Release 0.8.2

release/crankshaft.control

@@ -1,5 +1,5 @@
 comment = 'CartoDB Spatial Analysis extension'
-default_version = '0.8.1'
+default_version = '0.8.2'
 requires = 'plpythonu, postgis'
 superuser = true
 schema = cdb_crankshaft

release/python/0.8.2/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/0.8.2/crankshaft/crankshaft/analysis_data_provider.py

@@ -0,0 +1,149 @@
+"""class for fetching data"""
+import plpy
+import pysal_utils as pu
+
+NULL_VALUE_ERROR = ('No usable data passed to analysis. Check your input rows '
+                    'for null values and fill in appropriately.')
+
+
+def verify_data(func):
+    """decorator to verify data result before returning to algorithm"""
+    def wrapper(*args, **kwargs):
+        """Error checking"""
+        try:
+            data = func(*args, **kwargs)
+            if not data:
+                plpy.error(NULL_VALUE_ERROR)
+            else:
+                return data
+        except plpy.SPIError as err:
+            plpy.error('Analysis failed: {}'.format(err))
+
+        return []
+
+    return wrapper
+
+
+class AnalysisDataProvider(object):
+    """Data fetching class for pl/python functions"""
+    @verify_data
+    def get_getis(self, w_type, params):  # pylint: disable=no-self-use
+        """fetch data for getis ord's g"""
+        query = pu.construct_neighbor_query(w_type, params)
+        return plpy.execute(query)
+
+    @verify_data
+    def get_markov(self, w_type, params):  # pylint: disable=no-self-use
+        """fetch data for spatial markov"""
+        query = pu.construct_neighbor_query(w_type, params)
+        return plpy.execute(query)
+
+    @verify_data
+    def get_moran(self, w_type, params):  # pylint: disable=no-self-use
+        """fetch data for moran's i analyses"""
+        query = pu.construct_neighbor_query(w_type, params)
+        return plpy.execute(query)
+
+    @verify_data
+    def get_nonspatial_kmeans(self, params):  # pylint: disable=no-self-use
+        """
+            Fetch data for non-spatial k-means.
+
+            Inputs - a dict (params) with the following keys:
+                colnames: a (text) list of column names (e.g.,
+                          `['andy', 'cookie']`)
+                id_col: the name of the id column (e.g., `'cartodb_id'`)
+                subquery: the subquery for exposing the data (e.g.,
+                          SELECT * FROM favorite_things)
+            Output:
+                A SQL query for packaging the data for consumption within
+                `KMeans().nonspatial`. Format will be a list of length one,
+                with the first element a dict with keys ('rowid', 'attr1',
+                'attr2', ...)
+        """
+        agg_cols = ', '.join([
+            'array_agg({0}) As arr_col{1}'.format(val, idx+1)
+            for idx, val in enumerate(params['colnames'])
+        ])
+        query = '''
+            SELECT {cols}, array_agg({id_col}) As rowid
+            FROM ({subquery}) As a
+        '''.format(subquery=params['subquery'],
+                   id_col=params['id_col'],
+                   cols=agg_cols).strip()
+        return plpy.execute(query)
+
+    @verify_data
+    def get_segmentation_model_data(self, params):  # pylint: disable=R0201
+        """
+           fetch data for Segmentation
+        params = {"subquery": query,
+                  "target": variable,
+                  "features": feature_columns}
+        """
+        columns = ', '.join(['array_agg("{col}") As "{col}"'.format(col=col)
+                             for col in params['features']])
+        query = '''
+                SELECT
+                  array_agg("{target}") As target,
+                  {columns}
+                FROM ({subquery}) As q
+                '''.format(subquery=params['subquery'],
+                           target=params['target'],
+                           columns=columns)
+        return plpy.execute(query)
+
+    @verify_data
+    def get_segmentation_data(self, params):  # pylint: disable=no-self-use
+        """
+            params = {"subquery": target_query,
+                      "id_col": id_col}
+        """
+        query = '''
+                SELECT
+                  array_agg("{id_col}" ORDER BY "{id_col}") as "ids"
+                FROM ({subquery}) as q
+                 '''.format(**params)
+        return plpy.execute(query)
+
+    @verify_data
+    def get_segmentation_predict_data(self, params):  # pylint: disable=R0201
+        """
+            fetch data for Segmentation
+            params = {"subquery": target_query,
+                      "feature_columns": feature_columns}
+        """
+        joined_features = ', '.join(['"{}"::numeric'.format(a)
+                                     for a in params['feature_columns']])
+        query = '''
+                SELECT
+                  Array[{joined_features}] As features
+                FROM ({subquery}) as q
+                '''.format(subquery=params['subquery'],
+                           joined_features=joined_features)
+        return plpy.cursor(query)
+
+    @verify_data
+    def get_spatial_kmeans(self, params):  # pylint: disable=no-self-use
+        """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)
+        return plpy.execute(query)
+
+    @verify_data
+    def get_gwr(self, params):  # pylint: disable=no-self-use
+        """fetch data for gwr analysis"""
+        query = pu.gwr_query(params)
+        return plpy.execute(query)
+
+    @verify_data
+    def get_gwr_predict(self, params):  # pylint: disable=no-self-use
+        """fetch data for gwr predict"""
+        query = pu.gwr_predict_query(params)
+        return plpy.execute(query)

release/python/0.8.2/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/0.8.2/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(object):
+    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
+
+        params = 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, params)
+        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/0.8.2/crankshaft/crankshaft/clustering/kmeans.py

