predit with out ind

multimodal/kernels/lpMKL.py

@@ -237,7 +237,7 @@ class MKL(BaseEstimator, ClassifierMixin, MKernel):
             return C, weights
 
 
-    def predict(self, X, views_ind=None):
+    def predict(self, X):
         """
 
         Parameters
@@ -271,7 +271,7 @@ class MKL(BaseEstimator, ClassifierMixin, MKernel):
         """
         check_is_fitted(self, ['X_', 'C', 'K_', 'y_', 'weights'])
         X , test_kernels = self._global_kernel_transform(X,
-                                                         views_ind=views_ind,
+                                                         views_ind=self.X_.views_ind,
                                                          Y=self.X_)
         check_array(X)
         C = self.C
@@ -322,7 +322,15 @@ class MKL(BaseEstimator, ClassifierMixin, MKernel):
 
         Parameters
         ----------
-        X : {array-like} of shape = (n_samples, n_features)
+        X : dict dictionary with all views {array like} with shape = (n_samples, n_features)  for multi-view
+            for each view.
+            or
+            `MultiModalData` ,  `MultiModalArray`
+            or
+            {array-like,}, shape = (n_samples, n_features)
+            Training multi-view input samples. can be also Kernel where attibute 'kernel'
+            is set to precompute "precomputed"
+
         y : array-like, shape = (n_samples,)
             True labels for X.
 

multimodal/kernels/mvml.py

@@ -410,7 +410,7 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin):
             A_inv = spli.pinv(A)
         return A_inv
 
-    def predict(self, X, views_ind=None):
+    def predict(self, X):
         """
 
         Parameters
@@ -444,7 +444,7 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin):
         """
         check_is_fitted(self, ['X_', 'U_dict', 'K_', 'y_']) # , 'U_dict', 'K_' 'y_'
         X, test_kernels = self._global_kernel_transform(X,
-                                                        views_ind=views_ind,
+                                                        views_ind=self.X_.views_ind,
                                                         Y=self.X_)
 
         check_array(X)
@@ -454,12 +454,9 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin):
         else:
             pred = np.sign(pred)
             pred = pred.astype(int)
-            pred = np.where(pred == -1, 0 , pred)
+            pred = np.where(pred == -1, 0, pred)
             return np.take(self.classes_, pred)
 
-
-
-
     def _predict_mvml(self, test_kernels, g, w):
         """
 
@@ -599,7 +596,7 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin):
 
         return A_new
 
-    def score(self, X, y):
+    def score(self, X, y=None):
         """Return the mean accuracy on the given test data and labels.
 
         Parameters