compute alpha mumbo

doc/tutorial/auto_examples/index.rst

@@ -172,8 +172,8 @@ The following toy examples illustrate how the MVML algorithm
 .. _sphx_glr_tutorial_auto_examples_usecase:
 
 
-Use Case Examples
------------------
+Use Case Examples on Digit
+--------------------------
 
 The following toy examples illustrate how the multimodal as usecase on digit  dataset of sklearn
 
@@ -262,7 +262,7 @@ The following toy examples illustrate how the multimodal as usecase on digit  da
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="Use Case MKL">
+    <div class="sphx-glr-thumbcontainer" tooltip="Use Case MKL on digit">
 
 .. only:: html
 

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMKL.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Use Case MKL\n\nUse case for all classifier of multimodallearn MKL\nmulti class digit from sklearn, multivue\n - vue 0 digit data (color of sklearn)\n - vue 1 gradiant of image in first direction\n - vue 2 gradiant of image in second direction\n\n\n"
+        "\n# Use Case MKL on digit\n\nUse case for all classifier of multimodallearn MKL\nmulti class digit from sklearn, multivue\n - vue 0 digit data (color of sklearn)\n - vue 1 gradiant of image in first direction\n - vue 2 gradiant of image in second direction\n\n\n"
       ]
     },
     {
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "from __future__ import absolute_import\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.multiclass import OneVsOneClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom multimodal.datasets.base import load_dict, save_dict\nfrom multimodal.tests.data.get_dataset_path import get_dataset_path\nfrom multimodal.datasets.data_sample import MultiModalArray\nfrom multimodal.kernels.mvml import MVML\nfrom multimodal.kernels.lpMKL import MKL\n\nfrom usecase_function import plot_subplot\n\nif __name__ == '__main__':\n    # file = get_dataset_path(\"digit_histogram.npy\")\n    file = get_dataset_path(\"digit_col_grad.npy\")\n    y = np.load(get_dataset_path(\"digit_y.npy\"))\n    base_estimator = DecisionTreeClassifier(max_depth=4)\n    dic_digit = load_dict(file)\n    XX =MultiModalArray(dic_digit)\n    X_train, X_test, y_train, y_test = train_test_split(XX, y)\n\n    est4 = OneVsOneClassifier(MKL(lmbda=0.1, nystrom_param=0.2)).fit(X_train, y_train)\n    y_pred4 = est4.predict(X_test)\n    y_pred44 = est4.predict(X_train)\n    print(\"result of MKL on digit with oneversone\")\n    result4 = np.mean(y_pred4.ravel() == y_test.ravel()) * 100\n    print(result4)\n\n    fig = plt.figure(figsize=(12., 11.))\n    fig.suptitle(\"MKL : result\" + str(result4), fontsize=16)\n    plot_subplot(X_train, y_train, y_pred44  ,0, (4, 1, 1), \"train vue 0\" )\n    plot_subplot(X_test, y_test,y_pred4 , 0, (4, 1, 2), \"test vue 0\" )\n    plot_subplot(X_test, y_test, y_pred4,1, (4, 1, 3), \"test vue 1\" )\n    plot_subplot(X_test, y_test,y_pred4, 2, (4, 1, 4), \"test vue 2\" )\n    # plt.legend()\n    plt.show()"
+        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.multiclass import OneVsOneClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom multimodal.datasets.base import load_dict, save_dict\nfrom multimodal.tests.data.get_dataset_path import get_dataset_path\nfrom multimodal.datasets.data_sample import MultiModalArray\nfrom multimodal.kernels.mvml import MVML\nfrom multimodal.kernels.lpMKL import MKL\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib._color_data as mcd\n\n\ndef plot_subplot(X, Y, Y_pred, vue, subplot, title):\n    cn = mcd.CSS4_COLORS\n    classes = np.unique(Y)\n    n_classes = len(np.unique(Y))\n    axs = plt.subplot(subplot[0],subplot[1],subplot[2])\n    axs.set_title(title)\n    #plt.scatter(X._extract_view(vue), X._extract_view(vue), s=40, c='gray',\n    #            edgecolors=(0, 0, 0))\n    for index, k in zip(range(n_classes), cn.keys()):\n         Y_class, = np.where(Y==classes[index])\n         Y_class_pred = np.intersect1d(np.where(Y_pred==classes[index])[0], np.where(Y_pred==Y)[0])\n         plt.scatter(X._extract_view(vue)[Y_class],\n                     X._extract_view(vue)[Y_class],\n                     s=40, c=cn[k], edgecolors='blue', linewidths=2, label=\"class real class: \"+str(index)) #\n         plt.scatter(X._extract_view(vue)[Y_class_pred],\n                     X._extract_view(vue)[Y_class_pred],\n                     s=160, edgecolors='orange', linewidths=2, label=\"class prediction: \"+str(index))\n\n\nif __name__ == '__main__':\n    # file = get_dataset_path(\"digit_histogram.npy\")\n    file = get_dataset_path(\"digit_col_grad.npy\")\n    y = np.load(get_dataset_path(\"digit_y.npy\"))\n    base_estimator = DecisionTreeClassifier(max_depth=4)\n    dic_digit = load_dict(file)\n    XX =MultiModalArray(dic_digit)\n    X_train, X_test, y_train, y_test = train_test_split(XX, y)\n\n    est4 = OneVsOneClassifier(MKL(lmbda=0.1, nystrom_param=0.2)).fit(X_train, y_train)\n    y_pred4 = est4.predict(X_test)\n    y_pred44 = est4.predict(X_train)\n    print(\"result of MKL on digit with oneversone\")\n    result4 = np.mean(y_pred4.ravel() == y_test.ravel()) * 100\n    print(result4)\n\n    fig = plt.figure(figsize=(12., 11.))\n    fig.suptitle(\"MKL : result\" + str(result4), fontsize=16)\n    plot_subplot(X_train, y_train, y_pred44  ,0, (4, 1, 1), \"train vue 0 color\" )\n    plot_subplot(X_test, y_test,y_pred4 , 0, (4, 1, 2), \"test vue 0 color\" )\n    plot_subplot(X_test, y_test, y_pred4,1, (4, 1, 3), \"test vue 1 gradiant 0\" )\n    plot_subplot(X_test, y_test,y_pred4, 2, (4, 1, 4), \"test vue 2 gradiant 1\" )\n    # plt.legend()\n    plt.show()"
       ]
     }
   ],

