Revisions

doc/index.rst

@@ -21,10 +21,10 @@ Documentation
 
 
 
-   reference/api
    tutorial/install_devel
    tutorial/auto_examples/index
    tutorial/times
+   reference/api
    tutorial/credits
 
 

doc/tutorial/auto_examples/combo/sg_execution_times.rst

 
 :orphan:
 
-.. _sphx_glr_tutorial_auto_examples_cumbo_sg_execution_times:
+.. _sphx_glr_tutorial_auto_examples_combo_sg_execution_times:
 
 Computation times
 =================
-**00:01.102** total execution time for **tutorial_auto_examples_cumbo** files:
+**00:03.474** total execution time for **tutorial_auto_examples_combo** files:
 
 +--------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_examples_cumbo_plot_cumbo_2_views_2_classes.py` (``plot_cumbo_2_views_2_classes.py``) | 00:00.603 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_examples_combo_plot_combo_2_views_2_classes.py` (``plot_combo_2_views_2_classes.py``) | 00:02.387 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_examples_cumbo_plot_cumbo_3_views_3_classes.py` (``plot_cumbo_3_views_3_classes.py``) | 00:00.499 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_examples_combo_plot_combo_3_views_3_classes.py` (``plot_combo_3_views_3_classes.py``) | 00:01.088 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------------------------+-----------+--------+

doc/tutorial/auto_examples/index.rst

@@ -19,7 +19,7 @@ Multimodal Examples
 
 
 
-.. _sphx_glr_tutorial_auto_examples_cumbo:
+.. _sphx_glr_tutorial_auto_examples_combo:
 
 
 MuCuMBo Examples
@@ -37,9 +37,9 @@ cooperation between views for classification.
 
 .. only:: html
 
- .. figure:: /tutorial/auto_examples/cumbo/images/thumb/sphx_glr_plot_cumbo_2_views_2_classes_thumb.png
+ .. figure:: /tutorial/auto_examples/combo/images/thumb/sphx_glr_plot_combo_2_views_2_classes_thumb.png
 
-     :ref:`sphx_glr_tutorial_auto_examples_cumbo_plot_cumbo_2_views_2_classes.py`
+     :ref:`sphx_glr_tutorial_auto_examples_combo_plot_combo_2_views_2_classes.py`
 
 .. raw:: html
 
@@ -49,7 +49,7 @@ cooperation between views for classification.
 .. toctree::
    :hidden:
 
-   /tutorial/auto_examples/cumbo/plot_cumbo_2_views_2_classes
+   /tutorial/auto_examples/combo/plot_combo_2_views_2_classes
 
 .. raw:: html
 
@@ -57,9 +57,9 @@ cooperation between views for classification.
 
 .. only:: html
 
- .. figure:: /tutorial/auto_examples/cumbo/images/thumb/sphx_glr_plot_cumbo_3_views_3_classes_thumb.png
+ .. figure:: /tutorial/auto_examples/combo/images/thumb/sphx_glr_plot_combo_3_views_3_classes_thumb.png
 
-     :ref:`sphx_glr_tutorial_auto_examples_cumbo_plot_cumbo_3_views_3_classes.py`
+     :ref:`sphx_glr_tutorial_auto_examples_combo_plot_combo_3_views_3_classes.py`
 
 .. raw:: html
 
@@ -69,7 +69,7 @@ cooperation between views for classification.
 .. toctree::
    :hidden:
 
-   /tutorial/auto_examples/cumbo/plot_cumbo_3_views_3_classes
+   /tutorial/auto_examples/combo/plot_combo_3_views_3_classes
 .. raw:: html
 
     <div class="sphx-glr-clear"></div>
@@ -242,13 +242,13 @@ The following toy examples illustrate how the multimodal as usecase on digit  da
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="multi class digit from sklearn, multivue  - vue 0 digit data (color of sklearn)  - vue 1 gradia...">
+    <div class="sphx-glr-thumbcontainer" tooltip="Use Case MKL on digit">
 
 .. only:: html
 
- .. figure:: /tutorial/auto_examples/usecase/images/thumb/sphx_glr_plot_usecase_exampleMuCuBo_thumb.png
+ .. figure:: /tutorial/auto_examples/usecase/images/thumb/sphx_glr_plot_usecase_exampleMKL_thumb.png
 