@@ -0,0 +1,113 @@
+from sklearn.cluster import KMeans
+import numpy as np
+
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+
+
+class Kmeans(object):
+    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"}
+
+        result = self.data_provider.get_spatial_kmeans(params)
+
+        # Unpack query response
+        xs = result[0]['xs']
+        ys = result[0]['ys']
+        ids = result[0]['ids']
+
+        km = KMeans(n_clusters=no_clusters, n_init=no_init)
+        labels = km.fit_predict(zip(xs, ys))
+        return zip(ids, labels)
+
+    def nonspatial(self, subquery, colnames, no_clusters=5,
+                   standardize=True, id_col='cartodb_id'):
+        """
+        Arguments:
+            query (string): A SQL query to retrieve the data required to do the
+                            k-means clustering analysis, like so:
+                            SELECT * FROM iris_flower_data
+            colnames (list): a list of the column names which contain the data
+                             of interest, like so: ['sepal_width',
+                                                    'petal_width',
+                                                    'sepal_length',
+                                                    'petal_length']
+            no_clusters (int): number of clusters (greater than zero)
+            id_col (string): name of the input id_column
+
+        Returns:
+            A list of tuples with the following columns:
+            cluster labels: a label for the cluster that the row belongs to
+            centers: center of the cluster that this row belongs to
+            silhouettes: silhouette measure for this value
+            rowid: row that these values belong to (corresponds to the value in
+                   `id_col`)
+        """
+        import json
+        from sklearn import metrics
+
+        params = {
+            "colnames": colnames,
+            "subquery": subquery,
+            "id_col": id_col
+        }
+
+        data = self.data_provider.get_nonspatial_kmeans(params)
+
+        # fill array with values for k-means clustering
+        if standardize:
+            cluster_columns = _scale_data(
+              _extract_columns(data))
+        else:
+            cluster_columns = _extract_columns(data)
+
+        kmeans = KMeans(n_clusters=no_clusters,
+                        random_state=0).fit(cluster_columns)
+
+        centers = [json.dumps(dict(zip(colnames, c)))
+                   for c in kmeans.cluster_centers_[kmeans.labels_]]
+
+        silhouettes = metrics.silhouette_samples(cluster_columns,
+                                                 kmeans.labels_,
+                                                 metric='sqeuclidean')
+
+        return zip(kmeans.labels_,
+                   centers,
+                   silhouettes,
+                   [kmeans.inertia_] * kmeans.labels_.shape[0],
+                   data[0]['rowid'])
+
+
+# -- Preprocessing steps
+
+def _extract_columns(data):
+    """
+        Extract the features from the query and pack them into a NumPy array
+        data (list of dicts): result of the kmeans request
+    """
+    # number of columns minus rowid column
+    n_cols = len(data[0]) - 1
+    return np.array([data[0]['arr_col{0}'.format(i+1)]
+                     for i in xrange(n_cols)],
+                    dtype=float).T
+
+
+def _scale_data(features):
+    """
+        Scale all input columns to center on 0 with a standard devation of 1
+        features (numpy matrix): features of dimension (n_features, n_samples)
+    """
+    from sklearn.preprocessing import StandardScaler
+    scaler = StandardScaler()
+    return scaler.fit_transform(features)

