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Merge branch 'pysal_gwr' of github.com:TaylorOshan/crankshaft into pysal_gwr

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This commit is contained in:
Andy Eschbacher
2016-11-29 15:52:11 -05:00
parent 77b7217368 413e6aa5c7
commit 7efb064fd9
2 changed files with 14 additions and 9 deletions
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6
src/pg/sql/21_gwr.sql
View File

@@ -1,11 +1,11 @@
CREATE OR REPLACE FUNCTION
CDB_GWR(subquery text, dep_var text, ind_vars text[],
fixed boolean default False, kernel text default 'bisquare')
RETURNS table(coeffs JSON, stand_errs JSON, t_vals JSON, predicted numeric, residuals numeric, r_squared numeric, rowid bigint)
bw numeric default null, fixed boolean default False, kernel text default 'bisquare')
RETURNS table(coeffs JSON, stand_errs JSON, t_vals JSON, predicted numeric, residuals numeric, r_squared numeric, rowid bigint, bandwidth numeric)
AS $$
from crankshaft.regression import gwr_cs
return gwr_cs.gwr(subquery, dep_var, ind_vars, fixed, kernel)
return gwr_cs.gwr(subquery, dep_var, ind_vars, bw, fixed, kernel)
$$ LANGUAGE plpythonu;

17
src/py/crankshaft/crankshaft/regression/gwr_cs.py
View File

@@ -6,12 +6,13 @@ import crankshaft.pysal_utils as pu
import json
def gwr(subquery, dep_var, ind_vars,
def gwr(subquery, dep_var, ind_vars, bw=None,
fixed=False, kernel='bisquare'):
"""
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'
"""
@@ -55,9 +56,12 @@ def gwr(subquery, dep_var, ind_vars,
# add intercept variable name
ind_vars.insert(0, 'intercept')
# calculate bandwidth
bw = Sel_BW(coords, Y, X,
fixed=fixed, kernel=kernel).search()
# calculate bandwidth if none is supplied
plpy.notice(str(bw))
if bw is None:
bw = Sel_BW(coords, Y, X,
fixed=fixed, kernel=kernel).search()
plpy.notice(str(bw))
model = GWR(coords, Y, X, bw,
fixed=fixed, kernel=kernel).fit()
@@ -72,6 +76,7 @@ def gwr(subquery, dep_var, ind_vars,
predicted = model.predy.flatten()
residuals = model.resid_response
r_squared = model.localR2.flatten()
bw = np.repeat(float(bw), n)
for idx in xrange(n):
coefficients.append(json.dumps({var: model.params[idx, k]
@@ -82,6 +87,6 @@ def gwr(subquery, dep_var, ind_vars,
for k, var in enumerate(ind_vars)}))
plpy.notice(str(zip(coefficients, stand_errs, t_vals,
predicted, residuals, r_squared, rowid)))
predicted, residuals, r_squared, rowid, bw)))
return zip(coefficients, stand_errs, t_vals,
predicted, residuals, r_squared, rowid)
predicted, residuals, r_squared, rowid, bw)