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMKL.py

 # -*- coding: utf-8 -*-
 """
-============
-Use Case MKL
-============
+=====================
+Use Case MKL on digit
+=====================
 Use case for all classifier of multimodallearn MKL
 multi class digit from sklearn, multivue
  - vue 0 digit data (color of sklearn)
@@ -10,7 +10,7 @@ multi class digit from sklearn, multivue
  - vue 2 gradiant of image in second direction
 
 """
-from __future__ import absolute_import
+
 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.multiclass import OneVsOneClassifier
@@ -22,7 +22,29 @@ from multimodal.datasets.data_sample import MultiModalArray
 from multimodal.kernels.mvml import MVML
 from multimodal.kernels.lpMKL import MKL
 
-from usecase_function import plot_subplot
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib._color_data as mcd
+
+
+def plot_subplot(X, Y, Y_pred, vue, subplot, title):
+    cn = mcd.CSS4_COLORS
+    classes = np.unique(Y)
+    n_classes = len(np.unique(Y))
+    axs = plt.subplot(subplot[0],subplot[1],subplot[2])
+    axs.set_title(title)
+    #plt.scatter(X._extract_view(vue), X._extract_view(vue), s=40, c='gray',
+    #            edgecolors=(0, 0, 0))
+    for index, k in zip(range(n_classes), cn.keys()):
+         Y_class, = np.where(Y==classes[index])
+         Y_class_pred = np.intersect1d(np.where(Y_pred==classes[index])[0], np.where(Y_pred==Y)[0])
+         plt.scatter(X._extract_view(vue)[Y_class],
+                     X._extract_view(vue)[Y_class],
+                     s=40, c=cn[k], edgecolors='blue', linewidths=2, label="class real class: "+str(index)) #
+         plt.scatter(X._extract_view(vue)[Y_class_pred],
+                     X._extract_view(vue)[Y_class_pred],
+                     s=160, edgecolors='orange', linewidths=2, label="class prediction: "+str(index))
+
 