release/python/0.8.2/crankshaft/crankshaft/clustering/moran.py

@@ -0,0 +1,341 @@
+"""
+Moran's I geostatistics (global clustering & outliers presence)
+Functionality relies on a combination of `PySAL
+<http://pysal.readthedocs.io/en/latest/>`__ and the data providered provided in
+the class instantiation (which defaults to PostgreSQL's plpy module's `database
+access functions <https://www.postgresql.org/docs/10/static/plpython.html>`__).
+"""
+
+from collections import OrderedDict
+import pysal as ps
+
+# crankshaft module
+import crankshaft.pysal_utils as pu
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+
+# High level interface ---------------------------------------
+
+
+class Moran(object):
+    """Class for calculation of Moran's I statistics (global, local, and local
+    rate)
+
+    Parameters:
+      data_provider (:obj:`AnalysisDataProvider`): Class for fetching data. See
+        the `crankshaft.analysis_data_provider` module for more information.
+    """
+    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.
+
+        Args:
+
+          subquery (str): Query to give access to the data needed. This query
+            must give access to ``attr_name``, ``geom_col``, and ``id_col``.
+          attr_name (str): Column name of data to analyze
+          w_type (str): Type of spatial weight. Must be one of `knn`
+            or `queen`. See `PySAL documentation
+            <http://pysal.readthedocs.io/en/latest/users/tutorials/weights.html>`__
+            for more information.
+          num_ngbrs (int): If using `knn` for ``w_type``, this
+            specifies the number of neighbors to be used to define the spatial
+            neighborhoods.
+          permutations (int): Number of permutations for performing
+            conditional randomization to find the p-value. Higher numbers
+            takes a longer time for getting results.
+          geom_col (str): Name of the geometry column in the dataset for
+            finding the spatial neighborhoods.
+          id_col (str): Row index for each value. Usually the database index.
+
+        """
+        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 (local)
+
+        Args:
+
+          subquery (str): Query to give access to the data needed. This query
+            must give access to ``attr_name``, ``geom_col``, and ``id_col``.
+          attr (str): Column name of data to analyze
+          w_type (str): Type of spatial weight. Must be one of `knn`
+            or `queen`. See `PySAL documentation
+            <http://pysal.readthedocs.io/en/latest/users/tutorials/weights.html>`__
+            for more information.
+          num_ngbrs (int): If using `knn` for ``w_type``, this
+            specifies the number of neighbors to be used to define the spatial
+            neighborhoods.
+          permutations (int): Number of permutations for performing
+            conditional randomization to find the p-value. Higher numbers
+            takes a longer time for getting results.
+          geom_col (str): Name of the geometry column in the dataset for
+            finding the spatial neighborhoods.
+          id_col (str): Row index for each value. Usually the database index.
+
+        Returns:
+          list of tuples: Where each tuple consists of the following values:
+            - quadrants classification (one of `HH`, `HL`, `LL`, or `LH`)
+            - p-value
+            - spatial lag
+            - standardized spatial lag (centered on the mean, normalized by the
+              standard deviation)
+            - original value
+            - standardized value
+            - Moran's I statistic
+            - original row index
+        """
+
+        # 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)
+
+        # calculate spatial lag
+        lag = ps.weights.spatial_lag.lag_spatial(weight, lisa.y)
+        lag_std = ps.weights.spatial_lag.lag_spatial(weight, lisa.z)
+
+        return zip(
+            quads,
+            lisa.p_sim,
+            lag,
+            lag_std,
+            lisa.y,
+            lisa.z,
+            lisa.Is,
+            weight.id_order
+        )
+
+    def global_rate_stat(self, subquery, numerator, denominator,
+                         w_type, num_ngbrs, permutations, geom_col, id_col):
+        """
+        Moran's I Rate (global)
+
+        Args:
+
+          subquery (str): Query to give access to the data needed. This query
+            must give access to ``attr_name``, ``geom_col``, and ``id_col``.
+          numerator (str): Column name of numerator to analyze
+          denominator (str): Column name of the denominator
+          w_type (str): Type of spatial weight. Must be one of `knn`
+            or `queen`. See `PySAL documentation
+            <http://pysal.readthedocs.io/en/latest/users/tutorials/weights.html>`__
+            for more information.
+          num_ngbrs (int): If using `knn` for ``w_type``, this
+            specifies the number of neighbors to be used to define the spatial
+            neighborhoods.
+          permutations (int): Number of permutations for performing
+            conditional randomization to find the p-value. Higher numbers
+            takes a longer time for getting results.
+          geom_col (str): Name of the geometry column in the dataset for
+            finding the spatial neighborhoods.
+          id_col (str): Row index for each value. Usually the database index.
+        """
+        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
+
+        Args:
+
+          subquery (str): Query to give access to the data needed. This query
+            must give access to ``attr_name``, ``geom_col``, and ``id_col``.
+          numerator (str): Column name of numerator to analyze
+          denominator (str): Column name of the denominator
+          w_type (str): Type of spatial weight. Must be one of `knn`
+            or `queen`. See `PySAL documentation
+            <http://pysal.readthedocs.io/en/latest/users/tutorials/weights.html>`__
+            for more information.
+          num_ngbrs (int): If using `knn` for ``w_type``, this
+            specifies the number of neighbors to be used to define the spatial
+            neighborhoods.
+          permutations (int): Number of permutations for performing
+            conditional randomization to find the p-value. Higher numbers
+            takes a longer time for getting results.
+          geom_col (str): Name of the geometry column in the dataset for
+            finding the spatial neighborhoods.
+          id_col (str): Row index for each value. Usually the database index.
+
+        Returns:
+          list of tuples: Where each tuple consists of the following values:
+            - quadrants classification (one of `HH`, `HL`, `LL`, or `LH`)
+            - p-value
+            - spatial lag
+            - standardized spatial lag (centered on the mean, normalized by the
+              standard deviation)
+            - original value (roughly numerator divided by denominator)
+            - standardized value
+            - Moran's I statistic
+            - original row index
+        """
+        # 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)
+
+        # spatial lag
+        lag = ps.weights.spatial_lag.lag_spatial(weight, lisa.y)
+        lag_std = ps.weights.spatial_lag.lag_spatial(weight, lisa.z)
+
+        return zip(
+            quads,
+            lisa.p_sim,
+            lag,
+            lag_std,
+            lisa.y,
+            lisa.z,
+            lisa.Is,
+            weight.id_order
+        )
+
+    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
+    Args:
+      coord (int): quadrant of a specific measurement
+    Returns:
+      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'
+    return None
+
+
+def quad_position(quads):
+    """
+    Map all quads
+
+    Args:
+      quads (:obj:`numpy.ndarray`): an array of quads classified by
+      1-4 (PySAL default)
+    Returns:
+      list: an array of quads classied by 'HH', 'LL', etc.
+    """
+    return [map_quads(q) for q in quads]

release/python/0.8.2/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/0.8.2/crankshaft/crankshaft/pysal_utils/pysal_utils.py