-     :ref:`sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMuCuBo.py`
+     :ref:`sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMKL.py`
 
 .. raw:: html
 
@@ -258,17 +258,17 @@ The following toy examples illustrate how the multimodal as usecase on digit  da
 .. toctree::
    :hidden:
 
-   /tutorial/auto_examples/usecase/plot_usecase_exampleMuCuBo
+   /tutorial/auto_examples/usecase/plot_usecase_exampleMKL
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="Use Case MKL on digit">
+    <div class="sphx-glr-thumbcontainer" tooltip="multi class digit from sklearn, multivue  - vue 0 digit data (color of sklearn)  - vue 1 gradia...">
 
 .. only:: html
 
- .. figure:: /tutorial/auto_examples/usecase/images/thumb/sphx_glr_plot_usecase_exampleMKL_thumb.png
+ .. figure:: /tutorial/auto_examples/usecase/images/thumb/sphx_glr_plot_usecase_exampleMuComBo_thumb.png
 
-     :ref:`sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMKL.py`
+     :ref:`sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMuComBo.py`
 
 .. raw:: html
 
@@ -278,7 +278,7 @@ The following toy examples illustrate how the multimodal as usecase on digit  da
 .. toctree::
    :hidden:
 
-   /tutorial/auto_examples/usecase/plot_usecase_exampleMKL
+   /tutorial/auto_examples/usecase/plot_usecase_exampleMuComBo
 .. raw:: html
 
     <div class="sphx-glr-clear"></div>
@@ -291,15 +291,15 @@ The following toy examples illustrate how the multimodal as usecase on digit  da
     :class: sphx-glr-footer-gallery
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-python
 
-    :download:`Download all examples in Python source code: auto_examples_python.zip <//home/dominique/projets/ANR-Lives/scikit-multimodallearn/doc/tutorial/auto_examples/auto_examples_python.zip>`
+    :download:`Download all examples in Python source code: auto_examples_python.zip <//home/baptiste/Documents/Gitwork/scikit-multimodallearn/doc/tutorial/auto_examples/auto_examples_python.zip>`
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
 
-    :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip <//home/dominique/projets/ANR-Lives/scikit-multimodallearn/doc/tutorial/auto_examples/auto_examples_jupyter.zip>`
+    :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip <//home/baptiste/Documents/Gitwork/scikit-multimodallearn/doc/tutorial/auto_examples/auto_examples_jupyter.zip>`
 
 
 .. only:: html

doc/tutorial/auto_examples/mumbo/sg_execution_times.rst

@@ -3,8 +3,8 @@
 
 .. _sphx_glr_tutorial_auto_examples_mumbo_sg_execution_times:
 
-Computation times
-=================
+Mumbo computation times
+=======================
 **00:02.013** total execution time for **tutorial_auto_examples_mumbo** files:
 
 +--------------------------------------------------------------------------------------------------------------------+-----------+--------+

doc/tutorial/auto_examples/mvml/sg_execution_times.rst

@@ -3,8 +3,8 @@
 
 .. _sphx_glr_tutorial_auto_examples_mvml_sg_execution_times:
 
-Computation times
-=================
+MVML computation times
+======================
 **00:03.630** total execution time for **tutorial_auto_examples_mvml** files:
 
 +-------------------------------------------------------------------------------+-----------+--------+

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMKL.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "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()"
+        "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.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    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

@@ -19,7 +19,6 @@ from sklearn.tree import DecisionTreeClassifier
 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 multimodal.kernels.lpMKL import MKL
 
 import numpy as np
@@ -50,7 +49,6 @@ if __name__ == '__main__':
     # file = get_dataset_path("digit_histogram.npy")
     file = get_dataset_path("digit_col_grad.npy")
     y = np.load(get_dataset_path("digit_y.npy"))
-    base_estimator = DecisionTreeClassifier(max_depth=4)
     dic_digit = load_dict(file)
     XX =MultiModalArray(dic_digit)
     X_train, X_test, y_train, y_test = train_test_split(XX, y)

