merge in pg work
This commit is contained in:
Taylor Oshan
commit
1c0e4fae47
11
src/pg/sql/21_gwr.sql
Normal file
11
src/pg/sql/21_gwr.sql
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@ -0,0 +1,11 @@
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CREATE OR REPLACE FUNCTION
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CDB_GWR(subquery text, dep_var text, ind_vars text[],
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fixed boolean default False, kernel text default 'bisquare')
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RETURNS table(v1 numeric, v2 numeric, v3 numeric, v4 numeric, v5 numeric, v6 numeric, rowid bigint)
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AS $$
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from crankshaft.regression import gwr
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return gwr(subquery, dep_var, ind_vars, fixed, kernel)
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$$ LANGUAGE plpythonu;
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@ -3,3 +3,4 @@ import crankshaft.random_seeds
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import crankshaft.clustering
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import crankshaft.clustering
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import crankshaft.space_time_dynamics
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import crankshaft.space_time_dynamics
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import crankshaft.segmentation
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import crankshaft.segmentation
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import crankshaft.regression
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@ -42,7 +42,7 @@ def get_weight(query_res, w_type='knn', num_ngbrs=5):
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return built_weight
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return built_weight
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def query_attr_select(params):
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def query_attr_select(params, table_ref=True):
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"""
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"""
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Create portion of SELECT statement for attributes inolved in query.
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Create portion of SELECT statement for attributes inolved in query.
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@param params: dict of information used in query (column names,
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@param params: dict of information used in query (column names,
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@ -50,11 +50,15 @@ def query_attr_select(params):
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"""
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"""
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attr_string = ""
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attr_string = ""
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template = "i.\"%(col)s\"::numeric As attr%(alias_num)s, "
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template = "\"%(col)s\"::numeric As attr%(alias_num)s, "
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if 'time_cols' in params:
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if table_ref:
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# if markov analysis
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template = "i." + template
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attrs = params['time_cols']
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if ('time_cols' in params) or ('ind_vars' in params):
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# if markov or gwr analysis
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attrs = (params['time_cols'] if 'time_cols' in params
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else params['ind_vars'])
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for idx, val in enumerate(attrs):
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for idx, val in enumerate(attrs):
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attr_string += template % {"col": val, "alias_num": idx + 1}
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attr_string += template % {"col": val, "alias_num": idx + 1}
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@ -71,7 +75,7 @@ def query_attr_select(params):
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return attr_string
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return attr_string
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def query_attr_where(params):
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def query_attr_where(params, table_ref=True):
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"""
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"""
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Construct where conditions when building neighbors query
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Construct where conditions when building neighbors query
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Create portion of WHERE clauses for weeding out NULL-valued geometries
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Create portion of WHERE clauses for weeding out NULL-valued geometries
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@ -90,11 +94,14 @@ def query_attr_where(params):
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NULL AND idx_replace."time3" IS NOT NULL'
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NULL AND idx_replace."time3" IS NOT NULL'
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"""
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"""
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attr_string = []
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attr_string = []
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template = "idx_replace.\"%s\" IS NOT NULL"
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template = "\"%s\" IS NOT NULL"