@@ -0,0 +1,251 @@
+"""
+    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
+    """
+
+    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 AND
+                      j."{geom_col}" IS NOT NULL
+                ORDER BY j."{geom_col}" <-> i."{geom_col}" ASC
+                LIMIT {num_ngbrs})) As neighbors
+             FROM ({subquery}) As i
+            WHERE %(attr_where_i)s AND i."{geom_col}" IS NOT NULL
+            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)

release/python/0.8.2/crankshaft/crankshaft/random_seeds.py

@@ -0,0 +1,12 @@
+"""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/0.8.2/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/0.8.2/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/0.8.2/crankshaft/crankshaft/regression/glm/__init__.py

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

release/python/0.8.2/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/0.8.2/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/0.8.2/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/0.8.2/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/0.8.2/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/0.8.2/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/0.8.2/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/0.8.2/crankshaft/crankshaft/regression/gwr/__init__.py

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

release/python/0.8.2/crankshaft/crankshaft/regression/gwr/base/__init__.py

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

release/python/0.8.2/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/0.8.2/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/0.8.2/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/0.8.2/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/0.8.2/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/0.8.2/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/0.8.2/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/0.8.2/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/0.8.2/crankshaft/crankshaft/segmentation/__init__.py

@@ -0,0 +1,2 @@
+"""Import all functions from for segmentation"""
+from segmentation import *

release/python/0.8.2/crankshaft/crankshaft/segmentation/segmentation.py

@@ -0,0 +1,228 @@
+"""
+Segmentation creation and prediction
+"""
+
+import numpy as np
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn import metrics
+from sklearn.cross_validation import train_test_split
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+
+# NOTE: added optional param here
+
+
+class Segmentation(object):
+    """
+        Add docstring
+    """
+
+    def __init__(self, data_provider=None):
+        if data_provider is None:
+            self.data_provider = AnalysisDataProvider()
+        else:
+            self.data_provider = data_provider
+
+    def create_and_predict_segment_agg(self, target, features, target_features,
+                                       target_ids, model_parameters):
+        """
+        Version of create_and_predict_segment that works on arrays that come
+            straight form the SQL calling the function.
+
+            Input:
+                @param target: The 1D array of length NSamples containing the
+                target variable we want the model to predict
+                @param features: The 2D array of size NSamples * NFeatures that
+                    form the input 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, accuracy_array)
+
+    def create_and_predict_segment(self, query, variable, feature_columns,
+                                   target_query, model_params,
+                                   id_col='cartodb_id'):
+        """
+        generate a segment with machine learning
+        Stuart Lynn
+                @param query: subquery that data is pulled from for packaging
+                @param variable: name of the target variable
+                @param feature_columns: list of column names
+                @target_query: The query to run to obtain the data to predict
+                @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)
+        """
+        params = {"subquery": target_query,
+                  "id_col": id_col}
+
+        (target, features, target_mean,
+            feature_means) = self.clean_data(query, variable, feature_columns)
+
+        model, accuracy = train_model(target, features, model_params, 0.2)
+        result = self.predict_segment(model, feature_columns, target_query,
+                                      feature_means)
+        accuracy_array = [accuracy] * result.shape[0]
+
+        rowid = self.data_provider.get_segmentation_data(params)
+        '''
+        rowid = [{'ids': [2.9, 4.9, 4, 5, 6]}]
+        '''
+        return zip(rowid[0]['ids'], result, accuracy_array)
+
+    def predict_segment(self, model, feature_columns, target_query,
+                        feature_means):
+        """
+        Use the provided model to predict the values for the new feature set
+            Input:
+                @param model: The pretrained model
+                @features_col: 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 cartodb_ids associated with it.
+        """
+
+        batch_size = 1000
+        params = {"subquery": target_query,
+                  "feature_columns": feature_columns}
+
+        results = []
+        cursors = self.data_provider.get_segmentation_predict_data(params)
+        '''
+         cursors = [{'features': [[m1[0],m2[0],m3[0]],[m1[1],m2[1],m3[1]],
+                                  [m1[2],m2[2],m3[2]]]}]
+        '''
+
+        while True:
+            rows = cursors.fetch(batch_size)
+            if not rows:
+                break
+            batch = np.row_stack([np.array(row['features'])
+                                  for row in rows]).astype(float)
+
+            batch = replace_nan_with_mean(batch, feature_means)[0]
+            prediction = model.predict(batch)
+            results.append(prediction)
+
+        # NOTE: we removed the cartodb_ids calculation in here
+        return np.concatenate(results)
+
+    def clean_data(self, query, variable, feature_columns):
+        """
+            Add docstring
+        """
+        params = {"subquery": query,
+                  "target": variable,
+                  "features": feature_columns}
+
+        data = self.data_provider.get_segmentation_model_data(params)
+
+        '''
+        data = [{'target': [2.9, 4.9, 4, 5, 6],
+        'feature1': [1,2,3,4], 'feature2' : [2,3,4,5]}]
+        '''
+
+        # extract target data from data_provider object
+        target = np.array(data[0]['target'], dtype=float)
+
+        # put n feature data arrays into an n x m array of arrays
+        features = np.column_stack([np.array(data[0][col])
+                                    for col in feature_columns]).astype(float)
+
+        features, feature_means = replace_nan_with_mean(features)
+        target, target_mean = replace_nan_with_mean(target)
+        return target, features, target_mean, feature_means
+
+
+def replace_nan_with_mean(array, means=None):
+    """
+        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
+    nanvals = np.isnan(array)
+    indices = np.where(nanvals)
+
+    def loops(array, axis):
+        try:
+            return np.shape(array)[axis]
+        except IndexError:
+            return 1
+    ran = loops(array, 1)
+
+    if means is None:
+        means = {}
+
+        if ran == 1:
+            array = np.array(array)
+            means[0] = np.mean(array[~np.isnan(array)])
+            for row in zip(*indices):
+                array[row] = means[0]
+        else:
+            for col in range(ran):
+                means[col] = np.mean(array[~np.isnan(array[:, col]), col])
+            for row, col in zip(*indices):
+                array[row, col] = means[col]
+    else:
+        if ran == 1:
+            for row in zip(*indices):
+                array[row] = means[0]
+        else:
+            for row, col in zip(*indices):
+                array[row, col] = means[col]
+
+    return array, means
+
+
+def train_model(target, features, model_params, test_split):
+    """
+        Train the Gradient Boosting model on the provided data to calculate
+        the accuracy of the model
+        Input:
+            @param target: 1D Array of the variable that the model is to be
+                trained to predict
+            @param features: 2D Array NSamples *NFeatures to use in training
+                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_test, target_test)
+    return model, accuracy
+
+
+def calculate_model_accuracy(model, features_test, target_test):
+    """
+        Calculate the mean squared error of the model prediction
+        Input:
+            @param model: model trained from input features
+            @param features_test: test features set to make prediction from
+            @param target_test: test target set to compare predictions to
+        Output:
+            mean squared error of the model prection compared target_test
+    """
+    prediction = model.predict(features_test)
+    return metrics.mean_squared_error(prediction, target_test)

