764766	13233
+37	0101000020BF08000066666666423825410000008006AC4C41	3.93999999999999995	8.80000000000000071	7.59999999999999964	93.7000000000000028	695329.199999999953	3758093	13223
+47	0101000020BF08000066666666B195254100000080D0774C41	7.62999999999999989	6.59999999999999964	12	26.6999999999999993	707288.699999999953	3731361	13097
+108	0101000020BF080000333333339AFA264100000000173D4B41	34.0900000000000034	19.8999999999999986	8	100	752973.099999999977	3570222	13249
+75	0101000020BF0800009A9999992EEC29410000008048F74B41	42.3900000000000006	17.5	13.3000000000000007	42.7000000000000028	849431.300000000047	3665553	13009
+83	0101000020BF08000066666666C39D2D4100000080E3C54B41	41.509999999999998	27.8000000000000007	7.70000000000000018	53.7999999999999972	970465.699999999953	3640263	13165
+91	0101000020BF080000333333339939254100000000ABA74B41	25.4899999999999984	13.6999999999999993	13.5999999999999996	95.7999999999999972	695500.599999999977	3624790	13145
+131	0101000020BF080000CDCCCCCC2AEB2A410000000043F74A41	34.4500000000000028	27.3000000000000007	8.59999999999999964	72.5999999999999943	882069.400000000023	3534470	13271
+136	0101000020BF0800009A99999959562A4100000000D8DB4A41	31.3299999999999983	22	7.59999999999999964	47	863020.800000000047	3520432	13017
+140	0101000020BF080000CDCCCCCCFF4B2B410000008038A54A41	25.4600000000000009	22.5	11.0999999999999996	64.5999999999999943	894463.900000000023	3492465	13069
+45	0101000020BF080000333333338034294100000000B35D4C41	34.740000000000002	15	11	73	825920.099999999977	3717990	13211
+58	0101000020BF080000000000003EBC27410000008062744C41	8.02999999999999936	6.20000000000000018	18.1000000000000014	59.2000000000000028	777759	3729605	13247
+66	0101000020BF0800009A999999401F2D410000000063364C41	41.9600000000000009	18.1999999999999993	17.3000000000000007	9.90000000000000036	954272.300000000047	3697862	13245
+67	0101000020BF080000CDCCCCCCA09B244100000080601E4C41	13.3800000000000008	19.1000000000000014	5.70000000000000018	100	675280.400000000023	3685569	13149
+74	0101000020BF080000CDCCCCCC6387254100000000842F4C41	22.5899999999999999	11.4000000000000004	13.3000000000000007	76.0999999999999943	705457.900000000023	3694344	13077
+112	0101000020BF0800009A999999C7662D410000008033294B41	29.1900000000000013	21.8999999999999986	6.5	79.2999999999999972	963427.800000000047	3560039	13267
+117	0101000020BF0800009A9999993E6A2841000000004E314B41	48.9799999999999969	32.8999999999999986	9.5	72.5999999999999943	800031.300000000047	3564188	13093
+127	0101000020BF080000CDCCCCCC209628410000008067FC4A41	40.6599999999999966	29	10	48.3999999999999986	805648.400000000023	3537103	13081
+166	0101000020BF08000066666666E4462841000000800E1B4A41	37.9299999999999997	22.6000000000000014	13.4000000000000004	55.2000000000000028	795506.199999999953	3421725	13275
+48	0101000020BF080000000000003EBC27410000008062744C41	8.02999999999999936	6.20000000000000018	18.1000000000000014	59.2000000000000028	777759	3729605	13247
+3	0101000020BF08000033333333A0B6274100000080AD704D41	0.100000000000000006	18.3000000000000007	10.0999999999999996	100	777040.099999999977	3858779	13291
+1	0101000020BF080000000000008B2A294100000080727C4D41	0.349999999999999978	13.5999999999999996	11.5999999999999996	100	824645.5	3864805	13241
+2	0101000020BF080000666666663B5A284100000000C08B4D41	0	14	11.4000000000000004	100	797981.699999999953	3872640	13281
+4	0101000020BF0800009A9999996F8F264100000000F67F4D41	0.0299999999999999989	17.1999999999999993	7.79999999999999982	100	739255.800000000047	3866604	13111
+18	0101000020BF08000033333333C5ED234100000000C0184D41	8.60999999999999943	14.5999999999999996	5.90000000000000036	77.4000000000000057	653026.599999999977	3813760	13055
+6	0101000020BF080000CDCCCCCC56F6244100000000D5694D41	4.05999999999999961	11.0999999999999996	12	70	686891.400000000023	3855274	13313
+5	0101000020BF0800009A999999F499254100000000B6674D41	0.260000000000000009	11.3000000000000007	5.5	89	707834.300000000047	3854188	13213
+7	0101000020BF080000CDCCCCCCCF7224410000000097774D41	0.910000000000000031	12	8.09999999999999964	43.6000000000000014	670055.900000000023	3862318	13047
+8	0101000020BF080000CDCCCCCC6C1B2441000000803B504D41	3.72999999999999998	12.8000000000000007	8.40000000000000036	44.7999999999999972	658870.400000000023	3842167	13295
+10	0101000020BF0800009A9999993C5C26410000008064554D41	0.260000000000000009	16.6000000000000014	8.59999999999999964	100	732702.300000000047	3844809	13123
+11	0101000020BF08000033333333CAFD284100000080DD4B4D41	5.41999999999999993	11.5999999999999996	12	88.5	818917.099999999977	3839931	13137
+12	0101000020BF08000033333333D3512841000000001F4E4D41	2.58999999999999986	12.5	13.5999999999999996	100	796905.599999999977	3841086	13311
+13	0101000020BF08000000000000F093274100000080363D4D41	1.40999999999999992	15.3000000000000007	11.0999999999999996	78.5999999999999943	772600	3832429	13187
+14	0101000020BF080000CDCCCCCCCBB2294100000080C1324D41	11.8100000000000005	17	13.0999999999999996	64.5	842085.900000000023	3827075	13257
+15	0101000020BF080000333333333A382541000000801B294D41	3.7799999999999998	11.0999999999999996	9.19999999999999929	79.7000000000000028	695325.099999999977	3822135	13129
+17	0101000020BF080000CDCCCCCCE235244100000000B0E94C41	13.5600000000000005	13.5999999999999996	13.6999999999999993	36.1000000000000014	662257.400000000023	3789664	13115
+19	0101000020BF0800009A9999990ACF294100000080E5174D41	9.89000000000000057	16.5	9.5	87.2000000000000028	845701.300000000047	3813323	13119
+20	0101000020BF080000666666662D65264100000000EE164D41	1.47999999999999998	12.8000000000000007	9	100	733846.699999999953	3812828	13227
+21	0101000020BF080000CDCCCCCCBB922A4100000080FF114D41	20.4100000000000001	14.1999999999999993	9.09999999999999964	73.7999999999999972	870749.900000000023	3810303	13147
+25	0101000020BF08000000000000075525410000000000F14C41	9.21000000000000085	10.6999999999999993	9	75.2000000000000028	699011.5	3793408	13015
+22	0101000020BF08000000000000673E28410000000068044D41	8.48000000000000043	10.5999999999999996	15.4000000000000004	81.0999999999999943	794419.5	3803344	13139
+23	0101000020BF0800009A999999EA00294100000000C00C4D41	3.49000000000000021	15.0999999999999996	6.40000000000000036	100	819317.300000000047	3807616	13011
+24	0101000020BF080000CDCCCCCC41682641000000005FF24C41	1.77000000000000002	6.09999999999999964	18.3999999999999986	57.7999999999999972	734240.900000000023	3794110	13057
+44	0101000020BF0800009A9999999B52244100000000E7894C41	6.46999999999999975	14.4000000000000004	7.5	67.7999999999999972	665933.800000000047	3740622	13143
+26	0101000020BF0800009A999999AA5B27410000008066E84C41	0	6.79999999999999982	15.5999999999999996	93.7000000000000028	765397.300000000047	3789005	13117
+27	0101000020BF080000666666666AD72A410000008068E14C41	29.9899999999999984	19.6999999999999993	8	70	879541.199999999953	3785425	13105
+28	0101000020BF0800003333333312E528410000008086DE4C41	9.58000000000000007	14.0999999999999996	9	78	815753.099999999977	3783949	13157
+29	0101000020BF0800009A999999FDE52941000000805EE14C41	8.32000000000000028	15.6999999999999993	9.69999999999999929	100	848638.800000000047	3785405	13195
+31	0101000020BF080000CDCCCCCC4064264100000000807B4C41	49.9200000000000017	18.3999999999999986	31.6000000000000014	4.20000000000000018	733728.400000000023	3733248	13121
+32	0101000020BF08000033333333359427410000000029B84C41	5.11000000000000032	4	29.6000000000000014	13.5999999999999996	772634.599999999977	3764306	13135
+33	0101000020BF0800003333333346872841000000808BC24C41	11.4399999999999995	14.6999999999999993	9.19999999999999929	64.5999999999999943	803747.099999999977	3769623	13013
+35	0101000020BF0800009A99999977582A410000008074A94C41	24.7399999999999984	16.1999999999999993	12.8000000000000007	100	863291.800000000047	3756777	13221
+36	0101000020BF0800009A9999994D1D26410000008041AA4C41	9.83999999999999986	5.59999999999999964	33	5.79999999999999982	724646.800000000047	3757187	13067
+38	0101000020BF08000033333333F9672941000000806CB54C41	26.2300000000000004	27	37.5	17.6000000000000014	832508.599999999977	3762905	13059
+46	0101000020BF08000033333333F5B624410000000071544C41	15.4600000000000009	14.4000000000000004	12	68.5	678778.599999999977	3713250	13045
+39	0101000020BF08000000000000B9322B4100000080C49B4C41	45.9399999999999977	22.6000000000000014	10.4000000000000004	59.6000000000000014	891228.5	3749769	13317
+40	0101000020BF08000000000000C90E2C410000000039A14C41	38.1899999999999977	17.8000000000000007	8.19999999999999929	100	919396.5	3752562	13181
+41	0101000020BF080000CDCCCCCC1F5A294100000080FB9D4C41	7.37000000000000011	7.90000000000000036	28.3999999999999986	95.2000000000000028	830735.900000000023	3750903	13219
+42	0101000020BF080000CDCCCCCC7F2B2741000000806A7F4C41	42.2299999999999969	9.90000000000000036	32.7000000000000028	2.5	759231.900000000023	3735253	13089
+43	0101000020BF0800009A999999006D284100000080F18D4C41	18.370000000000001	13.1999999999999993	9.40000000000000036	61.2000000000000028	800384.300000000047	3742691	13297
+49	0101000020BF0800009A999999321C2A41000000002D664C41	49.8900000000000006	25.1000000000000014	8.80000000000000071	75.7000000000000028	855577.300000000047	3722330	13133
+50	0101000020BF080000CDCCCCCC31FD2A4100000080BA5C4C41	61.3599999999999994	31.8999999999999986	5.59999999999999964	100	884376.900000000023	3717493	13265
+86	0101000020BF080000000000004E6B294100000000DEA54B41	45.9299999999999997	26	4.79999999999999982	100	832935	3623868	13289
+51	0101000020BF080000CDCCCCCC7FC32C4100000000BA654C41	10.9299999999999997	6.59999999999999964	23.8999999999999986	30.6000000000000014	942527.900000000023	3722100	13073
+87	0101000020BF08000066666666B4C72C4100000000AD974B41	32.5799999999999983	25.6999999999999993	9.09999999999999964	64.2000000000000028	943066.199999999953	3616602	13107
+52	0101000020BF080000CDCCCCCC5F352841000000001B614C41	22.3500000000000014	14.4000000000000004	9.5	76	793263.900000000023	3719734	13217
+53	0101000020BF080000CDCCCCCCC5012C4100000000885A4C41	36.3800000000000026	21.6000000000000014	10.4000000000000004	65.9000000000000057	917730.900000000023	3716368	13189
+55	0101000020BF080000CDCCCCCCC04C274100000000023A4C41	10.2400000000000002	6.09999999999999964	10.6999999999999993	76	763488.400000000023	3699716	13151
+54	0101000020BF080000333333338E8A2B4100000000533A4C41	60.2299999999999969	32.6000000000000014	4.20000000000000018	100	902471.099999999977	3699878	13301
+56	0101000020BF0800009A99999985C026410000000077514C41	23.8200000000000003	8.59999999999999964	14.6999999999999993	4.40000000000000036	745538.800000000047	3711726	13063
+57	0101000020BF0800009A999999401F2D410000000063364C41	41.9600000000000009	18.1999999999999993	17.3000000000000007	9.90000000000000036	954272.300000000047	3697862	13245
+59	0101000020BF080000666666669952264100000000C23B4C41	5.12999999999999989	2.60000000000000009	25.8000000000000007	53.8999999999999986	731468.699999999953	3700612	13113
+60	0101000020BF08000033333333A2A2284100000000FA304C41	34.7999999999999972	17.3999999999999986	10.8000000000000007	100	807249.099999999977	3695092	13159
+61	0101000020BF080000000000003EBC27410000008062744C41	8.02999999999999936	6.20000000000000018	18.1000000000000014	59.2000000000000028	777759	3729605	13247
+146	0101000020BF080000CDCCCCCCA951274100000080EFA84A41	50.1499999999999986	24.3999999999999986	17	10	764116.900000000023	3494367	13095
+142	0101000020BF0800009A9999994B1B2A41000000803AC04A41	30.5	27.1999999999999993	8.30000000000000071	63.3999999999999986	855461.800000000047	3506293	13155
+62	0101000020BF08000033333333D2A32941000000004B314C41	32.7899999999999991	16.3999999999999986	11.6999999999999993	66.5	840169.099999999977	3695254	13237
+63	0101000020BF080000CDCCCCCC6387254100000000842F4C41	22.5899999999999999	11.4000000000000004	13.3000000000000007	76.0999999999999943	705457.900000000023	3694344	13077
+64	0101000020BF0800009A9999995DA82A4100000080C2264C41	79.6400000000000006	30.1000000000000014	6.79999999999999982	100	873518.800000000047	3689861	13141
+65	0101000020BF08000066666666E0E02741000000004C1C4C41	35.4799999999999969	15.5999999999999996	7.20000000000000018	73.4000000000000057	782448.199999999953	3684504	13035
+68	0101000020BF0800009A999999C5B82B4100000000BC1E4C41	12.6899999999999995	16.8000000000000007	5.29999999999999982	100	908386.800000000047	3685752	13125
+69	0101000020BF0800003333333398332C410000000038FC4B41	55.9200000000000017	31.3000000000000007	6.20000000000000018	100	924108.099999999977	3668080	13163
+114	0101000020BF0800009A999999BFF52D4100000080393F4B41	33.8800000000000026	25.3999999999999986	8.59999999999999964	100	981727.800000000047	3571315	13109
+70	0101000020BF0800009A99999993A62D4100000000B1024C41	52.1899999999999977	30.3000000000000007	9.59999999999999964	72.2999999999999972	971593.800000000047	3671394	13033
+71	0101000020BF08000066666666C809274100000080521D4C41	29.0799999999999983	15.5999999999999996	11.0999999999999996	53.6000000000000014	754916.199999999953	3685029	13255
+72	0101000020BF0800009A999999E04D2B410000008023D64B41	51.8599999999999994	21.6000000000000014	9.80000000000000071	67.0999999999999943	894704.300000000047	3648583	13303
+73	0101000020BF0800009A999999BFD4254100000080F9EC4B41	44.6199999999999974	22.3999999999999986	6.70000000000000018	82.2999999999999972	715359.800000000047	3660275	13199
+76	0101000020BF0800009A999999F1D4244100000000EFEC4B41	30.0300000000000011	16.3000000000000007	13.5999999999999996	44	682616.800000000047	3660254	13285
+77	0101000020BF08000066666666851E284100000000A0ED4B41	31.7800000000000011	13.8000000000000007	12.9000000000000004	75.0999999999999943	790338.699999999953	3660608	13207
+79	0101000020BF08000033333333EE10294100000080B7EC4B41	25.6000000000000014	10.8000000000000007	12	81.9000000000000057	821367.099999999977	3660143	13169
+80	0101000020BF0800009A999999E9B52641000000804CF74B41	20.0399999999999991	13.4000000000000004	9.30000000000000071	100	744180.800000000047	3665561	13231
+92	0101000020BF080000CDCCCCCCBC002B4100000080DD754B41	33.3200000000000003	20.5	12	52.8999999999999986	884830.400000000023	3599291	13175
+89	0101000020BF08000066666666B9BA2B410000000039A74B41	33.8900000000000006	22.1999999999999993	4.90000000000000036	100	908636.699999999953	3624562	13167
+81	0101000020BF08000000000000F8A32E41000000001FC94B41	44.6899999999999977	22.8999999999999986	8.59999999999999964	79.2999999999999972	1004028	3641918	13251
+82	0101000020BF080000CDCCCCCC59352A410000008041C14B41	41.990000000000002	15.3000000000000007	8.80000000000000071	100	858796.900000000023	3637891	13319
+84	0101000020BF080000666666664AF4264100000000CCC34B41	27.7800000000000011	14.6999999999999993	9	65.2999999999999972	752165.199999999953	3639192	13293
+85	0101000020BF080000CDCCCCCC11B62841000000007ABE4B41	41.6799999999999997	19.1999999999999993	17	16.1000000000000014	809736.900000000023	3636468	13021
+88	0101000020BF080000CDCCCCCC13682641000000007DA44B41	62.3400000000000034	24.8999999999999986	7.09999999999999964	97.9000000000000057	734217.900000000023	3623162	13263
+90	0101000020BF0800000000000071E8274100000080D7A44B41	30.6600000000000001	14	5.70000000000000018	100	783416.5	3623343	13079
+98	0101000020BF080000CDCCCCCC516D2D4100000000FD744B41	30.9400000000000013	24.1000000000000014	9.90000000000000036	52.1000000000000014	964264.900000000023	3598842	13043
+95	0101000020BF080000CDCCCCCCDA5A28410000008001894B41	47.5300000000000011	24	15.1999999999999993	61.2999999999999972	798061.400000000023	3609091	13225
+93	0101000020BF0800009A999999E52C27410000008039874B41	43.2100000000000009	29.5	7.09999999999999964	100	759410.800000000047	3608179	13269
+120	0101000020BF0800009A99999931762541000000802C1B4B41	63.4600000000000009	31.3999999999999986	8	100	703256.800000000047	3552857	13259
+94	0101000020BF080000CDCCCCCCAE5C2E410000008036784B41	25.9499999999999993	27.5	19.8999999999999986	63.2000000000000028	994903.400000000023	3600493	13031
+96	0101000020BF080000CDCCCCCC4DD8284100000080CC644B41	21.8000000000000007	10.5999999999999996	16	20.8999999999999986	814118.900000000023	3590553	13153
+134	0101000020BF080000000000002F8327410000008050DB4A41	19.2199999999999989	12.5999999999999996	13.6999999999999993	78.2000000000000028	770455.5	3520161	13177
+97	0101000020BF08000000000000B67D2F4100000080AA6F4B41	14.0299999999999994	12.6999999999999993	7.59999999999999964	89.0999999999999943	1031899	3596117	13103
+100	0101000020BF080000666666664363254100000000CA734B41	37.9500000000000028	18.6000000000000014	16.6000000000000014	3.20000000000000018	700833.699999999953	3598228	13215
+101	0101000020BF080000333333334B0C2C4100000000D16D4B41	33.1000000000000014	27.1000000000000014	6.29999999999999982	53.2999999999999972	919077.599999999977	3595170	13283
+102	0101000020BF0800009A999999995D264100000080C4584B41	41.3200000000000003	28.1999999999999993	4.59999999999999964	100	732876.800000000047	3584393	13197
+105	0101000020BF080000666666667E83254100000000844B4B41	30.9400000000000013	10.4000000000000004	20.1999999999999993	13.6999999999999993	704959.199999999953	3577608	13053
+119	0101000020BF0800009A9999997053264100000000460B4B41	50.2000000000000028	22.5	5.5	100	731576.300000000047	3544716	13307
+106	0101000020BF0800009A999999BEB62741000000809A594B41	58.7199999999999989	29.1999999999999993	10.0999999999999996	65.5999999999999943	777055.300000000047	3584821	13193
+107	0101000020BF080000CDCCCCCC46422A4100000080863C4B41	27.6400000000000006	21.8000000000000007	8	70.7000000000000028	860451.400000000023	3569933	13091
+109	0101000020BF080000333333333772294100000080AB374B41	32.4600000000000009	24.3000000000000007	10.6999999999999993	56.5	833819.599999999977	3567447	13235
+110	0101000020BF080000CDCCCCCC6E1A2C4100000080AC394B41	28.2699999999999996	24.5	10.0999999999999996	98.5999999999999943	920887.400000000023	3568473	13209
+111	0101000020BF080000CDCCCCCC4CBD2C4100000000F1374B41	23.379999999999999	24	11.4000000000000004	35.7000000000000028	941734.400000000023	3567586	13279
+113	0101000020BF0800009A999999B06D2B4100000000BC2F4B41	30.0599999999999987	30.3000000000000007	8.59999999999999964	100	898776.300000000047	3563384	13309
+133	0101000020BF08000033333333EABD25410000008045DA4A41	58.1700000000000017	35.8999999999999986	6	53.5	712437.099999999977	3519627	13243
+115	0101000020BF080000000000007A2B304100000080C5224B41	38.0200000000000031	17.1999999999999993	18.6000000000000014	5.09999999999999964	1059706	3556747	13051
+145	0101000020BF080000CDCCCCCC04692C410000008061B94A41	15.4199999999999999	24.1000000000000014	6.59999999999999964	61.7000000000000028	930946.400000000023	3502787	13005
+116	0101000020BF0800000000000052392F4100000000531F4B41	14.8499999999999996	13.1999999999999993	11.8000000000000007	80.5999999999999943	1023145	3554982	13029
+118	0101000020BF08000033333333824C27410000000004194B41	46.5300000000000011	24.8000000000000007	15.9000000000000004	45.3999999999999986	763457.099999999977	3551752	13261
+123	0101000020BF08000000000000ACF72E4100000080A4FC4A41	39.1499999999999986	17.1999999999999993	13.4000000000000004	32.8999999999999986	1014742	3537225	13179
+124	0101000020BF080000CDCCCCCC2AEB2A410000000043F74A41	34.4500000000000028	27.3000000000000007	8.59999999999999964	72.5999999999999943	882069.400000000023	3534470	13271
+125	0101000020BF080000000000002F9729410000008039FF4A41	31.7600000000000016	28.6000000000000014	7.59999999999999964	100	838551.5	3538547	13315
+128	0101000020BF080000CDCCCCCC3BF22B41000000803AF04A41	15.3599999999999994	18.8000000000000007	8.30000000000000071	65.0999999999999943	915741.900000000023	3530869	13161
+126	0101000020BF0800000000000000A82E4100000000C5D64A41	21.75	23.6999999999999993	5.20000000000000018	100	1004544	3517834	13183
+132	0101000020BF0800009A999999EFAC26410000000026E04A41	59.8999999999999986	29.1000000000000014	9.19999999999999929	50.2999999999999972	743031.800000000047	3522636	13273
+129	0101000020BF08000033333333A9BA2C410000000072DE4A41	20.7600000000000016	19.8999999999999986	8.19999999999999929	75.5999999999999943	941396.599999999977	3521764	13001
+130	0101000020BF080000CDCCCCCC36F62441000000000EE34A41	49.9299999999999997	33	7.29999999999999982	100	686875.400000000023	3524124	13239
+135	0101000020BF0800009A999999A11D2E4100000080D9A84A41	19.4499999999999993	21.1999999999999993	9.59999999999999964	59.8999999999999986	986832.800000000047	3494323	13305
+137	0101000020BF080000333333338F0129410000008017D14A41	40.6599999999999966	31.3000000000000007	7.20000000000000018	44.5	819399.599999999977	3514927	13287
+138	0101000020BF08000033333333F471284100000000309B4A41	30.7100000000000009	26.1999999999999993	6.29999999999999982	71.0999999999999943	801018.099999999977	3487328	13321
+141	0101000020BF080000CDCCCCCCFF4B2B410000008038A54A41	25.4600000000000009	22.5	11.0999999999999996	64.5999999999999943	894463.900000000023	3492465	13069
+143	0101000020BF08000000000000C8722F4100000080FBB44A41	43.3400000000000034	22.3000000000000007	8.69999999999999929	100	1030500	3500535	13191
+144	0101000020BF080000CDCCCCCCE33B25410000008099AA4A41	60.759999999999998	35.7000000000000028	11.1999999999999993	100	695793.900000000023	3495219	13061
+147	0101000020BF080000666666660A1E26410000008096A54A41	58.8900000000000006	31.8000000000000007	10.0999999999999996	100	724741.199999999953	3492653	13037
+148	0101000020BF0800009A9999998461294100000080F19B4A41	26.6799999999999997	22.8999999999999986	14	51.1000000000000014	831682.300000000047	3487715	13277
+152	0101000020BF080000333333330E78254100000000C8734A41	44.0900000000000034	31.3999999999999986	9.40000000000000036	52.7999999999999972	703495.099999999977	3467152	13099
+149	0101000020BF0800009A9999997B192D4100000000DE904A41	11.6899999999999995	21.3000000000000007	6.29999999999999982	74.4000000000000057	953533.800000000047	3482044	13229
+150	0101000020BF08000000000000663B2F41000000806B7B4A41	25.5700000000000003	14.3000000000000007	19.8999999999999986	20.3000000000000007	1023411	3471063	13127
+151	0101000020BF08000033333333DBA22C4100000080C94B4A41	25.879999999999999	21.1000000000000014	10.4000000000000004	54.2000000000000028	938349.599999999977	3446675	13299
+153	0101000020BF0800009A999999173E2A410000008044724A41	11.6199999999999992	19.3000000000000007	7.5	66.2000000000000028	859915.800000000047	3466377	13019
+154	0101000020BF0800000000000082542B4100000000167D4A41	26.8599999999999994	26	6.40000000000000036	100	895553	3471916	13003
+155	0101000020BF080000333333336DBF264100000080A6824A41	51.6700000000000017	24.8000000000000007	9.40000000000000036	100	745398.599999999977	3474765	13007
+156	0101000020BF080000333333333D62274100000000F5594A41	47.9099999999999966	28.6999999999999993	7.79999999999999982	56.2000000000000028	766238.599999999977	3453930	13205
+157	0101000020BF080000CDCCCCCCB1E22D4100000080546D4A41	4.58000000000000007	18.1999999999999993	5.79999999999999982	100	979288.900000000023	3463849	13025
+162	0101000020BF080000333333331CB62B4100000000FA274A41	27.2899999999999991	26.3999999999999986	6.70000000000000018	58.6000000000000014	908046.099999999977	3428340	13065
+158	0101000020BF0800003333333310A129410000008057504A41	29.9400000000000013	22.3999999999999986	6.5	62	839816.099999999977	3449007	13075
+159	0101000020BF0800009A999999E7AD284100000000FD5D4A41	24.1600000000000001	22.8000000000000007	10	59.3999999999999986	808691.800000000047	3455994	13071
+163	0101000020BF0800009A99999998AA2A4100000080B63E4A41	26.5799999999999983	25.8999999999999986	5.40000000000000036	100	873804.300000000047	3439981	13173
+161	0101000020BF08000000000000C0C62E4100000080B63A4A41	20.1900000000000013	11.5	13.5	47.1000000000000014	1008480	3437933	13039
+172	0101000020BF080000000000005C43294100000000E31A4A41	41.4699999999999989	25.8999999999999986	9.09999999999999964	65.5999999999999943	827822	3421638	13027
+164	0101000020BF0800009A99999968602D4100000080A0304A41	27.0500000000000007	18.3000000000000007	6.40000000000000036	100	962612.300000000047	3432769	13049
+165	0101000020BF080000000000005C43294100000000E31A4A41	41.4699999999999989	25.8999999999999986	9.09999999999999964	65.5999999999999943	827822	3421638	13027
+167	0101000020BF0800003333333304592741000000803C1B4A41	31.5	22.3000000000000007	7.70000000000000018	55.3999999999999986	765058.099999999977	3421817	13131
+168	0101000020BF080000CDCCCCCCA85B264100000000341B4A41	39.4699999999999989	23.3000000000000007	11.6999999999999993	58	732628.400000000023	3421800	13087
+169	0101000020BF08000033333333DF7F254100000000991B4A41	32.740000000000002	29.1000000000000014	7.79999999999999982	69.4000000000000057	704495.599999999977	3422002	13253
+170	0101000020BF080000333333331A642A410000008058164A41	31.879999999999999	19.8999999999999986	16.3000000000000007	47.6000000000000014	864781.099999999977	3419313	13185
+171	0101000020BF080000000000001C5D2B4100000000DEF24941	11.4800000000000004	14.5999999999999996	4.70000000000000018	100	896654	3401148	13101
+\.
+
+
+--
+-- PostgreSQL database dump complete
+--

src/pg/test/sql/01_install_test.sql

@@ -1,6 +1,6 @@
 -- Install dependencies
 CREATE EXTENSION plpythonu;
-CREATE EXTENSION postgis VERSION '2.2.2';
+CREATE EXTENSION postgis;
 
 -- Create role publicuser if it does not exist
 DO

src/pg/test/sql/21_gwr_test.sql

@@ -0,0 +1,31 @@
+-- test of Geographically Weighted Regression (GWR)
+SET client_min_messages TO WARNING;
+\set ECHO none
+\pset format unaligned
+\i test/fixtures/gwr_georgia.sql
+
+SELECT 
+    rowid,
+    round((coeffs->>'pctrural')::numeric, 4) As coeff_pctrural,
+    round((stand_errs->>'pctrural')::numeric, 4) As std_errs_pctrural,
+    round((t_vals->>'pctrural')::numeric, 4) As t_vals_pctrural,
+    round(predicted, 4) As predicted,
+    round(residuals, 4) As residuals,
+    round(r_squared, 4) As r_squared,
+    bandwidth As bandwidth
+FROM
+cdb_crankshaft.CDB_GWR('SELECT * FROM g_utm_testing', 'pctbach', 
+        Array['pctrural', 'pctpov', 'pctblack']::text[],
+        90.0, 
+        False,
+        'bisquare', 
+        'the_geom',
+        'areakey')
+WHERE rowid in (13001, 13027, 13039, 13231, 13321, 13293)
+ORDER BY rowid ASC;
+
+
+-- comparison data from known calculated values in 
+--   https://github.com/TaylorOshan/pysal/blob/1d6af33bda46b1d623f70912c56155064463383f/pysal/examples/georgia/georgia_BS_NN_listwise.csv
+-- Note: values output from this analysis were correct with 1% of the values in that table, possibly due to projection differences.
+

src/py/Makefile

@@ -2,11 +2,11 @@ include ../../Makefile.global
 
 # Install the package locally for development
 install:
-	pip install --upgrade ./crankshaft
+	$(PIP) install --upgrade ./crankshaft
 
 # Test develpment install
 test:
-	nosetests crankshaft/test/
+	$(NOSETESTS) crankshaft/test/
 
 release: ../../release/$(EXTENSION).control $(SOURCES_DATA)
 	mkdir -p ../../release/python/$(EXTVERSION)
@@ -14,4 +14,4 @@ release: ../../release/$(EXTENSION).control $(SOURCES_DATA)
 	$(SED) -i -r 's/version='"'"'[0-9]+\.[0-9]+\.[0-9]+'"'"'/version='"'"'$(EXTVERSION)'"'"'/g'  ../../release/python/$(EXTVERSION)/$(PACKAGE)/setup.py
 
 deploy: 
-	pip install $(RUN_OPTIONS) --upgrade ../../release/python/$(RELEASE_VERSION)/$(PACKAGE)
+	$(PIP) install $(RUN_OPTIONS) --upgrade ../../release/python/$(RELEASE_VERSION)/$(PACKAGE)

src/py/crankshaft/crankshaft/__init__.py

@@ -3,4 +3,5 @@ import crankshaft.random_seeds
 import crankshaft.clustering
 import crankshaft.space_time_dynamics
 import crankshaft.segmentation
+import crankshaft.regression
 import analysis_data_provider

src/py/crankshaft/crankshaft/analysis_data_provider.py

@@ -65,3 +65,21 @@ class AnalysisDataProvider:
             return data
         except plpy.SPIError, err:
             plpy.error('Analysis failed: %s' % err)
+
+    def get_gwr(self, params):
+        """fetch data for gwr analysis"""
+        query = pu.gwr_query(params)
+        try:
+            query_result = plpy.execute(query)
+            return query_result
+        except plpy.SPIError, err:
+            plpy.error('Analysis failed: %s' % err)
+
+    def get_gwr_predict(self, params):
+        """fetch data for gwr predict"""
+        query = pu.gwr_predict_query(params)
+        try:
+            query_result = plpy.execute(query)
+            return query_result
+        except plpy.SPIError, err:
+            plpy.error('Analysis failed: %s' % err)

src/py/crankshaft/crankshaft/pysal_utils/pysal_utils.py

@@ -42,7 +42,7 @@ def get_weight(query_res, w_type='knn', num_ngbrs=5):
     return built_weight
 
 
-def query_attr_select(params):
+def query_attr_select(params, table_ref=True):
     """
         Create portion of SELECT statement for attributes inolved in query.
         Defaults to order in the params
@@ -58,11 +58,17 @@ def query_attr_select(params):
     """
 
     attr_string = ""
-    template = "i.\"%(col)s\"::numeric As attr%(alias_num)s, "
+    template = "\"%(col)s\"::numeric As attr%(alias_num)s, "
 
-    if 'time_cols' in params:
-        # if markov analysis
-        attrs = params['time_cols']
+    if table_ref:
+        template = "i." + template
+
+    if ('time_cols' in params) or ('ind_vars' in params):
+        # if markov or gwr analysis
+        attrs = (params['time_cols'] if 'time_cols' in params
+                 else params['ind_vars'])
+        if 'ind_vars' in params:
+            template = "array_agg(\"%(col)s\"::numeric) As attr%(alias_num)s, "
 
         for idx, val in enumerate(attrs):
             attr_string += template % {"col": val, "alias_num": idx + 1}
@@ -79,7 +85,7 @@ def query_attr_select(params):
     return attr_string
 
 
-def query_attr_where(params):
+def query_attr_where(params, table_ref=True):
     """
       Construct where conditions when building neighbors query
         Create portion of WHERE clauses for weeding out NULL-valued geometries
@@ -98,11 +104,14 @@ def query_attr_where(params):
           NULL AND idx_replace."time3" IS NOT NULL'
     """
     attr_string = []
-    template = "idx_replace.\"%s\" IS NOT NULL"
+    template = "\"%s\" IS NOT NULL"
+    if table_ref:
+        template = "idx_replace." + template
 
-    if 'time_cols' in params:
-        # markov where clauses
-        attrs = params['time_cols']
+    if ('time_cols' in params) or ('ind_vars' in params):
+        # markov or gwr where clauses
+        attrs = (params['time_cols'] if 'time_cols' in params
+                 else params['ind_vars'])
         # add values to template
         for attr in attrs:
             attr_string.append(template % attr)
@@ -132,8 +141,8 @@ def knn(params):
         @param vars: dict of values to fill template
     """
 