 if __name__ == '__main__':
     # file = get_dataset_path("digit_histogram.npy")
@@ -42,10 +64,10 @@ if __name__ == '__main__':
 
     fig = plt.figure(figsize=(12., 11.))
     fig.suptitle("MKL : result" + str(result4), fontsize=16)
-    plot_subplot(X_train, y_train, y_pred44  ,0, (4, 1, 1), "train vue 0" )
-    plot_subplot(X_test, y_test,y_pred4 , 0, (4, 1, 2), "test vue 0" )
-    plot_subplot(X_test, y_test, y_pred4,1, (4, 1, 3), "test vue 1" )
-    plot_subplot(X_test, y_test,y_pred4, 2, (4, 1, 4), "test vue 2" )
+    plot_subplot(X_train, y_train, y_pred44  ,0, (4, 1, 1), "train vue 0 color" )
+    plot_subplot(X_test, y_test,y_pred4 , 0, (4, 1, 2), "test vue 0 color" )
+    plot_subplot(X_test, y_test, y_pred4,1, (4, 1, 3), "test vue 1 gradiant 0" )
+    plot_subplot(X_test, y_test,y_pred4, 2, (4, 1, 4), "test vue 2 gradiant 1" )
     # plt.legend()
     plt.show()
 

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMKL.py.md5

-4f807359096f5f5b3a7ee6b3ea540b91
+f7b5c3f0fd24e4628f03aa7019eea376
\ No newline at end of file

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMKL.rst

@@ -7,9 +7,9 @@
 .. _sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMKL.py:
 
 
-============
-Use Case MKL
-============
+=====================
+Use Case MKL on digit
+=====================
 Use case for all classifier of multimodallearn MKL
 multi class digit from sklearn, multivue
  - vue 0 digit data (color of sklearn)
@@ -30,8 +30,8 @@ multi class digit from sklearn, multivue
  .. code-block:: none
 
     result of MKL on digit with oneversone
-    98.44444444444444
-    /home/dominique/projets/ANR-Lives/scikit-multimodallearn/examples/usecase/plot_usecase_exampleMKL.py:50: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
+    96.88888888888889
+    /home/dominique/projets/ANR-Lives/scikit-multimodallearn/examples/usecase/plot_usecase_exampleMKL.py:72: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
       plt.show()
 
 
@@ -44,7 +44,7 @@ multi class digit from sklearn, multivue
 
 .. code-block:: default
 
-    from __future__ import absolute_import
+
     import numpy as np
     import matplotlib.pyplot as plt
     from sklearn.multiclass import OneVsOneClassifier
@@ -56,7 +56,29 @@ multi class digit from sklearn, multivue
     from multimodal.kernels.mvml import MVML
     from multimodal.kernels.lpMKL import MKL
 
-    from usecase_function import plot_subplot
+    import numpy as np
+    import matplotlib.pyplot as plt
+    import matplotlib._color_data as mcd
+
+
+    def plot_subplot(X, Y, Y_pred, vue, subplot, title):
+        cn = mcd.CSS4_COLORS
+        classes = np.unique(Y)
+        n_classes = len(np.unique(Y))
+        axs = plt.subplot(subplot[0],subplot[1],subplot[2])
+        axs.set_title(title)
+        #plt.scatter(X._extract_view(vue), X._extract_view(vue), s=40, c='gray',
+        #            edgecolors=(0, 0, 0))
+        for index, k in zip(range(n_classes), cn.keys()):
+             Y_class, = np.where(Y==classes[index])
+             Y_class_pred = np.intersect1d(np.where(Y_pred==classes[index])[0], np.where(Y_pred==Y)[0])
+             plt.scatter(X._extract_view(vue)[Y_class],
+                         X._extract_view(vue)[Y_class],
+                         s=40, c=cn[k], edgecolors='blue', linewidths=2, label="class real class: "+str(index)) #
+             plt.scatter(X._extract_view(vue)[Y_class_pred],
+                         X._extract_view(vue)[Y_class_pred],
+                         s=160, edgecolors='orange', linewidths=2, label="class prediction: "+str(index))
+
 