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if table_ref:
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template = "idx_replace." + template
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if 'time_cols' in params:
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if ('time_cols' in params) or ('ind_vars' in params):
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# markov where clauses
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# markov or gwr where clauses
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attrs = params['time_cols']
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attrs = (params['time_cols'] if 'time_cols' in params
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else params['ind_vars'])
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# add values to template
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# add values to template
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for attr in attrs:
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for attr in attrs:
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attr_string.append(template % attr)
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attr_string.append(template % attr)
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@ -104,13 +111,16 @@ def query_attr_where(params):
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# get keys
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# get keys
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attrs = sorted([k for k in params
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attrs = sorted([k for k in params
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if k not in ('id_col', 'geom_col', 'subquery',
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if k not in ('id_col', 'geom_col', 'subquery',
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'num_ngbrs', 'subquery')])
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'num_ngbrs', 'ind_vars')])
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# add values to template
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# add values to template
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for attr in attrs:
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for attr in attrs:
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attr_string.append(template % params[attr])
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attr_string.append(template % params[attr])
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if len(attrs) == 2:
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if len(attrs) == 2:
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attr_string.append("idx_replace.\"%s\" <> 0" % params[attrs[1]])
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check_zero = "\"%s\" <> 0" % params[attrs[1]]
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if table_ref is not None:
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check_zero = "idx_replace." + check_zero
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attr_string.append(check_zero)
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out = " AND ".join(attr_string)
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out = " AND ".join(attr_string)
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@ -122,8 +132,8 @@ def knn(params):
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@param vars: dict of values to fill template
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@param vars: dict of values to fill template
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"""
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"""
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attr_select = query_attr_select(params)
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attr_select = query_attr_select(params, table_ref=True)
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attr_where = query_attr_where(params)
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attr_where = query_attr_where(params, table_ref=True)
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replacements = {"attr_select": attr_select,
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replacements = {"attr_select": attr_select,
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"attr_where_i": attr_where.replace("idx_replace", "i"),
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"attr_where_i": attr_where.replace("idx_replace", "i"),
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@ -177,6 +187,31 @@ def queen(params):
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return query.format(**params)
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return query.format(**params)
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def gwr_query(params):
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"""
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GWR query
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"""
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replacements = {"ind_vars_select": query_attr_select(params,
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table_ref=None),
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"ind_vars_where": query_attr_where(params,
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table_ref=None)}
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query = '''
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SELECT
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array_agg(ST_X({geom_col})) As x,
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array_agg(ST_Y({geom_col})) As y,
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array_agg({dep_var}) As dep_var,
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%(ind_vars_select)s,
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array_agg({id_col}) As rowid
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FROM ({subquery}) As q
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WHERE
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{dep_var} IS NOT NULL AND
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%(ind_vars_where)s