-    attr_select = query_attr_select(params)
-    attr_where = query_attr_where(params)
+    attr_select = query_attr_select(params, table_ref=True)
+    attr_where = query_attr_where(params, table_ref=True)
 
     replacements = {"attr_select": attr_select,
                     "attr_where_i": attr_where.replace("idx_replace", "i"),
@@ -187,6 +196,56 @@ def queen(params):
 
     return query.format(**params)
 
+
+def gwr_query(params):
+    """
+    GWR query
+    """
+
+    replacements = {"ind_vars_select": query_attr_select(params,
+                                                         table_ref=None),
+                    "ind_vars_where": query_attr_where(params,
+                                                       table_ref=None)}
+
+    query = '''
+      SELECT
+        array_agg(ST_X(ST_Centroid("{geom_col}"))) As x,
+        array_agg(ST_Y(ST_Centroid("{geom_col}"))) As y,
+        array_agg("{dep_var}") As dep_var,
+        %(ind_vars_select)s
+        array_agg("{id_col}") As rowid
+      FROM ({subquery}) As q
+      WHERE
+        "{dep_var}" IS NOT NULL AND
+        %(ind_vars_where)s
+        ''' % replacements
+
+    return query.format(**params).strip()
+
+
+def gwr_predict_query(params):
+    """
+    GWR query
+    """
+
+    replacements = {"ind_vars_select": query_attr_select(params,
+                                                         table_ref=None),
+                    "ind_vars_where": query_attr_where(params,
+                                                       table_ref=None)}
+
+    query = '''
+      SELECT
+        array_agg(ST_X(ST_Centroid({geom_col}))) As x,
+        array_agg(ST_Y(ST_Centroid({geom_col}))) As y,
+        array_agg({dep_var}) As dep_var,
+        %(ind_vars_select)s
+        array_agg({id_col}) As rowid
+      FROM ({subquery}) As q
+      WHERE
+        %(ind_vars_where)s
+        ''' % replacements
+
+    return query.format(**params).strip()
 # to add more weight methods open a ticket or pull request
 
 

src/py/crankshaft/crankshaft/regression/__init__.py

@@ -0,0 +1,3 @@
+from crankshaft.regression.gwr import *
+from crankshaft.regression.glm import *
+from crankshaft.regression.gwr_cs import *

src/py/crankshaft/crankshaft/regression/glm/GLM_validate_estimation.ipynb

@@ -0,0 +1,444 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#Import GLM and pysal\n",
+    "import os\n",
+    "import numpy as np\n",
+    "os.chdir('/Users/toshan/dev/pysal/pysal/contrib/glm')\n",
+    "from glm import GLM\n",
+    "import pysal\n",
+    "import pandas as pd\n",
+    "import statsmodels.formula.api as smf\n",
+    "import statsmodels.api as sm\n",
+    "from family import Gaussian, Binomial, Poisson, QuasiPoisson\n",
+    "\n",
+    "from statsmodels.api import families"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#Prepare some test data - columbus example\n",
+    "db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')\n",
+    "y = np.array(db.by_col(\"HOVAL\"))\n",
+    "y = np.reshape(y, (49,1))\n",
+    "X = []\n",
+    "#X.append(np.ones(len(y)))\n",
+    "X.append(db.by_col(\"INC\"))\n",
+    "X.append(db.by_col(\"CRIME\"))\n",
+    "X = np.array(X).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 46.42818268]\n",
+      " [  0.62898397]\n",
+      " [ -0.48488854]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#First fit pysal OLS model\n",
+    "from pysal.spreg import ols\n",
+    "OLS = ols.OLS(y, X)\n",
+    "print OLS.betas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'family.Gaussian'>\n",
+      "<class 'family.Gaussian'>\n",
+      "<class 'family.Gaussian'>\n",
+      "[ 46.42818268   0.62898397  -0.48488854]\n",
+      "[ 46.42818268   0.62898397  -0.48488854]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Then fit Gaussian GLM\n",
+    "\n",
+    "#create Gaussian GLM model object\n",
+    "model = GLM(y, X, Gaussian())\n",
+    "model\n",
+    "\n",
+    "#Fit model to estimate coefficients and return GLMResults object\n",
+    "results = model.fit()\n",
+    "\n",
+    "#Check coefficients - R betas [46.4282, 0.6290, -0.4849]\n",
+    "print results.params\n",
+    "\n",
+    "# Gaussian GLM results from statsmodels\n",
+    "sm_model = smf.GLM(y, sm.add_constant(X), family=families.Gaussian())\n",
+    "sm_results = sm_model.fit()\n",
+    "print sm_results.params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 2\n",
+      "<class 'family.Gaussian'>\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "<class 'family.Gaussian'>\n",
+      "<class 'family.Gaussian'>\n",
+      "<class 'family.Gaussian'>\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print results.df_model, sm_results.df_model\n",
+    "print np.allclose(results.aic, sm_results.aic)\n",
+    "print np.allclose(results.bic, sm_results.bic)\n",
+    "print np.allclose(results.deviance, sm_results.deviance)\n",
+    "print np.allclose(results.df_model, sm_results.df_model)\n",
+    "print np.allclose(results.df_resid, sm_results.df_resid)\n",
+    "print np.allclose(results.llf, sm_results.llf)\n",
+    "print np.allclose(results.mu, sm_results.mu)\n",
+    "print np.allclose(results.n, sm_results.nobs)\n",
+    "print np.allclose(results.null, sm_results.null)\n",
+    "print np.allclose(results.null_deviance, sm_results.null_deviance)\n",
+    "print np.allclose(results.params, sm_results.params)\n",
+    "print np.allclose(results.pearson_chi2, sm_results.pearson_chi2)\n",
+    "print np.allclose(results.resid_anscombe, sm_results.resid_anscombe)\n",
+    "print np.allclose(results.resid_deviance, sm_results.resid_deviance)\n",
+    "print np.allclose(results.resid_pearson, sm_results.resid_pearson)\n",
+    "print np.allclose(results.resid_response, sm_results.resid_response)\n",
+    "print np.allclose(results.resid_working, sm_results.resid_working)\n",
+    "print np.allclose(results.scale, sm_results.scale)\n",
+    "print np.allclose(results.normalized_cov_params, sm_results.normalized_cov_params)\n",
+    "print np.allclose(results.cov_params(), sm_results.cov_params())\n",
+    "print np.allclose(results.bse, sm_results.bse)\n",
+    "print np.allclose(results.conf_int(), sm_results.conf_int())\n",
+    "print np.allclose(results.pvalues, sm_results.pvalues)\n",
+    "print np.allclose(results.tvalues, sm_results.tvalues)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'family.Poisson'>\n",
+      "<class 'family.Poisson'>\n",
+      "<class 'family.Poisson'>\n",
+      "[ 3.92159085  0.01183491 -0.01371397]\n",
+      "[ 3.92159085  0.01183491 -0.01371397]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Now fit a Poisson GLM \n",
+    "\n",
+    "poisson_y = np.round(y).astype(int)\n",
+    "\n",
+    "#create Poisson GLM model object\n",
+    "model = GLM(poisson_y, X, Poisson())\n",
+    "model\n",
+    "\n",
+    "#Fit model to estimate coefficients and return GLMResults object\n",
+    "results = model.fit()\n",
+    "\n",
+    "#Check coefficients - R betas [3.91926, 0.01198, -0.01371]\n",
+    "print results.params.T\n",
+    "\n",
+    "# Poisson GLM results from statsmodels\n",
+    "sm_results = smf.GLM(poisson_y, sm.add_constant(X), family=families.Poisson()).fit()\n",
+    "print sm_results.params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'family.Poisson'>\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "<class 'family.Poisson'>\n",
+      "<class 'family.Poisson'>\n",
+      "<class 'family.Poisson'>\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "[ 0.13049161  0.00511599  0.00193769] [ 0.13049161  0.00511599  0.00193769]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print np.allclose(results.aic, sm_results.aic)\n",
+    "print np.allclose(results.bic, sm_results.bic)\n",
+    "print np.allclose(results.deviance, sm_results.deviance)\n",
+    "print np.allclose(results.df_model, sm_results.df_model)\n",
+    "print np.allclose(results.df_resid, sm_results.df_resid)\n",
+    "print np.allclose(results.llf, sm_results.llf)\n",
+    "print np.allclose(results.mu, sm_results.mu)\n",
+    "print np.allclose(results.n, sm_results.nobs)\n",
+    "print np.allclose(results.null, sm_results.null)\n",
+    "print np.allclose(results.null_deviance, sm_results.null_deviance)\n",
+    "print np.allclose(results.params, sm_results.params)\n",
+    "print np.allclose(results.pearson_chi2, sm_results.pearson_chi2)\n",
+    "print np.allclose(results.resid_anscombe, sm_results.resid_anscombe)\n",
+    "print np.allclose(results.resid_deviance, sm_results.resid_deviance)\n",
+    "print np.allclose(results.resid_pearson, sm_results.resid_pearson)\n",
+    "print np.allclose(results.resid_response, sm_results.resid_response)\n",
+    "print np.allclose(results.resid_working, sm_results.resid_working)\n",
+    "print np.allclose(results.scale, sm_results.scale)\n",
+    "print np.allclose(results.normalized_cov_params, sm_results.normalized_cov_params)\n",
+    "print np.allclose(results.cov_params(), sm_results.cov_params())\n",
+    "print np.allclose(results.bse, sm_results.bse)\n",
+    "print np.allclose(results.conf_int(), sm_results.conf_int())\n",
+    "print np.allclose(results.pvalues, sm_results.pvalues)\n",
+    "print np.allclose(results.tvalues, sm_results.tvalues)\n",
+    "print results.bse, sm_results.bse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-5.33638276  0.0287754 ]\n",
+      "[-5.33638276  0.0287754 ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Now fit a binomial GLM\n",
+    "londonhp = pd.read_csv('/Users/toshan/projects/londonhp.csv')\n",
+    "#londonhp = pd.read_csv('/Users/qszhao/Dropbox/pysal/pysal/contrib/gwr/londonhp.csv')\n",
+    "y = londonhp['BATH2'].values\n",
+    "y = np.reshape(y, (316,1))\n",
+    "X = londonhp['FLOORSZ'].values\n",
+    "X = np.reshape(X, (316,1))\n",
+    "\n",
+    "#create logistic GLM model object\n",
+    "model = GLM(y, X, Binomial())\n",
+    "model\n",
+    "\n",
+    "#Fit model to estimate coefficients and return GLMResults object\n",
+    "results = model.fit()\n",
+    "\n",
+    "#Check coefficients - R betas [-5.33638, 0.02878]\n",
+    "print results.params.T\n",
+    "\n",
+    "# Logistic GLM results from statsmodels\n",
+    "sm_results = smf.GLM(y, sm.add_constant(X), family=families.Binomial()).fit()\n",
+    "print sm_results.params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 1\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print results.df_model, sm_results.df_model\n",
+    "print np.allclose(results.aic, sm_results.aic)\n",
+    "print np.allclose(results.bic, sm_results.bic)\n",
+    "print np.allclose(results.deviance, sm_results.deviance)\n",
+    "print np.allclose(results.df_model, sm_results.df_model)\n",
+    "print np.allclose(results.df_resid, sm_results.df_resid)\n",
+    "print np.allclose(results.llf, sm_results.llf)\n",
+    "print np.allclose(results.mu, sm_results.mu)\n",
+    "print np.allclose(results.n, sm_results.nobs)\n",
+    "print np.allclose(results.null, sm_results.null)\n",
+    "print np.allclose(results.null_deviance, sm_results.null_deviance)\n",
+    "print np.allclose(results.params, sm_results.params)\n",
+    "print np.allclose(results.pearson_chi2, sm_results.pearson_chi2)\n",
+    "print np.allclose(results.resid_anscombe, sm_results.resid_anscombe)\n",
+    "print np.allclose(results.resid_deviance, sm_results.resid_deviance)\n",
+    "print np.allclose(results.resid_pearson, sm_results.resid_pearson)\n",
+    "print np.allclose(results.resid_response, sm_results.resid_response)\n",
+    "print np.allclose(results.resid_working, sm_results.resid_working)\n",
+    "print np.allclose(results.scale, sm_results.scale)\n",
+    "print np.allclose(results.normalized_cov_params, sm_results.normalized_cov_params)\n",
+    "print np.allclose(results.cov_params(), sm_results.cov_params())\n",
+    "print np.allclose(results.bse, sm_results.bse)\n",
+    "print np.allclose(results.conf_int(), sm_results.conf_int())\n",
+    "print np.allclose(results.pvalues, sm_results.pvalues)\n",
+    "print np.allclose(results.tvalues, sm_results.tvalues)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'family.QuasiPoisson'>\n",
+      "<class 'family.QuasiPoisson'>\n",
+      "<class 'family.QuasiPoisson'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "#create QUasiPoisson GLM model object\n",
+    "model = GLM(poisson_y, X, QuasiPoisson())\n",
+    "model\n",
+    "\n",
+    "#Fit model to estimate coefficients and return GLMResults object\n",
+    "results = model.fit()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

src/py/crankshaft/crankshaft/regression/glm/__init__.py

@@ -0,0 +1,4 @@
+import glm
+import family
+import utils
+import iwls

src/py/crankshaft/crankshaft/regression/glm/base.py

@@ -0,0 +1,959 @@
+
+from __future__ import print_function
+import numpy as np
+from scipy import stats
+from utils import cache_readonly
+
+class Results(object):
+    """
+    Class to contain model results
+    Parameters
+    ----------
+    model : class instance
+        the previously specified model instance
+    params : array
+        parameter estimates from the fit model
+    """
+    def __init__(self, model, params, **kwd):
+        self.__dict__.update(kwd)
+        self.initialize(model, params, **kwd)
+        self._data_attr = []
+
+    def initialize(self, model, params, **kwd):
+        self.params = params
+        self.model = model
+        if hasattr(model, 'k_constant'):
+            self.k_constant = model.k_constant
+
+    def predict(self, exog=None, transform=True, *args, **kwargs):
+        """
+        Call self.model.predict with self.params as the first argument.
+        Parameters
+        ----------
+        exog : array-like, optional
+            The values for which you want to predict.
+        transform : bool, optional
+            If the model was fit via a formula, do you want to pass
+            exog through the formula. Default is True. E.g., if you fit
+            a model y ~ log(x1) + log(x2), and transform is True, then
+            you can pass a data structure that contains x1 and x2 in
+            their original form. Otherwise, you'd need to log the data
+            first.
+        args, kwargs :
+            Some models can take additional arguments or keywords, see the
+            predict method of the model for the details.
+        Returns
+        -------
+        prediction : ndarray or pandas.Series
+            See self.model.predict
+        """
+        if transform and hasattr(self.model, 'formula') and exog is not None:
+            from patsy import dmatrix
+            exog = dmatrix(self.model.data.design_info.builder,
+                           exog)
+
+        if exog is not None:
+            exog = np.asarray(exog)
+            if exog.ndim == 1 and (self.model.exog.ndim == 1 or
+                                   self.model.exog.shape[1] == 1):
+                exog = exog[:, None]
+            exog = np.atleast_2d(exog)  # needed in count model shape[1]
+
+        return self.model.predict(self.params, exog, *args, **kwargs)
+
+
+#TODO: public method?
+class LikelihoodModelResults(Results):
+    """
+    Class to contain results from likelihood models
+    Parameters
+    -----------
+    model : LikelihoodModel instance or subclass instance
+        LikelihoodModelResults holds a reference to the model that is fit.
+    params : 1d array_like
+        parameter estimates from estimated model
+    normalized_cov_params : 2d array
+       Normalized (before scaling) covariance of params. (dot(X.T,X))**-1
+    scale : float
+        For (some subset of models) scale will typically be the
+        mean square error from the estimated model (sigma^2)
+    Returns
+    -------
+    **Attributes**
+    mle_retvals : dict
+        Contains the values returned from the chosen optimization method if
+        full_output is True during the fit.  Available only if the model
+        is fit by maximum likelihood.  See notes below for the output from
+        the different methods.
+    mle_settings : dict
+        Contains the arguments passed to the chosen optimization method.
+        Available if the model is fit by maximum likelihood.  See
+        LikelihoodModel.fit for more information.
+    model : model instance
+        LikelihoodResults contains a reference to the model that is fit.
+    params : ndarray
+        The parameters estimated for the model.
+    scale : float
+        The scaling factor of the model given during instantiation.
+    tvalues : array
+        The t-values of the standard errors.
+    Notes
+    -----
+    The covariance of params is given by scale times normalized_cov_params.
+    Return values by solver if full_output is True during fit:
+        'newton'
+            fopt : float
+                The value of the (negative) loglikelihood at its
+                minimum.
+            iterations : int
+                Number of iterations performed.
+            score : ndarray
+                The score vector at the optimum.
+            Hessian : ndarray
+                The Hessian at the optimum.
+            warnflag : int
+                1 if maxiter is exceeded. 0 if successful convergence.
+            converged : bool
+                True: converged. False: did not converge.
+            allvecs : list
+                List of solutions at each iteration.
+        'nm'
+            fopt : float
+                The value of the (negative) loglikelihood at its
+                minimum.
+            iterations : int
+                Number of iterations performed.
+            warnflag : int
+                1: Maximum number of function evaluations made.
+                2: Maximum number of iterations reached.
+            converged : bool
+                True: converged. False: did not converge.
+            allvecs : list
+                List of solutions at each iteration.
+        'bfgs'
+            fopt : float
+                Value of the (negative) loglikelihood at its minimum.
+            gopt : float
+                Value of gradient at minimum, which should be near 0.
+            Hinv : ndarray
+                value of the inverse Hessian matrix at minimum.  Note
+                that this is just an approximation and will often be
+                different from the value of the analytic Hessian.
+            fcalls : int
+                Number of calls to loglike.
+            gcalls : int
+                Number of calls to gradient/score.
+            warnflag : int
+                1: Maximum number of iterations exceeded. 2: Gradient
+                and/or function calls are not changing.
+            converged : bool
+                True: converged.  False: did not converge.
+            allvecs : list
+                Results at each iteration.
+        'lbfgs'
+            fopt : float
+                Value of the (negative) loglikelihood at its minimum.
+            gopt : float
+                Value of gradient at minimum, which should be near 0.
+            fcalls : int
+                Number of calls to loglike.
+            warnflag : int
+                Warning flag:
+                - 0 if converged
+                - 1 if too many function evaluations or too many iterations
+                - 2 if stopped for another reason
+            converged : bool
+                True: converged.  False: did not converge.
+        'powell'
+            fopt : float
+                Value of the (negative) loglikelihood at its minimum.
+            direc : ndarray
+                Current direction set.
+            iterations : int
+                Number of iterations performed.
+            fcalls : int
+                Number of calls to loglike.
+            warnflag : int
+                1: Maximum number of function evaluations. 2: Maximum number
+                of iterations.
+            converged : bool
+                True : converged. False: did not converge.
+            allvecs : list
+                Results at each iteration.
+        'cg'
+            fopt : float
+                Value of the (negative) loglikelihood at its minimum.
+            fcalls : int
+                Number of calls to loglike.
+            gcalls : int
+                Number of calls to gradient/score.
+            warnflag : int
+                1: Maximum number of iterations exceeded. 2: Gradient and/
+                or function calls not changing.
+            converged : bool
+                True: converged. False: did not converge.
+            allvecs : list
+                Results at each iteration.
+        'ncg'
+            fopt : float
+                Value of the (negative) loglikelihood at its minimum.
+            fcalls : int
+                Number of calls to loglike.
+            gcalls : int
+                Number of calls to gradient/score.
+            hcalls : int
+                Number of calls to hessian.
+            warnflag : int
+                1: Maximum number of iterations exceeded.
+            converged : bool
+                True: converged. False: did not converge.
+            allvecs : list
+                Results at each iteration.
+        """
+
+    # by default we use normal distribution
+    # can be overwritten by instances or subclasses
+    use_t = False
+
+    def __init__(self, model, params, normalized_cov_params=None, scale=1.,
+                 **kwargs):
+        super(LikelihoodModelResults, self).__init__(model, params)
+        self.normalized_cov_params = normalized_cov_params
+        self.scale = scale
+
+        # robust covariance
+        # We put cov_type in kwargs so subclasses can decide in fit whether to
+        # use this generic implementation
+        if 'use_t' in kwargs:
+            use_t = kwargs['use_t']
+            if use_t is not None:
+                self.use_t = use_t
+        if 'cov_type' in kwargs:
+            cov_type = kwargs.get('cov_type', 'nonrobust')
+            cov_kwds = kwargs.get('cov_kwds', {})
+
+            if cov_type == 'nonrobust':
+                self.cov_type = 'nonrobust'
+                self.cov_kwds = {'description' : 'Standard Errors assume that the ' +
+                                 'covariance matrix of the errors is correctly ' +
+                                 'specified.'}
+            else:
+                from statsmodels.base.covtype import get_robustcov_results
+                if cov_kwds is None:
+                    cov_kwds = {}
+                use_t = self.use_t
+                # TODO: we shouldn't need use_t in get_robustcov_results
+                get_robustcov_results(self, cov_type=cov_type, use_self=True,
+                                           use_t=use_t, **cov_kwds)
+
+
+    def normalized_cov_params(self):
+        raise NotImplementedError
+
+
+    def _get_robustcov_results(self, cov_type='nonrobust', use_self=True,
+                                   use_t=None, **cov_kwds):
+        from statsmodels.base.covtype import get_robustcov_results
+        if cov_kwds is None:
+            cov_kwds = {}
+
+        if cov_type == 'nonrobust':
+            self.cov_type = 'nonrobust'
+            self.cov_kwds = {'description' : 'Standard Errors assume that the ' +
+                             'covariance matrix of the errors is correctly ' +
+                             'specified.'}
+        else:
+            # TODO: we shouldn't need use_t in get_robustcov_results
+            get_robustcov_results(self, cov_type=cov_type, use_self=True,
+                                       use_t=use_t, **cov_kwds)
+
+    @cache_readonly
+    def llf(self):
+        return self.model.loglike(self.params)
+
+    @cache_readonly
+    def bse(self):
+        return np.sqrt(np.diag(self.cov_params()))
+
+    @cache_readonly
+    def tvalues(self):
+        """
+        Return the t-statistic for a given parameter estimate.
+        """
+        return self.params / self.bse
+
+    @cache_readonly
+    def pvalues(self):
+        if self.use_t:
+            df_resid = getattr(self, 'df_resid_inference', self.df_resid)
+            return stats.t.sf(np.abs(self.tvalues), df_resid)*2
+        else:
+            return stats.norm.sf(np.abs(self.tvalues))*2
+
+
+    def cov_params(self, r_matrix=None, column=None, scale=None, cov_p=None,
+            other=None):
+        """
+        Returns the variance/covariance matrix.
+        The variance/covariance matrix can be of a linear contrast
+        of the estimates of params or all params multiplied by scale which
+        will usually be an estimate of sigma^2.  Scale is assumed to be
+        a scalar.
+        Parameters
+        ----------
+        r_matrix : array-like
+            Can be 1d, or 2d.  Can be used alone or with other.
+        column :  array-like, optional
+            Must be used on its own.  Can be 0d or 1d see below.
+        scale : float, optional
+            Can be specified or not.  Default is None, which means that
+            the scale argument is taken from the model.
+        other : array-like, optional
+            Can be used when r_matrix is specified.
+        Returns
+        -------
+        cov : ndarray
+            covariance matrix of the parameter estimates or of linear
+            combination of parameter estimates. See Notes.
+        Notes
+        -----
+        (The below are assumed to be in matrix notation.)
+        If no argument is specified returns the covariance matrix of a model
+        ``(scale)*(X.T X)^(-1)``
+        If contrast is specified it pre and post-multiplies as follows
+        ``(scale) * r_matrix (X.T X)^(-1) r_matrix.T``
+        If contrast and other are specified returns
+        ``(scale) * r_matrix (X.T X)^(-1) other.T``
+        If column is specified returns
+        ``(scale) * (X.T X)^(-1)[column,column]`` if column is 0d
+        OR
+        ``(scale) * (X.T X)^(-1)[column][:,column]`` if column is 1d
+        """
+        if (hasattr(self, 'mle_settings') and
+                self.mle_settings['optimizer'] in ['l1', 'l1_cvxopt_cp']):
+            dot_fun = nan_dot
+        else:
+            dot_fun = np.dot
+
+        if (cov_p is None and self.normalized_cov_params is None and
+            not hasattr(self, 'cov_params_default')):
+            raise ValueError('need covariance of parameters for computing '
+                             '(unnormalized) covariances')
+        if column is not None and (r_matrix is not None or other is not None):
+            raise ValueError('Column should be specified without other '
+                             'arguments.')
+        if other is not None and r_matrix is None:
+            raise ValueError('other can only be specified with r_matrix')
+
+        if cov_p is None:
+            if hasattr(self, 'cov_params_default'):
+                cov_p = self.cov_params_default
+            else:
+                if scale is None:
+                    scale = self.scale
+                cov_p = self.normalized_cov_params * scale
+
+        if column is not None:
+            column = np.asarray(column)
+            if column.shape == ():
+                return cov_p[column, column]
+            else:
+                #return cov_p[column][:, column]
+                return cov_p[column[:, None], column]
+        elif r_matrix is not None:
+            r_matrix = np.asarray(r_matrix)
+            if r_matrix.shape == ():
+                raise ValueError("r_matrix should be 1d or 2d")
+            if other is None:
+                other = r_matrix
+            else:
+                other = np.asarray(other)
+            tmp = dot_fun(r_matrix, dot_fun(cov_p, np.transpose(other)))
+            return tmp
+        else:  # if r_matrix is None and column is None:
+            return cov_p
+
+    #TODO: make sure this works as needed for GLMs
+    def t_test(self, r_matrix, cov_p=None, scale=None,
+               use_t=None):
+        """
+        Compute a t-test for a each linear hypothesis of the form Rb = q
+        Parameters
+        ----------
+        r_matrix : array-like, str, tuple
+            - array : If an array is given, a p x k 2d array or length k 1d
+              array specifying the linear restrictions. It is assumed
+              that the linear combination is equal to zero.
+            - str : The full hypotheses to test can be given as a string.
+              See the examples.
+            - tuple : A tuple of arrays in the form (R, q). If q is given,
+              can be either a scalar or a length p row vector.
+        cov_p : array-like, optional
+            An alternative estimate for the parameter covariance matrix.
+            If None is given, self.normalized_cov_params is used.
+        scale : float, optional
+            An optional `scale` to use.  Default is the scale specified
+            by the model fit.
+        use_t : bool, optional
+            If use_t is None, then the default of the model is used.
+            If use_t is True, then the p-values are based on the t
+            distribution.
+            If use_t is False, then the p-values are based on the normal
+            distribution.
+        Returns
+        -------
+        res : ContrastResults instance
+            The results for the test are attributes of this results instance.
+            The available results have the same elements as the parameter table
+            in `summary()`.
+        Examples
+        --------
+        >>> import numpy as np
+        >>> import statsmodels.api as sm
+        >>> data = sm.datasets.longley.load()
+        >>> data.exog = sm.add_constant(data.exog)
+        >>> results = sm.OLS(data.endog, data.exog).fit()
+        >>> r = np.zeros_like(results.params)
+        >>> r[5:] = [1,-1]
+        >>> print(r)
+        [ 0.  0.  0.  0.  0.  1. -1.]
+        r tests that the coefficients on the 5th and 6th independent
+        variable are the same.
+        >>> T_test = results.t_test(r)
+        >>> print(T_test)
+        <T contrast: effect=-1829.2025687192481, sd=455.39079425193762,
+        t=-4.0167754636411717, p=0.0015163772380899498, df_denom=9>
+        >>> T_test.effect
+        -1829.2025687192481
+        >>> T_test.sd
+        455.39079425193762
+        >>> T_test.tvalue
+        -4.0167754636411717
+        >>> T_test.pvalue
+        0.0015163772380899498
+        Alternatively, you can specify the hypothesis tests using a string
+        >>> from statsmodels.formula.api import ols
+        >>> dta = sm.datasets.longley.load_pandas().data
+        >>> formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR'
+        >>> results = ols(formula, dta).fit()
+        >>> hypotheses = 'GNPDEFL = GNP, UNEMP = 2, YEAR/1829 = 1'
+        >>> t_test = results.t_test(hypotheses)
+        >>> print(t_test)
+        See Also
+        ---------
+        tvalues : individual t statistics
+        f_test : for F tests
+        patsy.DesignInfo.linear_constraint
+        """
+        from patsy import DesignInfo
+        names = self.model.data.param_names
+        LC = DesignInfo(names).linear_constraint(r_matrix)
+        r_matrix, q_matrix = LC.coefs, LC.constants
+        num_ttests = r_matrix.shape[0]
+        num_params = r_matrix.shape[1]
+
+        if (cov_p is None and self.normalized_cov_params is None and
+            not hasattr(self, 'cov_params_default')):
+            raise ValueError('Need covariance of parameters for computing '
+                             'T statistics')
+        if num_params != self.params.shape[0]:
+            raise ValueError('r_matrix and params are not aligned')
+        if q_matrix is None:
+            q_matrix = np.zeros(num_ttests)
+        else:
+            q_matrix = np.asarray(q_matrix)
+            q_matrix = q_matrix.squeeze()
+        if q_matrix.size > 1:
+            if q_matrix.shape[0] != num_ttests:
+                raise ValueError("r_matrix and q_matrix must have the same "
+                                 "number of rows")
+
+        if use_t is None:
+            #switch to use_t false if undefined
+            use_t = (hasattr(self, 'use_t') and self.use_t)
+
+        _t = _sd = None
+
+        _effect = np.dot(r_matrix, self.params)
+        # nan_dot multiplies with the convention nan * 0 = 0
+
+        # Perform the test
+        if num_ttests > 1:
+            _sd = np.sqrt(np.diag(self.cov_params(
+                r_matrix=r_matrix, cov_p=cov_p)))
+        else:
+            _sd = np.sqrt(self.cov_params(r_matrix=r_matrix, cov_p=cov_p))
+        _t = (_effect - q_matrix) * recipr(_sd)
+
+        df_resid = getattr(self, 'df_resid_inference', self.df_resid)
+
+        if use_t:
+            return ContrastResults(effect=_effect, t=_t, sd=_sd,
+                                   df_denom=df_resid)
+        else:
+            return ContrastResults(effect=_effect, statistic=_t, sd=_sd,
+                                   df_denom=df_resid,
+                                   distribution='norm')
+
+    def f_test(self, r_matrix, cov_p=None, scale=1.0, invcov=None):
+        """
+        Compute the F-test for a joint linear hypothesis.
+        This is a special case of `wald_test` that always uses the F
+        distribution.
+        Parameters
+        ----------
+        r_matrix : array-like, str, or tuple
+            - array : An r x k array where r is the number of restrictions to
+              test and k is the number of regressors. It is assumed
+              that the linear combination is equal to zero.
+            - str : The full hypotheses to test can be given as a string.
+              See the examples.
+            - tuple : A tuple of arrays in the form (R, q), ``q`` can be
+              either a scalar or a length k row vector.
+        cov_p : array-like, optional
+            An alternative estimate for the parameter covariance matrix.
+            If None is given, self.normalized_cov_params is used.
+        scale : float, optional
+            Default is 1.0 for no scaling.
+        invcov : array-like, optional
+            A q x q array to specify an inverse covariance matrix based on a
+            restrictions matrix.
+        Returns
+        -------
+        res : ContrastResults instance
+            The results for the test are attributes of this results instance.
+        Examples
+        --------
+        >>> import numpy as np
+        >>> import statsmodels.api as sm
+        >>> data = sm.datasets.longley.load()
+        >>> data.exog = sm.add_constant(data.exog)
+        >>> results = sm.OLS(data.endog, data.exog).fit()
+        >>> A = np.identity(len(results.params))
+        >>> A = A[1:,:]
+        This tests that each coefficient is jointly statistically
+        significantly different from zero.
+        >>> print(results.f_test(A))
+        <F contrast: F=330.28533923463488, p=4.98403052872e-10,
+         df_denom=9, df_num=6>
+        Compare this to
+        >>> results.fvalue
+        330.2853392346658
+        >>> results.f_pvalue
+        4.98403096572e-10
+        >>> B = np.array(([0,0,1,-1,0,0,0],[0,0,0,0,0,1,-1]))
+        This tests that the coefficient on the 2nd and 3rd regressors are
+        equal and jointly that the coefficient on the 5th and 6th regressors
+        are equal.
+        >>> print(results.f_test(B))
+        <F contrast: F=9.740461873303655, p=0.00560528853174, df_denom=9,
+         df_num=2>
+        Alternatively, you can specify the hypothesis tests using a string
+        >>> from statsmodels.datasets import longley
+        >>> from statsmodels.formula.api import ols
+        >>> dta = longley.load_pandas().data
+        >>> formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR'
+        >>> results = ols(formula, dta).fit()
+        >>> hypotheses = '(GNPDEFL = GNP), (UNEMP = 2), (YEAR/1829 = 1)'
+        >>> f_test = results.f_test(hypotheses)
+        >>> print(f_test)
+        See Also
+        --------
+        statsmodels.stats.contrast.ContrastResults
+        wald_test
+        t_test
+        patsy.DesignInfo.linear_constraint
+        Notes
+        -----
+        The matrix `r_matrix` is assumed to be non-singular. More precisely,
+        r_matrix (pX pX.T) r_matrix.T
+        is assumed invertible. Here, pX is the generalized inverse of the
+        design matrix of the model. There can be problems in non-OLS models
+        where the rank of the covariance of the noise is not full.
+        """
+        res = self.wald_test(r_matrix, cov_p=cov_p, scale=scale,
+                             invcov=invcov, use_f=True)
+        return res
+
+    #TODO: untested for GLMs?
+    def wald_test(self, r_matrix, cov_p=None, scale=1.0, invcov=None,
+                  use_f=None):
+        """
+        Compute a Wald-test for a joint linear hypothesis.
+        Parameters
+        ----------
+        r_matrix : array-like, str, or tuple
+            - array : An r x k array where r is the number of restrictions to
+              test and k is the number of regressors. It is assumed that the
+              linear combination is equal to zero.
+            - str : The full hypotheses to test can be given as a string.
+              See the examples.
+            - tuple : A tuple of arrays in the form (R, q), ``q`` can be
+              either a scalar or a length p row vector.
+        cov_p : array-like, optional
+            An alternative estimate for the parameter covariance matrix.
+            If None is given, self.normalized_cov_params is used.
+        scale : float, optional
+            Default is 1.0 for no scaling.
+        invcov : array-like, optional
+            A q x q array to specify an inverse covariance matrix based on a
+            restrictions matrix.
+        use_f : bool
+            If True, then the F-distribution is used. If False, then the
+            asymptotic distribution, chisquare is used. If use_f is None, then
+            the F distribution is used if the model specifies that use_t is True.
+            The test statistic is proportionally adjusted for the distribution
+            by the number of constraints in the hypothesis.
+        Returns
+        -------
+        res : ContrastResults instance
+            The results for the test are attributes of this results instance.
+        See also
+        --------
+        statsmodels.stats.contrast.ContrastResults
+        f_test
+        t_test
+        patsy.DesignInfo.linear_constraint
+        Notes
+        -----
+        The matrix `r_matrix` is assumed to be non-singular. More precisely,
+        r_matrix (pX pX.T) r_matrix.T
+        is assumed invertible. Here, pX is the generalized inverse of the
+        design matrix of the model. There can be problems in non-OLS models
+        where the rank of the covariance of the noise is not full.
+        """
+        if use_f is None:
+            #switch to use_t false if undefined
+            use_f = (hasattr(self, 'use_t') and self.use_t)
+
+        from patsy import DesignInfo
+        names = self.model.data.param_names
+        LC = DesignInfo(names).linear_constraint(r_matrix)
+        r_matrix, q_matrix = LC.coefs, LC.constants
+
+        if (self.normalized_cov_params is None and cov_p is None and
+                invcov is None and not hasattr(self, 'cov_params_default')):
+            raise ValueError('need covariance of parameters for computing '
+                             'F statistics')
+
+        cparams = np.dot(r_matrix, self.params[:, None])
+        J = float(r_matrix.shape[0])  # number of restrictions
+        if q_matrix is None:
+            q_matrix = np.zeros(J)
+        else:
+            q_matrix = np.asarray(q_matrix)
+        if q_matrix.ndim == 1:
+            q_matrix = q_matrix[:, None]
+            if q_matrix.shape[0] != J:
+                raise ValueError("r_matrix and q_matrix must have the same "
+                                 "number of rows")
+        Rbq = cparams - q_matrix
+        if invcov is None:
+            cov_p = self.cov_params(r_matrix=r_matrix, cov_p=cov_p)
+            if np.isnan(cov_p).max():
+                raise ValueError("r_matrix performs f_test for using "
+                                 "dimensions that are asymptotically "
+                                 "non-normal")
+            invcov = np.linalg.inv(cov_p)
+
+        if (hasattr(self, 'mle_settings') and
+                self.mle_settings['optimizer'] in ['l1', 'l1_cvxopt_cp']):
+            F = nan_dot(nan_dot(Rbq.T, invcov), Rbq)
+        else:
+            F = np.dot(np.dot(Rbq.T, invcov), Rbq)
+
+        df_resid = getattr(self, 'df_resid_inference', self.df_resid)
+        if use_f:
+            F /= J
+            return ContrastResults(F=F, df_denom=df_resid,
+                                   df_num=invcov.shape[0])
+        else:
+            return ContrastResults(chi2=F, df_denom=J, statistic=F,
+                                   distribution='chi2', distargs=(J,))
+
+
+    def wald_test_terms(self, skip_single=False, extra_constraints=None,
+                   combine_terms=None):
+        """
+        Compute a sequence of Wald tests for terms over multiple columns
+        This computes joined Wald tests for the hypothesis that all
+        coefficients corresponding to a `term` are zero.
+        `Terms` are defined by the underlying formula or by string matching.
+        Parameters
+        ----------
+        skip_single : boolean
+            If true, then terms that consist only of a single column and,
+            therefore, refers only to a single parameter is skipped.
+            If false, then all terms are included.
+        extra_constraints : ndarray
+            not tested yet
+        combine_terms : None or list of strings
+            Each string in this list is matched to the name of the terms or
+            the name of the exogenous variables. All columns whose name
+            includes that string are combined in one joint test.
+        Returns
+        -------
+        test_result : result instance
+            The result instance contains `table` which is a pandas DataFrame
+            with the test results: test statistic, degrees of freedom and
+            pvalues.
+        Examples
+        --------
+        >>> res_ols = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)",
+                          data).fit()
+        >>> res_ols.wald_test_terms()
+        <class 'statsmodels.stats.contrast.WaldTestResults'>
+                                                  F                P>F  df constraint  df denom
+        Intercept                        279.754525  2.37985521351e-22              1        51
+        C(Duration, Sum)                   5.367071    0.0245738436636              1        51
+        C(Weight, Sum)                    12.432445  3.99943118767e-05              2        51
+        C(Duration, Sum):C(Weight, Sum)    0.176002      0.83912310946              2        51
+        >>> res_poi = Poisson.from_formula("Days ~ C(Weight) * C(Duration)",
+                                           data).fit(cov_type='HC0')
+        >>> wt = res_poi.wald_test_terms(skip_single=False,
+                                         combine_terms=['Duration', 'Weight'])
+        >>> print(wt)
+                                    chi2             P>chi2  df constraint
+        Intercept              15.695625  7.43960374424e-05              1
+        C(Weight)              16.132616  0.000313940174705              2
+        C(Duration)             1.009147     0.315107378931              1
+        C(Weight):C(Duration)   0.216694     0.897315972824              2
+        Duration               11.187849     0.010752286833              3
+        Weight                 30.263368  4.32586407145e-06              4
+        """
+        # lazy import
+        from collections import defaultdict
+
+        result = self
+        if extra_constraints is None:
+            extra_constraints = []
+        if combine_terms is None:
+            combine_terms = []
+        design_info = getattr(result.model.data.orig_exog, 'design_info', None)
+
+        if design_info is None and extra_constraints is None:
+            raise ValueError('no constraints, nothing to do')
+
+
+        identity = np.eye(len(result.params))
+        constraints = []
+        combined = defaultdict(list)
+        if design_info is not None:
+            for term in design_info.terms:
+                cols = design_info.slice(term)
+                name = term.name()
+                constraint_matrix = identity[cols]
+
+                # check if in combined
+                for cname in combine_terms:
+                    if cname in name:
+                        combined[cname].append(constraint_matrix)
+
+                k_constraint = constraint_matrix.shape[0]
+                if skip_single:
+                    if k_constraint == 1:
+                        continue
+
+                constraints.append((name, constraint_matrix))
+
+            combined_constraints = []
+            for cname in combine_terms:
+                combined_constraints.append((cname, np.vstack(combined[cname])))
+        else:
+            # check by exog/params names if there is no formula info
+            for col, name in enumerate(result.model.exog_names):
+                constraint_matrix = identity[col]
+
+                # check if in combined
+                for cname in combine_terms:
+                    if cname in name:
+                        combined[cname].append(constraint_matrix)
+
+                if skip_single:
+                    continue
+
+                constraints.append((name, constraint_matrix))
+
+            combined_constraints = []
+            for cname in combine_terms:
+                combined_constraints.append((cname, np.vstack(combined[cname])))
+
+        use_t = result.use_t
+        distribution = ['chi2', 'F'][use_t]
+
+        res_wald = []
+        index = []
+        for name, constraint in constraints + combined_constraints + extra_constraints:
+            wt = result.wald_test(constraint)
+            row = [wt.statistic.item(), wt.pvalue, constraint.shape[0]]
+            if use_t:
+                row.append(wt.df_denom)
+            res_wald.append(row)
+            index.append(name)
+
+        # distribution nerutral names
+        col_names = ['statistic', 'pvalue', 'df_constraint']
+        if use_t:
+            col_names.append('df_denom')
+        # TODO: maybe move DataFrame creation to results class
+        from pandas import DataFrame
+        table = DataFrame(res_wald, index=index, columns=col_names)
+        res = WaldTestResults(None, distribution, None, table=table)
+        # TODO: remove temp again, added for testing
+        res.temp = constraints + combined_constraints + extra_constraints
+        return res
+
+
+    def conf_int(self, alpha=.05, cols=None, method='default'):
+        """
+        Returns the confidence interval of the fitted parameters.
+        Parameters
+        ----------
+        alpha : float, optional
+            The significance level for the confidence interval.
+            ie., The default `alpha` = .05 returns a 95% confidence interval.
+        cols : array-like, optional
+            `cols` specifies which confidence intervals to return
+        method : string
+            Not Implemented Yet
+            Method to estimate the confidence_interval.
+            "Default" : uses self.bse which is based on inverse Hessian for MLE
+            "hjjh" :
+            "jac" :
+            "boot-bse"
+            "boot_quant"
+            "profile"
+        Returns
+        --------
+        conf_int : array
+            Each row contains [lower, upper] limits of the confidence interval
+            for the corresponding parameter. The first column contains all
+            lower, the second column contains all upper limits.
+        Examples
+        --------
+        >>> import statsmodels.api as sm
+        >>> data = sm.datasets.longley.load()
+        >>> data.exog = sm.add_constant(data.exog)
+        >>> results = sm.OLS(data.endog, data.exog).fit()
+        >>> results.conf_int()
+        array([[-5496529.48322745, -1467987.78596704],
+               [    -177.02903529,      207.15277984],
+               [      -0.1115811 ,        0.03994274],
+               [      -3.12506664,       -0.91539297],
+               [      -1.5179487 ,       -0.54850503],
+               [      -0.56251721,        0.460309  ],
+               [     798.7875153 ,     2859.51541392]])
+        >>> results.conf_int(cols=(2,3))
+        array([[-0.1115811 ,  0.03994274],
+               [-3.12506664, -0.91539297]])
+        Notes
+        -----
+        The confidence interval is based on the standard normal distribution.
+        Models wish to use a different distribution should overwrite this
+        method.
+        """
+        bse = self.bse
+
+        if self.use_t:
+            dist = stats.t
+            df_resid = getattr(self, 'df_resid_inference', self.df_resid)
+            q = dist.ppf(1 - alpha / 2, df_resid)
+        else:
+            dist = stats.norm
+            q = dist.ppf(1 - alpha / 2)
+
+        if cols is None:
+            lower = self.params - q * bse
+            upper = self.params + q * bse
+        else:
+            cols = np.asarray(cols)
+            lower = self.params[cols] - q * bse[cols]
+            upper = self.params[cols] + q * bse[cols]
+        return np.asarray(lzip(lower, upper))
+
+    def save(self, fname, remove_data=False):
+        '''
+        save a pickle of this instance
+        Parameters
+        ----------
+        fname : string or filehandle
+            fname can be a string to a file path or filename, or a filehandle.
+        remove_data : bool
+            If False (default), then the instance is pickled without changes.
+            If True, then all arrays with length nobs are set to None before
+            pickling. See the remove_data method.
+            In some cases not all arrays will be set to None.
+        Notes
+        -----
+        If remove_data is true and the model result does not implement a
+        remove_data method then this will raise an exception.
+        '''
+
+        from statsmodels.iolib.smpickle import save_pickle
+
+        if remove_data:
+            self.remove_data()
+
+        save_pickle(self, fname)
+
+    @classmethod
+    def load(cls, fname):
+        '''
+        load a pickle, (class method)
+        Parameters
+        ----------
+        fname : string or filehandle
+            fname can be a string to a file path or filename, or a filehandle.
+        Returns
+        -------
+        unpickled instance
+        '''
+
+        from statsmodels.iolib.smpickle import load_pickle
+        return load_pickle(fname)
+
+    def remove_data(self):
+        '''remove data arrays, all nobs arrays from result and model
+        This reduces the size of the instance, so it can be pickled with less
+        memory. Currently tested for use with predict from an unpickled
+        results and model instance.
+        .. warning:: Since data and some intermediate results have been removed
+           calculating new statistics that require them will raise exceptions.
+           The exception will occur the first time an attribute is accessed
+           that has been set to None.
+        Not fully tested for time series models, tsa, and might delete too much
+        for prediction or not all that would be possible.
+        The list of arrays to delete is maintained as an attribute of the
+        result and model instance, except for cached values. These lists could
+        be changed before calling remove_data.
+        '''
+        def wipe(obj, att):
+            #get to last element in attribute path
+            p = att.split('.')
+            att_ = p.pop(-1)
+            try:
+                obj_ = reduce(getattr, [obj] + p)
+
+                #print(repr(obj), repr(att))
+                #print(hasattr(obj_, att_))
+                if hasattr(obj_, att_):
+                    #print('removing3', att_)
+                    setattr(obj_, att_, None)
+            except AttributeError:
+                pass
+
+        model_attr = ['model.' + i for i in self.model._data_attr]
+        for att in self._data_attr + model_attr:
+            #print('removing', att)
+            wipe(self, att)
+
+        data_in_cache = getattr(self, 'data_in_cache', [])
+        data_in_cache += ['fittedvalues', 'resid', 'wresid']
+        for key in data_in_cache:
+            try:
+                self._cache[key] = None
+            except (AttributeError, KeyError):
+                pass
+
+def lzip(*args, **kwargs):
+    return list(zip(*args, **kwargs))