     if __name__ == '__main__':
         # file = get_dataset_path("digit_histogram.npy")
@@ -76,10 +98,10 @@ multi class digit from sklearn, multivue
 
         fig = plt.figure(figsize=(12., 11.))
         fig.suptitle("MKL : result" + str(result4), fontsize=16)
-        plot_subplot(X_train, y_train, y_pred44  ,0, (4, 1, 1), "train vue 0" )
-        plot_subplot(X_test, y_test,y_pred4 , 0, (4, 1, 2), "test vue 0" )
-        plot_subplot(X_test, y_test, y_pred4,1, (4, 1, 3), "test vue 1" )
-        plot_subplot(X_test, y_test,y_pred4, 2, (4, 1, 4), "test vue 2" )
+        plot_subplot(X_train, y_train, y_pred44  ,0, (4, 1, 1), "train vue 0 color" )
+        plot_subplot(X_test, y_test,y_pred4 , 0, (4, 1, 2), "test vue 0 color" )
+        plot_subplot(X_test, y_test, y_pred4,1, (4, 1, 3), "test vue 1 gradiant 0" )
+        plot_subplot(X_test, y_test,y_pred4, 2, (4, 1, 4), "test vue 2 gradiant 1" )
         # plt.legend()
         plt.show()
 
@@ -87,7 +109,7 @@ multi class digit from sklearn, multivue
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  12.697 seconds)
+   **Total running time of the script:** ( 0 minutes  20.457 seconds)
 
 
 .. _sphx_glr_download_tutorial_auto_examples_usecase_plot_usecase_exampleMKL.py:

