fix doc test bug

multimodal/datasets/data_sample.py

@@ -361,7 +361,10 @@ class MultiModalArray(np.ndarray, MultiModalData):
             try:
                 new_data = np.asarray(data)
                 if views_ind is None:
-                    views_ind = np.array([0, new_data.shape[1]])
+                    if new_data.shape[1] > 1:
+                        views_ind = np.array([0, new_data.shape[1] // 2, new_data.shape[1]])
+                    else:
+                        views_ind = np.array([0, new_data.shape[1]])
             except  Exception as e:
                 raise ValueError('Reshape your data')
             if new_data.ndim < 2 :

multimodal/kernels/mkernel.py

@@ -85,7 +85,7 @@ class MKernel(metaclass=ABCMeta):
         if not isinstance(X_, MultiModalArray):
             try:
                 X_ = np.asarray(X)
-                X_ = MultiModalArray(X_)
+                X_ = MultiModalArray(X_, views_ind)
             except Exception as e:
                 pass
                 # raise TypeError('Reshape your data')

multimodal/kernels/mvml.py

@@ -104,36 +104,17 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin):
     >>> from multimodal.kernels.mvml import MVML
     >>> from sklearn.datasets import load_iris
     >>> X, y = load_iris(return_X_y=True)
+    >>> y[y>0] = 1
     >>> views_ind = [0, 2, 4]  # view 0: sepal data, view 1: petal data
     >>> clf = MVML()
-    clf.get_params()
+    >>> clf.get_params()
     {'eta': 1, 'kernel': 'linear', 'kernel_params': None, 'learn_A': 1, 'learn_w': 0, 'lmbda': 0.1, 'n_loops': 6, 'nystrom_param': 1.0, 'precision': 0.0001}
     >>> clf.fit(X, y, views_ind)  # doctest: +NORMALIZE_WHITESPACE
-    MumboClassifier(base_estimator=None, best_view_mode='edge',
-        n_estimators=50, random_state=0)
+    MVML(eta=1, kernel='linear', kernel_params=None, learn_A=1, learn_w=0,
+       lmbda=0.1, n_loops=6, nystrom_param=1.0, precision=0.0001)
     >>> print(clf.predict([[ 5.,  3.,  1.,  1.]]))
-    [1]
-    >>> views_ind = [[0, 2], [1, 3]]  # view 0: length data, view 1: width data
-    >>> clf = MumboClassifier(random_state=0)
-    >>> clf.fit(X, y, views_ind)  # doctest: +NORMALIZE_WHITESPACE
-    MumboClassifier(base_estimator=None, best_view_mode='edge',
-        n_estimators=50, random_state=0)
-    >>> print(clf.predict([[ 5.,  3.,  1.,  1.]]))
-    [1]
+    0
 
-    >>> from sklearn.tree import DecisionTreeClassifier
-    >>> base_estimator = DecisionTreeClassifier(max_depth=2)
-    >>> clf = MumboClassifier(base_estimator=base_estimator, random_state=0)
-    >>> clf.fit(X, y, views_ind)  # doctest: +NORMALIZE_WHITESPACE
-    MumboClassifier(base_estimator=DecisionTreeClassifier(class_weight=None,
-            criterion='gini', max_depth=2, max_features=None,
-            max_leaf_nodes=None, min_impurity_decrease=0.0,
-            min_impurity_split=None, min_samples_leaf=1, min_samples_split=2,
-            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
-            splitter='best'),
-        best_view_mode='edge', n_estimators=50, random_state=0)
-    >>> print(clf.predict([[ 5.,  3.,  1.,  1.]]))
-    [1]
     """
     # r_cond = 10-30
     def __init__(self, lmbda=0.1, eta=1, nystrom_param=1.0, kernel="linear",
@@ -471,8 +452,8 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin):
             return pred
         else:
             pred = np.sign(pred)
-            pred[pred==-1] = 0
             pred = pred.astype(int)
+            pred = np.where(pred == -1, 0 , pred)
             return np.take(self.classes_, pred)