release/python/0.8.2/crankshaft/crankshaft/space_time_dynamics/__init__.py

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

release/python/0.8.2/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(object):
+    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}
+
+        result = self.data_provider.get_markov(w_type, params)
+
+        # build weight
+        weights = pu.get_weight(result, w_type)
+        weights.transform = 'r'
+
+        # prep time data
+        t_data = get_time_data(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/0.8.2/crankshaft/requirements.txt

@@ -0,0 +1,5 @@
+joblib==0.9.4
+numpy==1.11.0
+scipy==0.17.0
+pysal==1.14.3
+scikit-learn==0.17.0

release/python/0.8.2/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='0.8.2',
+
+    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==0.9.4', 'numpy==1.11.0', 'scipy==0.17.0', 'pysal==1.14.3', 'scikit-learn==0.17.0'],
+
+    requires=['pysal', 'numpy', 'sklearn'],
+
+    test_suite='test'
+)

release/python/0.8.2/crankshaft/test/fixtures/data.json

@@ -0,0 +1 @@
+[{"ids": [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, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]}]

release/python/0.8.2/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/0.8.2/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/0.8.2/crankshaft/test/fixtures/markov.json

@@ -0,0 +1 @@
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release/python/0.8.2/crankshaft/test/fixtures/moran.json

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release/python/0.8.2/crankshaft/test/fixtures/neighbors.json

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release/python/0.8.2/crankshaft/test/fixtures/predict_data.json

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release/python/0.8.2/crankshaft/test/fixtures/segmentation_result.json

