Added test of y_training shape so that if it is given as a 2d array a…

…nd incorrectly transposed it will be fixed, also the return values will be given in the same form as the training values
JohnFNovak · Apr 2, 2013 · 0a1e6f7 · 0a1e6f7
1 parent be2984a
commit 0a1e6f7
Showing 1 changed file with 8 additions and 0 deletions.
diff --git a/sklearn/gaussian_process/gaussian_process.py b/sklearn/gaussian_process/gaussian_process.py
@@ -274,8 +274,12 @@ def fit(self, X, y):
         self.y_shape = np.array(y).shape
         y = array2d(y)
 
+        # If the y values are given to fit() as a list:
         if len(self.y_shape) == 1:
             y = y.T
+        # If the y vales are given to fit() as an array, but transposed wrong
+        elif self.y_shape[0] == 1 and self.y_shape[1] == X.shape[0]:
+            y = y.T
 
         # Check shapes of DOE & observations
         n_samples_X, n_features = X.shape
@@ -461,6 +465,8 @@ def predict(self, X, eval_MSE=False, batch_size=None):
 
             if len(self.y_shape) == 1:
                 y = y.ravel()
+            elif self.y_shape[0] == 1 and self.y_shape[1] == X.shape[0]:
+                y = y.T
 
             # Mean Squared Error
             if eval_MSE:
@@ -496,6 +502,8 @@ def predict(self, X, eval_MSE=False, batch_size=None):
 
                 if len(self.y_shape) == 1:
                     MSE = MSE.ravel()
+                elif self.y_shape[0] == 1 and self.y_shape[1] == X.shape[0]:
+                    MSE = MSE.T
 
                 return y, MSE