diff --git a/multimodal/datasets/digit.py b/multimodal/datasets/digit.py
new file mode 100644
index 0000000000000000000000000000000000000000..2411306adce3217e8e660db4e5f0a3053f646efa
--- /dev/null
+++ b/multimodal/datasets/digit.py
@@ -0,0 +1,62 @@
+from sklearn import datasets
+import numpy as np
+import PIL
+import matplotlib.pyplot as plt
+import os
+import matplotlib.pyplot as plt
+from multimodal.datasets.base import load_dict, save_dict
+from multimodal.tests.data.get_dataset_path import get_dataset_path
+from multimodal.datasets.data_sample import MultiModalArray
+from multimodal.kernels.mvml import MVML
+#Load the digits dataset
+digits = datasets.load_digits()
+
+#Display the first digit
+plt.figure(1, figsize=(3, 3))
+plt.imshow(digits.images[-1], cmap=plt.cm.gray_r, interpolation='nearest')
+plt.show()
+colors = digits.data
+gradiant = np.gradient(digits.images, axis=[1,2])
+print(gradiant[0].shape)
+gradiant0 = gradiant[0].reshape(colors.shape[0], colors.shape[1])
+gradiant1 = gradiant[1].reshape(colors.shape[0], colors.shape[1])
+for ind in range(digits.images.shape[0]):
+ ima0 = digits.images[ind, :,:]
+ ima1 = gradiant[0][ind, :,:]
+ ima2 = gradiant[1][ind, :,:]
+ ama_pil0 = PIL.Image.fromarray(ima0, mode=None)
+ ama_pil1 = PIL.Image.fromarray(ima1, mode=None)
+ ama_pil2 = PIL.Image.fromarray(ima2, mode=None)
+ histo_color = np.asarray(ama_pil0.histogram())
+ histo_gradiant0 = np.asarray(ama_pil1.histogram())
+ histo_gradiant1 = np.asarray(ama_pil2.histogram())
+ if ind==0:
+ list_histogram_color = histo_color
+ list_histogram_gradiant0 = histo_gradiant0
+ list_histogram_gradiant1 = histo_gradiant1
+ else:
+ list_histogram_color = np.vstack((list_histogram_color, histo_color))
+ list_histogram_gradiant0 = np.vstack((list_histogram_gradiant0, histo_gradiant0))
+ list_histogram_gradiant1 = np.vstack((list_histogram_gradiant1, histo_gradiant1))
+
+dict_digit = {0: list_histogram_color, 1: list_histogram_gradiant0, 2: list_histogram_gradiant1}
+
+
+print(list_histogram_color.shape)
+print(list_histogram_gradiant0.shape)
+print(list_histogram_gradiant1.shape)
+file = get_dataset_path("digit_histogram.npy")
+save_dict(dict_digit, file)
+
+d2 = load_dict(file)
+
+figure = plt.figure(figsize=(27, 9))
+ax = plt.subplot(2,1,1)
+
+ax.scatter(list_histogram_color[:,3], list_histogram_color[:,4], c=digits.target, edgecolors='k')
+ax = plt.subplot(2,1,2)
+ax.scatter(list_histogram_color[:,0], list_histogram_color[:,1], c=digits.target, edgecolors='k')
+plt.show()
+
+mvml = MVML(lmbda=0.1, eta=1, nystrom_param=0.2)
+mvml.fit(d2)
diff --git a/multimodal/kernels/lpMKL.py b/multimodal/kernels/lpMKL.py
index 76436b6082041c36a3298a660e80f36120adf5c5..5bca05f8501b03b5e8e813ed9b4cb5688ad2701a 100644
--- a/multimodal/kernels/lpMKL.py
+++ b/multimodal/kernels/lpMKL.py
@@ -6,6 +6,8 @@ from sklearn.utils.validation import check_X_y
from sklearn.utils.validation import check_array
from sklearn.utils.validation import check_is_fitted
from multimodal.kernels.mkernel import MKernel
+from sklearn.utils.multiclass import type_of_target
+from sklearn.utils.multiclass import check_classification_targets
class MKL(BaseEstimator, ClassifierMixin, MKernel):
@@ -122,9 +124,18 @@ class MKL(BaseEstimator, ClassifierMixin, MKernel):
self : object
Returns self.