@@ -0,0 +1 @@
+[[4.6399276705817796, 0.0052868236922298225], [5.115554441401355, 0.0052868236922298225], [3.9279922238303424, 0.0052868236922298225], [3.3819641948267578, 0.0052868236922298225], [2.9132843041389509, 0.0052868236922298225], [2.876066696867833, 0.0052868236922298225], [4.0106272888112651, 0.0052868236922298225], [3.5783652270475974, 0.0052868236922298225], [2.9165716286821199, 0.0052868236922298225], [3.4108311334783568, 0.0052868236922298225], [4.3202132937804372, 0.0052868236922298225], [3.7479855400737048, 0.0052868236922298225], [2.9370765208742595, 0.0052868236922298225], [4.4630858731319449, 0.0052868236922298225], [2.9921697215186938, 0.0052868236922298225], [3.7783567974677217, 0.0052868236922298225], [4.2514291487926652, 0.0052868236922298225], [3.9658039808720535, 0.0052868236922298225], [3.723696295039459, 0.0052868236922298225], [4.2305764993690955, 0.0052868236922298225], [3.1241034993855421, 0.0052868236922298225], [4.0343877737948652, 0.0052868236922298225], [4.7864094703726359, 0.0052868236922298225], [3.4423141823770624, 0.0052868236922298225], [3.424225241703863, 0.0052868236922298225], [3.309201541170641, 0.0052868236922298225], [3.037867375630356, 0.0052868236922298225], [3.8380172470256544, 0.0052868236922298225], [3.8840548342704815, 0.0052868236922298225], [2.8781306594987903, 0.0052868236922298225], [3.4874554940106037, 0.0052868236922298225], [2.8254928573623284, 0.0052868236922298225], [3.0980811019970185, 0.0052868236922298225], [3.3153313414322114, 0.0052868236922298225], [3.7254807947737478, 0.0052868236922298225], [2.2352532389466111, 0.0052868236922298225], [3.398793991587584, 0.0052868236922298225], [4.393489711684496, 0.0052868236922298225], [4.6820658816158236, 0.0052868236922298225], [3.2930725801147198, 0.0052868236922298225], [3.3013108011535843, 0.0052868236922298225], [4.5169704979664962, 0.0052868236922298225], [4.2356395759837682, 0.0052868236922298225], [4.685867240919821, 0.0052868236922298225], [3.3666476683180364, 0.0052868236922298225], [3.1633810641520688, 0.0052868236922298225], [3.9284828602074846, 0.0052868236922298225], [3.8813794254923417, 0.0052868236922298225], [3.9767682468020018, 0.0052868236922298225], [3.6296971637437938, 0.0052868236922298225], [3.2336758867109574, 0.0052868236922298225], [3.3438434216857305, 0.0052868236922298225], [4.059745940545219, 0.0052868236922298225], [4.8003413624883429, 0.0052868236922298225], [3.8343150532526087, 0.0052868236922298225], [3.8884993452951977, 0.0052868236922298225], [4.5967216279010819, 0.0052868236922298225], [4.6317641832280811, 0.0052868236922298225], [3.5805166062443643, 0.0052868236922298225], [4.1049176867051367, 0.0052868236922298225], [3.9515389747788823, 0.0052868236922298225], [3.4250648002120125, 0.0052868236922298225], [4.4759157545508605, 0.0052868236922298225], [4.0134207861425963, 0.0052868236922298225], [3.8799241476802888, 0.0052868236922298225], [4.9781411173602796, 0.0052868236922298225], [4.5230126868924323, 0.0052868236922298225], [4.1529682867170568, 0.0052868236922298225], [4.4754108304977711, 0.0052868236922298225], [4.3132882554878655, 0.0052868236922298225], [4.0547786635287659, 0.0052868236922298225], [2.5688836012215037, 0.0052868236922298225], [3.889152819366271, 0.0052868236922298225], [3.3884811287288952, 0.0052868236922298225], [3.8286491083541225, 0.0052868236922298225], [3.4842580970352057, 0.0052868236922298225], [3.2207170727086329, 0.0052868236922298225], [3.9452244740355038, 0.0052868236922298225], [4.2400946327715978, 0.0052868236922298225], [3.8398869646230049, 0.0052868236922298225], [3.1242158541684319, 0.0052868236922298225], [3.2123888635213436, 0.0052868236922298225], [3.5900402737995578, 0.0052868236922298225], [4.2464905311370957, 0.0052868236922298225], [2.5886568078161565, 0.0052868236922298225], [4.6008521636045012, 0.0052868236922298225], [4.2038409929353815, 0.0052868236922298225], [3.3327313501720157, 0.0052868236922298225], [3.7948100469546913, 0.0052868236922298225], [4.0382728370257404, 0.0052868236922298225], [4.3126973580418575, 0.0052868236922298225], [3.976738340646583, 0.0052868236922298225], [3.4720389796281514, 0.0052868236922298225], [4.3014283833530316, 0.0052868236922298225], [3.0187012207036723, 0.0052868236922298225], [3.6486981350943344, 0.0052868236922298225], [2.8338354315095078, 0.0052868236922298225], [4.3507896147137961, 0.0052868236922298225], [3.4753809797796484, 0.0052868236922298225], [2.2399367208816638, 0.0052868236922298225]]