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMVML.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Use Case of MVML\n\nUse case for all classifier of multimodallearn MVML\n\nmulti class digit from sklearn, multivue\n - vue 0 digit data (color of sklearn)\n - vue 1 gradiant of image in first direction\n - vue 2 gradiant of image in second direction\n\n\n"
+        "\n# Use Case of MVML on digit\n\nUse case for all classifier of multimodallearn MVML\n\nmulti class digit from sklearn, multivue\n - vue 0 digit data (color of sklearn)\n - vue 1 gradiant of image in first direction\n - vue 2 gradiant of image in second direction\n\n\n"
       ]
     },
     {
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "from __future__ import absolute_import\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.multiclass import OneVsOneClassifier\nfrom sklearn.model_selection import train_test_split\nfrom multimodal.datasets.base import load_dict, save_dict\nfrom multimodal.tests.data.get_dataset_path import get_dataset_path\nfrom multimodal.datasets.data_sample import MultiModalArray\nfrom multimodal.kernels.mvml import MVML\nfrom usecase_function import plot_subplot\n\n\nif __name__ == '__main__':\n    # file = get_dataset_path(\"digit_histogram.npy\")\n    file = get_dataset_path(\"digit_col_grad.npy\")\n    y = np.load(get_dataset_path(\"digit_y.npy\"))\n    dic_digit = load_dict(file)\n    XX =MultiModalArray(dic_digit)\n    X_train, X_test, y_train, y_test = train_test_split(XX, y)\n    est1 = OneVsOneClassifier(MVML(lmbda=0.1, eta=1, nystrom_param=0.2)).fit(X_train, y_train)\n    y_pred1 = est1.predict(X_test)\n    y_pred11 = est1.predict(X_train)\n    print(\"result of MVML on digit with oneversone\")\n    result1 = np.mean(y_pred1.ravel() == y_test.ravel()) * 100\n    print(result1)\n\n    fig = plt.figure(figsize=(12., 11.))\n    fig.suptitle(\"MVML: result\" + str(result1), fontsize=16)\n    plot_subplot(X_train, y_train, y_pred11\n                 , 0, (4, 1, 1), \"train vue 0\" )\n    plot_subplot(X_test, y_test,y_pred1, 0, (4, 1, 2), \"test vue 0\" )\n    plot_subplot(X_test, y_test, y_pred1, 1, (4, 1, 3), \"test vue 1\" )\n    plot_subplot(X_test, y_test,y_pred1, 2, (4, 1, 4), \"test vue 2\" )\n    #plt.legend()\n    plt.show()"
+        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.multiclass import OneVsOneClassifier\nfrom sklearn.model_selection import train_test_split\nfrom multimodal.datasets.base import load_dict, save_dict\nfrom multimodal.tests.data.get_dataset_path import get_dataset_path\nfrom multimodal.datasets.data_sample import MultiModalArray\nfrom multimodal.kernels.mvml import MVML\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib._color_data as mcd\n\n\ndef plot_subplot(X, Y, Y_pred, vue, subplot, title):\n    cn = mcd.CSS4_COLORS\n    classes = np.unique(Y)\n    n_classes = len(np.unique(Y))\n    axs = plt.subplot(subplot[0],subplot[1],subplot[2])\n    axs.set_title(title)\n    #plt.scatter(X._extract_view(vue), X._extract_view(vue), s=40, c='gray',\n    #            edgecolors=(0, 0, 0))\n    for index, k in zip(range(n_classes), cn.keys()):\n         Y_class, = np.where(Y==classes[index])\n         Y_class_pred = np.intersect1d(np.where(Y_pred==classes[index])[0], np.where(Y_pred==Y)[0])\n         plt.scatter(X._extract_view(vue)[Y_class],\n                     X._extract_view(vue)[Y_class],\n                     s=40, c=cn[k], edgecolors='blue', linewidths=2, label=\"class real class: \"+str(index)) #\n         plt.scatter(X._extract_view(vue)[Y_class_pred],\n                     X._extract_view(vue)[Y_class_pred],\n                     s=160, edgecolors='orange', linewidths=2, label=\"class prediction: \"+str(index))\n\n\n\nif __name__ == '__main__':\n    # file = get_dataset_path(\"digit_histogram.npy\")\n    file = get_dataset_path(\"digit_col_grad.npy\")\n    y = np.load(get_dataset_path(\"digit_y.npy\"))\n    dic_digit = load_dict(file)\n    XX =MultiModalArray(dic_digit)\n    X_train, X_test, y_train, y_test = train_test_split(XX, y)\n    est1 = OneVsOneClassifier(MVML(lmbda=0.1, eta=1, nystrom_param=0.2)).fit(X_train, y_train)\n    y_pred1 = est1.predict(X_test)\n    y_pred11 = est1.predict(X_train)\n    print(\"result of MVML on digit with oneversone\")\n    result1 = np.mean(y_pred1.ravel() == y_test.ravel()) * 100\n    print(result1)\n\n    fig = plt.figure(figsize=(12., 11.))\n    fig.suptitle(\"MVML: result\" + str(result1), fontsize=16)\n    plot_subplot(X_train, y_train, y_pred11\n                 , 0, (4, 1, 1), \"train vue 0 color\" )\n    plot_subplot(X_test, y_test,y_pred1, 0, (4, 1, 2), \"test vue 0 color\" )\n    plot_subplot(X_test, y_test, y_pred1, 1, (4, 1, 3), \"test vue 1 gradiant 0\" )\n    plot_subplot(X_test, y_test,y_pred1, 2, (4, 1, 4), \"test vue 2 gradiant 1\" )\n    #plt.legend()\n    plt.show()"
       ]
     }
   ],