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

-f7b5c3f0fd24e4628f03aa7019eea376
+3360d3ee5508f0e16023ee336767f17c
\ No newline at end of file

doc/tutorial/auto_examples/usecase/plot_usecase_exampleMKL.rst

+.. only:: html
+
     .. note::
         :class: sphx-glr-download-link-note
 
@@ -30,8 +32,8 @@ multi class digit from sklearn, multivue
  .. code-block:: none
 
     result of MKL on digit with oneversone
-    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.
+    97.77777777777777
+    /home/baptiste/Documents/Gitwork/scikit-multimodallearn/examples/usecase/plot_usecase_exampleMKL.py:70: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
       plt.show()
 
 
@@ -53,7 +55,6 @@ multi class digit from sklearn, multivue
     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 multimodal.kernels.lpMKL import MKL
 
     import numpy as np
@@ -84,7 +85,6 @@ multi class digit from sklearn, multivue
         # file = get_dataset_path("digit_histogram.npy")
         file = get_dataset_path("digit_col_grad.npy")
         y = np.load(get_dataset_path("digit_y.npy"))
-        base_estimator = DecisionTreeClassifier(max_depth=4)
         dic_digit = load_dict(file)
         XX =MultiModalArray(dic_digit)
         X_train, X_test, y_train, y_test = train_test_split(XX, y)
@@ -109,7 +109,7 @@ multi class digit from sklearn, multivue
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  20.457 seconds)
+   **Total running time of the script:** ( 1 minutes  59.263 seconds)
 
 
 .. _sphx_glr_download_tutorial_auto_examples_usecase_plot_usecase_exampleMKL.py:
@@ -122,13 +122,13 @@ multi class digit from sklearn, multivue
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-python
 
      :download:`Download Python source code: plot_usecase_exampleMKL.py <plot_usecase_exampleMKL.py>`
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
 
      :download:`Download Jupyter notebook: plot_usecase_exampleMKL.ipynb <plot_usecase_exampleMKL.ipynb>`
 

doc/tutorial/auto_examples/usecase/sg_execution_times.rst

@@ -5,16 +5,16 @@
 
 Computation times
 =================
-**01:55.487** total execution time for **tutorial_auto_examples_usecase** files:
+**02:26.402** total execution time for **tutorial_auto_examples_usecase** files:
 
-+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMVML.py` (``plot_usecase_exampleMVML.py``)     | 01:14.485 | 0.0 MB |
-+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMKL.py` (``plot_usecase_exampleMKL.py``)       | 00:20.457 | 0.0 MB |
-+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMuCuBo.py` (``plot_usecase_exampleMuCuBo.py``) | 00:14.171 | 0.0 MB |
-+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMumBo.py` (``plot_usecase_exampleMumBo.py``)   | 00:06.374 | 0.0 MB |
-+------------------------------------------------------------------------------------------------------------------+-----------+--------+
++--------------------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMKL.py` (``plot_usecase_exampleMKL.py``)         | 01:59.263 | 0.0 MB |
++--------------------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMuComBo.py` (``plot_usecase_exampleMuComBo.py``) | 00:27.139 | 0.0 MB |
++--------------------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMVML.py` (``plot_usecase_exampleMVML.py``)       | 00:00.000 | 0.0 MB |
++--------------------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_auto_examples_usecase_plot_usecase_exampleMumBo.py` (``plot_usecase_exampleMumBo.py``)     | 00:00.000 | 0.0 MB |
++--------------------------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_tutorial_auto_examples_usecase_usecase_function.py` (``usecase_function.py``)                       | 00:00.000 | 0.0 MB |
-+------------------------------------------------------------------------------------------------------------------+-----------+--------+
++--------------------------------------------------------------------------------------------------------------------+-----------+--------+

doc/tutorial/auto_examples/usecase/usecase_function.rst

+.. only:: html
+
     .. note::
         :class: sphx-glr-download-link-note
 
@@ -60,13 +62,13 @@ Function plot_subplot
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-python
 
      :download:`Download Python source code: usecase_function.py <usecase_function.py>`
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
 