release/python/0.8.2/crankshaft/test/fixtures/true_result.json

@@ -0,0 +1 @@
+[[[4.4227215674645395]], [[5.2712118012993789]], [[3.6279373760418334]], [[3.38304104035302]], [[2.7761519796383083]], [[2.7263669419052903]], [[3.862757275091802]], [[3.7743654860778144]], [[2.9952706103894648]], [[3.7012102596745233]], [[4.2706362174772199]], [[3.7479335482775493]], [[2.7992585644337975]], [[4.6602663596480252]], [[2.8365997356035244]], [[4.1625232506719607]], [[4.288029411774362]], [[3.6502805624336396]], [[3.312942887719065]], [[4.5186384902849328]], [[2.9653532564494514]], [[4.3289422901142238]], [[4.7419880551200571]], [[3.6531881499003931]], [[3.6621884978514769]], [[3.4539621369025717]], [[3.0816377852518206]], [[3.4093586802263656]], [[4.1113582546549052]], [[3.1102565821185824]], [[3.6886391238733465]], [[2.6769960732095788]], [[3.3418345719183726]], [[3.3658004839965203]], [[3.5570805554883793]], [[2.1390737237132882]], [[3.5264121431452518]], [[4.5056952369329686]], [[4.6877372215758752]], [[3.5241022266554354]], [[3.4536533934696991]], [[4.7767903633790905]], [[4.0451460130466712]], [[4.5192404874918441]], [[3.3565389305543119]], [[3.1007664721556902]], [[3.837506835252591]], [[3.6718974066615448]], [[4.1994400482374701]], [[3.4464591829709863]], [[3.0305242012162878]], [[2.988742131620918]], [[4.2253988205149868]], [[4.7061635792179537]], [[3.5766936522234265]], [[3.7851875270538882]], [[4.4060743798682109]], [[4.6094932701511038]], [[3.8298278075415855]], [[4.1051259417055608]], [[3.9208808676586342]], [[3.5541468789732118]], [[4.2476793895442491]], [[4.4288656054562781]], [[4.285411557315129]], [[4.9136046105564342]], [[4.3470960822962557]], [[4.3856116783980914]], [[4.2073129171306984]], [[4.6041990539557842]], [[3.8444647328578898]], [[2.4961542431159094]], [[3.5327401988792424]], [[3.3732721581082883]], [[3.5637204210138624]], [[3.713349537021855]], [[2.8878000202718845]], [[3.6480052797146962]], [[4.3019684391870783]], [[4.0143985414914329]], [[3.0027858714530842]], [[3.0672345691071476]], [[3.6281764007528063]], [[4.315026861113993]], [[2.5281093390733806]], [[4.3926338598315251]], [[4.4814940137640589]], [[3.2358701805945751]], [[3.5738341758988197]], [[4.0125117105508474]], [[4.1332723757858041]], [[3.9190386346055655]], [[3.3570061842111683]], [[4.3000992650570122]], [[3.2744982636432503]], [[3.4530052231252344]], [[2.9362664904878524]], [[4.5160823458017774]], [[3.2157763779380728]], [[2.1699109068357223]]]

release/python/0.8.2/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/0.8.2/crankshaft/test/mock_plpy.py

@@ -0,0 +1,58 @@
+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 error(self, msg):
+        self.infos.append(msg)
+        self.notices.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/0.8.2/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/0.8.2/crankshaft/test/test_clustering_kmeans.py

@@ -0,0 +1,87 @@
+import unittest
+import numpy as np
+
+
+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):
+        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)
+
+
+class KMeansNonspatialTest(unittest.TestCase):
+    """Testing class for k-means non-spatial"""
+
+    def setUp(self):
+        self.params = {"subquery": "SELECT * FROM TABLE",
+                       "n_clusters": 5}
+
+    def test_kmeans_nonspatial(self):
+        """
+            test for k-means non-spatial
+        """
+        # data from:
+        # http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn-cluster-kmeans
+        data_raw = [OrderedDict([("arr_col1", [1, 1, 1, 4, 4, 4]),
+                                 ("arr_col2", [2, 4, 0, 2, 4, 0]),
+                                 ("rowid", [1, 2, 3, 4, 5, 6])])]
+
+        random_seeds.set_random_seeds(1234)
+        kmeans = Kmeans(FakeDataProvider(data_raw))
+        clusters = kmeans.nonspatial('subquery', ['col1', 'col2'], 2)
+
+        cl1 = clusters[0][0]
+        cl2 = clusters[3][0]
+
+        for idx, val in enumerate(clusters):
+            if idx < 3:
+                self.assertEqual(val[0], cl1)
+            else:
+                self.assertEqual(val[0], cl2)
+
+        # raises exception for no data
+        with self.assertRaises(Exception):
+            kmeans = Kmeans(FakeDataProvider([]))
+            kmeans.nonspatial('subquery', ['col1', 'col2'], 2)

release/python/0.8.2/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[6]) for row in result]
+        zipped_values = zip(result, self.moran_data)
+
+        for ([res_quad, res_val], [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[6]) for row in result]
+
+        zipped_values = zip(result, self.moran_data)
+
+        for ([res_quad, res_val], [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/0.8.2/crankshaft/test/test_pysal_utils.py

@@ -0,0 +1,83 @@
+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_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)

release/python/0.8.2/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/0.8.2/crankshaft/test/test_segmentation.py