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMVML.py

 # -*- coding: utf-8 -*-
 """
-================
-Use Case of MVML
-================
+=========================
+Use Case of MVML on digit
+========================
 Use case for all classifier of multimodallearn MVML
 
 multi class digit from sklearn, multivue
@@ -11,7 +11,7 @@ multi class digit from sklearn, multivue
  - vue 2 gradiant of image in second direction
 
 """
-from __future__ import absolute_import
+
 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.multiclass import OneVsOneClassifier
@@ -20,7 +20,29 @@ 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
-from usecase_function import plot_subplot
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib._color_data as mcd
+
+
+def plot_subplot(X, Y, Y_pred, vue, subplot, title):
+    cn = mcd.CSS4_COLORS
+    classes = np.unique(Y)
+    n_classes = len(np.unique(Y))
+    axs = plt.subplot(subplot[0],subplot[1],subplot[2])
+    axs.set_title(title)
+    #plt.scatter(X._extract_view(vue), X._extract_view(vue), s=40, c='gray',
+    #            edgecolors=(0, 0, 0))
+    for index, k in zip(range(n_classes), cn.keys()):
+         Y_class, = np.where(Y==classes[index])
+         Y_class_pred = np.intersect1d(np.where(Y_pred==classes[index])[0], np.where(Y_pred==Y)[0])
+         plt.scatter(X._extract_view(vue)[Y_class],
+                     X._extract_view(vue)[Y_class],
+                     s=40, c=cn[k], edgecolors='blue', linewidths=2, label="class real class: "+str(index)) #
+         plt.scatter(X._extract_view(vue)[Y_class_pred],
+                     X._extract_view(vue)[Y_class_pred],
+                     s=160, edgecolors='orange', linewidths=2, label="class prediction: "+str(index))
+
 
 
 if __name__ == '__main__':
@@ -40,10 +62,10 @@ if __name__ == '__main__':
     fig = plt.figure(figsize=(12., 11.))
     fig.suptitle("MVML: result" + str(result1), fontsize=16)
     plot_subplot(X_train, y_train, y_pred11
-                 , 0, (4, 1, 1), "train vue 0" )
-    plot_subplot(X_test, y_test,y_pred1, 0, (4, 1, 2), "test vue 0" )
-    plot_subplot(X_test, y_test, y_pred1, 1, (4, 1, 3), "test vue 1" )
-    plot_subplot(X_test, y_test,y_pred1, 2, (4, 1, 4), "test vue 2" )
+                 , 0, (4, 1, 1), "train vue 0 color" )
+    plot_subplot(X_test, y_test,y_pred1, 0, (4, 1, 2), "test vue 0 color" )
+    plot_subplot(X_test, y_test, y_pred1, 1, (4, 1, 3), "test vue 1 gradiant 0" )
+    plot_subplot(X_test, y_test,y_pred1, 2, (4, 1, 4), "test vue 2 gradiant 1" )
     #plt.legend()
     plt.show()
 

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMVML.py.md5

-b4b4bb03418027ba62ce77c251085cf5
+c401fe6af938dc5fef9c977303a2fdcf
\ No newline at end of file

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMVML.rst

@@ -7,9 +7,9 @@
 .. _sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMVML.py:
 
 
-================
-Use Case of MVML
-================
+=========================
+Use Case of MVML on digit
+========================
 Use case for all classifier of multimodallearn MVML
 
 multi class digit from sklearn, multivue
@@ -30,9 +30,54 @@ multi class digit from sklearn, multivue
 