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''' % replacements
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return query.format(**params).strip()
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# to add more weight methods open a ticket or pull request
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# to add more weight methods open a ticket or pull request
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2
src/py/crankshaft/crankshaft/regression/__init__.py
Normal file
2
src/py/crankshaft/crankshaft/regression/__init__.py
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@ -0,0 +1,2 @@
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from crankshaft.regression.gwr import *
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from crankshaft.regression.glm import *
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@ -16,14 +16,14 @@ def gwr(subquery, dep_var, ind_vars, fixed=False, kernel='bisquare'):
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x = np.array(query_result[0]['x'])
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x = np.array(query_result[0]['x'])
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y = np.array(query_result[0]['y'])
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y = np.array(query_result[0]['y'])
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coords = zip(x,y)
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coords = zip(x, y)
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Y = query_result[0]['dep'].reshape((-1,1))
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Y = query_result[0]['dep'].reshape((-1, 1))
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n = Y.shape[0]
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n = Y.shape[0]
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k = len(ind_vars)
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k = len(ind_vars)
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X = np.zeros((n, k))
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X = np.zeros((n, k))
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for attr in range(0,k):
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for attr in range(0, k):
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attr_name = 'attr' + str(attr+1)
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attr_name = 'attr' + str(attr+1)
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X[:, attr] = np.array(query_result[0][attr_name]).flatten()
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X[:, attr] = np.array(query_result[0][attr_name]).flatten()
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@ -1,169 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import sys\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import pysal as ps\n",
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"sys.path.append('/Users/toshan/Dropbox/GWR/PyGWRJing/PyGWR/')\n",
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"from M_FBGWR_May2016 import FBGWR\n",
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"from M_GWGLM import GWGLM\n",
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"from M_selection import Band_Sel\n",
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"import scipy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"path = ps.examples.get_path('GData_utm.csv')\n",
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"shp = pd.read_csv(path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Prep data into design matrix and coordinates\n",
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"\n",
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"#Dependent variable\n",
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"y = shp.PctBach.reshape((-1,1))\n",
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"\n",
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"#Design matrix - covariates - intercept added automatically\n",
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"pov = shp.PctPov.reshape((-1,1))\n",
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"rural = shp.PctRural.reshape((-1,1))\n",
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"blk = shp.PctBlack.reshape((-1,1))\n",
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"X = np.hstack([pov, rural, blk])\n",
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"labels = ['Intercept', 'PctPov', 'PctRural', 'PctBlack']\n",
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"\n",
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"#Coordinates for calibration points\n",
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"u = shp.X\n",
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"v = shp.Y\n",
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"coords = zip(u,v)\n",
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"\n",
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"coords_dict = {}\n",
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"for i, x in enumerate(coords):\n",
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" coords_dict[i] = x"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(60.0, <Kernel.GWR_W object at 0x10b73a450>, [(89.0, 1088.4132720309442), (73.0, 1082.7676645259439), (62.0, 1079.8414440181946), (62.0, 1079.8414440181946), (62.0, 1079.8414440181946), (60.0, 1079.6668177916372), (60.0, 1079.6668177916372), (60.0, 1079.6668177916372)])\n",
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"CPU times: user 1.75 s, sys: 80.7 ms, total: 1.83 s\n",
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"Wall time: 1.75 s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"print Band_Sel(y, None, X, coords_dict)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from pysal.contrib.gwr.sel_bw import Sel_BW\n",
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"from pysal.contrib.gwr.gwr import GWR"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
|
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"60.0\n",
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"CPU times: user 1.1 s, sys: 3.5 ms, total: 1.11 s\n",