@@ -0,0 +1,139 @@
+"""Tests for segmentation functionality"""
+import unittest
+import json
+from collections import OrderedDict
+
+import numpy as np
+
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+from crankshaft.segmentation import Segmentation
+from helper import fixture_file
+from mock_plpy import MockCursor
+
+
+class RawDataProvider(AnalysisDataProvider):
+    """Data Provider to overwrite the default SQL provider"""
+    def __init__(self, data, model, predict):
+        self.data = data
+        self.model = model
+        self.predict = predict
+
+    def get_segmentation_data(self, params):  # pylint: disable=unused-argument
+        """return data"""
+        return self.data
+
+    def get_segmentation_model_data(self, params):  # pylint: disable=W0613
+        """return model data"""
+        return self.model
+
+    def get_segmentation_predict_data(self, params):  # pylint: disable=W0613
+        """return predict data"""
+        return self.predict
+
+
+class SegmentationTest(unittest.TestCase):
+    """Testing class for Segmentation functions"""
+
+    def setUp(self):
+        self.params = {
+            "query": 'SELECT * FROM segmentation_data',
+            "variable": 'price',
+            "feature_columns": ['m1', 'm2', 'm3', 'm4', 'm5', 'm6'],
+            "target_query": 'SELECT * FROM segmentation_result',
+            "id_col": 'cartodb_id',
+            "model_params": {
+                'n_estimators': 1200,
+                'max_depth': 3,
+                'subsample': 0.5,
+                'learning_rate': 0.01,
+                'min_samples_leaf': 1
+            }
+        }
+        self.model_data = json.loads(
+            open(fixture_file('model_data.json')).read())
+        self.data = json.loads(
+            open(fixture_file('data.json')).read())
+        self.predict_data = json.loads(
+            open(fixture_file('predict_data.json')).read())
+        self.result_seg = json.loads(
+            open(fixture_file('segmentation_result.json')).read())
+        self.true_result = json.loads(
+            open(fixture_file('true_result.json')).read())
+
+    def test_replace_nan_with_mean(self):
+        """test segmentation.test_replace_nan_with_mean"""
+        from crankshaft.segmentation import replace_nan_with_mean
+        test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan])
+        result = replace_nan_with_mean(test_array, means=None)[0]
+        expectation = np.array([1.2, 2.2, 3.2, 2.2, 2.2], dtype=float)
+        self.assertItemsEqual(result, expectation)
+
+    def test_create_and_predict_segment(self):
+        """test segmentation.test_create_and_predict"""
+        from crankshaft.segmentation import replace_nan_with_mean
+        results = []
+        feature_columns = ['m1', 'm2']
+        feat = np.column_stack([np.array(self.model_data[0][col])
+                                for col in feature_columns]).astype(float)
+        feature_means = replace_nan_with_mean(feat)[1]
+
+        # data_model is of the form:
+        #  [OrderedDict([('target', target),
+        #                ('features', feat),
+        #                ('target_mean', target_mean),
+        #                ('feature_means', feature_means),
+        #                ('feature_columns', feature_columns)])]
+        data_model = self.model_data
+        cursor = self.predict_data
+        batch = []
+
+        batches = np.row_stack([np.array(row['features'])
+                                for row in cursor]).astype(float)
+        batches = replace_nan_with_mean(batches, feature_means)[0]
+        batch.append(batches)
+
+        data_predict = [OrderedDict([('features', d['features']),
+                                     ('batch', batch)])
+                        for d in self.predict_data]
+        data_predict = MockCursor(data_predict)
+
+        model_parameters = {
+            'n_estimators': 1200,
+            'max_depth': 3,
+            'subsample': 0.5,
+            'learning_rate': 0.01,
+            'min_samples_leaf': 1
+        }
+        data = [OrderedDict([('ids', d['ids'])])
+                for d in self.data]
+
+        seg = Segmentation(RawDataProvider(data, data_model,
+                                           data_predict))
+
+        result = seg.create_and_predict_segment(
+            'SELECT * FROM segmentation_test',
+            'x_value',
+            ['m1', 'm2'],
+            'SELECT * FROM segmentation_result',
+            model_parameters,
+            id_col='cartodb_id')
+        results = [(row[1], row[2]) for row in result]
+        zipped_values = zip(results, self.result_seg)
+        pre_res = [r[0] for r in self.true_result]
+        acc_res = [r[1] for r in self.result_seg]
+
+        # test values
+        for (res_pre, _), (exp_pre, _) in zipped_values:
+            diff = abs(res_pre - exp_pre) / np.mean([res_pre, exp_pre])
+            self.assertTrue(diff <= 0.05, msg='diff: {}'.format(diff))
+            diff = abs(res_pre - exp_pre) / np.mean([res_pre, exp_pre])
+            self.assertTrue(diff <= 0.05, msg='diff: {}'.format(diff))
+        prediction = [r[0] for r in results]
+
+        accuracy = np.sqrt(np.mean(
+            (np.array(prediction) - np.array(pre_res))**2
+        ))
+
+        self.assertEqual(len(results), len(self.result_seg))
+        self.assertTrue(accuracy < 0.3 * np.mean(pre_res))
+        self.assertTrue(results[0][1] < 0.01)

release/python/0.8.2/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))