src/py/crankshaft/crankshaft/regression/glm/glm.py

@@ -0,0 +1,326 @@
+
+import numpy as np
+import numpy.linalg as la
+from pysal.spreg.utils import RegressionPropsY, spdot
+import pysal.spreg.user_output as USER
+from utils import cache_readonly
+from base import LikelihoodModelResults
+import family
+from iwls import iwls
+
+__all__ = ['GLM']
+
+class GLM(RegressionPropsY):
+    """
+    Generalised linear models. Can currently estimate Guassian, Poisson and
+    Logisitc regression coefficients. GLM object prepares model input and fit
+    method performs estimation which then returns a GLMResults object.
+
+    Parameters
+    ----------
+        y             : array
+                        n*1, dependent variable.
+        X             : array
+                        n*k, independent variable, exlcuding the constant.
+        family        : string
+                        Model type: 'Gaussian', 'Poisson', 'Binomial'
+
+    Attributes
+    ----------
+        y             : array
+                        n*1, dependent variable.
+        X             : array
+                        n*k, independent variable, including constant.
+        family        : string
+                        Model type: 'Gaussian', 'Poisson', 'logistic'
+        n             : integer
+                        Number of observations
+        k             : integer
+                        Number of independent variables
+        df_model      : float
+                        k-1, where k is the number of variables (including
+                        intercept)
+        df_residual   : float
+                        observations minus variables (n-k)
+        mean_y        : float
+                        Mean of y
+        std_y         : float
+                        Standard deviation of y
+        fit_params     : dict
+                        Parameters passed into fit method to define estimation
+                        routine.
+        normalized_cov_params   : array
+                                k*k, approximates [X.T*X]-1
+    """
+    def __init__(self, y, X, family=family.Gaussian(), constant=True):
+        """
+        Initialize class
+        """
+        self.n = USER.check_arrays(y, X)
+        USER.check_y(y, self.n)
+        self.y = y
+        if constant:
+            self.X = USER.check_constant(X)
+        else:
+            self.X = X
+        self.family = family
+        self.k = self.X.shape[1]
+        self.fit_params = {}
+
+    def fit(self, ini_betas=None, tol=1.0e-6, max_iter=200, solve='iwls'):
+        """
+        Method that fits a model with a particular estimation routine.
+
+        Parameters
+        ----------
+
+        ini_betas     : array
+                        k*1, initial coefficient values, including constant.
+                        Default is None, which calculates initial values during
+                        estimation.
+        tol:            float
+                        Tolerence for estimation convergence.
+        max_iter       : integer
+                        Maximum number of iterations if convergence not
+                        achieved.
+        solve         :string
+                       Technique to solve MLE equations.
+                       'iwls' = iteratively (re)weighted least squares (default)
+        """
+        self.fit_params['ini_betas'] = ini_betas
+        self.fit_params['tol'] = tol
+        self.fit_params['max_iter'] = max_iter
+        self.fit_params['solve']=solve
+        if solve.lower() == 'iwls':
+            params, predy, w, n_iter = iwls(self.y, self.X, self.family,
+                    ini_betas=ini_betas, tol=tol, max_iter=max_iter)
+            self.fit_params['n_iter'] = n_iter
+        return GLMResults(self, params.flatten(), predy, w)
+
+    @cache_readonly
+    def df_model(self):
+        return self.X.shape[1] - 1
+
+    @cache_readonly
+    def df_resid(self):
+        return self.n - self.df_model - 1
+
+class GLMResults(LikelihoodModelResults):
+    """
+    Results of estimated GLM and diagnostics.
+
+    Parameters
+    ----------
+        model         : GLM object
+                        Pointer to GLM object with estimation parameters.
+        params         : array
+                        k*1, estimared coefficients
+        mu         : array
+                        n*1, predicted y values.
+        w             : array
+                        n*1, final weight used for iwls
+
+    Attributes
+    ----------
+        model         : GLM Object
+                        Points to GLM object for which parameters have been
+                        estimated.
+        y             : array
+                        n*1, dependent variable.
+        x             : array
+                        n*k, independent variable, including constant.
+        family        : string
+                        Model type: 'Gaussian', 'Poisson', 'Logistic'
+        n             : integer
+                        Number of observations
+        k             : integer
+                        Number of independent variables
+        df_model      : float
+                        k-1, where k is the number of variables (including
+                        intercept)
+        df_residual   : float
+                        observations minus variables (n-k)
+        fit_params    : dict
+                        parameters passed into fit method to define estimation
+                        routine.
+        scale         : float
+                        sigma squared used for subsequent computations.
+        params         : array
+                        n*k, estimared beta coefficients
+        w             : array
+                        n*1, final weight values of x
+        mu            : array
+                        n*1, predicted value of y (i.e., fittedvalues)
+        cov_params    : array
+                        Variance covariance matrix (kxk) of betas which has been
+                        appropriately scaled by sigma-squared
+        bse           : array
+                        k*1, standard errors of betas
+        pvalues       : array
+                        k*1, two-tailed pvalues of parameters
+        tvalues       : array
+                        k*1, the tvalues of the standard errors
+        null          : array
+                        n*1, predicted values of y for null model
+        deviance      : float
+                        value of the deviance function evalued at params;
+                        see family.py for distribution-specific deviance
+        null_deviance : float
+                        value of the deviance function for the model fit with
+                        a constant as the only regressor
+        llf           : float
+                        value of the loglikelihood function evalued at params;
+                        see family.py for distribution-specific loglikelihoods
+        llnull       : float
+                        value of log-likelihood function evaluated at null
+        aic           : float 
+                        AIC
+        bic           : float 
+                        BIC
+        D2            : float
+                        percent deviance explained
+        adj_D2        : float
+                        adjusted percent deviance explained
+        pseudo_R2       : float
+                        McFadden's pseudo R2  (coefficient of determination) 
+        adj_pseudoR2    : float
+                        adjusted McFadden's pseudo R2
+        resid_response          : array
+                                  response residuals; defined as y-mu
+        resid_pearson           : array
+                                  Pearson residuals; defined as (y-mu)/sqrt(VAR(mu))
+                                  where VAR is the distribution specific variance
+                                  function; see family.py and varfuncs.py for more information.
+        resid_working           : array
+                                  Working residuals; the working residuals are defined as
+                                  resid_response/link'(mu); see links.py for the
+                                  derivatives of the link functions.
+
+        resid_anscombe          : array
+                                 Anscombe residuals; see family.py for 
+                                 distribution-specific Anscombe residuals.
+        
+        resid_deviance          : array
+                                 deviance residuals; see family.py for 
+                                 distribution-specific deviance residuals.
+
+        pearson_chi2            : float
+                                  chi-Squared statistic is defined as the sum 
+                                  of the squares of the Pearson residuals
+
+        normalized_cov_params   : array
+                                k*k, approximates [X.T*X]-1
+    """
+    def __init__(self, model, params, mu, w):
+        self.model = model
+        self.n = model.n
+        self.y = model.y.T.flatten()
+        self.X = model.X
+        self.k = model.k
+        self.family = model.family
+        self.fit_params = model.fit_params
+        self.params = params
+        self.w = w
+        self.mu = mu.flatten()
+        self._cache = {}
+    
+    @cache_readonly
+    def df_model(self):
+        return self.model.df_model
+
+    @cache_readonly
+    def df_resid(self):
+        return self.model.df_resid
+
+    @cache_readonly
+    def normalized_cov_params(self):
+        return la.inv(spdot(self.w.T, self.w))
+
+    @cache_readonly
+    def resid_response(self):
+        return (self.y-self.mu)
+
+    @cache_readonly
+    def resid_pearson(self):
+        return  ((self.y-self.mu) /
+                np.sqrt(self.family.variance(self.mu)))
+
+    @cache_readonly
+    def resid_working(self):
+        return (self.resid_response / self.family.link.deriv(self.mu))
+
+    @cache_readonly
+    def resid_anscombe(self):
+        return (self.family.resid_anscombe(self.y, self.mu))
+
+    @cache_readonly
+    def resid_deviance(self):
+        return (self.family.resid_dev(self.y, self.mu))
+
+    @cache_readonly
+    def pearson_chi2(self):
+        chisq = (self.y - self.mu)**2 / self.family.variance(self.mu)
+        chisqsum = np.sum(chisq)
+        return chisqsum
+
+    @cache_readonly
+    def null(self):
+        y = np.reshape(self.y, (-1,1))
+        model = self.model
+        X = np.ones((len(y), 1))
+        null_mod =  GLM(y, X, family=self.family, constant=False)
+        return null_mod.fit().mu
+   
+    @cache_readonly
+    def scale(self):
+        if isinstance(self.family, (family.Binomial, family.Poisson)):
+            return 1.
+        else:
+            return (((np.power(self.resid_response, 2) /
+                         self.family.variance(self.mu))).sum() /
+                        (self.df_resid))
+    @cache_readonly
+    def deviance(self):
+        return self.family.deviance(self.y, self.mu)
+
+    @cache_readonly
+    def null_deviance(self):
+        return self.family.deviance(self.y, self.null)
+   
+    @cache_readonly
+    def llnull(self):
+        return self.family.loglike(self.y, self.null, scale=self.scale)
+
+    @cache_readonly
+    def llf(self):
+        return self.family.loglike(self.y, self.mu, scale=self.scale)
+    
+    @cache_readonly
+    def aic(self):
+        if isinstance(self.family, family.QuasiPoisson):
+        	return np.nan
+        else:
+            return -2 * self.llf + 2*(self.df_model+1)
+
+    @cache_readonly
+    def bic(self):
+        return (self.deviance -
+                (self.model.n - self.df_model - 1) *
+                np.log(self.model.n))
+    
+    @cache_readonly
+    def D2(self):
+        return 1 - (self.deviance / self.null_deviance)
+
+    @cache_readonly
+    def adj_D2(self):
+        return 1.0 - (float(self.n) - 1.0)/(float(self.n) - float(self.k)) * (1.0-self.D2)
+    
+    @cache_readonly
+    def pseudoR2(self):
+        return 1 - (self.llf/self.llnull)
+    
+    @cache_readonly
+    def adj_pseudoR2(self):
+        return 1 - ((self.llf-self.k)/self.llnull)
+    

src/py/crankshaft/crankshaft/regression/glm/iwls.py

@@ -0,0 +1,84 @@
+import numpy as np
+import numpy.linalg as la
+from scipy import sparse as sp
+from scipy.sparse import linalg as spla
+from pysal.spreg.utils import spdot, spmultiply
+from family import Binomial, Poisson
+
+def _compute_betas(y, x):
+    """
+    compute MLE coefficients using iwls routine
+
+    Methods: p189, Iteratively (Re)weighted Least Squares (IWLS),
+    Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+    Geographically weighted regression: the analysis of spatially varying relationships.
+    """
+    xT = x.T
+    xtx = spdot(xT, x)
+    xtx_inv = la.inv(xtx)
+    xtx_inv = sp.csr_matrix(xtx_inv)
+    xTy = spdot(xT, y, array_out=False)
+    betas = spdot(xtx_inv, xTy)
+    return betas
+
+def _compute_betas_gwr(y, x, wi):
+    """
+    compute MLE coefficients using iwls routine
+
+    Methods: p189, Iteratively (Re)weighted Least Squares (IWLS),
+    Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+    Geographically weighted regression: the analysis of spatially varying relationships.
+    """
+    xT = (x * wi).T
+    xtx = np.dot(xT, x)
+    xtx_inv = la.inv(xtx)
+    xtx_inv_xt = np.dot(xtx_inv, xT)
+    betas = np.dot(xtx_inv_xt, y)
+    return betas, xtx_inv_xt
+
+def iwls(y, x, family, offset=1.0, ini_betas=None, tol=1.0e-8, max_iter=200, wi=None):
+    """
+    Iteratively re-weighted least squares estimation routine
+    """
+    n_iter = 0
+    diff = 1.0e6
+    if ini_betas is None:
+        betas = np.zeros((x.shape[1], 1), np.float)
+    else:
+        betas = ini_betas
+    if isinstance(family, Binomial):
+        y = family.link._clean(y)
+    if isinstance(family, Poisson):
+    	y_off = y/offset
+        y_off = family.starting_mu(y_off)
+        v = family.predict(y_off)
+        mu = family.starting_mu(y)
+    else:
+        mu = family.starting_mu(y)
+        v = family.predict(mu)
+    
+    while diff > tol and n_iter < max_iter:
+    	n_iter += 1
+        w = family.weights(mu)
+        z = v + (family.link.deriv(mu)*(y-mu))
+        w = np.sqrt(w)
+        if type(x) != np.ndarray:
+        	w = sp.csr_matrix(w)
+        	z = sp.csr_matrix(z)
+        wx = spmultiply(x, w, array_out=False)
+        wz = spmultiply(z, w, array_out=False)
+        if wi is None:
+            n_betas = _compute_betas(wz, wx)
+        else:
+            n_betas, xtx_inv_xt = _compute_betas_gwr(wz, wx, wi)
+        v = spdot(x, n_betas)
+        mu  = family.fitted(v)
+        if isinstance(family, Poisson):
+            mu = mu * offset
+        diff = min(abs(n_betas-betas))
+        betas = n_betas
+        
+    if wi is None:
+        return betas, mu, wx, n_iter
+    else:
+        return betas, mu, v, w, z, xtx_inv_xt, n_iter

src/py/crankshaft/crankshaft/regression/glm/links.py

@@ -0,0 +1,953 @@
+'''
+Defines the link functions to be used with GLM and GEE families.
+'''
+
+import numpy as np
+import scipy.stats
+FLOAT_EPS = np.finfo(float).eps
+
+
+class Link(object):
+    """
+    A generic link function for one-parameter exponential family.
+
+    `Link` does nothing, but lays out the methods expected of any subclass.
+    """
+
+    def __call__(self, p):
+        """
+        Return the value of the link function.  This is just a placeholder.
+
+        Parameters
+        ----------
+        p : array-like
+            Probabilities
+
+        Returns
+        -------
+        g(p) : array-like
+            The value of the link function g(p) = z
+        """
+        return NotImplementedError
+
+    def inverse(self, z):
+        """
+        Inverse of the link function.  Just a placeholder.
+
+        Parameters
+        ----------
+        z : array-like
+            `z` is usually the linear predictor of the transformed variable
+            in the IRLS algorithm for GLM.
+
+        Returns
+        -------
+        g^(-1)(z) : array
+            The value of the inverse of the link function g^(-1)(z) = p
+
+
+        """
+        return NotImplementedError
+
+    def deriv(self, p):
+        """
+        Derivative of the link function g'(p).  Just a placeholder.
+
+        Parameters
+        ----------
+        p : array-like
+
+        Returns
+        -------
+        g'(p) : array
+            The value of the derivative of the link function g'(p)
+        """
+        return NotImplementedError
+
+    def deriv2(self, p):
+        """Second derivative of the link function g''(p)
+
+        implemented through numerical differentiation
+        """
+        from statsmodels.tools.numdiff import approx_fprime_cs
+        # TODO: workaround proplem with numdiff for 1d
+        return np.diag(approx_fprime_cs(p, self.deriv))
+
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse link function g^(-1)(z).
+
+        Notes
+        -----
+        This reference implementation gives the correct result but is
+        inefficient, so it can be overriden in subclasses.
+
+        Parameters
+        ----------
+        z : array-like
+            `z` is usually the linear predictor for a GLM or GEE model.
+
+        Returns
+        -------
+        g'^(-1)(z) : array
+            The value of the derivative of the inverse of the link function
+
+        """
+        return 1 / self.deriv(self.inverse(z))
+
+
+class Logit(Link):
+    """
+    The logit transform
+
+    Notes
+    -----
+    call and derivative use a private method _clean to make trim p by
+    machine epsilon so that p is in (0,1)
+
+    Alias of Logit:
+    logit = Logit()
+    """
+
+    def _clean(self, p):
+        """
+        Clip logistic values to range (eps, 1-eps)
+
+        Parameters
+        -----------
+        p : array-like
+            Probabilities
+
+        Returns
+        --------
+        pclip : array
+            Clipped probabilities
+        """
+        return np.clip(p, FLOAT_EPS, 1. - FLOAT_EPS)
+
+    def __call__(self, p):
+        """
+        The logit transform
+
+        Parameters
+        ----------
+        p : array-like
+            Probabilities
+
+        Returns
+        -------
+        z : array
+            Logit transform of `p`
+
+        Notes
+        -----
+        g(p) = log(p / (1 - p))
+        """
+        p = self._clean(p)
+        return np.log(p / (1. - p))
+
+    def inverse(self, z):
+        """
+        Inverse of the logit transform
+
+        Parameters
+        ----------
+        z : array-like
+            The value of the logit transform at `p`
+
+        Returns
+        -------
+        p : array
+            Probabilities
+
+        Notes
+        -----
+        g^(-1)(z) = exp(z)/(1+exp(z))
+        """
+        z = np.asarray(z)
+        t = np.exp(-z)
+        return 1. / (1. + t)
+
+    def deriv(self, p):
+
+        """
+        Derivative of the logit transform
+
+        Parameters
+        ----------
+        p: array-like
+            Probabilities
+
+        Returns
+        -------
+        g'(p) : array
+            Value of the derivative of logit transform at `p`
+
+        Notes
+        -----
+        g'(p) = 1 / (p * (1 - p))
+
+        Alias for `Logit`:
+        logit = Logit()
+        """
+        p = self._clean(p)
+        return 1. / (p * (1 - p))
+
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse of the logit transform
+
+        Parameters
+        ----------
+        z : array-like
+            `z` is usually the linear predictor for a GLM or GEE model.
+
+        Returns
+        -------
+        g'^(-1)(z) : array
+            The value of the derivative of the inverse of the logit function
+
+        """
+        t = np.exp(z)
+        return t/(1 + t)**2
+
+
+    def deriv2(self, p):
+        """
+        Second derivative of the logit function.
+
+        Parameters
+        ----------
+        p : array-like
+            probabilities
+
+        Returns
+        -------
+        g''(z) : array
+            The value of the second derivative of the logit function
+        """
+        v = p * (1 - p)
+        return (2*p - 1) / v**2
+
+class logit(Logit):
+    pass
+
+
+class Power(Link):
+    """
+    The power transform
+
+    Parameters
+    ----------
+    power : float
+        The exponent of the power transform
+
+    Notes
+    -----
+    Aliases of Power:
+    inverse = Power(power=-1)
+    sqrt = Power(power=.5)
+    inverse_squared = Power(power=-2.)
+    identity = Power(power=1.)
+    """
+
+    def __init__(self, power=1.):
+        self.power = power
+
+    def __call__(self, p):
+        """
+        Power transform link function
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        z : array-like
+            Power transform of x
+
+        Notes
+        -----
+        g(p) = x**self.power
+        """
+
+        z = np.power(p, self.power)
+        return z
+
+    def inverse(self, z):
+        """
+        Inverse of the power transform link function
+
+        Parameters
+        ----------
+        `z` : array-like
+            Value of the transformed mean parameters at `p`
+
+        Returns
+        -------
+        `p` : array
+            Mean parameters
+
+        Notes
+        -----
+        g^(-1)(z`) = `z`**(1/`power`)
+        """
+
+        p = np.power(z, 1. / self.power)
+        return p
+
+    def deriv(self, p):
+        """
+        Derivative of the power transform
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        --------
+        g'(p) : array
+            Derivative of power transform of `p`
+
+        Notes
+        -----
+        g'(`p`) = `power` * `p`**(`power` - 1)
+        """
+        return self.power * np.power(p, self.power - 1)
+
+    def deriv2(self, p):
+        """
+        Second derivative of the power transform
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        --------
+        g''(p) : array
+            Second derivative of the power transform of `p`
+
+        Notes
+        -----
+        g''(`p`) = `power` * (`power` - 1) * `p`**(`power` - 2)
+        """
+        return self.power * (self.power - 1) * np.power(p, self.power - 2)
+
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse of the power transform
+
+        Parameters
+        ----------
+        z : array-like
+            `z` is usually the linear predictor for a GLM or GEE model.
+
+        Returns
+        -------
+        g^(-1)'(z) : array
+            The value of the derivative of the inverse of the power transform
+        function
+        """
+        return np.power(z, (1 - self.power)/self.power) / self.power
+
+
+class inverse_power(Power):
+    """
+    The inverse transform
+
+    Notes
+    -----
+    g(p) = 1/p
+
+    Alias of statsmodels.family.links.Power(power=-1.)
+    """
+    def __init__(self):
+        super(inverse_power, self).__init__(power=-1.)
+
+
+class sqrt(Power):
+    """
+    The square-root transform
+
+    Notes
+    -----
+    g(`p`) = sqrt(`p`)
+
+    Alias of statsmodels.family.links.Power(power=.5)
+    """
+    def __init__(self):
+        super(sqrt, self).__init__(power=.5)
+
+
+class inverse_squared(Power):
+    """
+    The inverse squared transform
+
+    Notes
+    -----
+    g(`p`) = 1/(`p`\ \*\*2)
+
+    Alias of statsmodels.family.links.Power(power=2.)
+    """
+    def __init__(self):
+        super(inverse_squared, self).__init__(power=-2.)
+
+
+class identity(Power):
+    """
+    The identity transform
+
+    Notes
+    -----
+    g(`p`) = `p`
+
+    Alias of statsmodels.family.links.Power(power=1.)
+    """
+    def __init__(self):
+        super(identity, self).__init__(power=1.)
+
+
+class Log(Link):
+    """
+    The log transform
+
+    Notes
+    -----
+    call and derivative call a private method _clean to trim the data by
+    machine epsilon so that p is in (0,1). log is an alias of Log.
+    """
+
+    def _clean(self, x):
+        return np.clip(x, FLOAT_EPS, np.inf)
+
+    def __call__(self, p, **extra):
+        """
+        Log transform link function
+
+        Parameters
+        ----------
+        x : array-like
+            Mean parameters
+
+        Returns
+        -------
+        z : array
+            log(x)
+
+        Notes
+        -----
+        g(p) = log(p)
+        """
+        x = self._clean(p)
+        return np.log(x)
+
+    def inverse(self, z):
+        """
+        Inverse of log transform link function
+
+        Parameters
+        ----------
+        z : array
+            The inverse of the link function at `p`
+
+        Returns
+        -------
+        p : array
+            The mean probabilities given the value of the inverse `z`
+
+        Notes
+        -----
+        g^{-1}(z) = exp(z)
+        """
+        return np.exp(z)
+
+    def deriv(self, p):
+        """
+        Derivative of log transform link function
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        g'(p) : array
+            derivative of log transform of x
+
+        Notes
+        -----
+        g'(x) = 1/x
+        """
+        p = self._clean(p)
+        return 1. / p
+
+    def deriv2(self, p):
+        """
+        Second derivative of the log transform link function
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        g''(p) : array
+            Second derivative of log transform of x
+
+        Notes
+        -----
+        g''(x) = -1/x^2
+        """
+        p = self._clean(p)
+        return -1. / p**2
+
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse of the log transform link function
+
+        Parameters
+        ----------
+        z : array
+            The inverse of the link function at `p`
+
+        Returns
+        -------
+        g^(-1)'(z) : array
+            The value of the derivative of the inverse of the log function,
+            the exponential function
+        """
+        return np.exp(z)
+
+
+class log(Log):
+    """
+    The log transform
+
+    Notes
+    -----
+    log is a an alias of Log.
+    """
+    pass
+
+
+# TODO: the CDFLink is untested
+class CDFLink(Logit):
+    """
+    The use the CDF of a scipy.stats distribution
+
+    CDFLink is a subclass of logit in order to use its _clean method
+    for the link and its derivative.
+
+    Parameters
+    ----------
+    dbn : scipy.stats distribution
+        Default is dbn=scipy.stats.norm
+
+    Notes
+    -----
+    The CDF link is untested.
+    """
+
+    def __init__(self, dbn=scipy.stats.norm):
+        self.dbn = dbn
+
+    def __call__(self, p):
+        """
+        CDF link function
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        z : array
+            (ppf) inverse of CDF transform of p
+
+        Notes
+        -----
+        g(`p`) = `dbn`.ppf(`p`)
+        """
+        p = self._clean(p)
+        return self.dbn.ppf(p)
+
+    def inverse(self, z):
+        """
+        The inverse of the CDF link
+
+        Parameters
+        ----------
+        z : array-like
+            The value of the inverse of the link function at `p`
+
+        Returns
+        -------
+        p : array
+            Mean probabilities.  The value of the inverse of CDF link of `z`
+
+        Notes
+        -----
+        g^(-1)(`z`) = `dbn`.cdf(`z`)
+        """
+        return self.dbn.cdf(z)
+
+    def deriv(self, p):
+        """
+        Derivative of CDF link
+
+        Parameters
+        ----------
+        p : array-like
+            mean parameters
+
+        Returns
+        -------
+        g'(p) : array
+            The derivative of CDF transform at `p`
+
+        Notes
+        -----
+        g'(`p`) = 1./ `dbn`.pdf(`dbn`.ppf(`p`))
+        """
+        p = self._clean(p)
+        return 1. / self.dbn.pdf(self.dbn.ppf(p))
+
+    def deriv2(self, p):
+        """
+        Second derivative of the link function g''(p)
+
+        implemented through numerical differentiation
+        """
+        from statsmodels.tools.numdiff import approx_fprime
+        p = np.atleast_1d(p)
+        # Note: special function for norm.ppf does not support complex
+        return np.diag(approx_fprime(p, self.deriv, centered=True))
+
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse of the CDF transformation link function
+
+        Parameters
+        ----------
+        z : array
+            The inverse of the link function at `p`
+
+        Returns
+        -------
+        g^(-1)'(z) : array
+            The value of the derivative of the inverse of the logit function
+        """
+        return 1/self.deriv(self.inverse(z))
+
+
+class probit(CDFLink):
+    """
+    The probit (standard normal CDF) transform
+
+    Notes
+    --------
+    g(p) = scipy.stats.norm.ppf(p)
+
+    probit is an alias of CDFLink.
+    """
+    pass
+
+
+class cauchy(CDFLink):
+    """
+    The Cauchy (standard Cauchy CDF) transform
+
+    Notes
+    -----
+    g(p) = scipy.stats.cauchy.ppf(p)
+
+    cauchy is an alias of CDFLink with dbn=scipy.stats.cauchy
+    """
+
+    def __init__(self):
+        super(cauchy, self).__init__(dbn=scipy.stats.cauchy)
+
+    def deriv2(self, p):
+        """
+        Second derivative of the Cauchy link function.
+
+        Parameters
+        ----------
+        p: array-like
+            Probabilities
+
+        Returns
+        -------
+        g''(p) : array
+            Value of the second derivative of Cauchy link function at `p`
+        """
+        a = np.pi * (p - 0.5)
+        d2 = 2 * np.pi**2 * np.sin(a) / np.cos(a)**3
+        return d2
+
+class CLogLog(Logit):
+    """
+    The complementary log-log transform
+
+    CLogLog inherits from Logit in order to have access to its _clean method
+    for the link and its derivative.
+
+    Notes
+    -----
+    CLogLog is untested.
+    """
+    def __call__(self, p):
+        """
+        C-Log-Log transform link function
+
+        Parameters
+        ----------
+        p : array
+            Mean parameters
+
+        Returns
+        -------
+        z : array
+            The CLogLog transform of `p`
+
+        Notes
+        -----
+        g(p) = log(-log(1-p))
+        """
+        p = self._clean(p)
+        return np.log(-np.log(1 - p))
+
+    def inverse(self, z):
+        """
+        Inverse of C-Log-Log transform link function
+
+
+        Parameters
+        ----------
+        z : array-like
+            The value of the inverse of the CLogLog link function at `p`
+
+        Returns
+        -------
+        p : array
+            Mean parameters
+
+        Notes
+        -----
+        g^(-1)(`z`) = 1-exp(-exp(`z`))
+        """
+        return 1 - np.exp(-np.exp(z))
+
+    def deriv(self, p):
+        """
+        Derivative of C-Log-Log transform link function
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        g'(p) : array
+            The derivative of the CLogLog transform link function
+
+        Notes
+        -----
+        g'(p) = - 1 / ((p-1)*log(1-p))
+        """
+        p = self._clean(p)
+        return 1. / ((p - 1) * (np.log(1 - p)))
+
+    def deriv2(self, p):
+        """
+        Second derivative of the C-Log-Log ink function
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        g''(p) : array
+            The second derivative of the CLogLog link function
+        """
+        p = self._clean(p)
+        fl = np.log(1 - p)
+        d2 = -1 / ((1 - p)**2 * fl)
+        d2 *= 1 + 1 / fl
+        return d2
+
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse of the C-Log-Log transform link function
+
+        Parameters
+        ----------
+        z : array-like
+            The value of the inverse of the CLogLog link function at `p`
+
+        Returns
+        -------
+        g^(-1)'(z) : array
+            The derivative of the inverse of the CLogLog link function
+        """
+        return np.exp(z - np.exp(z))
+
+
+class cloglog(CLogLog):
+    """
+    The CLogLog transform link function.
+
+    Notes
+    -----
+    g(`p`) = log(-log(1-`p`))
+
+    cloglog is an alias for CLogLog
+    cloglog = CLogLog()
+    """
+    pass
+
+
+class NegativeBinomial(object):
+    '''
+    The negative binomial link function
+
+    Parameters
+    ----------
+    alpha : float, optional
+        Alpha is the ancillary parameter of the Negative Binomial link
+        function. It is assumed to be nonstochastic.  The default value is 1.
+        Permissible values are usually assumed to be in (.01, 2).
+    '''
+
+    def __init__(self, alpha=1.):
+        self.alpha = alpha
+
+    def _clean(self, x):
+        return np.clip(x, FLOAT_EPS, np.inf)
+
+    def __call__(self, p):
+        '''
+        Negative Binomial transform link function
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        z : array
+            The negative binomial transform of `p`
+
+        Notes
+        -----
+        g(p) = log(p/(p + 1/alpha))
+        '''
+        p = self._clean(p)
+        return np.log(p/(p + 1/self.alpha))
+
+    def inverse(self, z):
+        '''
+        Inverse of the negative binomial transform
+
+        Parameters
+        -----------
+        z : array-like
+            The value of the inverse of the negative binomial link at `p`.
+
+        Returns
+        -------
+        p : array
+            Mean parameters
+
+        Notes
+        -----
+        g^(-1)(z) = exp(z)/(alpha*(1-exp(z)))
+        '''
+        return -1/(self.alpha * (1 - np.exp(-z)))
+
+    def deriv(self, p):
+        '''
+        Derivative of the negative binomial transform
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        g'(p) : array
+            The derivative of the negative binomial transform link function
+
+        Notes
+        -----
+        g'(x) = 1/(x+alpha*x^2)
+        '''
+        return 1/(p + self.alpha * p**2)
+
+    def deriv2(self,p):
+        '''
+        Second derivative of the negative binomial link function.
+
+        Parameters
+        ----------
+        p : array-like
+            Mean parameters
+
+        Returns
+        -------
+        g''(p) : array
+            The second derivative of the negative binomial transform link
+            function
+
+        Notes
+        -----
+        g''(x) = -(1+2*alpha*x)/(x+alpha*x^2)^2
+        '''
+        numer = -(1 + 2 * self.alpha * p)
+        denom = (p + self.alpha * p**2)**2
+        return numer / denom
+
+    def inverse_deriv(self, z):
+        '''
+        Derivative of the inverse of the negative binomial transform
+
+        Parameters
+        -----------
+        z : array-like
+            Usually the linear predictor for a GLM or GEE model
+
+        Returns
+        -------
+        g^(-1)'(z) : array
+            The value of the derivative of the inverse of the negative
+            binomial link
+        '''
+        t = np.exp(z)
+        return t / (self.alpha * (1-t)**2)
+
+
+class nbinom(NegativeBinomial):
+    """
+    The negative binomial link function.
+
+    Notes
+    -----
+    g(p) = log(p/(p + 1/alpha))
+
+    nbinom is an alias of NegativeBinomial.
+    nbinom = NegativeBinomial(alpha=1.)
+    """
+    pass