  .. code-block:: none
 
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 2}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 4}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 6}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 5, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 6}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 3, 'precond_A_1': 4}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 4}
+    WARNING:root:warning appears during fit process{'precond_A': 3, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 4}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 6}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 6}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 6}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 4}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 1}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 2}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 2}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 1}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 2}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 3, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 3, 'precond_A_1': 4}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 3, 'precond_A_1': 4}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 6}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 3, 'precond_A_1': 4}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 3, 'precond_A_1': 2}
+    WARNING:root:warning appears during fit process{'precond_A': 5, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 5, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 4}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 6}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 4}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 4}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 6}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 1}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 5}
+    WARNING:root:warning appears during fit process{'precond_A': 4, 'precond_A_1': 5}
     result of MVML on digit with oneversone
-    98.88888888888889
-    /home/dominique/projets/ANR-Lives/scikit-multimodallearn/examples/usecase/plot_usecase_exampleMVML.py:48: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
+    96.88888888888889
+    /home/dominique/projets/ANR-Lives/scikit-multimodallearn/examples/usecase/plot_usecase_exampleMVML.py:70: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
       plt.show()
 
 
@@ -45,7 +90,7 @@ multi class digit from sklearn, multivue
 
 .. code-block:: default
 
-    from __future__ import absolute_import
+
     import numpy as np
     import matplotlib.pyplot as plt
     from sklearn.multiclass import OneVsOneClassifier
@@ -54,7 +99,29 @@ multi class digit from sklearn, multivue
     from multimodal.tests.data.get_dataset_path import get_dataset_path
     from multimodal.datasets.data_sample import MultiModalArray
     from multimodal.kernels.mvml import MVML
-    from usecase_function import plot_subplot
+    import numpy as np
+    import matplotlib.pyplot as plt
+    import matplotlib._color_data as mcd
+
+
+    def plot_subplot(X, Y, Y_pred, vue, subplot, title):
+        cn = mcd.CSS4_COLORS
+        classes = np.unique(Y)
+        n_classes = len(np.unique(Y))
+        axs = plt.subplot(subplot[0],subplot[1],subplot[2])
+        axs.set_title(title)
+        #plt.scatter(X._extract_view(vue), X._extract_view(vue), s=40, c='gray',
+        #            edgecolors=(0, 0, 0))
+        for index, k in zip(range(n_classes), cn.keys()):
+             Y_class, = np.where(Y==classes[index])
+             Y_class_pred = np.intersect1d(np.where(Y_pred==classes[index])[0], np.where(Y_pred==Y)[0])
+             plt.scatter(X._extract_view(vue)[Y_class],
+                         X._extract_view(vue)[Y_class],
+                         s=40, c=cn[k], edgecolors='blue', linewidths=2, label="class real class: "+str(index)) #
+             plt.scatter(X._extract_view(vue)[Y_class_pred],
+                         X._extract_view(vue)[Y_class_pred],
+                         s=160, edgecolors='orange', linewidths=2, label="class prediction: "+str(index))
+
 
 
     if __name__ == '__main__':
@@ -74,10 +141,10 @@ multi class digit from sklearn, multivue
         fig = plt.figure(figsize=(12., 11.))
         fig.suptitle("MVML: result" + str(result1), fontsize=16)
         plot_subplot(X_train, y_train, y_pred11
-                     , 0, (4, 1, 1), "train vue 0" )
-        plot_subplot(X_test, y_test,y_pred1, 0, (4, 1, 2), "test vue 0" )
-        plot_subplot(X_test, y_test, y_pred1, 1, (4, 1, 3), "test vue 1" )
-        plot_subplot(X_test, y_test,y_pred1, 2, (4, 1, 4), "test vue 2" )
+                     , 0, (4, 1, 1), "train vue 0 color" )
+        plot_subplot(X_test, y_test,y_pred1, 0, (4, 1, 2), "test vue 0 color" )
+        plot_subplot(X_test, y_test, y_pred1, 1, (4, 1, 3), "test vue 1 gradiant 0" )
+        plot_subplot(X_test, y_test,y_pred1, 2, (4, 1, 4), "test vue 2 gradiant 1" )
         #plt.legend()
         plt.show()
 
@@ -85,7 +152,7 @@ multi class digit from sklearn, multivue
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  39.921 seconds)
+   **Total running time of the script:** ( 1 minutes  14.485 seconds)
 
 
 .. _sphx_glr_download_tutorial_auto_examples_usecase_plot_usecase_exampleMVML.py: