set coef_ to fortran layout after fit - this will enhance the test time performance for predicting singe data points.

pprett · pprett · commit 6cadc54d1d06 · 2012-01-09T09:06:21.000+01:00
diff --git a/sklearn/linear_model/base.py b/sklearn/linear_model/base.py
@@ -249,8 +249,8 @@ def _validate_sample_weight(self, sample_weight, n_samples):
         return sample_weight
 
     def _set_coef(self, coef_):
-        """Make sure that coef_ is 2d. """
-        self.coef_ = array2d(coef_)
+        """Make sure that coef_ is fortran-style and 2d. """
+        self.coef_ = np.asfortranarray(array2d(coef_))
 
     def _allocate_parameter_mem(self, n_classes, n_features, coef_init=None,
                                 intercept_init=None):
diff --git a/sklearn/linear_model/stochastic_gradient.py b/sklearn/linear_model/stochastic_gradient.py
@@ -183,6 +183,8 @@ def _fit_multiclass(self, X, y, sample_weight):
             self.coef_[i] = coef
             self.intercept_[i] = intercept
 
+        self._set_coef(self.coef_)
+
 
 def _train_ova_classifier(i, c, X, y, coef_, intercept_, loss_function,
                           penalty_type, alpha, rho, n_iter, fit_intercept,
