From 1133a020c30c5ec221708741019e40712c12558f Mon Sep 17 00:00:00 2001
From: Dominique Benielli <dominique.benielli@lis-lab.fr>
Date: Tue, 18 Feb 2020 10:18:50 +0100
Subject: [PATCH] predit with out ind
---
multimodal/kernels/lpMKL.py | 14 +++++++++++---
multimodal/kernels/mvml.py | 11 ++++-------
2 files changed, 15 insertions(+), 10 deletions(-)
diff --git a/multimodal/kernels/lpMKL.py b/multimodal/kernels/lpMKL.py
index 44a15eb..76436b6 100644
--- a/multimodal/kernels/lpMKL.py
+++ b/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.
diff --git a/multimodal/kernels/mvml.py b/multimodal/kernels/mvml.py
index 706bc28..3a09996 100644
--- a/multimodal/kernels/mvml.py
+++ b/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
--
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