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"Wall time: 1.1 s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"print Sel_BW(coords, y, X, [], kernel='bisquare', constant=False).search()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%time\n",
|
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"results = FBGWR(y, X, coords_dict, tolFB=1e-03)"
|
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]
|
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},
|
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{
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"cell_type": "code",
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"execution_count": null,
|
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"metadata": {
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"collapsed": false,
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"scrolled": false
|
|
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},
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"outputs": [],
|
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"source": [
|
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"%%time\n",
|
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"sel = Sel_BW(coords, y, X, [], kernel='bisquare', fb=True, constant=False)\n",
|
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||||||
"results = sel.search(tol_fb=1e-03)"
|
|
||||||
]
|
|
||||||
}
|
|
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],
|
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||||||
"metadata": {
|
|
||||||
"anaconda-cloud": {},
|
|
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"kernelspec": {
|
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||||||
"display_name": "Python [fbgwr]",
|
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||||||
"language": "python",
|
|
||||||
"name": "Python [fbgwr]"
|
|
||||||
},
|
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||||||
"language_info": {
|
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"codemirror_mode": {
|
|
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"name": "ipython",
|
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"version": 2
|
|
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},
|
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"file_extension": ".py",
|
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||||||
"mimetype": "text/x-python",
|
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||||||
"name": "python",
|
|
||||||
"nbconvert_exporter": "python",
|
|
||||||
"pygments_lexer": "ipython2",
|
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||||||
"version": "2.7.12"
|
|
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}
|
|
||||||
},
|
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"nbformat": 4,
|
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"nbformat_minor": 0
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}
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@ -1,6 +0,0 @@
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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 1
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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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@ -1,571 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pysal as ps\n",
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|
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"from pysal.weights.Distance import Kernel\n",
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|
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"import sys\n",
|
|
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"sys.path.append('/Users/toshan/dev/pysal/pysal/weights')\n",
|
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"from Distance import Kernel as kn\n",
|
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"from util import full2W as f2w"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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|
||||||
"collapsed": true
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"n = 5 #number of observations\n",
|
|
||||||
"m = 3 #number of calibration points"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 17,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"x = np.random.randint(1,1000, n)\n",
|
|
||||||
"y = np.random.randint(1,1000, n)\n",
|
|
||||||
"D1 = zip(x,y)\n",
|
|
||||||
"W1 = kn(D1)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 18,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"x = np.random.randint(1,1000, m)\n",
|
|
||||||
"y = np.random.randint(1,1000, m)\n",
|
|
||||||
"D2 = zip(x,y)\n",
|
|
||||||
"W2 = kn(D2)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 39,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"array([[ 0.39894228, 0.1009935 , 0.05619733, 0.04896355, 0.32650695,\n",
|
|
||||||
" 0.37879836, 0.31411809, 0.1073076 ],\n",
|
|
||||||
" [ 0.1009935 , 0.39894228, 0.36195557, 0.36626279, 0.07720723,\n",
|
|
||||||
" 0.11754135, 0.03669346, 0.33721825],\n",
|
|
||||||
" [ 0.05619733, 0.36195557, 0.39894228, 0.39108403, 0.0345001 ,\n",
|
|
||||||
" 0.06151089, 0.02103471, 0.24420603],\n",
|
|
||||||
" [ 0.04896355, 0.36626279, 0.39108403, 0.39894228, 0.0335092 ,\n",
|
|
||||||
" 0.05713611, 0.01604658, 0.26976648],\n",
|
|
||||||
" [ 0.32650695, 0.07720723, 0.0345001 , 0.0335092 , 0.39894228,\n",
|
|
||||||
" 0.37224696, 0.18985479, 0.11774827],\n",
|
|
||||||
" [ 0.37879836, 0.11754135, 0.06151089, 0.05713611, 0.37224696,\n",
|
|
||||||
" 0.39894228, 0.24197075, 0.14519262],\n",
|
|
||||||
" [ 0.31411809, 0.03669346, 0.02103471, 0.01604658, 0.18985479,\n",
|
|
||||||
" 0.24197075, 0.39894228, 0.03119528],\n",
|
|
||||||
" [ 0.1073076 , 0.33721825, 0.24420603, 0.26976648, 0.11774827,\n",
|
|
||||||
" 0.14519262, 0.03119528, 0.39894228]])"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 39,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"D3 = np.vstack([D1,D2])\n",
|
|
||||||
"W3 = Kernel(D3, function='gaussian')\n",
|
|
||||||
"W3 = kn(D3, function='gaussian', truncate=False)\n",
|
|
||||||
"\n",
|
|
||||||
"W3.full()[0]"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 40,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"a\n",
|
|
||||||
"a\n",
|
|
||||||
"a\n",
|
|
||||||
"a\n",
|
|
||||||
"a\n",
|
|
||||||
"a\n",
|
|
||||||
"a\n",
|
|
||||||
"a\n",
|
|
||||||
"a\n",
|
|
||||||
"a\n",
|
|
||||||
"a\n",
|
|
||||||
"a\n",
|
|
||||||
"a\n",
|
|
||||||
"a\n",
|
|
||||||
"a\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"\n",
|
|
||||||
"coord_ids = np.arange(n)\n",
|
|
||||||
"points_ids = np.arange(n, n+m)\n",
|
|
||||||
"all_ids = np.arange(n+m)\n",
|
|
||||||
"dists = np.zeros((m,n))\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"for i in all_ids:\n",
|
|
||||||
" if i in points_ids:\n",
|
|
||||||
" for j in coord_ids:\n",
|
|
||||||
" if j in W3[j].keys():\n",
|
|
||||||
" if i >= n:\n",
|
|
||||||
" print 'a'\n",
|
|
||||||
" dists[i-n][j] = W3.full()[0][i][j]\n",
|
|
||||||
" elif j >= m:\n",
|
|
||||||
" print 'b'\n",
|
|
||||||
" dists[i][j-m] = W3.full()[0][i][j]\n",
|
|
||||||
" elif (i >= n) & (j >= m):\n",
|
|
||||||
" print 'c'\n",
|
|
||||||
" dists[i-n][j-m] = W3.full()[0][i][j]\n",
|
|
||||||
" else:\n",
|
|
||||||
" print 'd'\n",
|
|
||||||
" dists[i][j] = W3.full()[0][i][j]\n",
|
|
||||||
"\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 41,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"array([[ 0.37879836, 0.11754135, 0.06151089, 0.05713611, 0.37224696],\n",
|
|
||||||
" [ 0.31411809, 0.03669346, 0.02103471, 0.01604658, 0.18985479],\n",
|
|
||||||
" [ 0.1073076 , 0.33721825, 0.24420603, 0.26976648, 0.11774827]])"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 41,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"dists"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 22,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"ename": "ValueError",
|
|
||||||
"evalue": "3 is not in list",
|
|
||||||
"output_type": "error",
|
|
||||||
"traceback": [
|
|
||||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
||||||
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
|
||||||
"\u001b[0;32m<ipython-input-22-3036666a721b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf2w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
|
||||||
"\u001b[0;32m//anaconda/lib/python2.7/site-packages/pysal/weights/weights.pyc\u001b[0m in \u001b[0;36mfull\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 953\u001b[0m \"\"\"\n\u001b[0;32m--> 954\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 955\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 956\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtowsp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;32m//anaconda/lib/python2.7/site-packages/pysal/weights/util.pyc\u001b[0m in \u001b[0;36mfull\u001b[0;34m(w)\u001b[0m\n\u001b[1;32m 711\u001b[0m \u001b[0mw_i\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 712\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwij\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_i\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 713\u001b[0;31m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 714\u001b[0m \u001b[0mwfull\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwij\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 715\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mwfull\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;31mValueError\u001b[0m: 3 is not in list"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"w = f2w(dists)\n",
|
|
||||||
"w.full()[0]"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 23,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"new_neighbs = {}\n",
|
|
||||||
"new_weights = {}\n",
|
|
||||||
"for each in W2.neighbors:\n",
|
|
||||||
" three = set(W3.neighbors[each])\n",
|
|
||||||
" two = set(W2.neighbors[each])\n",
|
|
||||||
" new_neighbs[each] = list(three.difference(two))\n",
|
|
||||||
" new_weights[each] = {}\n",
|
|
||||||
" for weight in new_neighbs[each]:\n",
|
|
||||||
" new_weights[each][weight] = W3[each][weight]"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 24,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"'\\nfor ids in all_ids:\\n if ids in points_ids:\\n _all = set(W3.neighbors[ids])\\n points = set(W2.neighbors[ids])\\n new_neighbs[ids] = list(_all.difference(points))\\n new_weights[ids] = {}\\n for weight in new_neighbs[ids]:\\n new_weights[ids][weight] = W3[ids][weight]\\n\\n else:\\n new_weights[ids] = {}\\n'"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 24,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"coord_ids = W1.id_order\n",
|
|
||||||
"points_ids = W2.id_order\n",
|
|
||||||
"all_ids = W3.id_order\n",
|
|
||||||
"dists = np.zeros((2,3))\n",
|
|
||||||
"\n",
|
|
||||||
"'''\n",
|
|
||||||
"for ids in all_ids:\n",
|
|
||||||
" if ids in points_ids:\n",
|
|
||||||
" _all = set(W3.neighbors[ids])\n",
|
|
||||||
" points = set(W2.neighbors[ids])\n",
|
|
||||||
" new_neighbs[ids] = list(_all.difference(points))\n",
|
|
||||||
" new_weights[ids] = {}\n",
|
|
||||||
" for weight in new_neighbs[ids]:\n",
|
|
||||||
" new_weights[ids][weight] = W3[ids][weight]\n",
|
|
||||||
"\n",
|
|
||||||
" else:\n",
|
|
||||||
" new_weights[ids] = {}\n",
|
|
||||||
"''' \n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 10,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"3 0\n",
|
|
||||||
"a\n",
|
|
||||||
"3 1\n",
|
|
||||||
"a\n",
|
|
||||||
"3 2\n",
|
|
||||||
"a\n",
|
|
||||||
"4 0\n",
|
|
||||||
"a\n",
|
|
||||||
"4 1\n",
|
|
||||||
"a\n",
|
|
||||||
"4 2\n",
|
|
||||||
"a\n",
|
|
||||||
"5 0\n",
|
|
||||||
"a\n",
|
|
||||||
"5 1\n",
|
|
||||||
"a\n",
|
|
||||||
"5 2\n",
|
|
||||||
"a\n",
|
|
||||||
"6 0\n",
|
|
||||||
"a\n",
|
|
||||||
"6 1\n",
|
|
||||||
"a\n",
|
|
||||||
"6 2\n",
|
|
||||||
"a\n",
|
|
||||||
"7 0\n",
|
|
||||||
"a\n",
|
|
||||||
"7 1\n",
|
|
||||||
"a\n",
|
|
||||||
"7 2\n",
|
|
||||||
"a\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"n = 3 #number of observations\n",
|
|
||||||
"m = 2 #number of \n",
|
|
||||||
"for i in all_ids:\n",
|
|
||||||
" if i in points_ids:\n",
|
|
||||||
" for j in coord_ids:\n",
|
|
||||||
" if j in W3[j].keys():\n",
|
|
||||||
" print i,j\n",
|
|
||||||
" if i >= n:\n",
|
|