src/py/crankshaft/crankshaft/regression/glm/tests/test_glm.py

@@ -0,0 +1,993 @@
+"""
+Tests for generalized linear models. Majority of code either directly borrowed
+or closely adapted from statsmodels package. Model results verfiied using glm
+function in R and GLM function in statsmodels.
+"""
+
+__author__ = 'Taylor Oshan tayoshan@gmail.com'
+
+from pysal.contrib.glm.glm import GLM
+from pysal.contrib.glm.family import Gaussian, Poisson, Binomial, QuasiPoisson
+import numpy as np
+import pysal
+import unittest
+import math
+
+
+class TestGaussian(unittest.TestCase):
+    """
+    Tests for Poisson GLM
+    """
+
+    def setUp(self):
+        db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+        y = np.array(db.by_col("HOVAL"))
+        self.y = np.reshape(y, (49,1))
+        X = []
+        X.append(db.by_col("INC"))
+        X.append(db.by_col("CRIME"))
+        self.X = np.array(X).T
+
+    def testIWLS(self):
+        model = GLM(self.y, self.X, family=Gaussian())
+        results = model.fit()
+        self.assertEqual(results.n, 49)
+        self.assertEqual(results.df_model, 2)
+        self.assertEqual(results.df_resid, 46)
+        self.assertEqual(results.aic, 408.73548964604873)
+        self.assertEqual(results.bic, 10467.991340493107)
+        self.assertEqual(results.deviance, 10647.015074206196)
+        self.assertEqual(results.llf, -201.36774482302437)
+        self.assertEqual(results.null_deviance, 16367.794631703124)
+        self.assertEqual(results.scale, 231.45684943926514)
+        np.testing.assert_allclose(results.params, [ 46.42818268,   0.62898397,
+            -0.48488854])
+        np.testing.assert_allclose(results.bse, [ 13.19175703,   0.53591045,
+            0.18267291])
+        np.testing.assert_allclose(results.cov_params(), 
+                [[  1.74022453e+02,  -6.52060364e+00,  -2.15109867e+00],
+                [ -6.52060364e+00,   2.87200008e-01,   6.80956787e-02],
+                [ -2.15109867e+00,   6.80956787e-02,   3.33693910e-02]])
+        np.testing.assert_allclose(results.tvalues, [ 3.51948437,  1.17367365,
+            -2.65440864])
+        np.testing.assert_allclose(results.pvalues, [ 0.00043239,  0.24052577,
+            0.00794475], atol=1.0e-8)
+        np.testing.assert_allclose(results.conf_int(), 
+                [[ 20.57281401,  72.28355135],
+                [ -0.42138121,   1.67934915],
+                [ -0.84292086,  -0.12685622]])
+        np.testing.assert_allclose(results.normalized_cov_params, 
+                [[  7.51857004e-01,  -2.81720055e-02,  -9.29373521e-03],
+                [ -2.81720055e-02,   1.24083607e-03,   2.94204638e-04],
+                [ -9.29373521e-03,   2.94204638e-04,   1.44171110e-04]])
+        np.testing.assert_allclose(results.mu, 
+                [ 51.08752105,  50.66601521,  41.61367567,  33.53969014,
+                28.90638232,  43.87074227,  51.64910882,  34.92671563,
+                42.69267622,  38.49449134,  20.92815471,  25.25228436,
+                29.78223486,  25.02403635,  29.07959539,  24.63352275,
+                34.71372149,  33.40443052,  27.29864225,  65.86219802,
+                33.69854751,  37.44976435,  50.01304928,  36.81219959,
+                22.02674837,  31.64775955,  27.63563294,  23.7697291 ,
+                22.43119725,  21.76987089,  48.51169321,  49.05891819,
+                32.31656426,  44.20550354,  35.49244888,  51.27811308,
+                36.55047181,  27.37048914,  48.78812922,  57.31744163,
+                51.22914162,  54.70515578,  37.06622277,  44.5075759 ,
+                41.24328983,  49.93821824,  44.85644299,  40.93838609,  47.32045464])
+        self.assertEqual(results.pearson_chi2, 10647.015074206196)
+        np.testing.assert_allclose(results.resid_response, 
+                [ 29.37948195,  -6.09901421, -15.26367567,  -0.33968914,
+                -5.68138232, -15.12074227,  23.35089118,   2.19828437,
+                9.90732178,  57.90551066,  -1.22815371,  -5.35228436,
+                11.91776614,  17.87596565, -11.07959539,  -5.83352375,
+                7.03627851,  26.59556948,   3.30135775,  15.40479998,
+                -13.72354751,  -6.99976335,  -2.28004728,  16.38780141,
+                -4.12674837, -11.34776055,   6.46436506,  -0.9197291 ,
+                10.06880275,   0.73012911, -16.71169421,  -8.75891919,
+                -8.71656426, -15.75550254,  -8.49244888, -14.97811408,
+                6.74952719,  -4.67048814,  -9.18813122,   4.63255937,
+                -9.12914362, -10.37215578, -11.36622177, -11.0075759 ,
+                -13.51028983,  26.16177976,  -2.35644299, -14.13838709, -11.52045564])
+        np.testing.assert_allclose(results.resid_working, 
+                [ 29.37948195,  -6.09901421, -15.26367567,  -0.33968914,
+                -5.68138232, -15.12074227,  23.35089118,   2.19828437,
+                9.90732178,  57.90551066,  -1.22815371,  -5.35228436,
+                11.91776614,  17.87596565, -11.07959539,  -5.83352375,
+                7.03627851,  26.59556948,   3.30135775,  15.40479998,
+                -13.72354751,  -6.99976335,  -2.28004728,  16.38780141,
+                -4.12674837, -11.34776055,   6.46436506,  -0.9197291 ,
+                10.06880275,   0.73012911, -16.71169421,  -8.75891919,
+                -8.71656426, -15.75550254,  -8.49244888, -14.97811408,
+                6.74952719,  -4.67048814,  -9.18813122,   4.63255937,
+                -9.12914362, -10.37215578, -11.36622177, -11.0075759 ,
+                -13.51028983,  26.16177976,  -2.35644299, -14.13838709, -11.52045564])
+        np.testing.assert_allclose(results.resid_pearson, 
+                [ 29.37948195,  -6.09901421, -15.26367567,  -0.33968914,
+                -5.68138232, -15.12074227,  23.35089118,   2.19828437,
+                9.90732178,  57.90551066,  -1.22815371,  -5.35228436,
+                11.91776614,  17.87596565, -11.07959539,  -5.83352375,
+                7.03627851,  26.59556948,   3.30135775,  15.40479998,
+                -13.72354751,  -6.99976335,  -2.28004728,  16.38780141,
+                -4.12674837, -11.34776055,   6.46436506,  -0.9197291 ,
+                10.06880275,   0.73012911, -16.71169421,  -8.75891919,
+                -8.71656426, -15.75550254,  -8.49244888, -14.97811408,
+                6.74952719,  -4.67048814,  -9.18813122,   4.63255937,
+                -9.12914362, -10.37215578, -11.36622177, -11.0075759 ,
+                -13.51028983,  26.16177976,  -2.35644299, -14.13838709, -11.52045564])
+        np.testing.assert_allclose(results.resid_anscombe, 
+                [ 29.37948195,  -6.09901421, -15.26367567,  -0.33968914,
+                -5.68138232, -15.12074227,  23.35089118,   2.19828437,
+                9.90732178,  57.90551066,  -1.22815371,  -5.35228436,
+                11.91776614,  17.87596565, -11.07959539,  -5.83352375,
+                7.03627851,  26.59556948,   3.30135775,  15.40479998,
+                -13.72354751,  -6.99976335,  -2.28004728,  16.38780141,
+                -4.12674837, -11.34776055,   6.46436506,  -0.9197291 ,
+                10.06880275,   0.73012911, -16.71169421,  -8.75891919,
+                -8.71656426, -15.75550254,  -8.49244888, -14.97811408,
+                6.74952719,  -4.67048814,  -9.18813122,   4.63255937,
+                -9.12914362, -10.37215578, -11.36622177, -11.0075759 ,
+                -13.51028983,  26.16177976,  -2.35644299, -14.13838709, -11.52045564])
+        np.testing.assert_allclose(results.resid_deviance, 
+                [ 29.37948195,  -6.09901421, -15.26367567,  -0.33968914,
+                -5.68138232, -15.12074227,  23.35089118,   2.19828437,
+                9.90732178,  57.90551066,  -1.22815371,  -5.35228436,
+                11.91776614,  17.87596565, -11.07959539,  -5.83352375,
+                7.03627851,  26.59556948,   3.30135775,  15.40479998,
+                -13.72354751,  -6.99976335,  -2.28004728,  16.38780141,
+                -4.12674837, -11.34776055,   6.46436506,  -0.9197291 ,
+                10.06880275,   0.73012911, -16.71169421,  -8.75891919,
+                -8.71656426, -15.75550254,  -8.49244888, -14.97811408,
+                6.74952719,  -4.67048814,  -9.18813122,   4.63255937,
+                -9.12914362, -10.37215578, -11.36622177, -11.0075759 ,
+                -13.51028983,  26.16177976,  -2.35644299, -14.13838709, -11.52045564])
+        np.testing.assert_allclose(results.null, 
+                [ 38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,
+                38.43622447,  38.43622447,  38.43622447,  38.43622447,  38.43622447])
+        self.assertAlmostEqual(results.D2, .349514377851)
+        self.assertAlmostEqual(results.adj_D2, 0.32123239427957673)
+
+class TestPoisson(unittest.TestCase):
+
+    def setUp(self):
+        db = pysal.open(pysal.examples.get_path('columbus.dbf'),'r')
+        y = np.array(db.by_col("HOVAL"))
+        y = np.reshape(y, (49,1))
+        self.y = np.round(y).astype(int)
+        X = []
+        X.append(db.by_col("INC"))
+        X.append(db.by_col("CRIME"))
+        self.X = np.array(X).T
+
+    def testIWLS(self):
+        model = GLM(self.y, self.X, family=Poisson())
+        results = model.fit()
+        self.assertEqual(results.n, 49)
+        self.assertEqual(results.df_model, 2)
+        self.assertEqual(results.df_resid, 46)
+        self.assertAlmostEqual(results.aic, 500.85184179938756)
+        self.assertAlmostEqual(results.bic, 51.436404535087661)
+        self.assertAlmostEqual(results.deviance, 230.46013824817649)
+        self.assertAlmostEqual(results.llf, -247.42592089969378)
+        self.assertAlmostEqual(results.null_deviance, 376.97293610347361)
+        self.assertEqual(results.scale, 1.0)
+        np.testing.assert_allclose(results.params, [ 3.92159085,  0.01183491,
+            -0.01371397], atol=1.0e-8)
+        np.testing.assert_allclose(results.bse, [ 0.13049161,  0.00511599,
+            0.00193769], atol=1.0e-8)
+        np.testing.assert_allclose(results.cov_params(), 
+                [[  1.70280610e-02,  -6.18628383e-04,  -2.21386966e-04],
+                [ -6.18628383e-04,   2.61733917e-05,   6.77496445e-06],
+                [ -2.21386966e-04,   6.77496445e-06,   3.75463502e-06]])
+        np.testing.assert_allclose(results.tvalues, [ 30.0524361 ,   2.31331634,
+            -7.07748998])
+        np.testing.assert_allclose(results.pvalues, [  2.02901657e-198,
+            2.07052532e-002,   1.46788805e-012])
+        np.testing.assert_allclose(results.conf_int(), 
+                [[  3.66583199e+00,   4.17734972e+00],
+                [  1.80774841e-03,   2.18620753e-02],
+                [ -1.75117666e-02,  -9.91616901e-03]])
+        np.testing.assert_allclose(results.normalized_cov_params, 
+                [[  1.70280610e-02,  -6.18628383e-04,  -2.21386966e-04],
+                [ -6.18628383e-04,   2.61733917e-05,   6.77496445e-06],
+                [ -2.21386966e-04,   6.77496445e-06,   3.75463502e-06]])
+        np.testing.assert_allclose(results.mu, 
+                [ 51.26831574,  50.15022766,  40.06142973,  34.13799739,
+                28.76119226,  42.6836241 ,  55.64593703,  34.08277997,
+                40.90389582,  37.19727958,  23.47459217,  26.12384057,
+                29.78303507,  25.96888223,  29.14073823,  26.04369592,
+                34.18996367,  32.28924005,  27.42284396,  72.69207879,
+                33.05316347,  36.52276972,  49.2551479 ,  35.33439632,
+                24.07252457,  31.67153709,  27.81699478,  25.38021219,
+                24.31759259,  23.13586161,  48.40724678,  48.57969818,
+                31.92596006,  43.3679231 ,  34.32925819,  51.78908089,
+                34.49778584,  27.56236198,  48.34273194,  57.50829097,
+                50.66038226,  54.68701352,  35.77103116,  43.21886784,
+                40.07615759,  49.98658004,  43.13352883,  40.28520774,  46.28910294])
+        self.assertAlmostEqual(results.pearson_chi2, 264.62262932090221)
+        np.testing.assert_allclose(results.resid_response, 
+                [ 28.73168426,  -5.15022766, -14.06142973,  -1.13799739,
+                -5.76119226, -13.6836241 ,  19.35406297,   2.91722003,
+                12.09610418,  58.80272042,  -3.47459217,  -6.12384057,
+                12.21696493,  17.03111777, -11.14073823,  -7.04369592,
+                7.81003633,  27.71075995,   3.57715604,   8.30792121,
+                -13.05316347,  -6.52276972,  -1.2551479 ,  17.66560368,
+                -6.07252457, -11.67153709,   6.18300522,  -2.38021219,
+                7.68240741,  -1.13586161, -16.40724678,  -8.57969818,
+                -7.92596006, -15.3679231 ,  -7.32925819, -15.78908089,
+                8.50221416,  -4.56236198,  -8.34273194,   4.49170903,
+                -8.66038226, -10.68701352,  -9.77103116,  -9.21886784,
+                -12.07615759,  26.01341996,  -1.13352883, -13.28520774, -10.28910294])
+        np.testing.assert_allclose(results.resid_working, 
+                [ 1473.02506034,  -258.28508941,  -563.32097891,   -38.84895192,
+                -165.69875817,  -584.06666725,  1076.97496919,    99.42696848,
+                494.77778514,  2187.30123163,   -81.56463405,  -159.97823479,
+                363.858295  ,   442.27909165,  -324.64933645,  -183.44387481,
+                267.02485844,   894.75938   ,    98.09579187,   603.9200634 ,
+                -431.44834594,  -238.2296165 ,   -61.82249568,   624.20344168,
+                -146.18099686,  -369.65551968,   171.99262399,   -60.41029031,
+                186.81765356,   -26.27913713,  -794.22964417,  -416.79914795,
+                -253.04388425,  -666.47490701,  -251.6079969 ,  -817.70198717,
+                293.30756327,  -125.74947222,  -403.31045369,   258.31051005,
+                -438.73827602,  -584.440853  ,  -349.51985996,  -398.42903071,
+                -483.96599444,  1300.32189904,   -48.89309853,  -535.19735391,
+                -476.27334527])
+        np.testing.assert_allclose(results.resid_pearson, 
+                [ 4.01269878, -0.72726045, -2.221602  , -0.19477008, -1.07425881,
+                -2.09445239,  2.59451042,  0.49969118,  1.89131202,  9.64143836,
+                -0.71714142, -1.19813392,  2.23861212,  3.34207756, -2.0637814 ,
+                -1.3802231 ,  1.33568403,  4.87662684,  0.68309584,  0.97442591,
+                -2.27043598, -1.07931992, -0.17884182,  2.97186889, -1.23768025,
+                -2.07392709,  1.1723155 , -0.47246327,  1.55789092, -0.23614708,
+                -2.35819937, -1.23096188, -1.40274877, -2.33362391, -1.25091503,
+                -2.19400568,  1.44755952, -0.8690235 , -1.19989348,  0.59230634,
+                -1.21675413, -1.44515442, -1.63370888, -1.40229988, -1.90759306,
+                3.67934693, -0.17259375, -2.09312684, -1.51230062])
+        np.testing.assert_allclose(results.resid_anscombe, 
+                [ 3.70889134, -0.74031295, -2.37729865, -0.19586855, -1.11374751,
+                -2.22611959,  2.46352013,  0.49282126,  1.80857757,  8.06444452,
+                -0.73610811, -1.25061371,  2.10820431,  3.05467547, -2.22437611,
+                -1.45136173,  1.28939698,  4.35942058,  0.66904552,  0.95674923,
+                -2.45438937, -1.11429881, -0.17961012,  2.76715848, -1.29658591,
+                -2.22816691,  1.13269136, -0.48017382,  1.48562248, -0.23812278,
+                -2.51664399, -1.2703721 , -1.4683091 , -2.49907536, -1.30026484,
+                -2.32398309,  1.39380683, -0.89495368, -1.23735395,  0.58485202,
+                -1.25435224, -1.4968484 , -1.71888038, -1.45756652, -2.01906267,
+                3.41729922, -0.17335867, -2.22921828, -1.57470549])
+        np.testing.assert_allclose(results.resid_deviance, 
+                [ 3.70529668, -0.74027329, -2.37536322, -0.19586751, -1.11349765,
+                -2.22466106,  2.46246446,  0.4928057 ,  1.80799655,  8.02696525,
+                -0.73602255, -1.25021555,  2.10699958,  3.05084608, -2.22214376,
+                -1.45072221,  1.28913747,  4.35106213,  0.6689982 ,  0.95669662,
+                -2.45171913, -1.11410444, -0.17960956,  2.76494217, -1.29609865,
+                -2.22612429,  1.13247453, -0.48015254,  1.48508549, -0.23812   ,
+                -2.51476072, -1.27015583, -1.46777697, -2.49699318, -1.29992892,
+                -2.32263069,  1.39348459, -0.89482132, -1.23715363,  0.58483655,
+                -1.25415329, -1.49653039, -1.7181055 , -1.45719072, -2.01791949,
+                3.41437156, -0.1733581 , -2.22765605, -1.57426046])
+        np.testing.assert_allclose(results.null, 
+                [ 38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,  38.42857143])
+        self.assertAlmostEqual(results.D2, .388656011675)
+        self.assertAlmostEqual(results.adj_D2, 0.36207583826952761)#.375648692774)
+
+    def testQuasi(self):
+        model = GLM(self.y, self.X, family=QuasiPoisson())
+        results = model.fit()
+        self.assertEqual(results.n, 49)
+        self.assertEqual(results.df_model, 2)
+        self.assertEqual(results.df_resid, 46)
+        self.assertTrue(math.isnan(results.aic))
+        self.assertAlmostEqual(results.bic, 51.436404535087661)
+        self.assertAlmostEqual(results.deviance, 230.46013824817649)
+        self.assertTrue(math.isnan(results.llf))
+        self.assertAlmostEqual(results.null_deviance, 376.97293610347361)
+        self.assertAlmostEqual(results.scale, 5.7526658548022223)
+        np.testing.assert_allclose(results.params, [ 3.92159085,  0.01183491,
+            -0.01371397], atol=1.0e-8)
+        np.testing.assert_allclose(results.bse, [ 0.31298042,  0.01227057,
+            0.00464749], atol=1.0e-8)
+        np.testing.assert_allclose(results.cov_params(), 
+                [[  9.79567451e-02,  -3.55876238e-03,  -1.27356524e-03],
+                [ -3.55876238e-03,   1.50566777e-04,   3.89741067e-05],
+                [ -1.27356524e-03,   3.89741067e-05,   2.15991606e-05]])
+        np.testing.assert_allclose(results.tvalues, [ 12.52982796,   0.96449604,
+            -2.95083339])
+        np.testing.assert_allclose(results.pvalues, [  5.12737770e-36,
+            3.34797291e-01,   3.16917819e-03])
+        np.testing.assert_allclose(results.conf_int(), 
+                [[ 3.3081605 ,  4.53502121],
+                [-0.01221495,  0.03588478],
+                [-0.02282288, -0.00460506]], atol=1.0e-8)
+        np.testing.assert_allclose(results.normalized_cov_params, 
+                [[  1.70280610e-02,  -6.18628383e-04,  -2.21386966e-04],
+                [ -6.18628383e-04,   2.61733917e-05,   6.77496445e-06],
+                [ -2.21386966e-04,   6.77496445e-06,   3.75463502e-06]])
+        np.testing.assert_allclose(results.mu, 
+                [ 51.26831574,  50.15022766,  40.06142973,  34.13799739,
+                28.76119226,  42.6836241 ,  55.64593703,  34.08277997,
+                40.90389582,  37.19727958,  23.47459217,  26.12384057,
+                29.78303507,  25.96888223,  29.14073823,  26.04369592,
+                34.18996367,  32.28924005,  27.42284396,  72.69207879,
+                33.05316347,  36.52276972,  49.2551479 ,  35.33439632,
+                24.07252457,  31.67153709,  27.81699478,  25.38021219,
+                24.31759259,  23.13586161,  48.40724678,  48.57969818,
+                31.92596006,  43.3679231 ,  34.32925819,  51.78908089,
+                34.49778584,  27.56236198,  48.34273194,  57.50829097,
+                50.66038226,  54.68701352,  35.77103116,  43.21886784,
+                40.07615759,  49.98658004,  43.13352883,  40.28520774,  46.28910294])
+        self.assertAlmostEqual(results.pearson_chi2, 264.62262932090221)
+        np.testing.assert_allclose(results.resid_response, 
+                [ 28.73168426,  -5.15022766, -14.06142973,  -1.13799739,
+                -5.76119226, -13.6836241 ,  19.35406297,   2.91722003,
+                12.09610418,  58.80272042,  -3.47459217,  -6.12384057,
+                12.21696493,  17.03111777, -11.14073823,  -7.04369592,
+                7.81003633,  27.71075995,   3.57715604,   8.30792121,
+                -13.05316347,  -6.52276972,  -1.2551479 ,  17.66560368,
+                -6.07252457, -11.67153709,   6.18300522,  -2.38021219,
+                7.68240741,  -1.13586161, -16.40724678,  -8.57969818,
+                -7.92596006, -15.3679231 ,  -7.32925819, -15.78908089,
+                8.50221416,  -4.56236198,  -8.34273194,   4.49170903,
+                -8.66038226, -10.68701352,  -9.77103116,  -9.21886784,
+                -12.07615759,  26.01341996,  -1.13352883, -13.28520774, -10.28910294])
+        np.testing.assert_allclose(results.resid_working, 
+                [ 1473.02506034,  -258.28508941,  -563.32097891,   -38.84895192,
+                -165.69875817,  -584.06666725,  1076.97496919,    99.42696848,
+                494.77778514,  2187.30123163,   -81.56463405,  -159.97823479,
+                363.858295  ,   442.27909165,  -324.64933645,  -183.44387481,
+                267.02485844,   894.75938   ,    98.09579187,   603.9200634 ,
+                -431.44834594,  -238.2296165 ,   -61.82249568,   624.20344168,
+                -146.18099686,  -369.65551968,   171.99262399,   -60.41029031,
+                186.81765356,   -26.27913713,  -794.22964417,  -416.79914795,
+                -253.04388425,  -666.47490701,  -251.6079969 ,  -817.70198717,
+                293.30756327,  -125.74947222,  -403.31045369,   258.31051005,
+                -438.73827602,  -584.440853  ,  -349.51985996,  -398.42903071,
+                -483.96599444,  1300.32189904,   -48.89309853,  -535.19735391,
+                -476.27334527])
+        np.testing.assert_allclose(results.resid_pearson, 
+                [ 4.01269878, -0.72726045, -2.221602  , -0.19477008, -1.07425881,
+                -2.09445239,  2.59451042,  0.49969118,  1.89131202,  9.64143836,
+                -0.71714142, -1.19813392,  2.23861212,  3.34207756, -2.0637814 ,
+                -1.3802231 ,  1.33568403,  4.87662684,  0.68309584,  0.97442591,
+                -2.27043598, -1.07931992, -0.17884182,  2.97186889, -1.23768025,
+                -2.07392709,  1.1723155 , -0.47246327,  1.55789092, -0.23614708,
+                -2.35819937, -1.23096188, -1.40274877, -2.33362391, -1.25091503,
+                -2.19400568,  1.44755952, -0.8690235 , -1.19989348,  0.59230634,
+                -1.21675413, -1.44515442, -1.63370888, -1.40229988, -1.90759306,
+                3.67934693, -0.17259375, -2.09312684, -1.51230062])
+        np.testing.assert_allclose(results.resid_anscombe, 
+                [ 3.70889134, -0.74031295, -2.37729865, -0.19586855, -1.11374751,
+                -2.22611959,  2.46352013,  0.49282126,  1.80857757,  8.06444452,
+                -0.73610811, -1.25061371,  2.10820431,  3.05467547, -2.22437611,
+                -1.45136173,  1.28939698,  4.35942058,  0.66904552,  0.95674923,
+                -2.45438937, -1.11429881, -0.17961012,  2.76715848, -1.29658591,
+                -2.22816691,  1.13269136, -0.48017382,  1.48562248, -0.23812278,
+                -2.51664399, -1.2703721 , -1.4683091 , -2.49907536, -1.30026484,
+                -2.32398309,  1.39380683, -0.89495368, -1.23735395,  0.58485202,
+                -1.25435224, -1.4968484 , -1.71888038, -1.45756652, -2.01906267,
+                3.41729922, -0.17335867, -2.22921828, -1.57470549])
+        np.testing.assert_allclose(results.resid_deviance, 
+                [ 3.70529668, -0.74027329, -2.37536322, -0.19586751, -1.11349765,
+                -2.22466106,  2.46246446,  0.4928057 ,  1.80799655,  8.02696525,
+                -0.73602255, -1.25021555,  2.10699958,  3.05084608, -2.22214376,
+                -1.45072221,  1.28913747,  4.35106213,  0.6689982 ,  0.95669662,
+                -2.45171913, -1.11410444, -0.17960956,  2.76494217, -1.29609865,
+                -2.22612429,  1.13247453, -0.48015254,  1.48508549, -0.23812   ,
+                -2.51476072, -1.27015583, -1.46777697, -2.49699318, -1.29992892,
+                -2.32263069,  1.39348459, -0.89482132, -1.23715363,  0.58483655,
+                -1.25415329, -1.49653039, -1.7181055 , -1.45719072, -2.01791949,
+                3.41437156, -0.1733581 , -2.22765605, -1.57426046])
+        np.testing.assert_allclose(results.null, 
+                [ 38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,
+                38.42857143,  38.42857143,  38.42857143,  38.42857143,  38.42857143])
+        self.assertAlmostEqual(results.D2, .388656011675)
+        self.assertAlmostEqual(results.adj_D2, 0.36207583826952761)
+
+class TestBinomial(unittest.TestCase):
+
+    def setUp(self):
+        #London house price data
+        #y: 'BATH2'
+        y = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+            0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
+            0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
+       	self.y = y.reshape((316,1))
+        #X: 'FLOORSZ'
+       	X = np.array([ 77,  75,  64,  95, 107, 100,  81, 151,  98, 260, 171, 161,  91,
+            80,  50,  85,  52,  69,  60,  84, 155,  97,  69, 126,  90,  43,
+            51,  41, 140,  80,  52,  86,  66,  60,  40, 155, 138,  97, 115,
+            148, 206,  60,  53,  96,  88, 160,  31,  43, 154,  60, 131,  60,
+            46,  61, 125, 150,  76,  92,  96, 100, 105,  72,  48,  41,  72,
+            65,  60,  65,  98,  33, 144, 111,  91, 108,  38,  48,  95,  63,
+            98, 129, 108,  51, 131,  66,  48, 127,  76,  68,  52,  64,  57,
+            121,  67,  76, 112,  96,  90,  53,  93,  64,  97,  58,  44, 157,
+            53,  70,  71, 167,  47,  70,  96,  77,  75,  71,  67,  47,  71,
+            90,  69,  64,  65,  95,  60,  60,  65,  54, 121, 105,  50,  85,
+            69,  69,  62,  65,  93,  93,  70,  62, 155,  68, 117,  80,  80,
+            75,  98, 114,  86,  70,  50,  51, 163, 124,  59,  95,  51,  63,
+            85,  53,  46, 102, 114,  83,  47,  40,  63, 123, 100,  63, 110,
+            79,  98,  99, 120,  52,  48,  37,  81,  30,  88,  50,  35, 116,
+            67,  45,  80,  86, 109,  59,  75,  60,  71, 141, 121,  50, 168,
+            90,  51, 133,  75, 133, 127,  37,  68, 105,  61, 123, 151, 110,
+            77, 220,  94,  77,  70, 100,  98, 126,  55, 105,  60, 176, 104,
+            68,  62,  70,  48, 102,  80,  97,  66,  80, 102, 160,  55,  60,
+            71, 125,  85,  85, 190, 137,  48,  41,  42,  51,  57,  60, 114,
+            88,  84, 108,  66,  85,  42,  98,  90, 127, 100,  55,  76,  82,
+            63,  80,  71,  76, 121, 109,  92, 160, 109, 185, 100,  90,  90,
+            86,  88,  95, 116, 135,  61,  74,  60, 235,  76,  66, 100,  49,
+            50,  37, 100,  88,  90,  52,  95,  81,  79,  96,  75,  91,  86,
+            83, 180, 108,  80,  96,  49, 117, 117,  86,  46,  66,  95,  57,
+            120, 137,  68, 240])
+        self.X = X.reshape((316,1))
+
+    def testIWLS(self):
+        model = GLM(self.y, self.X, family=Binomial())
+        results = model.fit()
+        self.assertEqual(results.n, 316)
+        self.assertEqual(results.df_model, 1)
+        self.assertEqual(results.df_resid, 314)
+        self.assertEqual(results.aic, 155.19347530342466)
+        self.assertEqual(results.bic, -1656.1095797628657)
+        self.assertEqual(results.deviance, 151.19347530342466)
+        self.assertEqual(results.llf, -75.596737651712331)
+        self.assertEqual(results.null_deviance, 189.16038985881212)
+        self.assertEqual(results.scale, 1.0)
+        np.testing.assert_allclose(results.params, [-5.33638276,  0.0287754 ])
+        np.testing.assert_allclose(results.bse, [ 0.64499904,  0.00518312],
+                atol=1.0e-8)
+        np.testing.assert_allclose(results.cov_params(), 
+            [[  4.16023762e-01,  -3.14338457e-03],
+            [ -3.14338457e-03,   2.68646833e-05]])
+        np.testing.assert_allclose(results.tvalues, [-8.27347396,  5.55175826])
+        np.testing.assert_allclose(results.pvalues, [  1.30111233e-16,
+            2.82810512e-08])
+        np.testing.assert_allclose(results.conf_int(), 
+            [[-6.60055765, -4.07220787],
+            [ 0.01861668,  0.03893412]], atol=1.0e-8)
+        np.testing.assert_allclose(results.normalized_cov_params, 
+            [[  4.16023762e-01,  -3.14338457e-03],
+            [ -3.14338457e-03,   2.68646833e-05]])
+        np.testing.assert_allclose(results.mu, 
+            [ 0.04226237,  0.03999333,  0.02946178,  0.0689636 ,  0.09471181,
+            0.07879431,  0.04717464,  0.27065598,  0.07471691,  0.89522144,
+            0.39752487,  0.33102718,  0.06192993,  0.04589793,  0.01988679,
+            0.0526265 ,  0.02104007,  0.03386636,  0.02634295,  0.05121018,
+            0.29396682,  0.07275173,  0.03386636,  0.15307528,  0.06027915,
+            0.01631789,  0.02045547,  0.01541937,  0.2128508 ,  0.04589793,
+            0.02104007,  0.05407977,  0.0311527 ,  0.02634295,  0.01498855,
+            0.29396682,  0.20336776,  0.07275173,  0.11637537,  0.25395607,
+            0.64367488,  0.02634295,  0.02164101,  0.07083428,  0.05710047,
+            0.32468619,  0.01160845,  0.01631789,  0.28803008,  0.02634295,
+            0.17267234,  0.02634295,  0.01776301,  0.02709115,  0.14938186,
+            0.26501331,  0.04111287,  0.06362285,  0.07083428,  0.07879431,
+            0.08989109,  0.03680743,  0.0187955 ,  0.01541937,  0.03680743,
+            0.03029581,  0.02634295,  0.03029581,  0.07471691,  0.01228768,
+            0.23277197,  0.10505173,  0.06192993,  0.09720799,  0.01416217,
+            0.0187955 ,  0.0689636 ,  0.02865003,  0.07471691,  0.16460503,
+            0.09720799,  0.02045547,  0.17267234,  0.0311527 ,  0.0187955 ,
+            0.15684317,  0.04111287,  0.03293737,  0.02104007,  0.02946178,
+            0.02421701,  0.1353385 ,  0.03203302,  0.04111287,  0.10778798,
+            0.07083428,  0.06027915,  0.02164101,  0.06535882,  0.02946178,
+            0.07275173,  0.02490638,  0.01678627,  0.30605146,  0.02164101,
+            0.03482061,  0.03580075,  0.37030921,  0.0182721 ,  0.03482061,
+            0.07083428,  0.04226237,  0.03999333,  0.03580075,  0.03203302,
+            0.0182721 ,  0.03580075,  0.06027915,  0.03386636,  0.02946178,
