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@ -95,8 +95,9 @@ class GWR:
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for k, var in enumerate(ind_vars)}))
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t_vals.append(json.dumps({var: model.tvalues[idx, k]
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for k, var in enumerate(ind_vars)}))
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filtered_t_vals.append(json.dumps({var: filtered_t[idx, k]
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for k, var in enumerate(ind_vars)}))
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filtered_t_vals.append(
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json.dumps({var: filtered_t[idx, k]
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for k, var in enumerate(ind_vars)}))
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return zip(coeffs, stand_errs, t_vals, filtered_t_vals,
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predicted, residuals, r_squared, bw, rowid)
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@ -178,8 +179,8 @@ class GWR:
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fixed=fixed, kernel=kernel).search()
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# estimate model and predict at new locations
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model = PySAL_GWR(coords_train, Y_train, X_train, bw,
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fixed=fixed,
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model = PySAL_GWR(coords_train, Y_train, X_train,
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bw, fixed=fixed,
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kernel=kernel).predict(coords_test, X_test)
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coeffs = []
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@ -198,4 +199,4 @@ class GWR:
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for k, var in enumerate(ind_vars)}))
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return zip(coeffs, stand_errs, t_vals,
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r_squared, predicted, rowid)
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r_squared, predicted, rowid[test])
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