||||||
" print 'a'\n",
|
|
||||||
" dists[i-n][j] = W3.full()[0][i][j]\n",
|
|
||||||
" elif j >= m:\n",
|
|
||||||
" print 'b'\n",
|
|
||||||
" dists[i][j-m] = W3.full()[0][i][j]\n",
|
|
||||||
" elif (i >= n) & (j >= m):\n",
|
|
||||||
" print 'c'\n",
|
|
||||||
" dists[i-n][j-m] = W3.full()[0][i][j]\n",
|
|
||||||
" else:\n",
|
|
||||||
" print 'd'\n",
|
|
||||||
" dists[i][j] = W3.full()[0][i][j]\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 11,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"array([[ 0. , 0.45118883, 0.85404129],\n",
|
|
||||||
" [ 0.27754126, 0.40233088, 0.20596816],\n",
|
|
||||||
" [ 0. , 0.37941649, 0.6032733 ],\n",
|
|
||||||
" [ 0. , 0. , 0. ],\n",
|
|
||||||
" [ 0.35446728, 0.73305802, 0.50022967]])"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 11,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"dists"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 16,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"[0, 1]"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 16,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"points_ids"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 25,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"W4 = ps.W(new_neighbs, new_weights)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 26,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"{0: [3, 4, 5, 6, 7], 1: [3, 4, 5, 6, 7], 2: [3, 4, 5, 6, 7]}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 26,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"W4.neighbors"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 27,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"{0: {3: -1.0482874068379293,\n",
|
|
||||||
" 4: 0.36696681575222334,\n",
|
|
||||||
" 5: 0.678090925231672,\n",
|
|
||||||
" 6: 0.308555499289517,\n",
|
|
||||||
" 7: -0.620566108018012},\n",
|
|
||||||
" 1: {3: 0.5865615659374835,\n",
|
|
||||||
" 4: -0.8123595700046831,\n",
|
|
||||||
" 5: -0.5633467635255329,\n",
|
|
||||||
" 6: -1.1845906645769202,\n",
|
|
||||||
" 7: 0.42019589403802593},\n",
|
|
||||||
" 2: {3: 0.8005291815217084,\n",
|
|
||||||
" 4: -1.212624893235362,\n",
|
|
||||||
" 5: -0.9337024333901489,\n",
|
|
||||||
" 6: -1.4259607135415542,\n",
|
|
||||||
" 7: 0.009238162970930053}}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 27,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"W4.weights"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 28,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"[0, 1, 2]"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 28,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"W4.id_order"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 29,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"ename": "ValueError",
|
|
||||||
"evalue": "3 is not in list",
|
|
||||||
"output_type": "error",
|
|
||||||
"traceback": [
|
|
||||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
||||||
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
|
||||||
"\u001b[0;32m<ipython-input-29-bea43da3bca2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mW4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
|
||||||
"\u001b[0;32m//anaconda/lib/python2.7/site-packages/pysal/weights/weights.pyc\u001b[0m in \u001b[0;36mfull\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 953\u001b[0m \"\"\"\n\u001b[0;32m--> 954\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 955\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 956\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtowsp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;32m//anaconda/lib/python2.7/site-packages/pysal/weights/util.pyc\u001b[0m in \u001b[0;36mfull\u001b[0;34m(w)\u001b[0m\n\u001b[1;32m 711\u001b[0m \u001b[0mw_i\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 712\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwij\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_i\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 713\u001b[0;31m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 714\u001b[0m \u001b[0mwfull\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwij\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 715\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mwfull\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[0;31mValueError\u001b[0m: 3 is not in list"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"W4.full()[0]"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 244,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"{1: 1}"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 244,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"W4."
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 35,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": false
|
|
||||||
},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"array([5])"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 35,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"np.arange(5,6)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {
|
|
||||||
"collapsed": true
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": []
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"metadata": {
|
|
||||||
"anaconda-cloud": {},
|
|
||||||
"kernelspec": {
|
|
||||||
"display_name": "Python [Root]",
|
|
||||||
"language": "python",
|
|
||||||
"name": "Python [Root]"
|
|
||||||
},
|
|
||||||
"language_info": {
|
|
||||||
"codemirror_mode": {
|
|
||||||
"name": "ipython",
|
|
||||||
"version": 2
|
|
||||||
},
|
|
||||||
"file_extension": ".py",
|
|
||||||
"mimetype": "text/x-python",
|
|
||||||
"name": "python",
|
|
||||||
"nbconvert_exporter": "python",
|
|
||||||
"pygments_lexer": "ipython2",
|
|
||||||
"version": "2.7.12"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 0
|
|
||||||
}
|
|
||||||
@ -1,6 +0,0 @@
|
|||||||
{
|
|
||||||
"cells": [],
|
|
||||||
"metadata": {},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 0
|
|
||||||
}
|
|
||||||
1
src/py/crankshaft/crankshaft/regression/gwr/__init__.py
Normal file
1
src/py/crankshaft/crankshaft/regression/gwr/__init__.py
Normal file
@ -0,0 +1 @@
|
|||||||
|
from base import *
|
||||||
Loading…
Reference in New Issue
Block a user