+            0.03029581,  0.0689636 ,  0.02634295,  0.02634295,  0.03029581,
+            0.02225873,  0.1353385 ,  0.08989109,  0.01988679,  0.0526265 ,
+            0.03386636,  0.03386636,  0.02786   ,  0.03029581,  0.06535882,
+            0.06535882,  0.03482061,  0.02786   ,  0.29396682,  0.03293737,
+            0.12242534,  0.04589793,  0.04589793,  0.03999333,  0.07471691,
+            0.11344884,  0.05407977,  0.03482061,  0.01988679,  0.02045547,
+            0.34389327,  0.14576223,  0.02561486,  0.0689636 ,  0.02045547,
+            0.02865003,  0.0526265 ,  0.02164101,  0.01776301,  0.08307425,
+            0.11344884,  0.04982997,  0.0182721 ,  0.01498855,  0.02865003,
+            0.14221564,  0.07879431,  0.02865003,  0.10237696,  0.04465416,
+            0.07471691,  0.07673078,  0.13200634,  0.02104007,  0.0187955 ,
+            0.01376599,  0.04717464,  0.01128289,  0.05710047,  0.01988679,
+            0.01300612,  0.11936722,  0.03203302,  0.01726786,  0.04589793,
+            0.05407977,  0.09976271,  0.02561486,  0.03999333,  0.02634295,
+            0.03580075,  0.21771181,  0.1353385 ,  0.01988679,  0.37704374,
+            0.06027915,  0.02045547,  0.18104935,  0.03999333,  0.18104935,
+            0.15684317,  0.01376599,  0.03293737,  0.08989109,  0.02709115,
+            0.14221564,  0.27065598,  0.10237696,  0.04226237,  0.72991785,
+            0.06713876,  0.04226237,  0.03482061,  0.07879431,  0.07471691,
+            0.15307528,  0.02289366,  0.08989109,  0.02634295,  0.43243779,
+            0.08756457,  0.03293737,  0.02786   ,  0.03482061,  0.0187955 ,
+            0.08307425,  0.04589793,  0.07275173,  0.0311527 ,  0.04589793,
+            0.08307425,  0.32468619,  0.02289366,  0.02634295,  0.03580075,
+            0.14938186,  0.0526265 ,  0.0526265 ,  0.53268924,  0.19874565,
+            0.0187955 ,  0.01541937,  0.01586237,  0.02045547,  0.02421701,
+            0.02634295,  0.11344884,  0.05710047,  0.05121018,  0.09720799,
+            0.0311527 ,  0.0526265 ,  0.01586237,  0.07471691,  0.06027915,
+            0.15684317,  0.07879431,  0.02289366,  0.04111287,  0.04848506,
+            0.02865003,  0.04589793,  0.03580075,  0.04111287,  0.1353385 ,
+            0.09976271,  0.06362285,  0.32468619,  0.09976271,  0.49676673,
+            0.07879431,  0.06027915,  0.06027915,  0.05407977,  0.05710047,
+            0.0689636 ,  0.11936722,  0.18973955,  0.02709115,  0.03890304,
+            0.02634295,  0.80625182,  0.04111287,  0.0311527 ,  0.07879431,
+            0.0193336 ,  0.01988679,  0.01376599,  0.07879431,  0.05710047,
+            0.06027915,  0.02104007,  0.0689636 ,  0.04717464,  0.04465416,
+            0.07083428,  0.03999333,  0.06192993,  0.05407977,  0.04982997,
+            0.46087756,  0.09720799,  0.04589793,  0.07083428,  0.0193336 ,
+            0.12242534,  0.12242534,  0.05407977,  0.01776301,  0.0311527 ,
+            0.0689636 ,  0.02421701,  0.13200634,  0.19874565,  0.03293737,
+            0.82774282], atol=1.0e-8)
+        self.assertAlmostEqual(results.pearson_chi2, 271.21110541713801)
+        np.testing.assert_allclose(results.resid_response, 
+            [-0.04226237, -0.03999333, -0.02946178, -0.0689636 , -0.09471181,
+            -0.07879431, -0.04717464, -0.27065598, -0.07471691,  0.10477856,
+            -0.39752487,  0.66897282, -0.06192993, -0.04589793, -0.01988679,
+            -0.0526265 , -0.02104007, -0.03386636, -0.02634295, -0.05121018,
+            -0.29396682,  0.92724827, -0.03386636, -0.15307528, -0.06027915,
+            -0.01631789, -0.02045547, -0.01541937, -0.2128508 , -0.04589793,
+            -0.02104007, -0.05407977, -0.0311527 , -0.02634295, -0.01498855,
+            -0.29396682,  0.79663224, -0.07275173, -0.11637537,  0.74604393,
+            -0.64367488, -0.02634295, -0.02164101, -0.07083428, -0.05710047,
+            -0.32468619, -0.01160845, -0.01631789, -0.28803008, -0.02634295,
+            -0.17267234, -0.02634295, -0.01776301, -0.02709115,  0.85061814,
+            0.73498669, -0.04111287, -0.06362285, -0.07083428, -0.07879431,
+            0.91010891, -0.03680743, -0.0187955 , -0.01541937, -0.03680743,
+            -0.03029581, -0.02634295, -0.03029581, -0.07471691, -0.01228768,
+            0.76722803, -0.10505173, -0.06192993, -0.09720799, -0.01416217,
+            -0.0187955 , -0.0689636 , -0.02865003, -0.07471691, -0.16460503,
+            -0.09720799, -0.02045547,  0.82732766, -0.0311527 , -0.0187955 ,
+            -0.15684317, -0.04111287, -0.03293737, -0.02104007, -0.02946178,
+            -0.02421701, -0.1353385 , -0.03203302, -0.04111287, -0.10778798,
+            -0.07083428, -0.06027915, -0.02164101, -0.06535882, -0.02946178,
+            -0.07275173, -0.02490638, -0.01678627, -0.30605146, -0.02164101,
+            -0.03482061, -0.03580075,  0.62969079, -0.0182721 , -0.03482061,
+            -0.07083428, -0.04226237, -0.03999333, -0.03580075, -0.03203302,
+            -0.0182721 , -0.03580075, -0.06027915, -0.03386636, -0.02946178,
+            -0.03029581, -0.0689636 , -0.02634295, -0.02634295, -0.03029581,
+            -0.02225873, -0.1353385 , -0.08989109, -0.01988679, -0.0526265 ,
+            -0.03386636, -0.03386636, -0.02786   , -0.03029581, -0.06535882,
+            -0.06535882, -0.03482061, -0.02786   , -0.29396682, -0.03293737,
+            -0.12242534, -0.04589793, -0.04589793, -0.03999333, -0.07471691,
+            -0.11344884, -0.05407977, -0.03482061, -0.01988679, -0.02045547,
+            0.65610673,  0.85423777, -0.02561486, -0.0689636 , -0.02045547,
+            -0.02865003, -0.0526265 , -0.02164101, -0.01776301, -0.08307425,
+            -0.11344884, -0.04982997, -0.0182721 , -0.01498855, -0.02865003,
+            -0.14221564, -0.07879431, -0.02865003, -0.10237696, -0.04465416,
+            -0.07471691, -0.07673078, -0.13200634, -0.02104007, -0.0187955 ,
+            -0.01376599, -0.04717464, -0.01128289,  0.94289953, -0.01988679,
+            -0.01300612, -0.11936722, -0.03203302, -0.01726786, -0.04589793,
+            -0.05407977, -0.09976271, -0.02561486, -0.03999333, -0.02634295,
+            -0.03580075, -0.21771181,  0.8646615 , -0.01988679,  0.62295626,
+            -0.06027915, -0.02045547, -0.18104935,  0.96000667, -0.18104935,
+            -0.15684317, -0.01376599, -0.03293737, -0.08989109, -0.02709115,
+            -0.14221564,  0.72934402, -0.10237696, -0.04226237, -0.72991785,
+            -0.06713876, -0.04226237, -0.03482061, -0.07879431, -0.07471691,
+            -0.15307528,  0.97710634,  0.91010891, -0.02634295, -0.43243779,
+            -0.08756457, -0.03293737, -0.02786   , -0.03482061, -0.0187955 ,
+             0.91692575, -0.04589793, -0.07275173, -0.0311527 , -0.04589793,
+            -0.08307425,  0.67531381, -0.02289366, -0.02634295, -0.03580075,
+            -0.14938186, -0.0526265 , -0.0526265 ,  0.46731076, -0.19874565,
+            -0.0187955 , -0.01541937, -0.01586237, -0.02045547, -0.02421701,
+            -0.02634295, -0.11344884, -0.05710047, -0.05121018, -0.09720799,
+            0.9688473 , -0.0526265 , -0.01586237, -0.07471691, -0.06027915,
+            -0.15684317, -0.07879431, -0.02289366, -0.04111287, -0.04848506,
+            -0.02865003, -0.04589793, -0.03580075, -0.04111287, -0.1353385 ,
+            -0.09976271, -0.06362285,  0.67531381, -0.09976271, -0.49676673,
+            -0.07879431, -0.06027915, -0.06027915, -0.05407977, -0.05710047,
+            -0.0689636 , -0.11936722, -0.18973955, -0.02709115, -0.03890304,
+            -0.02634295,  0.19374818, -0.04111287, -0.0311527 , -0.07879431,
+            -0.0193336 , -0.01988679, -0.01376599, -0.07879431,  0.94289953,
+            -0.06027915, -0.02104007, -0.0689636 , -0.04717464, -0.04465416,
+            0.92916572, -0.03999333, -0.06192993, -0.05407977, -0.04982997,
+            -0.46087756, -0.09720799, -0.04589793, -0.07083428, -0.0193336 ,
+            -0.12242534, -0.12242534, -0.05407977, -0.01776301, -0.0311527 ,
+            -0.0689636 , -0.02421701, -0.13200634, -0.19874565, -0.03293737,
+            -0.82774282], atol=1.0e-8)
+        np.testing.assert_allclose(results.resid_working, 
+            [ -1.71062283e-03,  -1.53549840e-03,  -8.42423701e-04,
+            -4.42798906e-03,  -8.12073047e-03,  -5.71934606e-03,
+            -2.12046213e-03,  -5.34278480e-02,  -5.16550074e-03,
+            9.82823035e-03,  -9.52067472e-02,   1.48142818e-01,
+            -3.59779501e-03,  -2.00993083e-03,  -3.87619325e-04,
+            -2.62379729e-03,  -4.33370579e-04,  -1.10808799e-03,
+            -6.75670103e-04,  -2.48818484e-03,  -6.10129090e-02,
+            6.25511612e-02,  -1.10808799e-03,  -1.98451739e-02,
+            -3.41454749e-03,  -2.61928659e-04,  -4.09867263e-04,
+            -2.34090923e-04,  -3.56621577e-02,  -2.00993083e-03,
+            -4.33370579e-04,  -2.76645832e-03,  -9.40257152e-04,
+            -6.75670103e-04,  -2.21289369e-04,  -6.10129090e-02,
+            1.29061842e-01,  -4.90775251e-03,  -1.19671283e-02,
+            1.41347263e-01,  -1.47631680e-01,  -6.75670103e-04,
+            -4.58198217e-04,  -4.66208406e-03,  -3.07429001e-03,
+            -7.11923401e-02,  -1.33191898e-04,  -2.61928659e-04,
+            -5.90659690e-02,  -6.75670103e-04,  -2.46673839e-02,
+            -6.75670103e-04,  -3.09919962e-04,  -7.14047519e-04,
+            1.08085429e-01,   1.43161630e-01,  -1.62077632e-03,
+            -3.79032977e-03,  -4.66208406e-03,  -5.71934606e-03,
+            7.44566288e-02,  -1.30492035e-03,  -3.46630910e-04,
+            -2.34090923e-04,  -1.30492035e-03,  -8.90029618e-04,
+            -6.75670103e-04,  -8.90029618e-04,  -5.16550074e-03,
+            -1.49131762e-04,   1.37018624e-01,  -9.87652847e-03,
+            -3.59779501e-03,  -8.53083698e-03,  -1.97726627e-04,
+            -3.46630910e-04,  -4.42798906e-03,  -7.97307494e-04,
+            -5.16550074e-03,  -2.26348718e-02,  -8.53083698e-03,
+            -4.09867263e-04,   1.18189219e-01,  -9.40257152e-04,
+            -3.46630910e-04,  -2.07414715e-02,  -1.62077632e-03,
+            -1.04913757e-03,  -4.33370579e-04,  -8.42423701e-04,
+            -5.72261321e-04,  -1.58375811e-02,  -9.93244730e-04,
+            -1.62077632e-03,  -1.03659408e-02,  -4.66208406e-03,
+            -3.41454749e-03,  -4.58198217e-04,  -3.99257703e-03,
+            -8.42423701e-04,  -4.90775251e-03,  -6.04877746e-04,
+            -2.77048947e-04,  -6.50004229e-02,  -4.58198217e-04,
+            -1.17025566e-03,  -1.23580799e-03,   1.46831486e-01,
+            -3.27769165e-04,  -1.17025566e-03,  -4.66208406e-03,
+            -1.71062283e-03,  -1.53549840e-03,  -1.23580799e-03,
+            -9.93244730e-04,  -3.27769165e-04,  -1.23580799e-03,
+            -3.41454749e-03,  -1.10808799e-03,  -8.42423701e-04,
+            -8.90029618e-04,  -4.42798906e-03,  -6.75670103e-04,
+            -6.75670103e-04,  -8.90029618e-04,  -4.84422741e-04,
+            -1.58375811e-02,  -7.35405096e-03,  -3.87619325e-04,
+            -2.62379729e-03,  -1.10808799e-03,  -1.10808799e-03,
+            -7.54555329e-04,  -8.90029618e-04,  -3.99257703e-03,
+            -3.99257703e-03,  -1.17025566e-03,  -7.54555329e-04,
+            -6.10129090e-02,  -1.04913757e-03,  -1.31530576e-02,
+            -2.00993083e-03,  -2.00993083e-03,  -1.53549840e-03,
+            -5.16550074e-03,  -1.14104800e-02,  -2.76645832e-03,
+            -1.17025566e-03,  -3.87619325e-04,  -4.09867263e-04,
+            1.48037813e-01,   1.06365931e-01,  -6.39314594e-04,
+            -4.42798906e-03,  -4.09867263e-04,  -7.97307494e-04,
+            -2.62379729e-03,  -4.58198217e-04,  -3.09919962e-04,
+            -6.32800839e-03,  -1.14104800e-02,  -2.35929680e-03,
+            -3.27769165e-04,  -2.21289369e-04,  -7.97307494e-04,
+            -1.73489362e-02,  -5.71934606e-03,  -7.97307494e-04,
+            -9.40802551e-03,  -1.90495384e-03,  -5.16550074e-03,
+            -5.43585191e-03,  -1.51253748e-02,  -4.33370579e-04,
+            -3.46630910e-04,  -1.86893696e-04,  -2.12046213e-03,
+            -1.25867293e-04,   5.07657192e-02,  -3.87619325e-04,
+            -1.66959104e-04,  -1.25477263e-02,  -9.93244730e-04,
+            -2.93030065e-04,  -2.00993083e-03,  -2.76645832e-03,
+            -8.95970087e-03,  -6.39314594e-04,  -1.53549840e-03,
+            -6.75670103e-04,  -1.23580799e-03,  -3.70792339e-02,
+            1.01184411e-01,  -3.87619325e-04,   1.46321062e-01,
+            -3.41454749e-03,  -4.09867263e-04,  -2.68442736e-02,
+            3.68583645e-02,  -2.68442736e-02,  -2.07414715e-02,
+            -1.86893696e-04,  -1.04913757e-03,  -7.35405096e-03,
+            -7.14047519e-04,  -1.73489362e-02,   1.43973473e-01,
+            -9.40802551e-03,  -1.71062283e-03,  -1.43894386e-01,
+            -4.20497779e-03,  -1.71062283e-03,  -1.17025566e-03,
+            -5.71934606e-03,  -5.16550074e-03,  -1.98451739e-02,
+            2.18574168e-02,   7.44566288e-02,  -6.75670103e-04,
+            -1.06135519e-01,  -6.99614755e-03,  -1.04913757e-03,
+            -7.54555329e-04,  -1.17025566e-03,  -3.46630910e-04,
+            6.98449121e-02,  -2.00993083e-03,  -4.90775251e-03,
+            -9.40257152e-04,  -2.00993083e-03,  -6.32800839e-03,
+            1.48072729e-01,  -5.12120512e-04,  -6.75670103e-04,
+            -1.23580799e-03,  -1.89814939e-02,  -2.62379729e-03,
+            -2.62379729e-03,   1.16328328e-01,  -3.16494123e-02,
+            -3.46630910e-04,  -2.34090923e-04,  -2.47623705e-04,
+            -4.09867263e-04,  -5.72261321e-04,  -6.75670103e-04,
+            -1.14104800e-02,  -3.07429001e-03,  -2.48818484e-03,
+            -8.53083698e-03,   2.92419496e-02,  -2.62379729e-03,
+            -2.47623705e-04,  -5.16550074e-03,  -3.41454749e-03,
+            -2.07414715e-02,  -5.71934606e-03,  -5.12120512e-04,
+            -1.62077632e-03,  -2.23682205e-03,  -7.97307494e-04,
+            -2.00993083e-03,  -1.23580799e-03,  -1.62077632e-03,
+            -1.58375811e-02,  -8.95970087e-03,  -3.79032977e-03,
+            1.48072729e-01,  -8.95970087e-03,  -1.24186489e-01,
+            -5.71934606e-03,  -3.41454749e-03,  -3.41454749e-03,
+            -2.76645832e-03,  -3.07429001e-03,  -4.42798906e-03,
+            -1.25477263e-02,  -2.91702648e-02,  -7.14047519e-04,
+            -1.45456868e-03,  -6.75670103e-04,   3.02653681e-02,
+            -1.62077632e-03,  -9.40257152e-04,  -5.71934606e-03,
+            -3.66561274e-04,  -3.87619325e-04,  -1.86893696e-04,
+            -5.71934606e-03,   5.07657192e-02,  -3.41454749e-03,
+            -4.33370579e-04,  -4.42798906e-03,  -2.12046213e-03,
+            -1.90495384e-03,   6.11546973e-02,  -1.53549840e-03,
+            -3.59779501e-03,  -2.76645832e-03,  -2.35929680e-03,
+            -1.14513988e-01,  -8.53083698e-03,  -2.00993083e-03,
+            -4.66208406e-03,  -3.66561274e-04,  -1.31530576e-02,
+            -1.31530576e-02,  -2.76645832e-03,  -3.09919962e-04,
+            -9.40257152e-04,  -4.42798906e-03,  -5.72261321e-04,
+            -1.51253748e-02,  -3.16494123e-02,  -1.04913757e-03,
+            -1.18023417e-01])
+        np.testing.assert_allclose(results.resid_pearson, 
+            [-0.21006498, -0.20410641, -0.17423009, -0.27216147, -0.3234511 ,
+            -0.29246179, -0.22250903, -0.60917574, -0.28416602,  0.3421141 ,
+            -0.81229277,  1.42158361, -0.25694055, -0.21933056, -0.142444  ,
+            -0.23569027, -0.14660243, -0.18722578, -0.16448609, -0.2323235 ,
+            -0.64526275,  3.57006696, -0.18722578, -0.42513819, -0.25327023,
+            -0.12879668, -0.14450826, -0.12514332, -0.5200069 , -0.21933056,
+            -0.14660243, -0.23910582, -0.17931646, -0.16448609, -0.12335569,
+            -0.64526275,  1.97919183, -0.28010679, -0.36290807,  1.71396874,
+            -1.3440334 , -0.16448609, -0.14872695, -0.27610555, -0.24608613,
+            -0.69339243, -0.1083734 , -0.12879668, -0.63604537, -0.16448609,
+            -0.45684893, -0.16448609, -0.13447767, -0.16686977,  2.3862634 ,
+            1.66535145, -0.20706426, -0.26066405, -0.27610555, -0.29246179,
+            3.18191348, -0.19548397, -0.13840353, -0.12514332, -0.19548397,
+            -0.17675498, -0.16448609, -0.17675498, -0.28416602, -0.11153719,
+            1.81550268, -0.34261205, -0.25694055, -0.32813846, -0.11985666,
+            -0.13840353, -0.27216147, -0.17174127, -0.28416602, -0.44389026,
+            -0.32813846, -0.14450826,  2.18890738, -0.17931646, -0.13840353,
+            -0.43129917, -0.20706426, -0.18455132, -0.14660243, -0.17423009,
+            -0.1575374 , -0.39562855, -0.18191506, -0.20706426, -0.34757708,
+            -0.27610555, -0.25327023, -0.14872695, -0.26444152, -0.17423009,
+            -0.28010679, -0.15982038, -0.13066317, -0.66410018, -0.14872695,
+            -0.189939  , -0.19269154,  1.30401147, -0.13642648, -0.189939  ,
+            -0.27610555, -0.21006498, -0.20410641, -0.19269154, -0.18191506,
+            -0.13642648, -0.19269154, -0.25327023, -0.18722578, -0.17423009,
+            -0.17675498, -0.27216147, -0.16448609, -0.16448609, -0.17675498,
+            -0.15088226, -0.39562855, -0.3142763 , -0.142444  , -0.23569027,
+            -0.18722578, -0.18722578, -0.169288  , -0.17675498, -0.26444152,
+            -0.26444152, -0.189939  , -0.169288  , -0.64526275, -0.18455132,
+            -0.3735026 , -0.21933056, -0.21933056, -0.20410641, -0.28416602,
+            -0.35772404, -0.23910582, -0.189939  , -0.142444  , -0.14450826,
+            1.38125991,  2.42084442, -0.16213645, -0.27216147, -0.14450826,
+            -0.17174127, -0.23569027, -0.14872695, -0.13447767, -0.30099975,
+            -0.35772404, -0.22900483, -0.13642648, -0.12335569, -0.17174127,
+            -0.4071783 , -0.29246179, -0.17174127, -0.33771794, -0.21619749,
+            -0.28416602, -0.28828407, -0.38997712, -0.14660243, -0.13840353,
+            -0.11814455, -0.22250903, -0.10682532,  4.06361781, -0.142444  ,
+            -0.11479334, -0.36816723, -0.18191506, -0.1325567 , -0.21933056,
+            -0.23910582, -0.33289374, -0.16213645, -0.20410641, -0.16448609,
+            -0.19269154, -0.52754269,  2.52762346, -0.142444  ,  1.28538406,
+            -0.25327023, -0.14450826, -0.47018591,  4.89940505, -0.47018591,
+            -0.43129917, -0.11814455, -0.18455132, -0.3142763 , -0.16686977,
+            -0.4071783 ,  1.64156241, -0.33771794, -0.21006498, -1.6439517 ,
+            -0.26827373, -0.21006498, -0.189939  , -0.29246179, -0.28416602,
+            -0.42513819,  6.53301013,  3.18191348, -0.16448609, -0.87288109,
+            -0.30978696, -0.18455132, -0.169288  , -0.189939  , -0.13840353,
+             3.32226189, -0.21933056, -0.28010679, -0.17931646, -0.21933056,
+            -0.30099975,  1.44218477, -0.1530688 , -0.16448609, -0.19269154,
+            -0.41906522, -0.23569027, -0.23569027,  0.93662539, -0.4980393 ,
+            -0.13840353, -0.12514332, -0.12695686, -0.14450826, -0.1575374 ,
+            -0.16448609, -0.35772404, -0.24608613, -0.2323235 , -0.32813846,
+            5.57673284, -0.23569027, -0.12695686, -0.28416602, -0.25327023,
+            -0.43129917, -0.29246179, -0.1530688 , -0.20706426, -0.22573357,
+            -0.17174127, -0.21933056, -0.19269154, -0.20706426, -0.39562855,
+            -0.33289374, -0.26066405,  1.44218477, -0.33289374, -0.99355423,
+            -0.29246179, -0.25327023, -0.25327023, -0.23910582, -0.24608613,
+            -0.27216147, -0.36816723, -0.48391225, -0.16686977, -0.20119082,
+            -0.16448609,  0.49021146, -0.20706426, -0.17931646, -0.29246179,
+            -0.14040923, -0.142444  , -0.11814455, -0.29246179,  4.06361781,
+            -0.25327023, -0.14660243, -0.27216147, -0.22250903, -0.21619749,
+            3.6218033 , -0.20410641, -0.25694055, -0.23910582, -0.22900483,
+            -0.92458976, -0.32813846, -0.21933056, -0.27610555, -0.14040923,
+            -0.3735026 , -0.3735026 , -0.23910582, -0.13447767, -0.17931646,
+            -0.27216147, -0.1575374 , -0.38997712, -0.4980393 , -0.18455132,
+            -2.19209332])
+        np.testing.assert_allclose(results.resid_anscombe, 
+            [-0.31237627, -0.3036605 , -0.25978208, -0.40240831, -0.47552289,
+            -0.43149255, -0.33053793, -0.85617194, -0.41962951,  0.50181328,
+            -1.0954382 ,  1.66940149, -0.38048321, -0.3259044 , -0.21280762,
+            -0.34971301, -0.21896842, -0.27890356, -0.2454118 , -0.34482158,
+            -0.90063409,  2.80452413, -0.27890356, -0.61652596, -0.37518169,
+            -0.19255932, -0.2158664 , -0.18713159, -0.74270558, -0.3259044 ,
+            -0.21896842, -0.35467084, -0.2672722 , -0.2454118 , -0.18447466,
+            -0.90063409,  2.05763941, -0.41381347, -0.53089521,  1.88552083,
+            -1.60654218, -0.2454118 , -0.22211425, -0.40807333, -0.3647888 ,
+            -0.95861559, -0.16218047, -0.19255932, -0.88935802, -0.2454118 ,
+            -0.65930821, -0.2454118 , -0.20099345, -0.24892975,  2.28774016,
+            1.85167195, -0.30798858, -0.38585584, -0.40807333, -0.43149255,
+            2.65398426, -0.2910267 , -0.20681747, -0.18713159, -0.2910267 ,
+            -0.26350118, -0.2454118 , -0.26350118, -0.41962951, -0.16689207,
+             1.95381191, -0.50251231, -0.38048321, -0.48214234, -0.17927213,
+            -0.20681747, -0.40240831, -0.25611424, -0.41962951, -0.64189694,
+            -0.48214234, -0.2158664 ,  2.18071204, -0.2672722 , -0.20681747,
+            -0.62488429, -0.30798858, -0.27497271, -0.21896842, -0.25978208,
+            -0.23514749, -0.57618899, -0.27109582, -0.30798858, -0.50947546,
+            -0.40807333, -0.37518169, -0.22211425, -0.39130036, -0.25978208,
+            -0.41381347, -0.2385213 , -0.19533116, -0.92350689, -0.22211425,
+            -0.28288904, -0.28692985,  1.5730846 , -0.20388497, -0.28288904,
+            -0.40807333, -0.31237627, -0.3036605 , -0.28692985, -0.27109582,
+            -0.20388497, -0.28692985, -0.37518169, -0.27890356, -0.25978208,
+            -0.26350118, -0.40240831, -0.2454118 , -0.2454118 , -0.26350118,
+            -0.22530448, -0.57618899, -0.46253505, -0.21280762, -0.34971301,
+            -0.27890356, -0.27890356, -0.25249702, -0.26350118, -0.39130036,
+            -0.39130036, -0.28288904, -0.25249702, -0.90063409, -0.27497271,
+            -0.5456246 , -0.3259044 , -0.3259044 , -0.3036605 , -0.41962951,
+            -0.52366614, -0.35467084, -0.28288904, -0.21280762, -0.2158664 ,
+            1.63703418,  2.30570989, -0.24194253, -0.40240831, -0.2158664 ,
+            -0.25611424, -0.34971301, -0.22211425, -0.20099345, -0.44366892,
+            -0.52366614, -0.33999576, -0.20388497, -0.18447466, -0.25611424,
+            -0.59203547, -0.43149255, -0.25611424, -0.49563627, -0.32133344,
+            -0.41962951, -0.42552227, -0.56840788, -0.21896842, -0.20681747,
+            -0.17672552, -0.33053793, -0.15987433,  2.9768074 , -0.21280762,
+            -0.17173916, -0.53821445, -0.27109582, -0.19814236, -0.3259044 ,
+            -0.35467084, -0.48884654, -0.24194253, -0.3036605 , -0.2454118 ,
+            -0.28692985, -0.75249089,  2.35983933, -0.21280762,  1.55726719,
+            -0.37518169, -0.2158664 , -0.67712261,  3.23165236, -0.67712261,
+            -0.62488429, -0.17672552, -0.27497271, -0.46253505, -0.24892975,
+            -0.59203547,  1.83482464, -0.49563627, -0.31237627, -1.83652534,
+            -0.39681759, -0.31237627, -0.28288904, -0.43149255, -0.41962951,
+            -0.61652596,  3.63983609,  2.65398426, -0.2454118 , -1.16171662,
+            -0.45616505, -0.27497271, -0.25249702, -0.28288904, -0.20681747,
+            2.71015945, -0.3259044 , -0.41381347, -0.2672722 , -0.3259044 ,
+            -0.44366892,  1.68567947, -0.22853969, -0.2454118 , -0.28692985,
+            -0.60826548, -0.34971301, -0.34971301,  1.2290223 , -0.71397735,
+            -0.20681747, -0.18713159, -0.1898263 , -0.2158664 , -0.23514749,
+            -0.2454118 , -0.52366614, -0.3647888 , -0.34482158, -0.48214234,
+            3.41271513, -0.34971301, -0.1898263 , -0.41962951, -0.37518169,
+            -0.62488429, -0.43149255, -0.22853969, -0.30798858, -0.3352348 ,
+            -0.25611424, -0.3259044 , -0.28692985, -0.30798858, -0.57618899,
+            -0.48884654, -0.38585584,  1.68567947, -0.48884654, -1.28709718,
+            -0.43149255, -0.37518169, -0.37518169, -0.35467084, -0.3647888 ,
+            -0.40240831, -0.53821445, -0.69534436, -0.24892975, -0.29939131,
+            -0.2454118 ,  0.70366797, -0.30798858, -0.2672722 , -0.43149255,
+            -0.2097915 , -0.21280762, -0.17672552, -0.43149255,  2.9768074 ,
+            -0.37518169, -0.21896842, -0.40240831, -0.33053793, -0.32133344,
+            2.82351017, -0.3036605 , -0.38048321, -0.35467084, -0.33999576,
+            -1.21650102, -0.48214234, -0.3259044 , -0.40807333, -0.2097915 ,
+            -0.5456246 , -0.5456246 , -0.35467084, -0.20099345, -0.2672722 ,
+            -0.40240831, -0.23514749, -0.56840788, -0.71397735, -0.27497271,
+            -2.18250381])
+        np.testing.assert_allclose(results.resid_deviance, 
+            [-0.29387552, -0.2857098 , -0.24455876, -0.37803944, -0.44609851,
+            -0.40514674, -0.31088148, -0.79449324, -0.39409528,  0.47049798,
+            -1.00668653,  1.48698001, -0.35757692, -0.30654405, -0.20043547,
+            -0.32882173, -0.20622595, -0.26249995, -0.23106769, -0.32424676,
+            -0.83437766,  2.28941155, -0.26249995, -0.57644334, -0.35262564,
+            -0.18139734, -0.20331052, -0.17629229, -0.69186337, -0.30654405,
+            -0.20622595, -0.33345774, -0.251588  , -0.23106769, -0.17379306,
+            -0.83437766,  1.78479093, -0.38867448, -0.4974393 ,  1.65565332,
+            -1.43660134, -0.23106769, -0.20918228, -0.38332275, -0.34291558,
+            -0.88609006, -0.15281596, -0.18139734, -0.82428104, -0.23106769,
+            -0.61571821, -0.23106769, -0.18932865, -0.234371  ,  1.94999969,
+            1.62970871, -0.2897651 , -0.36259328, -0.38332275, -0.40514674,
+            2.19506559, -0.27386827, -0.19480442, -0.17629229, -0.27386827,
+            -0.24804925, -0.23106769, -0.24804925, -0.39409528, -0.15725009,
+            1.7074519 , -0.47114617, -0.35757692, -0.4522457 , -0.16889886,
+            -0.19480442, -0.37803944, -0.24111595, -0.39409528, -0.59975102,
+            -0.4522457 , -0.20331052,  1.87422489, -0.251588  , -0.19480442,
+            -0.5841272 , -0.2897651 , -0.25881274, -0.20622595, -0.24455876,
+            -0.22142749, -0.53929061, -0.25517563, -0.2897651 , -0.47760126,
+            -0.38332275, -0.35262564, -0.20918228, -0.36767536, -0.24455876,
+            -0.38867448, -0.2245965 , -0.18400413, -0.85481866, -0.20918228,
+            -0.26623785, -0.27002708,  1.40955093, -0.19204738, -0.26623785,
+            -0.38332275, -0.29387552, -0.2857098 , -0.27002708, -0.25517563,
+            -0.19204738, -0.27002708, -0.35262564, -0.26249995, -0.24455876,
+            -0.24804925, -0.37803944, -0.23106769, -0.23106769, -0.24804925,
+            -0.21218006, -0.53929061, -0.43402996, -0.20043547, -0.32882173,
+            -0.26249995, -0.26249995, -0.23772023, -0.24804925, -0.36767536,
+            -0.36767536, -0.26623785, -0.23772023, -0.83437766, -0.25881274,
+            -0.51106408, -0.30654405, -0.30654405, -0.2857098 , -0.39409528,
+            -0.49074728, -0.33345774, -0.26623785, -0.20043547, -0.20331052,
+            1.46111186,  1.96253843, -0.22780971, -0.37803944, -0.20331052,
+            -0.24111595, -0.32882173, -0.20918228, -0.18932865, -0.41648237,
+            -0.49074728, -0.31973217, -0.19204738, -0.17379306, -0.24111595,
+            -0.55389988, -0.40514674, -0.24111595, -0.46476893, -0.30226435,
+            -0.39409528, -0.39958581, -0.53211065, -0.20622595, -0.19480442,
+            -0.16650295, -0.31088148, -0.15064545,  2.39288231, -0.20043547,
+            -0.16181126, -0.5042114 , -0.25517563, -0.18664773, -0.30654405,
+            -0.33345774, -0.45846897, -0.22780971, -0.2857098 , -0.23106769,
+            -0.27002708, -0.7007597 ,  1.99998811, -0.20043547,  1.39670618,
+            -0.35262564, -0.20331052, -0.63203077,  2.53733821, -0.63203077,
+            -0.5841272 , -0.16650295, -0.25881274, -0.43402996, -0.234371  ,
+            -0.55389988,  1.61672923, -0.46476893, -0.29387552, -1.61804148,
+            -0.37282386, -0.29387552, -0.26623785, -0.40514674, -0.39409528,
+            -0.57644334,  2.74841605,  2.19506559, -0.23106769, -1.06433539,
+            -0.42810736, -0.25881274, -0.23772023, -0.26623785, -0.19480442,
+            2.23070414, -0.30654405, -0.38867448, -0.251588  , -0.30654405,
+            -0.41648237,  1.49993075, -0.21521982, -0.23106769, -0.27002708,
+            -0.5688444 , -0.32882173, -0.32882173,  1.12233423, -0.66569789,
+            -0.19480442, -0.17629229, -0.17882689, -0.20331052, -0.22142749,
+            -0.23106769, -0.49074728, -0.34291558, -0.32424676, -0.4522457 ,
+            2.63395309, -0.32882173, -0.17882689, -0.39409528, -0.35262564,
+            -0.5841272 , -0.40514674, -0.21521982, -0.2897651 , -0.3152773 ,
+            -0.24111595, -0.30654405, -0.27002708, -0.2897651 , -0.53929061,
+            -0.45846897, -0.36259328,  1.49993075, -0.45846897, -1.17192274,
+            -0.40514674, -0.35262564, -0.35262564, -0.33345774, -0.34291558,
+            -0.37803944, -0.5042114 , -0.64869028, -0.234371  , -0.28170899,
+            -0.23106769,  0.65629132, -0.2897651 , -0.251588  , -0.40514674,
+            -0.19760028, -0.20043547, -0.16650295, -0.40514674,  2.39288231,
+            -0.35262564, -0.20622595, -0.37803944, -0.31088148, -0.30226435,
+            2.30104857, -0.2857098 , -0.35757692, -0.33345774, -0.31973217,
+            -1.11158678, -0.4522457 , -0.30654405, -0.38332275, -0.19760028,
+            -0.51106408, -0.51106408, -0.33345774, -0.18932865, -0.251588  ,
+            -0.37803944, -0.22142749, -0.53211065, -0.66569789, -0.25881274,
+            -1.87550882])
+        np.testing.assert_allclose(results.null, 
+            [ 0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759,  0.08860759,  0.08860759,  0.08860759,  0.08860759,
+            0.08860759])
+        self.assertAlmostEqual(results.D2, .200712816165)
+        self.assertAlmostEqual(results.adj_D2, 0.19816731557930456)
+
+
+
+if __name__ == '__main__':
+	unittest.main()