"""
- self.X_, self.K_ = self._global_kernel_transform(X, views_ind=views_ind)
- self.classes_ = unique_labels(y)
+ self.X_, self.K_= self._global_kernel_transform(X, views_ind=views_ind)
check_X_y(self.X_, y)
+ check_classification_targets(y)
+ if type_of_target(y) in "binary":
+ self.classes_, y = np.unique(y, return_inverse=True)
+ y[y==0] = -1.0
+ elif type_of_target(y) in "continuous":
+ y = y.astype(float)
+ self.regression_ = True
+ else:
+ raise ValueError("MKL algorithms is a binary classifier"
+ " or performs regression with float target")
self.y_ = y
n = self.K_.shape[0]
self._calc_nystrom(self.K_, n)
@@ -270,13 +281,18 @@ class MKL(BaseEstimator, ClassifierMixin, MKernel):
Predicted classes.
"""
check_is_fitted(self, ['X_', 'C', 'K_', 'y_', 'weights'])
- X , test_kernels = self._global_kernel_transform(X,
+ X, K = self._global_kernel_transform(X,
views_ind=self.X_.views_ind,
Y=self.X_)
check_array(X)
C = self.C
weights = self.weights
- return self.lpMKL_predict(test_kernels, C, weights)
+ pred = self.lpMKL_predict(K, C, weights)
+
+ pred = np.sign(pred)
+ pred = pred.astype(int)
+ pred = np.where(pred == -1, 0, pred)
+ return np.take(self.classes_, pred)
def lpMKL_predict(self, X, C, weights):
diff --git a/multimodal/kernels/mvml.py b/multimodal/kernels/mvml.py
index 3a09996037633d171a2a23db622da894335a7e0d..2ce26b6fc3a807eab978675657eed6f09fad0c81 100644
--- a/multimodal/kernels/mvml.py
+++ b/multimodal/kernels/mvml.py
@@ -4,6 +4,7 @@ from scipy.sparse.linalg import splu
from scipy.sparse import csc_matrix
from sklearn.base import BaseEstimator
from sklearn.base import ClassifierMixin
+from sklearn.base import RegressorMixin
from sklearn.utils.multiclass import unique_labels
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils.validation import check_X_y
@@ -25,7 +26,7 @@ in International Conference on Artificial Intelligence and Statistics (AISTATS)
"""
-class MVML(MKernel, BaseEstimator, ClassifierMixin):
+class MVML(MKernel, BaseEstimator, ClassifierMixin, RegressorMixin):
r"""
The MVML Classifier
@@ -426,16 +427,7 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin):
for each view.
- {array like} with (n_samples, nviews * n_features) with 'views_ind' diferent to 'None'
- views_ind : array-like (default=[0, n_features//2, n_features])
- Paramater specifying how to extract the data views from X:
- - views_ind is a 1-D array of sorted integers, the entries
- indicate the limits of the slices used to extract the views,
- where view ``n`` is given by
- ``X[:, views_ind[n]:views_ind[n+1]]``.
-
- With this convention each view is therefore a view (in the NumPy
- sense) of X and no copy of the data is done.
Returns
-------
diff --git a/multimodal/tests/data/digit_histogram.npy b/multimodal/tests/data/digit_histogram.npy
new file mode 100644
index 0000000000000000000000000000000000000000..b08d2c6e85a3a92f30b5f88d95bdbd9edccb0f9d
Binary files /dev/null and b/multimodal/tests/data/digit_histogram.npy differ