src/py/crankshaft/crankshaft/regression/glm/utils.py

@@ -0,0 +1,350 @@
+
+from __future__ import absolute_import, print_function
+import numpy as np
+import warnings
+
+
+def _bit_length_26(x):
+    if x == 0:
+        return 0
+    elif x == 1:
+        return 1
+    else:
+        return len(bin(x)) - 2
+
+
+try:
+    from scipy.lib._version import NumpyVersion
+except ImportError:
+    import re
+    string_types = basestring 
+
+    class NumpyVersion():
+        """Parse and compare numpy version strings.
+        Numpy has the following versioning scheme (numbers given are examples; they
+        can be >9) in principle):
+        - Released version: '1.8.0', '1.8.1', etc.
+        - Alpha: '1.8.0a1', '1.8.0a2', etc.
+        - Beta: '1.8.0b1', '1.8.0b2', etc.
+        - Release candidates: '1.8.0rc1', '1.8.0rc2', etc.
+        - Development versions: '1.8.0.dev-f1234afa' (git commit hash appended)
+        - Development versions after a1: '1.8.0a1.dev-f1234afa',
+                                        '1.8.0b2.dev-f1234afa',
+                                        '1.8.1rc1.dev-f1234afa', etc.
+        - Development versions (no git hash available): '1.8.0.dev-Unknown'
+        Comparing needs to be done against a valid version string or other
+        `NumpyVersion` instance.
+        Parameters
+        ----------
+        vstring : str
+            Numpy version string (``np.__version__``).
+        Notes
+        -----
+        All dev versions of the same (pre-)release compare equal.
+        Examples
+        --------
+        >>> from scipy.lib._version import NumpyVersion
+        >>> if NumpyVersion(np.__version__) < '1.7.0':
+        ...     print('skip')
+        skip
+        >>> NumpyVersion('1.7')  # raises ValueError, add ".0"
+        """
+
+        def __init__(self, vstring):
+            self.vstring = vstring
+            ver_main = re.match(r'\d[.]\d+[.]\d+', vstring)
+            if not ver_main:
+                raise ValueError("Not a valid numpy version string")
+
+            self.version = ver_main.group()
+            self.major, self.minor, self.bugfix = [int(x) for x in
+                                                   self.version.split('.')]
+            if len(vstring) == ver_main.end():
+                self.pre_release = 'final'
+            else:
+                alpha = re.match(r'a\d', vstring[ver_main.end():])
+                beta = re.match(r'b\d', vstring[ver_main.end():])
+                rc = re.match(r'rc\d', vstring[ver_main.end():])
+                pre_rel = [m for m in [alpha, beta, rc] if m is not None]
+                if pre_rel:
+                    self.pre_release = pre_rel[0].group()
+                else:
+                    self.pre_release = ''
+
+            self.is_devversion = bool(re.search(r'.dev-', vstring))
+
+        def _compare_version(self, other):
+            """Compare major.minor.bugfix"""
+            if self.major == other.major:
+                if self.minor == other.minor:
+                    if self.bugfix == other.bugfix:
+                        vercmp = 0
+                    elif self.bugfix > other.bugfix:
+                        vercmp = 1
+                    else:
+                        vercmp = -1
+                elif self.minor > other.minor:
+                    vercmp = 1
+                else:
+                    vercmp = -1
+            elif self.major > other.major:
+                vercmp = 1
+            else:
+                vercmp = -1
+
+            return vercmp
+
+        def _compare_pre_release(self, other):
+            """Compare alpha/beta/rc/final."""
+            if self.pre_release == other.pre_release:
+                vercmp = 0
+            elif self.pre_release == 'final':
+                vercmp = 1
+            elif other.pre_release == 'final':
+                vercmp = -1
+            elif self.pre_release > other.pre_release:
+                vercmp = 1
+            else:
+                vercmp = -1
+
+            return vercmp
+
+        def _compare(self, other):
+            if not isinstance(other, (string_types, NumpyVersion)):
+                raise ValueError("Invalid object to compare with NumpyVersion.")
+
+            if isinstance(other, string_types):
+                other = NumpyVersion(other)
+
+            vercmp = self._compare_version(other)
+            if vercmp == 0:
+                # Same x.y.z version, check for alpha/beta/rc
+                vercmp = self._compare_pre_release(other)
+                if vercmp == 0:
+                    # Same version and same pre-release, check if dev version
+                    if self.is_devversion is other.is_devversion:
+                        vercmp = 0
+                    elif self.is_devversion:
+                        vercmp = -1
+                    else:
+                        vercmp = 1
+
+            return vercmp
+
+        def __lt__(self, other):
+            return self._compare(other) < 0
+
+        def __le__(self, other):
+            return self._compare(other) <= 0
+
+        def __eq__(self, other):
+            return self._compare(other) == 0
+
+        def __ne__(self, other):
+            return self._compare(other) != 0
+
+        def __gt__(self, other):
+            return self._compare(other) > 0
+
+        def __ge__(self, other):
+            return self._compare(other) >= 0
+
+        def __repr(self):
+            return "NumpyVersion(%s)" % self.vstring
+
+
+def _next_regular(target):
+    """
+    Find the next regular number greater than or equal to target.
+    Regular numbers are composites of the prime factors 2, 3, and 5.
+    Also known as 5-smooth numbers or Hamming numbers, these are the optimal
+    size for inputs to FFTPACK.
+    Target must be a positive integer.
+    """
+    if target <= 6:
+        return target
+
+    # Quickly check if it's already a power of 2
+    if not (target & (target - 1)):
+        return target
+
+    match = float('inf')  # Anything found will be smaller
+    p5 = 1
+    while p5 < target:
+        p35 = p5
+        while p35 < target:
+            # Ceiling integer division, avoiding conversion to float
+            # (quotient = ceil(target / p35))
+            quotient = -(-target // p35)
+            # Quickly find next power of 2 >= quotient
+            try:
+                p2 = 2 ** ((quotient - 1).bit_length())
+            except AttributeError:
+                # Fallback for Python <2.7
+                p2 = 2 ** _bit_length_26(quotient - 1)
+
+            N = p2 * p35
+            if N == target:
+                return N
+            elif N < match:
+                match = N
+            p35 *= 3
+            if p35 == target:
+                return p35
+        if p35 < match:
+            match = p35
+        p5 *= 5
+        if p5 == target:
+            return p5
+    if p5 < match:
+        match = p5
+    return match
+if NumpyVersion(np.__version__) >= '1.7.1':
+    np_matrix_rank = np.linalg.matrix_rank
+else:
+    def np_matrix_rank(M, tol=None):
+        """
+        Return matrix rank of array using SVD method
+        Rank of the array is the number of SVD singular values of the array that are
+        greater than `tol`.
+        Parameters
+        ----------
+        M : {(M,), (M, N)} array_like
+            array of <=2 dimensions
+        tol : {None, float}, optional
+        threshold below which SVD values are considered zero. If `tol` is
+        None, and ``S`` is an array with singular values for `M`, and
+        ``eps`` is the epsilon value for datatype of ``S``, then `tol` is
+        set to ``S.max() * max(M.shape) * eps``.
+        Notes
+        -----
+        The default threshold to detect rank deficiency is a test on the magnitude
+        of the singular values of `M`.  By default, we identify singular values less
+        than ``S.max() * max(M.shape) * eps`` as indicating rank deficiency (with
+        the symbols defined above). This is the algorithm MATLAB uses [1].  It also
+        appears in *Numerical recipes* in the discussion of SVD solutions for linear
+        least squares [2].
+        This default threshold is designed to detect rank deficiency accounting for
+        the numerical errors of the SVD computation.  Imagine that there is a column
+        in `M` that is an exact (in floating point) linear combination of other
+        columns in `M`. Computing the SVD on `M` will not produce a singular value
+        exactly equal to 0 in general: any difference of the smallest SVD value from
+        0 will be caused by numerical imprecision in the calculation of the SVD.
+        Our threshold for small SVD values takes this numerical imprecision into
+        account, and the default threshold will detect such numerical rank
+        deficiency.  The threshold may declare a matrix `M` rank deficient even if
+        the linear combination of some columns of `M` is not exactly equal to
+        another column of `M` but only numerically very close to another column of
+        `M`.
+        We chose our default threshold because it is in wide use.  Other thresholds
+        are possible.  For example, elsewhere in the 2007 edition of *Numerical
+        recipes* there is an alternative threshold of ``S.max() *
+        np.finfo(M.dtype).eps / 2. * np.sqrt(m + n + 1.)``. The authors describe
+        this threshold as being based on "expected roundoff error" (p 71).
+        The thresholds above deal with floating point roundoff error in the
+        calculation of the SVD.  However, you may have more information about the
+        sources of error in `M` that would make you consider other tolerance values
+        to detect *effective* rank deficiency.  The most useful measure of the
+        tolerance depends on the operations you intend to use on your matrix.  For
+        example, if your data come from uncertain measurements with uncertainties
+        greater than floating point epsilon, choosing a tolerance near that
+        uncertainty may be preferable.  The tolerance may be absolute if the
+        uncertainties are absolute rather than relative.
+        References
+        ----------
+        .. [1] MATLAB reference documention, "Rank"
+            http://www.mathworks.com/help/techdoc/ref/rank.html
+        .. [2] W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery,
+            "Numerical Recipes (3rd edition)", Cambridge University Press, 2007,
+            page 795.
+        Examples
+        --------
+        >>> from numpy.linalg import matrix_rank
+        >>> matrix_rank(np.eye(4)) # Full rank matrix
+        4
+        >>> I=np.eye(4); I[-1,-1] = 0. # rank deficient matrix
+        >>> matrix_rank(I)
+        3
+        >>> matrix_rank(np.ones((4,))) # 1 dimension - rank 1 unless all 0
+        1
+        >>> matrix_rank(np.zeros((4,)))
+        0
+        """
+        M = np.asarray(M)
+        if M.ndim > 2:
+            raise TypeError('array should have 2 or fewer dimensions')
+        if M.ndim < 2:
+            return int(not all(M == 0))
+        S = np.linalg.svd(M, compute_uv=False)
+        if tol is None:
+            tol = S.max() * max(M.shape) * np.finfo(S.dtype).eps
+        return np.sum(S > tol)
+
+
+
+class CacheWriteWarning(UserWarning):
+    pass
+
+class CachedAttribute(object):
+
+    def __init__(self, func, cachename=None, resetlist=None):
+        self.fget = func
+        self.name = func.__name__
+        self.cachename = cachename or '_cache'
+        self.resetlist = resetlist or ()
+
+    def __get__(self, obj, type=None):
+        if obj is None:
+            return self.fget
+        # Get the cache or set a default one if needed
+        _cachename = self.cachename
+        _cache = getattr(obj, _cachename, None)
+        if _cache is None:
+            setattr(obj, _cachename, resettable_cache())
+            _cache = getattr(obj, _cachename)
+        # Get the name of the attribute to set and cache
+        name = self.name
+        _cachedval = _cache.get(name, None)
+        # print("[_cachedval=%s]" % _cachedval)
+        if _cachedval is None:
+            # Call the "fget" function
+            _cachedval = self.fget(obj)
+            # Set the attribute in obj
+            # print("Setting %s in cache to %s" % (name, _cachedval))
+            try:
+                _cache[name] = _cachedval
+            except KeyError:
+                setattr(_cache, name, _cachedval)
+            # Update the reset list if needed (and possible)
+            resetlist = self.resetlist
+            if resetlist is not ():
+                try:
+                    _cache._resetdict[name] = self.resetlist
+                except AttributeError:
+                    pass
+        # else:
+        # print("Reading %s from cache (%s)" % (name, _cachedval))
+        return _cachedval
+
+    def __set__(self, obj, value):
+        errmsg = "The attribute '%s' cannot be overwritten" % self.name
+        warnings.warn(errmsg, CacheWriteWarning)
+
+
+class _cache_readonly(object):
+    """
+    Decorator for CachedAttribute
+    """
+
+    def __init__(self, cachename=None, resetlist=None):
+        self.func = None
+        self.cachename = cachename
+        self.resetlist = resetlist or None
+
+    def __call__(self, func):
+        return CachedAttribute(func,
+                               cachename=self.cachename,
+                               resetlist=self.resetlist)
+cache_readonly = _cache_readonly()
+
+

src/py/crankshaft/crankshaft/regression/glm/varfuncs.py

@@ -0,0 +1,284 @@
+"""
+Variance functions for use with the link functions in statsmodels.family.links
+"""
+
+__docformat__ = 'restructuredtext'
+
+import numpy as np
+FLOAT_EPS = np.finfo(float).eps
+
+class VarianceFunction(object):
+    """
+    Relates the variance of a random variable to its mean. Defaults to 1.
+
+    Methods
+    -------
+    call
+        Returns an array of ones that is the same shape as `mu`
+
+    Notes
+    -----
+    After a variance function is initialized, its call method can be used.
+
+    Alias for VarianceFunction:
+    constant = VarianceFunction()
+
+    See also
+    --------
+    statsmodels.family.family
+    """
+
+    def __call__(self, mu):
+        """
+        Default variance function
+
+        Parameters
+        -----------
+        mu : array-like
+            mean parameters
+
+        Returns
+        -------
+        v : array
+            ones(mu.shape)
+        """
+        mu = np.asarray(mu)
+        return np.ones(mu.shape, np.float64)
+
+
+    def deriv(self, mu):
+        """
+        Derivative of the variance function v'(mu)
+        """
+        from statsmodels.tools.numdiff import approx_fprime_cs
+        # TODO: diag workaround proplem with numdiff for 1d
+        return np.diag(approx_fprime_cs(mu, self))
+
+
+constant = VarianceFunction()
+constant.__doc__ = """
+The call method of constant returns a constant variance, i.e., a vector of ones.
+
+constant is an alias of VarianceFunction()
+"""
+
+class Power(object):
+    """
+    Power variance function
+
+    Parameters
+    ----------
+    power : float
+        exponent used in power variance function
+
+    Methods
+    -------
+    call
+        Returns the power variance
+
+    Formulas
+    --------
+    V(mu) = numpy.fabs(mu)**power
+
+    Notes
+    -----
+    Aliases for Power:
+    mu = Power()
+    mu_squared = Power(power=2)
+    mu_cubed = Power(power=3)
+    """
+
+    def __init__(self, power=1.):
+        self.power = power
+
+    def __call__(self, mu):
+        """
+        Power variance function
+
+        Parameters
+        ----------
+        mu : array-like
+            mean parameters
+
+        Returns
+        -------
+        variance : array
+            numpy.fabs(mu)**self.power
+        """
+        return np.power(np.fabs(mu), self.power)
+
+
+    def deriv(self, mu):
+        """
+        Derivative of the variance function v'(mu)
+        """
+        from statsmodels.tools.numdiff import approx_fprime_cs, approx_fprime
+        #return approx_fprime_cs(mu, self)  # TODO fix breaks in `fabs
+        # TODO: diag is workaround problem with numdiff for 1d
+        return np.diag(approx_fprime(mu, self))
+
+
+mu = Power()
+mu.__doc__ = """
+Returns np.fabs(mu)
+
+Notes
+-----
+This is an alias of Power()
+"""
+mu_squared = Power(power=2)
+mu_squared.__doc__ = """
+Returns np.fabs(mu)**2
+
+Notes
+-----
+This is an alias of statsmodels.family.links.Power(power=2)
+"""
+mu_cubed = Power(power=3)
+mu_cubed.__doc__ = """
+Returns np.fabs(mu)**3
+
+Notes
+-----
+This is an alias of statsmodels.family.links.Power(power=3)
+"""
+
+class Binomial(object):
+    """
+    Binomial variance function
+
+    Parameters
+    ----------
+    n : int, optional
+        The number of trials for a binomial variable.  The default is 1 for
+        p in (0,1)
+
+    Methods
+    -------
+    call
+        Returns the binomial variance
+
+    Formulas
+    --------
+    V(mu) = p * (1 - p) * n
+
+    where p = mu / n
+
+    Notes
+    -----
+    Alias for Binomial:
+    binary = Binomial()
+
+    A private method _clean trims the data by machine epsilon so that p is
+    in (0,1)
+    """
+
+    def __init__(self, n=1):
+        self.n = n
+
+    def _clean(self, p):
+        return np.clip(p, FLOAT_EPS, 1 - FLOAT_EPS)
+
+    def __call__(self, mu):
+        """
+        Binomial variance function
+
+        Parameters
+        -----------
+        mu : array-like
+            mean parameters
+
+        Returns
+        -------
+        variance : array
+           variance = mu/n * (1 - mu/n) * self.n
+        """
+        p = self._clean(mu / self.n)
+        return p * (1 - p) * self.n
+
+    #TODO: inherit from super
+    def deriv(self, mu):
+        """
+        Derivative of the variance function v'(mu)
+        """
+        from statsmodels.tools.numdiff import approx_fprime_cs, approx_fprime
+        # TODO: diag workaround proplem with numdiff for 1d
+        return np.diag(approx_fprime_cs(mu, self))
+
+
+binary = Binomial()
+binary.__doc__ = """
+The binomial variance function for n = 1
+
+Notes
+-----
+This is an alias of Binomial(n=1)
+"""
+
+class NegativeBinomial(object):
+    '''
+    Negative binomial variance function
+
+    Parameters
+    ----------
+    alpha : float
+        The ancillary parameter for the negative binomial variance function.
+        `alpha` is assumed to be nonstochastic.  The default is 1.
+
+    Methods
+    -------
+    call
+        Returns the negative binomial variance
+
+    Formulas
+    --------
+    V(mu) = mu + alpha*mu**2
+
+    Notes
+    -----
+    Alias for NegativeBinomial:
+    nbinom = NegativeBinomial()
+
+    A private method _clean trims the data by machine epsilon so that p is
+    in (0,inf)
+    '''
+
+    def __init__(self, alpha=1.):
+        self.alpha = alpha
+
+    def _clean(self, p):
+        return np.clip(p, FLOAT_EPS, np.inf)
+
+    def __call__(self, mu):
+        """
+        Negative binomial variance function
+
+        Parameters
+        ----------
+        mu : array-like
+            mean parameters
+
+        Returns
+        -------
+        variance : array
+            variance = mu + alpha*mu**2
+        """
+        p = self._clean(mu)
+        return p + self.alpha*p**2
+
+    def deriv(self, mu):
+        """
+        Derivative of the negative binomial variance function.
+        """
+
+        p = self._clean(mu)
+        return 1 + 2 * self.alpha * p
+
+nbinom = NegativeBinomial()
+nbinom.__doc__ = """
+Negative Binomial variance function.
+
+Notes
+-----
+This is an alias of NegativeBinomial(alpha=1.)
+"""

src/py/crankshaft/crankshaft/regression/gwr/__init__.py

@@ -0,0 +1 @@
+from base import *

src/py/crankshaft/crankshaft/regression/gwr/base/__init__.py

@@ -0,0 +1,4 @@
+import gwr
+import sel_bw
+import diagnostics
+import kernels

src/py/crankshaft/crankshaft/regression/gwr/base/diagnostics.py

@@ -0,0 +1,81 @@
+"""
+Diagnostics for estimated gwr modesl
+"""
+__author__ = "Taylor Oshan tayoshan@gmail.com"
+
+import numpy as np
+from crankshaft.regression.glm.family import Gaussian, Poisson, Binomial
+
+def get_AICc(gwr):
+    """
+    Get AICc value
+    
+    Gaussian: p61, (2.33), Fotheringham, Brunsdon and Charlton (2002)
+    
+    GWGLM: AICc=AIC+2k(k+1)/(n-k-1), Nakaya et al. (2005): p2704, (36)
+
+    """
+    n = gwr.n
+    k = gwr.tr_S
+    if isinstance(gwr.family, Gaussian):
+        aicc = -2.0*gwr.llf + 2.0*n*(k + 1.0)/(n-k-2.0)  
+    elif isinstance(gwr.family, (Poisson, Binomial)):
+        aicc = get_AIC(gwr) + 2.0 * k * (k+1.0) / (n - k - 1.0) 
+    return aicc
+
+def get_AIC(gwr):
+    """
+    Get AIC calue
+
+    Gaussian: p96, (4.22), Fotheringham, Brunsdon and Charlton (2002)
+
+    GWGLM:  AIC(G)=D(G) + 2K(G), where D and K denote the deviance and the effective
+    number of parameters in the model with bandwidth G, respectively.
+    
+    """   
+    k = gwr.tr_S
+    #deviance = -2*log-likelihood
+    y = gwr.y
+    mu = gwr.mu
+    if isinstance(gwr.family, Gaussian):
+        aic = -2.0 * gwr.llf + 2.0 * (k+1)
+    elif isinstance(gwr.family, (Poisson, Binomial)):
+        aic = np.sum(gwr.family.resid_dev(y, mu)**2) + 2.0 * k
+    return aic 
+
+def get_BIC(gwr):
+    """
+    Get BIC value
+
+    Gaussian: p61 (2.34), Fotheringham, Brunsdon and Charlton (2002)
+    BIC = -2log(L)+klog(n)
+
+    GWGLM: BIC = dev + tr_S * log(n)
+
+    """
+    n = gwr.n      # (scalar) number of observations
+    k = gwr.tr_S  
+    y = gwr.y
+    mu = gwr.mu
+    if isinstance(gwr.family, Gaussian):
+        bic = -2.0 * gwr.llf + (k+1) * np.log(n) 
+    elif isinstance(gwr.family, (Poisson, Binomial)):
+        bic = np.sum(gwr.family.resid_dev(y, mu)**2) + k * np.log(n)
+    return bic
+
+def get_CV(gwr):
+    """
+    Get CV value
+
+    Gaussian only
+
+    Methods: p60, (2.31) or p212 (9.4)
+    Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+    Geographically weighted regression: the analysis of spatially varying relationships.
+    Modification: sum of residual squared is divided by n according to GWR4 results
+
+    """
+    aa = gwr.resid_response.reshape((-1,1))/(1.0-gwr.influ)
+    cv = np.sum(aa**2)/gwr.n
+    return cv
+

src/py/crankshaft/crankshaft/regression/gwr/base/kernels.py

@@ -0,0 +1,120 @@
+# GWR kernel function specifications
+
+__author__ = "Taylor Oshan tayoshan@gmail.com" 
+
+#from pysal.weights.Distance import Kernel 
+import scipy
+from scipy.spatial.kdtree import KDTree
+import numpy as np
+
+#adaptive specifications should be parameterized with nn-1 to match original gwr
+#implementation. That is, pysal counts self neighbors with knn automatically.
+
+def fix_gauss(coords, bw, points=None):
+    w = _Kernel(coords, function='gwr_gaussian', bandwidth=bw,
+            truncate=False, points=points)
+    return w.kernel
+
+def adapt_gauss(coords, nn, points=None):
+    w = _Kernel(coords, fixed=False, k=nn-1, function='gwr_gaussian',
+            truncate=False, points=points)
+    return w.kernel
+
+def fix_bisquare(coords, bw, points=None):
+    w = _Kernel(coords, function='bisquare', bandwidth=bw, points=points)
+    return w.kernel
+
+def adapt_bisquare(coords, nn, points=None):
+    w = _Kernel(coords, fixed=False, k=nn-1, function='bisquare', points=points)
+    return w.kernel
+
+def fix_exp(coords, bw, points=None):
+    w = _Kernel(coords, function='exponential', bandwidth=bw,
+            truncate=False, points=points)
+    return w.kernel
+
+def adapt_exp(coords, nn, points=None):
+    w = _Kernel(coords, fixed=False, k=nn-1, function='exponential',
+            truncate=False, points=points)
+    return w.kernel
+
+from scipy.spatial.distance import cdist
+
+class _Kernel(object):
+    """
+
+    """
+    def __init__(self, data, bandwidth=None, fixed=True, k=None,
+                 function='triangular', eps=1.0000001, ids=None, truncate=True, 
+                 points=None): #Added truncate flag
+        if issubclass(type(data), scipy.spatial.KDTree):
+            self.data = data.data
+            data = self.data
+        else:
+            self.data = data
+        if k is not None:
+            self.k = int(k) + 1
+        else:
+            self.k = k
+        if points is None:
+            self.dmat = cdist(self.data, self.data)
+        else:
+            self.points = points
+            self.dmat = cdist(self.points, self.data)
+        self.function = function.lower()
+        self.fixed = fixed
+        self.eps = eps
+        self.trunc = truncate
+        if bandwidth:
+            try:
+                bandwidth = np.array(bandwidth)
+                bandwidth.shape = (len(bandwidth), 1)
+            except:
+                bandwidth = np.ones((len(data), 1), 'float') * bandwidth
+            self.bandwidth = bandwidth
+        else:
+            self._set_bw()
+        self.kernel = self._kernel_funcs(self.dmat/self.bandwidth)
+        
+        if self.trunc:
+            mask = np.repeat(self.bandwidth, len(self.data), axis=1)
+            self.kernel[(self.dmat >= mask)] = 0
+
+    def _set_bw(self):
+        if self.k is not None:
+            dmat = np.sort(self.dmat)[:,:self.k]
+        else:
+            dmat = self.dmat
+        if self.fixed:
+            # use max knn distance as bandwidth
+            bandwidth = dmat.max() * self.eps
+            n = len(self.data)
+            self.bandwidth = np.ones((n, 1), 'float') * bandwidth
+        else:
+            # use local max knn distance
+            self.bandwidth = dmat.max(axis=1) * self.eps
+            self.bandwidth.shape = (self.bandwidth.size, 1)
+            
+
+    def _kernel_funcs(self, zs):
+        # functions follow Anselin and Rey (2010) table 5.4
+        if self.function == 'triangular':
+            return 1 - zs 
+        elif self.function == 'uniform':
+            return np.ones(zi.shape) * 0.5 
+        elif self.function == 'quadratic':
+            return (3. / 4) * (1 - zs ** 2) 
+        elif self.function == 'quartic':
+            return (15. / 16) * (1 - zs ** 2) ** 2 
+        elif self.function == 'gaussian':
+            c = np.pi * 2
+            c = c ** (-0.5)
+            return c * np.exp(-(zs ** 2) / 2.)
+        elif self.function == 'gwr_gaussian':
+            return np.exp(-0.5*(zs)**2)
+        elif self.function == 'bisquare':
+            return (1-(zs)**2)**2
+        elif self.function =='exponential':
+            return np.exp(-zs)
+        else:
+            print('Unsupported kernel function', self.function)

src/py/crankshaft/crankshaft/regression/gwr/base/search.py

@@ -0,0 +1,208 @@
+#Bandwidth optimization methods
+
+__author__ = "Taylor Oshan"
+
+import numpy as np
+
+def golden_section(a, c, delta, function, tol, max_iter, int_score=False):
+    """
+    Golden section search routine
+    Method: p212, 9.6.4
+    Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+    Geographically weighted regression: the analysis of spatially varying relationships.
+
+    Parameters
+    ----------
+    a               : float
+                      initial max search section value
+    b               : float
+                      initial min search section value
+    delta           : float
+                      constant used to determine width of search sections
+    function        : function
+                      obejective function to be evaluated at different section
+                      values
+    int_score       : boolean
+                      False for float score, True for integer score
+    tol             : float
+                      tolerance used to determine convergence
+    max_iter        : integer
+                      maximum iterations if no convergence to tolerance
+
+    Returns
+    -------
+    opt_val         : float
+                      optimal value
+    opt_score       : kernel
+                      optimal score
+    output          : list of tuples
+                      searching history
+    """
+    b = a + delta * np.abs(c-a)
+    d = c - delta * np.abs(c-a)
+    score = 0.0
+    diff = 1.0e9
+    iters  = 0
+    output = []
+    while np.abs(diff) > tol and iters < max_iter:
+        iters += 1
+        if int_score:
+        	b = np.round(b)
+        	d = np.round(d)
+
+        score_a = function(a)
+        score_b = function(b)
+        score_c = function(c)
+        score_d = function(d)
+        
+        if score_b <= score_d:
+            opt_val = b
+            opt_score = score_b
+            c = d
+            d = b
+            b = a + delta * np.abs(c-a)
+            #if int_score:
+                #b = np.round(b)
+        else:
+            opt_val = d
+            opt_score = score_d
+            a = b
+            b = d
+            d = c - delta * np.abs(c-a)
+            #if int_score:
+                #d = np.round(b)
+        
+        #if int_score:
+        #	opt_val = np.round(opt_val)
+        output.append((opt_val, opt_score))
+        diff = score_b - score_d
+        score = opt_score
+    return np.round(opt_val, 2), opt_score, output
+
+def equal_interval(l_bound, u_bound, interval, function, int_score=False):
+    """
+    Interval search, using interval as stepsize
+
+    Parameters
+    ----------
+    l_bound         : float
+                      initial min search section value
+    u_bound         : float
+                      initial max search section value
+    interval        : float
+                      constant used to determine width of search sections
+    function        : function
+                      obejective function to be evaluated at different section
+                      values
+    int_score       : boolean
+                      False for float score, True for integer score
+
+    Returns
+    -------
+    opt_val         : float
+                      optimal value
+    opt_score       : kernel
+                      optimal score
+    output          : list of tuples
+                      searching history
+    """
+    a = l_bound
+    c = u_bound
+    b = a + interval
+    if int_score:
+        a = np.round(a,0)
+        c = np.round(c,0)
+        b = np.round(b,0)
+    
+    output = []
+
+    score_a = function(a)
+    score_c = function(c)
+
+    output.append((a,score_a))
+    output.append((c,score_c))
+
+    if score_a < score_c:
+        opt_val = a
+        opt_score = score_a
+    else:
+        opt_val = c
+        opt_score = score_c
+
+    while b < c:
+        score_b = function(b)
+
+        output.append((b,score_b))
+
+        if score_b < opt_score:
+            opt_val = b
+            opt_score = score_b
+        b = b + interval
+
+    return opt_val, opt_score, output
+
+
+def flexible_bw(init, y, X, n, k, family, tol, max_iter, rss_score,
+        gwr_func, bw_func, sel_func):
+    if init:
+        bw = sel_func(bw_func(y, X))
+        print bw
+        optim_model = gwr_func(y, X, bw)
+        err = optim_model.resid_response.reshape((-1,1))
+        est = optim_model.params
+    else:
+        model = GLM(y, X, family=self.family, constant=False).fit()
+        err = model.resid_response.reshape((-1,1))
+        est = np.repeat(model.params.T, n, axis=0)
+        
+    
+    XB = np.multiply(est, X)
+    if rss_score:
+        rss = np.sum((err)**2)
+    iters = 0
+    scores = []
+    delta = 1e6
+    BWs = []
+    VALs = []
+
+    while delta > tol and iters < max_iter:
+        iters += 1
+        new_XB = np.zeros_like(X)
+        bws = []
+        vals = []
+        ests = np.zeros_like(X)
+        f_XB = XB.copy()
+        f_err = err.copy()
+        for i in range(k):
+            temp_y = XB[:,i].reshape((-1,1))
+            temp_y = temp_y + err
+            temp_X = X[:,i].reshape((-1,1))
+            bw_class = bw_func(temp_y, temp_X)
+            bw = sel_func(bw_class)
+            optim_model = gwr_func(temp_y, temp_X, bw)
+            err = optim_model.resid_response.reshape((-1,1))
+            est = optim_model.params.reshape((-1,))
+
+            new_XB[:,i] = np.multiply(est, temp_X.reshape((-1,)))
+            bws.append(bw)
+            ests[:,i] = est
+            vals.append(bw_class.bw[1])
+            
+        predy = np.sum(np.multiply(ests, X), axis=1).reshape((-1,1))
+        num = np.sum((new_XB - XB)**2)/n
+        den = np.sum(np.sum(new_XB, axis=1)**2)
+        score = (num/den)**0.5
+        XB = new_XB
+            
+        if rss_score:
+            new_rss = np.sum((y - predy)**2)
+            score = np.abs((new_rss - rss)/new_rss)
+            rss = new_rss
+        print score
+        scores.append(score)
+        delta = score
+        BWs.append(bws) 
+        VALs.append(vals)
+
+    opt_bws = BWs[-1]
+    return opt_bws, np.array(BWs), np.array(VALs), np.array(scores), f_XB, f_err

src/py/crankshaft/crankshaft/regression/gwr/base/sel_bw.py

@@ -0,0 +1,286 @@
+# GWR Bandwidth selection class
+
+#Thinking about removing the search method and just having optimization begin in
+#class __init__
+
+#x_glob and offset parameters dont yet do anything; former is for semiparametric
+#GWR and later is for offset variable for Poisson model
+
+__author__ = "Taylor Oshan Tayoshan@gmail.com"
+
+from kernels import *
+from search import golden_section, equal_interval, flexible_bw
+from gwr import GWR
+from crankshaft.regression.glm.family import Gaussian, Poisson, Binomial
+import pysal.spreg.user_output as USER
+from diagnostics import get_AICc, get_AIC, get_BIC, get_CV
+from scipy.spatial.distance import pdist, squareform
+from pysal.common import KDTree
+import numpy as np
+
+kernels = {1: fix_gauss, 2: adapt_gauss, 3: fix_bisquare, 4:
+        adapt_bisquare, 5: fix_exp, 6:adapt_exp}
+getDiag = {'AICc': get_AICc,'AIC':get_AIC, 'BIC': get_BIC, 'CV': get_CV}
+
+class Sel_BW(object):
+    """
+    Select bandwidth for kernel
+
+    Methods: p211 - p213, bandwidth selection
+    Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002).
+    Geographically weighted regression: the analysis of spatially varying relationships.
+
+    Parameters
+    ----------
+    y              : array
+                     n*1, dependent variable.
+    x_glob         : array
+                     n*k1, fixed independent variable.
+    x_loc          : array
+                     n*k2, local independent variable, including constant.
+    coords         : list of tuples
+                     (x,y) of points used in bandwidth selection
+    family         : string
+                     GWR model type: 'Gaussian', 'logistic, 'Poisson''
+    offset         : array
+                     n*1, offset variable for Poisson model
+    kernel         : string
+                     kernel function: 'gaussian', 'bisquare', 'exponetial'
+    fixed          : boolean
+                     True for fixed bandwidth and False for adaptive (NN)
+    fb             : True for flexible (mutliple covaraite-specific) bandwidths
+                     False for a traditional (same for  all covariates)
+                     bandwdith; defualt is False.
+    constant       : boolean
+                     True to include intercept (default) in model and False to exclude
+                     intercept.
+
+
+    Attributes
+    ----------
+    y              : array
+                     n*1, dependent variable.
+    x_glob         : array
+                     n*k1, fixed independent variable.
+    x_loc          : array
+                     n*k2, local independent variable, including constant.
+    coords         : list of tuples
+                     (x,y) of points used in bandwidth selection
+    family         : string
+                     GWR model type: 'Gaussian', 'logistic, 'Poisson''
+    kernel         : string
+                     type of kernel used and wether fixed or adaptive
+    criterion      : string
+                     bw selection criterion: 'AICc', 'AIC', 'BIC', 'CV'
+    search         : string
+                     bw search method: 'golden', 'interval'
+    bw_min         : float
+                     min value used in bandwidth search
+    bw_max         : float
+                     max value used in bandwidth search
+    interval       : float
+                     interval increment used in interval search
+    tol            : float
+                     tolerance used to determine convergence
+    max_iter       : integer
+                     max interations if no convergence to tol
+    fb             : True for flexible (mutliple covaraite-specific) bandwidths
+                     False for a traditional (same for  all covariates)
+                     bandwdith; defualt is False.
+    constant       : boolean
+                     True to include intercept (default) in model and False to exclude
+                     intercept.
+    """
+    def __init__(self, coords, y, x_loc, x_glob=None, family=Gaussian(),
+            offset=None, kernel='bisquare', fixed=False, fb=False, constant=True):
+        self.coords = coords
+        self.y = y
+        self.x_loc = x_loc
+        if x_glob is not None:
+            self.x_glob = x_glob
+        else:
+            self.x_glob = []
+        self.family=family
+        self.fixed = fixed
+        self.kernel = kernel
+        if offset is None:
+        	self.offset = np.ones((len(y), 1))
+        else:
+            self.offset = offset * 1.0
+        self.fb = fb
+        self.constant = constant
+
+    def search(self, search='golden_section', criterion='AICc', bw_min=0.0, 
+            bw_max=0.0, interval=0.0, tol=1.0e-6, max_iter=200, init_fb=True,
+            tol_fb=1.0e-5, rss_score=False, max_iter_fb=200):
+        """
+        Parameters
+        ----------
+        criterion      : string
+                         bw selection criterion: 'AICc', 'AIC', 'BIC', 'CV'
+        search         : string
+                         bw search method: 'golden', 'interval'
+        bw_min         : float
+                         min value used in bandwidth search
+        bw_max         : float
+                         max value used in bandwidth search
+        interval       : float
+                         interval increment used in interval search
+        tol            : float
+                         tolerance used to determine convergence
+        max_iter       : integer
+                         max iterations if no convergence to tol
+        init_fb        : True to initialize flexible bandwidth search with
+                         esitmates from a traditional GWR and False to
+                         initialize flexible bandwidth search with global
+                         regression estimates
+        tol_fb         : convergence tolerence for the flexible bandwidth
+                         backfitting algorithm; a larger tolerance may stop the
+                         algorith faster though it may result in a less optimal
+                         model
+        max_iter_fb    : max iterations if no convergence to tol for flexible
+                         bandwidth backfittign algorithm
+        rss_score      : True to use the residual sum of sqaures to evaluate
+                         each iteration of the flexible bandwidth backfitting
+                         routine and False to use a smooth function; default is
+                         False
+
+        Returns
+        -------
+        bw             : scalar or array
+                         optimal bandwidth value or values; returns scalar for
+                         fb=False and array for fb=True; ordering of bandwidths
+                         matches the ordering of the covariates (columns) of the
+                         designs matrix, X
+        """     
+        self.search = search
+        self.criterion = criterion
+        self.bw_min = bw_min
+        self.bw_max = bw_max
+        self.interval = interval
+        self.tol = tol
+        self.max_iter = max_iter
+        self.init_fb = init_fb
+        self.tol_fb = tol_fb
+        self.rss_score = rss_score
+        self.max_iter_fb = max_iter_fb
+        
+
+        if self.fixed:
+            if self.kernel == 'gaussian':
+                ktype = 1
+            elif self.kernel == 'bisquare':
+                ktype = 3
+            elif self.kernel == 'exponential':
+                ktype = 5
+            else:
+                raise TypeError('Unsupported kernel function ', self.kernel)
+        else:
+            if self.kernel == 'gaussian':
+            	ktype = 2
+            elif self.kernel == 'bisquare':
+                ktype = 4
+            elif self.kernel == 'exponential':
+                ktype = 6
+            else:
+                raise TypeError('Unsupported kernel function ', self.kernel)
+
+        function = lambda bw: getDiag[criterion](
+                GWR(self.coords, self.y, self.x_loc, bw, family=self.family,
+                    kernel=self.kernel, fixed=self.fixed, offset=self.offset).fit())
+        
+        if ktype % 2 == 0:
+            int_score = True
+        else:
+            int_score = False
+        self.int_score = int_score
+        
+        if self.fb:
+            self._fbw()
+            print self.bw[1]
+            self.XB = self.bw[4]
+            self.err = self.bw[5]
+        else:
+            self._bw()
+
+        return self.bw[0]
+
+    def _bw(self):
+        gwr_func = lambda bw: getDiag[self.criterion](
+                GWR(self.coords, self.y, self.x_loc, bw, family=self.family,
+                    kernel=self.kernel, fixed=self.fixed, constant=self.constant).fit())
+        if self.search == 'golden_section':
+            a,c = self._init_section(self.x_glob, self.x_loc, self.coords,
+                    self.constant)
+            delta = 0.38197 #1 - (np.sqrt(5.0)-1.0)/2.0
+            self.bw = golden_section(a, c, delta, gwr_func, self.tol, 
+                    self.max_iter, self.int_score)
+        elif self.search == 'interval':
+            self.bw = equal_interval(self.bw_min, self.bw_max, self.interval,
+                    gwr_func, self.int_score)
+        else:
+            raise TypeError('Unsupported computational search method ', search)
+   
+    def _fbw(self):
+        y = self.y
+        if self.constant:
+        	X = USER.check_constant(self.x_loc)
+        else:
+            X = self.x_loc
+        n, k = X.shape
+        family = self.family
+        offset = self.offset
+        kernel = self.kernel
+        fixed = self.fixed
+        coords = self.coords
+        search = self.search
+        criterion = self.criterion
+        bw_min = self.bw_min
+        bw_max = self.bw_max
+        interval = self.interval
+        tol = self.tol
+        max_iter = self.max_iter
+        gwr_func = lambda y, X, bw: GWR(coords, y, X, bw, family=family, 
+                kernel=kernel, fixed=fixed, offset=offset, constant=False).fit()
+        bw_func = lambda y, X: Sel_BW(coords, y, X, x_glob=[], family=family,
+                kernel=kernel, fixed=fixed, offset=offset, constant=False)
+        sel_func = lambda bw_func: bw_func.search(search=search, 
+                        criterion=criterion, bw_min=bw_min, bw_max=bw_max, 
+                        interval=interval, tol=tol, max_iter=max_iter)
+        self.bw = flexible_bw(self.init_fb, y, X, n, k, family, self.tol_fb,
+               self.max_iter_fb, self.rss_score, gwr_func, bw_func, sel_func)
+
+
+
+    def _init_section(self, x_glob, x_loc, coords, constant):
+        if len(x_glob) > 0:
+            n_glob = x_glob.shape[1]
+        else:
+            n_glob = 0
+        if len(x_loc) > 0:
+            n_loc = x_loc.shape[1]
+        else:
+            n_loc = 0
+        if constant:
+            n_vars = n_glob + n_loc + 1
+        else:
+            n_vars = n_glob + n_loc
+        n = np.array(coords).shape[0]
+
+        if self.int_score:
+            a = 40 + 2 * n_vars
+            c = n
+        else:
+            nn = 40 + 2 * n_vars
+            sq_dists = squareform(pdist(coords))
+            sort_dists = np.sort(sq_dists, axis=1)
+            min_dists = sort_dists[:,nn-1]
+            max_dists = sort_dists[:,-1]
+            a = np.min(min_dists)/2.0
+            c = np.max(max_dists)/2.0
+      
+        if a < self.bw_min:
+            a = self.bw_min
+        if c > self.bw_max and self.bw_max > 0:
+            c = self.bw_max
+        return a, c

src/py/crankshaft/crankshaft/regression/gwr/base/tests/test_gwr.py

@@ -0,0 +1,853 @@
+"""
+GWR is tested against results from GWR4
+"""
+
+import unittest
+import pickle as pk
+from crankshaft.regression.gwr.gwr import GWR, FBGWR
+from crankshaft.regression.gwr.sel_bw import Sel_BW
+from crankshaft.regression.gwr.diagnostics import get_AICc, get_AIC, get_BIC, get_CV
+from crankshaft.regression.glm.family import Gaussian, Poisson, Binomial
+import numpy as np
+import pysal
+
+class TestGWRGaussian(unittest.TestCase):
+    def setUp(self):
+        data = pysal.open(pysal.examples.get_path('GData_utm.csv'))
+        self.coords = zip(data.by_col('X'), data.by_col('Y'))
+        self.y = np.array(data.by_col('PctBach')).reshape((-1,1))
+        rural  = np.array(data.by_col('PctRural')).reshape((-1,1))
+        pov = np.array(data.by_col('PctPov')).reshape((-1,1)) 
+        black = np.array(data.by_col('PctBlack')).reshape((-1,1))
+        self.X = np.hstack([rural, pov, black])
+        self.BS_F = pysal.open(pysal.examples.get_path('georgia_BS_F_listwise.csv'))
+        self.BS_NN = pysal.open(pysal.examples.get_path('georgia_BS_NN_listwise.csv'))
+        self.GS_F = pysal.open(pysal.examples.get_path('georgia_GS_F_listwise.csv'))
+        self.GS_NN = pysal.open(pysal.examples.get_path('georgia_GS_NN_listwise.csv'))
+        self.FB = pk.load(open(pysal.examples.get_path('FB.p'), 'r'))
+        self.XB = pk.load(open(pysal.examples.get_path('XB.p'), 'r'))
+        self.err = pk.load(open(pysal.examples.get_path('err.p'), 'r'))
+
+    def test_BS_F(self):
+        est_Int = self.BS_F.by_col(' est_Intercept')
+        se_Int = self.BS_F.by_col(' se_Intercept')
+        t_Int = self.BS_F.by_col(' t_Intercept')
+        est_rural = self.BS_F.by_col(' est_PctRural')
+        se_rural = self.BS_F.by_col(' se_PctRural')
+        t_rural = self.BS_F.by_col(' t_PctRural')
+        est_pov = self.BS_F.by_col(' est_PctPov')
+        se_pov = self.BS_F.by_col(' se_PctPov')
+        t_pov = self.BS_F.by_col(' t_PctPov')
+        est_black = self.BS_F.by_col(' est_PctBlack')
+        se_black = self.BS_F.by_col(' se_PctBlack')
+        t_black = self.BS_F.by_col(' t_PctBlack')
+        yhat = self.BS_F.by_col(' yhat')
+        res = np.array(self.BS_F.by_col(' residual'))
+        std_res = np.array(self.BS_F.by_col(' std_residual')).reshape((-1,1))
+        localR2 = np.array(self.BS_F.by_col(' localR2')).reshape((-1,1))
+        inf = np.array(self.BS_F.by_col(' influence')).reshape((-1,1))
+        cooksD = np.array(self.BS_F.by_col(' CooksD')).reshape((-1,1))
+        
+        model = GWR(self.coords, self.y, self.X, bw=209267.689, fixed=True)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        CV = get_CV(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 894.0)
+        self.assertAlmostEquals(np.floor(AIC), 890.0)
+        self.assertAlmostEquals(np.floor(BIC), 944.0)
+        self.assertAlmostEquals(np.round(CV,2), 18.25)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
+        np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04)
+        np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04)
+        np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04)
+        np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04)
+        np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04)
+        np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04)
+        np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02)
+        np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02)
+        np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05)
+        np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04)
+        np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04)
+        np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05)
+        np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04)
+        np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00)
+
+    def test_BS_NN(self):
+        est_Int = self.BS_NN.by_col(' est_Intercept')
+        se_Int = self.BS_NN.by_col(' se_Intercept')
+        t_Int = self.BS_NN.by_col(' t_Intercept')
+        est_rural = self.BS_NN.by_col(' est_PctRural')
+        se_rural = self.BS_NN.by_col(' se_PctRural')
+        t_rural = self.BS_NN.by_col(' t_PctRural')
+        est_pov = self.BS_NN.by_col(' est_PctPov')
+        se_pov = self.BS_NN.by_col(' se_PctPov')
+        t_pov = self.BS_NN.by_col(' t_PctPov')
+        est_black = self.BS_NN.by_col(' est_PctBlack')
+        se_black = self.BS_NN.by_col(' se_PctBlack')
+        t_black = self.BS_NN.by_col(' t_PctBlack')
+        yhat = self.BS_NN.by_col(' yhat')
+        res = np.array(self.BS_NN.by_col(' residual'))
+        std_res = np.array(self.BS_NN.by_col(' std_residual')).reshape((-1,1))
+        localR2 = np.array(self.BS_NN.by_col(' localR2')).reshape((-1,1))
+        inf = np.array(self.BS_NN.by_col(' influence')).reshape((-1,1))
+        cooksD = np.array(self.BS_NN.by_col(' CooksD')).reshape((-1,1))
+
+        model = GWR(self.coords, self.y, self.X, bw=90.000, fixed=False)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        CV = get_CV(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 896.0)
+        self.assertAlmostEquals(np.floor(AIC), 892.0)
+        self.assertAlmostEquals(np.floor(BIC), 941.0)
+        self.assertAlmostEquals(np.around(CV, 2), 19.19)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
+        np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04)
+        np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04)
+        np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04)
+        np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04)
+        np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04)
+        np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04)
+        np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02)
+        np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02)
+        np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05)
+        np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04)
+        np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04)
+        np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05)
+        np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04)
+        np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00)
+
+    def test_GS_F(self):
+        est_Int = self.GS_F.by_col(' est_Intercept')
+        se_Int = self.GS_F.by_col(' se_Intercept')
+        t_Int = self.GS_F.by_col(' t_Intercept')
+        est_rural = self.GS_F.by_col(' est_PctRural')
+        se_rural = self.GS_F.by_col(' se_PctRural')
+        t_rural = self.GS_F.by_col(' t_PctRural')
+        est_pov = self.GS_F.by_col(' est_PctPov')
+        se_pov = self.GS_F.by_col(' se_PctPov')
+        t_pov = self.GS_F.by_col(' t_PctPov')
+        est_black = self.GS_F.by_col(' est_PctBlack')
+        se_black = self.GS_F.by_col(' se_PctBlack')
+        t_black = self.GS_F.by_col(' t_PctBlack')
+        yhat = self.GS_F.by_col(' yhat')
+        res = np.array(self.GS_F.by_col(' residual'))
+        std_res = np.array(self.GS_F.by_col(' std_residual')).reshape((-1,1))
+        localR2 = np.array(self.GS_F.by_col(' localR2')).reshape((-1,1))
+        inf = np.array(self.GS_F.by_col(' influence')).reshape((-1,1))
+        cooksD = np.array(self.GS_F.by_col(' CooksD')).reshape((-1,1))
+        
+        model = GWR(self.coords, self.y, self.X, bw=87308.298,
+                kernel='gaussian', fixed=True)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        CV = get_CV(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 895.0)
+        self.assertAlmostEquals(np.floor(AIC), 890.0)
+        self.assertAlmostEquals(np.floor(BIC), 943.0)
+        self.assertAlmostEquals(np.around(CV, 2), 18.21)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
+        np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04)
+        np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04)
+        np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04)
+        np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04)
+        np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04)
+        np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04)
+        np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02)
+        np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02)
+        np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05)
+        np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04)
+        np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04)
+        np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05)
+        np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04)
+        np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00)
+        
+    def test_GS_NN(self):
+        est_Int = self.GS_NN.by_col(' est_Intercept')
+        se_Int = self.GS_NN.by_col(' se_Intercept')
+        t_Int = self.GS_NN.by_col(' t_Intercept')
+        est_rural = self.GS_NN.by_col(' est_PctRural')
+        se_rural = self.GS_NN.by_col(' se_PctRural')
+        t_rural = self.GS_NN.by_col(' t_PctRural')
+        est_pov = self.GS_NN.by_col(' est_PctPov')
+        se_pov = self.GS_NN.by_col(' se_PctPov')
+        t_pov = self.GS_NN.by_col(' t_PctPov')
+        est_black = self.GS_NN.by_col(' est_PctBlack')
+        se_black = self.GS_NN.by_col(' se_PctBlack')
+        t_black = self.GS_NN.by_col(' t_PctBlack')
+        yhat = self.GS_NN.by_col(' yhat')
+        res = np.array(self.GS_NN.by_col(' residual'))
+        std_res = np.array(self.GS_NN.by_col(' std_residual')).reshape((-1,1))
+        localR2 = np.array(self.GS_NN.by_col(' localR2')).reshape((-1,1))
+        inf = np.array(self.GS_NN.by_col(' influence')).reshape((-1,1))
+        cooksD = np.array(self.GS_NN.by_col(' CooksD')).reshape((-1,1))
+
+        model = GWR(self.coords, self.y, self.X, bw=49.000,
+                kernel='gaussian', fixed=False)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        CV = get_CV(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc),  896)
+        self.assertAlmostEquals(np.floor(AIC), 894.0)
+        self.assertAlmostEquals(np.floor(BIC), 922.0)
+        self.assertAlmostEquals(np.around(CV, 2), 17.91)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
+        np.testing.assert_allclose(est_rural, rslt.params[:,1], rtol=1e-04)
+        np.testing.assert_allclose(se_rural, rslt.bse[:,1], rtol=1e-04)
+        np.testing.assert_allclose(t_rural, rslt.tvalues[:,1], rtol=1e-04)
+        np.testing.assert_allclose(est_pov, rslt.params[:,2], rtol=1e-04)
+        np.testing.assert_allclose(se_pov, rslt.bse[:,2], rtol=1e-04)
+        np.testing.assert_allclose(t_pov, rslt.tvalues[:,2], rtol=1e-04)
+        np.testing.assert_allclose(est_black, rslt.params[:,3], rtol=1e-02)
+        np.testing.assert_allclose(se_black, rslt.bse[:,3], rtol=1e-02)
+        np.testing.assert_allclose(t_black, rslt.tvalues[:,3], rtol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05)
+        np.testing.assert_allclose(res, rslt.resid_response, rtol=1e-04)
+        np.testing.assert_allclose(std_res, rslt.std_res, rtol=1e-04)
+        np.testing.assert_allclose(localR2, rslt.localR2, rtol=1e-05)
+        np.testing.assert_allclose(inf, rslt.influ, rtol=1e-04)
+        np.testing.assert_allclose(cooksD, rslt.cooksD, rtol=1e-00)
+    
+    def test_FBGWR(self):
+        model = FBGWR(self.coords, self.y, self.X, [157.0, 65.0, 52.0],
+                XB=self.XB, err=self.err, constant=False)
+        rslt = model.fit()
+
+        np.testing.assert_allclose(rslt.predy, self.FB['predy'], atol=1e-07)
+        np.testing.assert_allclose(rslt.params, self.FB['params'], atol=1e-07)
+        np.testing.assert_allclose(rslt.resid_response, self.FB['u'], atol=1e-05)
+        np.testing.assert_almost_equal(rslt.resid_ss, 6339.3497144025841)
+
+    def test_Prediction(self):
+        coords =np.array(self.coords)
+        index = np.arange(len(self.y))
+        #train = index[0:-10]
+        test = index[-10:]
+
+        #y_train = self.y[train]
+        #X_train = self.X[train]
+        #coords_train = list(coords[train])
+
+        #y_test = self.y[test]
+        X_test = self.X[test]
+        coords_test = list(coords[test])
+
+
+        model = GWR(self.coords, self.y, self.X, 93, family=Gaussian(),
+                fixed=False, kernel='bisquare')
+        results = model.predict(coords_test, X_test)
+        
+        params = np.array([22.77198, -0.10254,    -0.215093,   -0.01405,
+            19.10531,    -0.094177,   -0.232529,   0.071913,
+            19.743421,   -0.080447,   -0.30893,    0.083206,
+            17.505759,   -0.078919,   -0.187955,   0.051719,
+            27.747402,   -0.165335,   -0.208553,   0.004067,
+            26.210627,   -0.138398,   -0.360514,   0.072199,
+            18.034833,   -0.077047,   -0.260556,   0.084319,
+            28.452802,   -0.163408,   -0.14097,    -0.063076,
+            22.353095,   -0.103046,   -0.226654,   0.002992,
+            18.220508,   -0.074034,   -0.309812,   0.108636]).reshape((10,4))
+        np.testing.assert_allclose(params, results.params, rtol=1e-03)
+
+        bse = np.array([2.080166,    0.021462,    0.102954,    0.049627,
+            2.536355,    0.022111,    0.123857,    0.051917,
+            1.967813,    0.019716,    0.102562,    0.054918,
+            2.463219,    0.021745,    0.110297,    0.044189,
+            1.556056,    0.019513,    0.12764,     0.040315,
+            1.664108,    0.020114,    0.131208,    0.041613,
+            2.5835,      0.021481,    0.113158,    0.047243,
+            1.709483,    0.019752,    0.116944,    0.043636,
+            1.958233,    0.020947,    0.09974,     0.049821,
+            2.276849,    0.020122,    0.107867,    0.047842]).reshape((10,4))
+        np.testing.assert_allclose(bse, results.bse, rtol=1e-03)
+
+        tvalues = np.array([10.947193,   -4.777659,   -2.089223,   -0.283103,
+            7.532584,    -4.259179,   -1.877395,   1.385161,
+            10.033179,   -4.080362,   -3.012133,   1.515096,
+            7.106862,    -3.629311,   -1.704079,   1.17042,
+            17.831878,   -8.473156,   -1.633924,   0.100891,
+            15.750552,   -6.880725,   -2.74765,    1.734978,
+            6.980774,    -3.586757,   -2.302575,   1.784818,
+            16.644095,   -8.273001,   -1.205451,   -1.445501,
+            11.414933,   -4.919384,   -2.272458,   0.060064,
+            8.00251, -3.679274,   -2.872176,   2.270738]).reshape((10,4))
+        np.testing.assert_allclose(tvalues, results.tvalues, rtol=1e-03)
+
+        localR2 = np.array([[ 0.53068693],
+                            [ 0.59582647],
+                            [ 0.59700925],
+                            [ 0.45769954],
+                            [ 0.54634509],
+                            [ 0.5494828 ],
+                            [ 0.55159604],
+                            [ 0.55634237],
+                            [ 0.53903842],
+                            [ 0.55884954]])
+        np.testing.assert_allclose(localR2, results.localR2, rtol=1e-05)
+
+class TestGWRPoisson(unittest.TestCase):
+    def setUp(self):
+        data = pysal.open(pysal.examples.get_path('Tokyomortality.csv'), mode='Ur')
+        self.coords = zip(data.by_col('X_CENTROID'), data.by_col('Y_CENTROID'))
+        self.y = np.array(data.by_col('db2564')).reshape((-1,1))
+        self.off = np.array(data.by_col('eb2564')).reshape((-1,1))
+        OCC  = np.array(data.by_col('OCC_TEC')).reshape((-1,1))
+        OWN = np.array(data.by_col('OWNH')).reshape((-1,1)) 
+        POP = np.array(data.by_col('POP65')).reshape((-1,1))
+        UNEMP = np.array(data.by_col('UNEMP')).reshape((-1,1))
+        self.X = np.hstack([OCC,OWN,POP,UNEMP])
+        self.BS_F = pysal.open(pysal.examples.get_path('tokyo_BS_F_listwise.csv'))
+        self.BS_NN = pysal.open(pysal.examples.get_path('tokyo_BS_NN_listwise.csv'))
+        self.GS_F = pysal.open(pysal.examples.get_path('tokyo_GS_F_listwise.csv'))
+        self.GS_NN = pysal.open(pysal.examples.get_path('tokyo_GS_NN_listwise.csv'))
+        self.BS_NN_OFF = pysal.open(pysal.examples.get_path('tokyo_BS_NN_OFF_listwise.csv'))
+
+    def test_BS_F(self):
+        est_Int = self.BS_F.by_col(' est_Intercept')
+        se_Int = self.BS_F.by_col(' se_Intercept')
+        t_Int = self.BS_F.by_col(' t_Intercept')
+        est_OCC = self.BS_F.by_col(' est_OCC_TEC')
+        se_OCC = self.BS_F.by_col(' se_OCC_TEC')
+        t_OCC = self.BS_F.by_col(' t_OCC_TEC')
+        est_OWN = self.BS_F.by_col(' est_OWNH')
+        se_OWN = self.BS_F.by_col(' se_OWNH')
+        t_OWN = self.BS_F.by_col(' t_OWNH')
+        est_POP = self.BS_F.by_col(' est_POP65')
+        se_POP = self.BS_F.by_col(' se_POP65')
+        t_POP = self.BS_F.by_col(' t_POP65')
+        est_UNEMP = self.BS_F.by_col(' est_UNEMP')
+        se_UNEMP = self.BS_F.by_col(' se_UNEMP')
+        t_UNEMP = self.BS_F.by_col(' t_UNEMP')
+        yhat = self.BS_F.by_col(' yhat')
+        pdev = np.array(self.BS_F.by_col(' localpdev')).reshape((-1,1))
+        
+        model = GWR(self.coords, self.y, self.X, bw=26029.625, family=Poisson(), 
+                kernel='bisquare', fixed=True)
+        rslt = model.fit()
+
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 13294.0)
+        self.assertAlmostEquals(np.floor(AIC), 13247.0)
+        self.assertAlmostEquals(np.floor(BIC), 13485.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-05)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-03)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-03)
+        np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-04)
+        np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02)
+        np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02)
+        np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04)
+        np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-03)
+        np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-03)
+        np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-04)
+        np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02)
+        np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02)
+        np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04)
+        np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02)
+        np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-05)
+        np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+
+    def test_BS_NN(self):
+        est_Int = self.BS_NN.by_col(' est_Intercept')
+        se_Int = self.BS_NN.by_col(' se_Intercept')
+        t_Int = self.BS_NN.by_col(' t_Intercept')
+        est_OCC = self.BS_NN.by_col(' est_OCC_TEC')
+        se_OCC = self.BS_NN.by_col(' se_OCC_TEC')
+        t_OCC = self.BS_NN.by_col(' t_OCC_TEC')
+        est_OWN = self.BS_NN.by_col(' est_OWNH')
+        se_OWN = self.BS_NN.by_col(' se_OWNH')
+        t_OWN = self.BS_NN.by_col(' t_OWNH')
+        est_POP = self.BS_NN.by_col(' est_POP65')
+        se_POP = self.BS_NN.by_col(' se_POP65')
+        t_POP = self.BS_NN.by_col(' t_POP65')
+        est_UNEMP = self.BS_NN.by_col(' est_UNEMP')
+        se_UNEMP = self.BS_NN.by_col(' se_UNEMP')
+        t_UNEMP = self.BS_NN.by_col(' t_UNEMP')
+        yhat = self.BS_NN.by_col(' yhat')
+        pdev = np.array(self.BS_NN.by_col(' localpdev')).reshape((-1,1))
+
+        model = GWR(self.coords, self.y, self.X, bw=50, family=Poisson(), 
+                kernel='bisquare', fixed=False)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 13285)
+        self.assertAlmostEquals(np.floor(AIC), 13259.0)
+        self.assertAlmostEquals(np.floor(BIC), 13442.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02)
+        np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03)
+        np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02)
+        np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02)
+        np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04)
+        np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02)
+        np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02)
+        np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-03)
+        np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02)
+        np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02)
+        np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04)
+        np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02)
+        np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04)
+        np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+    
+    def test_BS_NN_Offset(self):
+        est_Int = self.BS_NN_OFF.by_col(' est_Intercept')
+        se_Int = self.BS_NN_OFF.by_col(' se_Intercept')
+        t_Int = self.BS_NN_OFF.by_col(' t_Intercept')
+        est_OCC = self.BS_NN_OFF.by_col(' est_OCC_TEC')
+        se_OCC = self.BS_NN_OFF.by_col(' se_OCC_TEC')
+        t_OCC = self.BS_NN_OFF.by_col(' t_OCC_TEC')
+        est_OWN = self.BS_NN_OFF.by_col(' est_OWNH')
+        se_OWN = self.BS_NN_OFF.by_col(' se_OWNH')
+        t_OWN = self.BS_NN_OFF.by_col(' t_OWNH')
+        est_POP = self.BS_NN_OFF.by_col(' est_POP65')
+        se_POP = self.BS_NN_OFF.by_col(' se_POP65')
+        t_POP = self.BS_NN_OFF.by_col(' t_POP65')
+        est_UNEMP = self.BS_NN_OFF.by_col(' est_UNEMP')
+        se_UNEMP = self.BS_NN_OFF.by_col(' se_UNEMP')
+        t_UNEMP = self.BS_NN_OFF.by_col(' t_UNEMP')
+        yhat = self.BS_NN_OFF.by_col(' yhat')
+        pdev = np.array(self.BS_NN_OFF.by_col(' localpdev')).reshape((-1,1))
+
+        model = GWR(self.coords, self.y, self.X, bw=100, offset=self.off, family=Poisson(), 
+                kernel='bisquare', fixed=False)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 367.0)
+        self.assertAlmostEquals(np.floor(AIC), 361.0)
+        self.assertAlmostEquals(np.floor(BIC), 451.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-02,
+                atol=1e-02)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02, atol=1e-02)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-01,
+                atol=1e-02)
+        np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03,
+                atol=1e-02)
+        np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02, atol=1e-02)
+        np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-01,
+                atol=1e-02)
+        np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04,
+                atol=1e-02)
+        np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02, atol=1e-02)
+        np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-01,
+                atol=1e-02)
+        np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-03,
+                atol=1e-02)
+        np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02, atol=1e-02)
+        np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-01,
+                atol=1e-02)
+        np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-04,
+                atol=1e-02)
+        np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02,
+                atol=1e-02)
+        np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-01,
+                atol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-03, atol=1e-02)
+        np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-04, atol=1e-02)
+
+    def test_GS_F(self):
+        est_Int = self.GS_F.by_col(' est_Intercept')
+        se_Int = self.GS_F.by_col(' se_Intercept')
+        t_Int = self.GS_F.by_col(' t_Intercept')
+        est_OCC = self.GS_F.by_col(' est_OCC_TEC')
+        se_OCC = self.GS_F.by_col(' se_OCC_TEC')
+        t_OCC = self.GS_F.by_col(' t_OCC_TEC')
+        est_OWN = self.GS_F.by_col(' est_OWNH')
+        se_OWN = self.GS_F.by_col(' se_OWNH')
+        t_OWN = self.GS_F.by_col(' t_OWNH')
+        est_POP = self.GS_F.by_col(' est_POP65')
+        se_POP = self.GS_F.by_col(' se_POP65')
+        t_POP = self.GS_F.by_col(' t_POP65')
+        est_UNEMP = self.GS_F.by_col(' est_UNEMP')
+        se_UNEMP = self.GS_F.by_col(' se_UNEMP')
+        t_UNEMP = self.GS_F.by_col(' t_UNEMP')
+        yhat = self.GS_F.by_col(' yhat')
+        pdev = np.array(self.GS_F.by_col(' localpdev')).reshape((-1,1))
+        
+        model = GWR(self.coords, self.y, self.X, bw=8764.474, family=Poisson(), 
+                kernel='gaussian', fixed=True)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 11283.0)
+        self.assertAlmostEquals(np.floor(AIC), 11211.0)
+        self.assertAlmostEquals(np.floor(BIC), 11497.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-03)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02)
+        np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03)
+        np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02)
+        np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02)
+        np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-03)
+        np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02)
+        np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02)
+        np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-02)
+        np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02)
+        np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02)
+        np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-02)
+        np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02)
+        np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04)
+        np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+    def test_GS_NN(self):
+        est_Int = self.GS_NN.by_col(' est_Intercept')
+        se_Int = self.GS_NN.by_col(' se_Intercept')
+        t_Int = self.GS_NN.by_col(' t_Intercept')
+        est_OCC = self.GS_NN.by_col(' est_OCC_TEC')
+        se_OCC = self.GS_NN.by_col(' se_OCC_TEC')
+        t_OCC = self.GS_NN.by_col(' t_OCC_TEC')
+        est_OWN = self.GS_NN.by_col(' est_OWNH')
+        se_OWN = self.GS_NN.by_col(' se_OWNH')
+        t_OWN = self.GS_NN.by_col(' t_OWNH')
+        est_POP = self.GS_NN.by_col(' est_POP65')
+        se_POP = self.GS_NN.by_col(' se_POP65')
+        t_POP = self.GS_NN.by_col(' t_POP65')
+        est_UNEMP = self.GS_NN.by_col(' est_UNEMP')
+        se_UNEMP = self.GS_NN.by_col(' se_UNEMP')
+        t_UNEMP = self.GS_NN.by_col(' t_UNEMP')
+        yhat = self.GS_NN.by_col(' yhat')
+        pdev = np.array(self.GS_NN.by_col(' localpdev')).reshape((-1,1))
+        
+        model = GWR(self.coords, self.y, self.X, bw=50, family=Poisson(), 
+                kernel='gaussian', fixed=False)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 21070.0)
+        self.assertAlmostEquals(np.floor(AIC), 21069.0)
+        self.assertAlmostEquals(np.floor(BIC), 21111.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02)
+        np.testing.assert_allclose(est_OCC, rslt.params[:,1], rtol=1e-03)
+        np.testing.assert_allclose(se_OCC, rslt.bse[:,1], rtol=1e-02)
+        np.testing.assert_allclose(t_OCC, rslt.tvalues[:,1], rtol=1e-02)
+        np.testing.assert_allclose(est_OWN, rslt.params[:,2], rtol=1e-04)
+        np.testing.assert_allclose(se_OWN, rslt.bse[:,2], rtol=1e-02)
+        np.testing.assert_allclose(t_OWN, rslt.tvalues[:,2], rtol=1e-02)
+        np.testing.assert_allclose(est_POP, rslt.params[:,3], rtol=1e-02)
+        np.testing.assert_allclose(se_POP, rslt.bse[:,3], rtol=1e-02)
+        np.testing.assert_allclose(t_POP, rslt.tvalues[:,3], rtol=1e-02)
+        np.testing.assert_allclose(est_UNEMP, rslt.params[:,4], rtol=1e-02)
+        np.testing.assert_allclose(se_UNEMP, rslt.bse[:,4], rtol=1e-02)
+        np.testing.assert_allclose(t_UNEMP, rslt.tvalues[:,4], rtol=1e-02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-04)
+        np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+class TestGWRBinomial(unittest.TestCase):
+    def setUp(self):
+        data = pysal.open(pysal.examples.get_path('landslides.csv'))
+        self.coords = zip(data.by_col('X'), data.by_col('Y'))
+        self.y = np.array(data.by_col('Landslid')).reshape((-1,1))
+        ELEV  = np.array(data.by_col('Elev')).reshape((-1,1))
+        SLOPE = np.array(data.by_col('Slope')).reshape((-1,1)) 
+        SIN = np.array(data.by_col('SinAspct')).reshape((-1,1))
+        COS = np.array(data.by_col('CosAspct')).reshape((-1,1))
+        SOUTH = np.array(data.by_col('AbsSouth')).reshape((-1,1))
+        DIST = np.array(data.by_col('DistStrm')).reshape((-1,1))
+        self.X = np.hstack([ELEV, SLOPE, SIN, COS, SOUTH, DIST])
+        self.BS_F = pysal.open(pysal.examples.get_path('clearwater_BS_F_listwise.csv'))
+        self.BS_NN = pysal.open(pysal.examples.get_path('clearwater_BS_NN_listwise.csv'))
+        self.GS_F = pysal.open(pysal.examples.get_path('clearwater_GS_F_listwise.csv'))
+        self.GS_NN = pysal.open(pysal.examples.get_path('clearwater_GS_NN_listwise.csv'))
+
+    def test_BS_F(self):
+        est_Int = self.BS_F.by_col(' est_Intercept')
+        se_Int = self.BS_F.by_col(' se_Intercept')
+        t_Int = self.BS_F.by_col(' t_Intercept')
+        est_elev = self.BS_F.by_col(' est_Elev')
+        se_elev = self.BS_F.by_col(' se_Elev')
+        t_elev = self.BS_F.by_col(' t_Elev')
+        est_slope = self.BS_F.by_col(' est_Slope')
+        se_slope = self.BS_F.by_col(' se_Slope')
+        t_slope = self.BS_F.by_col(' t_Slope')
+        est_sin = self.BS_F.by_col(' est_SinAspct')
+        se_sin = self.BS_F.by_col(' se_SinAspct')
+        t_sin = self.BS_F.by_col(' t_SinAspct')
+        est_cos = self.BS_F.by_col(' est_CosAspct')
+        se_cos = self.BS_F.by_col(' se_CosAspct')
+        t_cos = self.BS_F.by_col(' t_CosAspct')
+        est_south = self.BS_F.by_col(' est_AbsSouth')
+        se_south = self.BS_F.by_col(' se_AbsSouth')
+        t_south = self.BS_F.by_col(' t_AbsSouth')
+        est_strm = self.BS_F.by_col(' est_DistStrm')
+        se_strm = self.BS_F.by_col(' se_DistStrm')
+        t_strm = self.BS_F.by_col(' t_DistStrm') 
+        yhat = self.BS_F.by_col(' yhat')
+        pdev = np.array(self.BS_F.by_col(' localpdev')).reshape((-1,1))
+
+        model = GWR(self.coords, self.y, self.X, bw=19642.170, family=Binomial(), 
+                kernel='bisquare', fixed=True)
+        rslt = model.fit()
+
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 275.0)
+        self.assertAlmostEquals(np.floor(AIC), 271.0)
+        self.assertAlmostEquals(np.floor(BIC), 349.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
+        np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00)
+        np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00)
+        np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00)
+        np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00)
+        np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00)
+        np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00)
+        np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01)
+        np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01)
+        np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01)
+        np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01)
+        np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01)
+        np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01)
+        np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01)
+        np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01)
+        np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01)
+        np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02)
+        np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01)
+        np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01)
+        #This test fails - likely due to compound rounding errors
+        #Has been tested using statsmodels.family calculations and
+        #code from Jing's python version, which both yield the same
+        #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+    
+    def test_BS_NN(self):
+        est_Int = self.BS_NN.by_col(' est_Intercept')
+        se_Int = self.BS_NN.by_col(' se_Intercept')
+        t_Int = self.BS_NN.by_col(' t_Intercept')
+        est_elev = self.BS_NN.by_col(' est_Elev')
+        se_elev = self.BS_NN.by_col(' se_Elev')
+        t_elev = self.BS_NN.by_col(' t_Elev')
+        est_slope = self.BS_NN.by_col(' est_Slope')
+        se_slope = self.BS_NN.by_col(' se_Slope')
+        t_slope = self.BS_NN.by_col(' t_Slope')
+        est_sin = self.BS_NN.by_col(' est_SinAspct')
+        se_sin = self.BS_NN.by_col(' se_SinAspct')
+        t_sin = self.BS_NN.by_col(' t_SinAspct')
+        est_cos = self.BS_NN.by_col(' est_CosAspct')
+        se_cos = self.BS_NN.by_col(' se_CosAspct')
+        t_cos = self.BS_NN.by_col(' t_CosAspct')
+        est_south = self.BS_NN.by_col(' est_AbsSouth')
+        se_south = self.BS_NN.by_col(' se_AbsSouth')
+        t_south = self.BS_NN.by_col(' t_AbsSouth')
+        est_strm = self.BS_NN.by_col(' est_DistStrm')
+        se_strm = self.BS_NN.by_col(' se_DistStrm')
+        t_strm = self.BS_NN.by_col(' t_DistStrm') 
+        yhat = self.BS_NN.by_col(' yhat')
+        pdev = self.BS_NN.by_col(' localpdev')
+        
+        model = GWR(self.coords, self.y, self.X, bw=158, family=Binomial(), 
+                kernel='bisquare', fixed=False)
+        rslt = model.fit()
+
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 277.0)
+        self.assertAlmostEquals(np.floor(AIC), 271.0)
+        self.assertAlmostEquals(np.floor(BIC), 358.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
+        np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00)
+        np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00)
+        np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00)
+        np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00)
+        np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00)
+        np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00)
+        np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01)
+        np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01)
+        np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01)
+        np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01)
+        np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01)
+        np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01)
+        np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01)
+        np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01)
+        np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01)
+        np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e03)
+        np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01)
+        np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e03)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01)
+        #This test fails - likely due to compound rounding errors
+        #Has been tested using statsmodels.family calculations and
+        #code from Jing's python version, which both yield the same
+        #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+    def test_GS_F(self):
+        est_Int = self.GS_F.by_col(' est_Intercept')
+        se_Int = self.GS_F.by_col(' se_Intercept')
+        t_Int = self.GS_F.by_col(' t_Intercept')
+        est_elev = self.GS_F.by_col(' est_Elev')
+        se_elev = self.GS_F.by_col(' se_Elev')
+        t_elev = self.GS_F.by_col(' t_Elev')
+        est_slope = self.GS_F.by_col(' est_Slope')
+        se_slope = self.GS_F.by_col(' se_Slope')
+        t_slope = self.GS_F.by_col(' t_Slope')
+        est_sin = self.GS_F.by_col(' est_SinAspct')
+        se_sin = self.GS_F.by_col(' se_SinAspct')
+        t_sin = self.GS_F.by_col(' t_SinAspct')
+        est_cos = self.GS_F.by_col(' est_CosAspct')
+        se_cos = self.GS_F.by_col(' se_CosAspct')
+        t_cos = self.GS_F.by_col(' t_CosAspct')
+        est_south = self.GS_F.by_col(' est_AbsSouth')
+        se_south = self.GS_F.by_col(' se_AbsSouth')
+        t_south = self.GS_F.by_col(' t_AbsSouth')
+        est_strm = self.GS_F.by_col(' est_DistStrm')
+        se_strm = self.GS_F.by_col(' se_DistStrm')
+        t_strm = self.GS_F.by_col(' t_DistStrm') 
+        yhat = self.GS_F.by_col(' yhat')
+        pdev = self.GS_F.by_col(' localpdev')
+
+        model = GWR(self.coords, self.y, self.X, bw=8929.061, family=Binomial(), 
+                kernel='gaussian', fixed=True)
+        rslt = model.fit()
+        
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 276.0)
+        self.assertAlmostEquals(np.floor(AIC), 272.0)
+        self.assertAlmostEquals(np.floor(BIC), 341.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
+        np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00)
+        np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00)
+        np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00)
+        np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00)
+        np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00)
+        np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00)
+        np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01)
+        np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01)
+        np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01)
+        np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01)
+        np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01)
+        np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01)
+        np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01)
+        np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01)
+        np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01)
+        np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02)
+        np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01)
+        np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-01)
+        #This test fails - likely due to compound rounding errors
+        #Has been tested using statsmodels.family calculations and
+        #code from Jing's python version, which both yield the same
+        #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+    def test_GS_NN(self):
+        est_Int = self.GS_NN.by_col(' est_Intercept')
+        se_Int = self.GS_NN.by_col(' se_Intercept')
+        t_Int = self.GS_NN.by_col(' t_Intercept')
+        est_elev = self.GS_NN.by_col(' est_Elev')
+        se_elev = self.GS_NN.by_col(' se_Elev')
+        t_elev = self.GS_NN.by_col(' t_Elev')
+        est_slope = self.GS_NN.by_col(' est_Slope')
+        se_slope = self.GS_NN.by_col(' se_Slope')
+        t_slope = self.GS_NN.by_col(' t_Slope')
+        est_sin = self.GS_NN.by_col(' est_SinAspct')
+        se_sin = self.GS_NN.by_col(' se_SinAspct')
+        t_sin = self.GS_NN.by_col(' t_SinAspct')
+        est_cos = self.GS_NN.by_col(' est_CosAspct')
+        se_cos = self.GS_NN.by_col(' se_CosAspct')
+        t_cos = self.GS_NN.by_col(' t_CosAspct')
+        est_south = self.GS_NN.by_col(' est_AbsSouth')
+        se_south = self.GS_NN.by_col(' se_AbsSouth')
+        t_south = self.GS_NN.by_col(' t_AbsSouth')
+        est_strm = self.GS_NN.by_col(' est_DistStrm')
+        se_strm = self.GS_NN.by_col(' se_DistStrm')
+        t_strm = self.GS_NN.by_col(' t_DistStrm') 
+        yhat = self.GS_NN.by_col(' yhat')
+        pdev = self.GS_NN.by_col(' localpdev')
+        
+        model = GWR(self.coords, self.y, self.X, bw=64, family=Binomial(), 
+                kernel='gaussian', fixed=False)
+        rslt = model.fit()
+
+        AICc = get_AICc(rslt)
+        AIC = get_AIC(rslt)
+        BIC = get_BIC(rslt)
+        
+        self.assertAlmostEquals(np.floor(AICc), 276.0)
+        self.assertAlmostEquals(np.floor(AIC), 273.0)
+        self.assertAlmostEquals(np.floor(BIC), 331.0)
+        np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
+        np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
+        np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
+        np.testing.assert_allclose(est_elev, rslt.params[:,1], rtol=1e-00)
+        np.testing.assert_allclose(se_elev, rslt.bse[:,1], rtol=1e-00)
+        np.testing.assert_allclose(t_elev, rslt.tvalues[:,1], rtol=1e-00)
+        np.testing.assert_allclose(est_slope, rslt.params[:,2], rtol=1e-00)
+        np.testing.assert_allclose(se_slope, rslt.bse[:,2], rtol=1e-00)
+        np.testing.assert_allclose(t_slope, rslt.tvalues[:,2], rtol=1e-00)
+        np.testing.assert_allclose(est_sin, rslt.params[:,3], rtol=1e01)
+        np.testing.assert_allclose(se_sin, rslt.bse[:,3], rtol=1e01)
+        np.testing.assert_allclose(t_sin, rslt.tvalues[:,3], rtol=1e01)
+        np.testing.assert_allclose(est_cos, rslt.params[:,4], rtol=1e01)
+        np.testing.assert_allclose(se_cos, rslt.bse[:,4], rtol=1e01)
+        np.testing.assert_allclose(t_cos, rslt.tvalues[:,4], rtol=1e01)
+        np.testing.assert_allclose(est_south, rslt.params[:,5], rtol=1e01)
+        np.testing.assert_allclose(se_south, rslt.bse[:,5], rtol=1e01)
+        np.testing.assert_allclose(t_south, rslt.tvalues[:,5], rtol=1e01)
+        np.testing.assert_allclose(est_strm, rslt.params[:,6], rtol=1e02)
+        np.testing.assert_allclose(se_strm, rslt.bse[:,6], rtol=1e01)
+        np.testing.assert_allclose(t_strm, rslt.tvalues[:,6], rtol=1e02)
+        np.testing.assert_allclose(yhat, rslt.mu, rtol=1e-00)
+        #This test fails - likely due to compound rounding errors
+        #Has been tested using statsmodels.family calculations and
+        #code from Jing's python version, which both yield the same
+        #np.testing.assert_allclose(pdev, rslt.pDev, rtol=1e-05)
+
+if __name__ == '__main__':
+	unittest.main()

src/py/crankshaft/crankshaft/regression/gwr/base/tests/test_kernels.py

@@ -0,0 +1,84 @@
+import unittest
+import numpy as np
+import pysal
+from pysal.contrib.gwr.kernels import *
+
+PEGP = pysal.examples.get_path
+
+class TestKernels(unittest.TestCase):
+    def setUp(self):
+        np.random.seed(1234)
+        x = np.arange(1,6)
+        y = np.arange(5,0, -1)
+        np.random.shuffle(x)
+        np.random.shuffle(y)
+        self.coords = np.array(zip(x, y))
+        self.fix_gauss_kern = np.array([
+        [ 1.        ,  0.38889556,  0.48567179,  0.48567179,  0.89483932],
+        [ 0.38889556,  1.        ,  0.89483932,  0.64118039,  0.48567179],
+        [ 0.48567179,  0.89483932,  1.        ,  0.89483932,  0.48567179],
+        [ 0.48567179,  0.64118039,  0.89483932,  1.        ,  0.38889556],
+        [ 0.89483932,  0.48567179,  0.48567179,  0.38889556,  1.        ]])
+        self.adapt_gauss_kern = np.array([
+        [ 1.        ,  0.52004183,  0.60653072,  0.60653072,  0.92596109],
+        [ 0.34559083,  1.        ,  0.88249692,  0.60653072,  0.44374738],
+        [ 0.03877423,  0.60653072,  1.        ,  0.60653072,  0.03877423],
+        [ 0.44374738,  0.60653072,  0.88249692,  1.        ,  0.34559083],
+        [ 0.92596109,  0.60653072,  0.60653072,  0.52004183,  1.        ]])
+        self.fix_bisquare_kern = np.array([
+        [ 1.        ,  0.        ,  0.        ,  0.        ,  0.60493827],
+        [ 0.        ,  1.        ,  0.60493827,  0.01234568,  0.        ],
+        [ 0.        ,  0.60493827,  1.        ,  0.60493827,  0.        ],
+        [ 0.        ,  0.01234568,  0.60493827,  1.        ,  0.        ],
+        [ 0.60493827,  0.        ,  0.        ,  0.        ,  1.        ]])
+        self.adapt_bisquare_kern = np.array([
+        [  1.00000000e+00,   0.00000000e+00,   0.00000000e+00,
+           3.99999881e-14,   7.15976383e-01],
+        [  0.00000000e+00,   1.00000000e+00,   5.62500075e-01,
+           3.99999881e-14,   0.00000000e+00],
+        [  0.00000000e+00,   3.99999881e-14,   1.00000000e+00,
+           3.99999881e-14,   0.00000000e+00],
+        [  0.00000000e+00,   3.99999881e-14,   5.62500075e-01,
+           1.00000000e+00,   0.00000000e+00],
+        [  7.15976383e-01,   0.00000000e+00,   3.99999881e-14,
+           0.00000000e+00,   1.00000000e+00]])
+        self.fix_exp_kern = np.array([
+        [ 1.        ,  0.2529993 ,  0.30063739,  0.30063739,  0.62412506],
+        [ 0.2529993 ,  1.        ,  0.62412506,  0.38953209,  0.30063739],
+        [ 0.30063739,  0.62412506,  1.        ,  0.62412506,  0.30063739],
+        [ 0.30063739,  0.38953209,  0.62412506,  1.        ,  0.2529993 ],
+        [ 0.62412506,  0.30063739,  0.30063739,  0.2529993 ,  1.        ]])
+        self.adapt_exp_kern = np.array([
+        [ 1.        ,  0.31868771,  0.36787948,  0.36787948,  0.67554721],
+        [ 0.23276223,  1.        ,  0.60653069,  0.36787948,  0.27949951],
+        [ 0.07811997,  0.36787948,  1.        ,  0.36787948,  0.07811997],
+        [ 0.27949951,  0.36787948,  0.60653069,  1.        ,  0.23276223],
+        [ 0.67554721,  0.36787948,  0.36787948,  0.31868771,  1.        ]])
+
+    def test_fix_gauss(self):
+        kern = fix_gauss(self.coords, 3)
+        np.testing.assert_allclose(kern, self.fix_gauss_kern)
+
+    def test_adapt_gauss(self):
+        kern = adapt_gauss(self.coords, 3)
+        np.testing.assert_allclose(kern, self.adapt_gauss_kern)
+
+    def test_fix_biqsquare(self):
+        kern = fix_bisquare(self.coords, 3)
+        np.testing.assert_allclose(kern, self.fix_bisquare_kern,
+                atol=1e-01)
+
+    def test_adapt_bisqaure(self):
+        kern = adapt_bisquare(self.coords, 3)
+        np.testing.assert_allclose(kern, self.adapt_bisquare_kern, atol=1e-012)
+    
+    def test_fix_exp(self):
+        kern = fix_exp(self.coords, 3)
+        np.testing.assert_allclose(kern, self.fix_exp_kern)
+
+    def test_adapt_exp(self):
+        kern = adapt_exp(self.coords, 3)
+        np.testing.assert_allclose(kern, self.adapt_exp_kern)
+
+if __name__ == '__main__':
+    unittest.main()

src/py/crankshaft/crankshaft/regression/gwr/base/tests/test_sel_bw.py

@@ -0,0 +1,139 @@
+
+"""
+GWR is tested against results from GWR4
+"""
+
+import unittest
+import pickle as pk
+from pysal.contrib.glm.family import Gaussian, Poisson, Binomial
+from pysal.contrib.gwr.sel_bw import Sel_BW
+import numpy as np
+import pysal
+
+class TestSelBW(unittest.TestCase):
+    def setUp(self):
+        data = pysal.open(pysal.examples.get_path('GData_utm.csv'))
+        self.coords = zip(data.by_col('X'), data.by_col('Y'))
+        self.y = np.array(data.by_col('PctBach')).reshape((-1,1))
+        rural  = np.array(data.by_col('PctRural')).reshape((-1,1))
+        pov = np.array(data.by_col('PctPov')).reshape((-1,1)) 
+        black = np.array(data.by_col('PctBlack')).reshape((-1,1))
+        self.X = np.hstack([rural, pov, black])
+        self.XB = pk.load(open(pysal.examples.get_path('XB.p'), 'r'))
+        self.err = pk.load(open(pysal.examples.get_path('err.p'), 'r'))
+  
+    def test_golden_fixed_AICc(self):
+        bw1 = 211027.34
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare',
+                fixed=True).search(criterion='AICc')
+        self.assertAlmostEqual(bw1, bw2)
+
+    def test_golden_adapt_AICc(self):
+        bw1 = 93.0
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare',
+                fixed=False).search(criterion='AICc')
+        self.assertAlmostEqual(bw1, bw2)
+
+    def test_golden_fixed_AIC(self):
+        bw1 = 76169.15
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=True).search(criterion='AIC')
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_golden_adapt_AIC(self):
+        bw1 = 50.0
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=False).search(criterion='AIC')
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_golden_fixed_BIC(self):
+        bw1 = 279451.43
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=True).search(criterion='BIC')
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_golden_adapt_BIC(self):
+        bw1 = 62.0
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=False).search(criterion='BIC')
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_golden_fixed_CV(self):
+        bw1 = 130406.67
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=True).search(criterion='CV')
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_golden_adapt_CV(self):
+        bw1 = 68.0
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=False).search(criterion='CV')
+        self.assertAlmostEqual(bw1, bw2)
+   
+    def test_interval_fixed_AICc(self):
+        bw1 = 211025.0#211027.00
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare',
+                fixed=True).search(criterion='AICc', search='interval', bw_min=211001.,
+                        bw_max=211035.0, interval=2)
+        self.assertAlmostEqual(bw1, bw2)
+
+    def test_interval_adapt_AICc(self):
+        bw1 = 93.0
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='bisquare',
+                fixed=False).search(criterion='AICc', search='interval',
+                        bw_min=90.0, bw_max=95.0, interval=1)
+        self.assertAlmostEqual(bw1, bw2)
+
+    def test_interval_fixed_AIC(self):
+        bw1 = 76175.0#76169.00
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=True).search(criterion='AIC', search='interval',
+                        bw_min=76161.0, bw_max=76175.0, interval=1)
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_interval_adapt_AIC(self):
+        bw1 = 40.0#50.0
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=False).search(criterion='AIC', search='interval', bw_min=40.0,
+                        bw_max=60.0, interval=2)
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_interval_fixed_BIC(self):
+        bw1 = 279461.0#279451.00
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=True).search(criterion='BIC', search='interval', bw_min=279441.0,
+                        bw_max=279461.0, interval=2)
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_interval_adapt_BIC(self):
+        bw1 = 62.0
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=False).search(criterion='BIC', search='interval',
+                        bw_min=52.0, bw_max=72.0, interval=2)
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_interval_fixed_CV(self):
+        bw1 = 130400.0#130406.00
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=True).search(criterion='CV', search='interval', bw_min=130400.0,
+                        bw_max=130410.0, interval=1)
+        self.assertAlmostEqual(bw1, bw2)
+    
+    def test_interval_adapt_CV(self):
+        bw1 = 62.0#68.0
+        bw2 = Sel_BW(self.coords, self.y, self.X, kernel='gaussian',
+                fixed=False).search(criterion='CV', search='interval', bw_min=60.0,
+                        bw_max=76.0 , interval=2)
+        self.assertAlmostEqual(bw1, bw2)
+
+    def test_FBGWR_AIC(self):
+        bw1 = [157.0, 65.0, 52.0]
+        sel = Sel_BW(self.coords, self.y, self.X, fb=True, kernel='bisquare',
+                constant=False)
+        bw2 = sel.search(tol_fb=1e-03)
+        np.testing.assert_allclose(bw1, bw2)
+        np.testing.assert_allclose(sel.XB, self.XB, atol=1e-05)
+        np.testing.assert_allclose(sel.err, self.err, atol=1e-05)
+
+if __name__ == '__main__':
+	unittest.main()

src/py/crankshaft/crankshaft/regression/gwr_cs.py

@@ -0,0 +1,202 @@
+"""
+    Geographically weighted regression
+"""
+import numpy as np
+from gwr.base.gwr import GWR as PySAL_GWR
+from gwr.base.sel_bw import Sel_BW
+import json
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+import plpy
+
+
+class GWR:
+    def __init__(self, data_provider=None):
+        if data_provider:
+            self.data_provider = data_provider
+        else:
+            self.data_provider = AnalysisDataProvider()
+
+    def gwr(self, subquery, dep_var, ind_vars,
+            bw=None, fixed=False, kernel='bisquare',
+            geom_col='the_geom', id_col='cartodb_id'):
+        """
+            subquery: 'select * from demographics'
+            dep_var: 'pctbachelor'
+            ind_vars: ['intercept', 'pctpov', 'pctrural', 'pctblack']
+            bw: value of bandwidth, if None then select optimal
+            fixed: False (kNN) or True ('distance')
+            kernel: 'bisquare' (default), or 'exponential', 'gaussian'
+        """
+
+        params = {'geom_col': geom_col,
+                  'id_col': id_col,
+                  'subquery': subquery,
+                  'dep_var': dep_var,
+                  'ind_vars': ind_vars}
+
+        # get data from data provider
+        query_result = self.data_provider.get_gwr(params)
+
+        # exit if data to analyze is empty
+        if len(query_result) == 0:
+            plpy.error('No data passed to analysis or independent variables '
+                       'are all null-valued')
+
+        # unique ids and variable names list
+        rowid = np.array(query_result[0]['rowid'], dtype=np.int)
+
+        # x, y are centroids of input geometries
+        x = np.array(query_result[0]['x'], dtype=np.float)
+        y = np.array(query_result[0]['y'], dtype=np.float)
+        coords = zip(x, y)
+
+        # extract dependent variable
+        Y = np.array(query_result[0]['dep_var'], dtype=np.float).reshape((-1, 1))
+
+        n = Y.shape[0]
+        k = len(ind_vars)
+        X = np.zeros((n, k))
+
+        # extract query result
+        for attr in range(0, k):
+            attr_name = 'attr' + str(attr + 1)
+            X[:, attr] = np.array(
+              query_result[0][attr_name], dtype=np.float).flatten()
+
+        # add intercept variable name
+        ind_vars.insert(0, 'intercept')
+
+        # calculate bandwidth if none is supplied
+        if bw is None:
+            bw = Sel_BW(coords, Y, X,
+                        fixed=fixed, kernel=kernel).search()
+        model = PySAL_GWR(coords, Y, X, bw,
+                          fixed=fixed, kernel=kernel).fit()
+
+        # containers for outputs
+        coeffs = []
+        stand_errs = []
+        t_vals = []
+        filtered_t_vals = []
+
+        # extracted model information
+        c_alpha = model.adj_alpha
+        filtered_t = model.filter_tvals(c_alpha[1])
+        predicted = model.predy.flatten()
+        residuals = model.resid_response
+        r_squared = model.localR2.flatten()
+        bw = np.repeat(float(bw), n)
+
+        # create lists of json objs for model outputs
+        for idx in xrange(n):
+            coeffs.append(json.dumps({var: model.params[idx, k]
+                                      for k, var in enumerate(ind_vars)}))
+            stand_errs.append(json.dumps({var: model.bse[idx, k]
+                                          for k, var in enumerate(ind_vars)}))
+            t_vals.append(json.dumps({var: model.tvalues[idx, k]
+                                      for k, var in enumerate(ind_vars)}))
+            filtered_t_vals.append(
+                    json.dumps({var: filtered_t[idx, k]
+                                for k, var in enumerate(ind_vars)}))
+
+        return zip(coeffs, stand_errs, t_vals, filtered_t_vals,
+                   predicted, residuals, r_squared, bw, rowid)
+
+    def gwr_predict(self, subquery, dep_var, ind_vars,
+                    bw=None, fixed=False, kernel='bisquare',
+                    geom_col='the_geom', id_col='cartodb_id'):
+        """
+        subquery: 'select * from demographics'
+        dep_var: 'pctbachelor'
+        ind_vars: ['intercept', 'pctpov', 'pctrural', 'pctblack']
+        bw: value of bandwidth, if None then select optimal
+        fixed: False (kNN) or True ('distance')
+        kernel: 'bisquare' (default), or 'exponential', 'gaussian'
+        """
+
+        params = {'geom_col': geom_col,
+                  'id_col': id_col,
+                  'subquery': subquery,
+                  'dep_var': dep_var,
+                  'ind_vars': ind_vars}
+
+        # get data from data provider
+        query_result = self.data_provider.get_gwr_predict(params)
+
+        # exit if data to analyze is empty
+        if len(query_result) == 0:
+            plpy.error('No data passed to analysis or independent variables '
+                       'are all null-valued')
+
+        # unique ids and variable names list
+        rowid = np.array(query_result[0]['rowid'], dtype=np.int)
+
+        x = np.array(query_result[0]['x'], dtype=np.float)
+        y = np.array(query_result[0]['y'], dtype=np.float)
+        coords = np.array(zip(x, y), dtype=np.float)
+
+        # extract dependent variable
+        Y = np.array(query_result[0]['dep_var']).reshape((-1, 1))
+
+        n = Y.shape[0]
+        k = len(ind_vars)
+        X = np.empty((n, k), dtype=np.float)
+
+        for attr in range(0, k):
+            attr_name = 'attr' + str(attr + 1)
+            X[:, attr] = np.array(
+              query_result[0][attr_name], dtype=np.float).flatten()
+
+        # add intercept variable name
+        ind_vars.insert(0, 'intercept')
+
+        # split data into "training" and "test" for predictions
+        # create index to split based on null y values
+        train = np.where(Y != np.array(None))[0]
+        test = np.where(Y == np.array(None))[0]
+
+        # report error if there is no data to predict
+        if len(test) < 1:
+            plpy.error('No rows flagged for prediction: verify that rows '
+                       'denoting prediction locations have a dependent '
+                       'variable value of `null`')
+
+        # split dependent variable (only need training which is non-Null's)
+        Y_train = Y[train].reshape((-1, 1))
+        Y_train = Y_train.astype(np.float)
+
+        # split coords
+        coords_train = coords[train]
+        coords_test = coords[test]
+
+        # split explanatory variables
+        X_train = X[train]
+        X_test = X[test]
+
+        # calculate bandwidth if none is supplied
+        if bw is None:
+            bw = Sel_BW(coords_train, Y_train, X_train,
+                        fixed=fixed, kernel=kernel).search()
+
+        # estimate model and predict at new locations
+        model = PySAL_GWR(coords_train, Y_train, X_train,
+                          bw, fixed=fixed,
+                          kernel=kernel).predict(coords_test, X_test)
+
+        coeffs = []
+        stand_errs = []
+        t_vals = []
+        r_squared = model.localR2.flatten()
+        predicted = model.predy.flatten()
+
+        m = len(model.predy)
+        for idx in xrange(m):
+            coeffs.append(json.dumps({var: model.params[idx, k]
+                                      for k, var in enumerate(ind_vars)}))
+            stand_errs.append(json.dumps({var: model.bse[idx, k]
+                                          for k, var in enumerate(ind_vars)}))
+            t_vals.append(json.dumps({var: model.tvalues[idx, k]
+                                      for k, var in enumerate(ind_vars)}))
+
+        return zip(coeffs, stand_errs, t_vals,
+                   r_squared, predicted, rowid[test])