      :download:`Download Jupyter notebook: usecase_function.ipynb <usecase_function.ipynb>`
 

doc/tutorial/estimator_template.rst

+
+.. _estim-template:
+
+Estimator template
+==================
+
+To add a multimodal estimator based on the groundwork of scikit-multimodallearn,
+please feel free to use the following template, while complying with the
+`Developer's Guide <http://scikit-learn.org/stable/developers>`_ of the
+scikit-learn project to ensure full compatibility.
+
+
+
+.. code-block:: default
+
+    import numpy as np
+    from sklearn.base import ClassifierMixin, BaseEstimator
+    from sklearn.utils import check_X_y
+    from sklearn.utils.multiclass import check_classification_targets
+    from sklearn.utils.validation import check_is_fitted
+    from multimodal.boosting.boost import UBoosting
+
+
+    class NewMultiModalEstimator(BaseEstimator, ClassifierMixin, UBoosting):
+        r""""
+        Your documentation
+        """
+
+        def __init__(self, your_attributes=None, ):
+            self.your_attributes = your_attributes
+
+        def fit(self, X, y, views_ind=None):
+            """Build a multimodal classifier from the training set (X, y).
+
+            Parameters
+            ----------
+            X : dict dictionary with all views
+                or
+                `MultiModalData` ,  `MultiModalArray`, `MultiModalSparseArray`
+                or
+                {array-like, sparse matrix}, shape = (n_samples, n_features)
+                Training multi-view input samples.
+                Sparse matrix can be CSC, CSR, COO, DOK, or LIL.
+                COO, DOK and LIL are converted to CSR.
+
+            y : array-like, shape = (n_samples,)
+                Target values (class labels).
+
+            views_ind : array-like (default=[0, n_features//2, n_features])
+                Paramater specifying how to extract the data views from X:
+
+                - If 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.
+
+                - If views_ind is an array of arrays of integers, then each array
+                  of integers ``views_ind[n]`` specifies the indices of the view
+                  ``n``, which is then given by ``X[:, views_ind[n]]``.
+
+                  With this convention each view creates therefore a partial copy
+                  of the data in X. This convention is thus more flexible but less
+                  efficient than the previous one.
+
+            Returns
+            -------
+            self : object
+                Returns self.
+
+            Raises
+            ------
+            ValueError  estimator must support sample_weight
+
+            ValueError where `X` and `view_ind` are not compatibles
+            """
+
+            # _global_X_transform processes the multimodal dataset to transform the
+            # in the MultiModalArray format.
+            self.X_ = self._global_X_transform(X, views_ind=views_ind)
+
+            # Ensure proper format for views_ind and return number of views.
+            views_ind_, n_views = self.X_._validate_views_ind(self.X_.views_ind,
+                                                              self.X_.shape[1])
+
+            # According to scikit learn guidelines.
+            check_X_y(self.X_, y)
+            if not isinstance(y, np.ndarray):
+                y = np.asarray(y)
+            check_classification_targets(y)
+            self._validate_estimator()
+
+            return self
+
+
+        def predict(self, X):
+            """Predict classes for X.
+
+            Parameters
+            ----------
+            X : {array-like, sparse matrix}, shape = (n_samples, n_features)
+                Multi-view input samples.
+                Sparse matrix can be CSC, CSR, COO, DOK, or LIL.
+                COO, DOK and LIL are converted to CSR.
+
+            Returns
+            -------
+            y : numpy.ndarray, shape = (n_samples,)
+                Predicted classes.
+
+            Raises
+            ------
+            ValueError   'X' input matrix must be have the same total number of features
+                         of 'X' fit data
+            """
+            # According to scikit learn guidelines
+            check_is_fitted(self, ("your_attributes"))
+
+            # _global_X_transform processes the multimodal dataset to transform the
+            # in the MultiModalArray format.
+            X = self._global_X_transform(X, views_ind=self.X_.views_ind)
+
+            # Ensure that X is in the proper format.
+            X = self._validate_X_predict(X)
+
+            # Returning fake multi-class labels
+            return np.random.randint(0, 5, size=X.shape[0])
\ No newline at end of file

doc/tutorial/install_devel.rst

@@ -38,7 +38,8 @@ The development of scikit-multimodallearn follows the guidelines provided by the
 scikit-learn community.
 
 Refer to the `Developer's Guide <http://scikit-learn.org/stable/developers>`_
-of the scikit-learn project for more details.
+of the scikit-learn project for general details. Expanding the library can be
+done by following the template provided in :ref:`estim-template` .
 
 Source code
 -----------

doc/tutorial/times.rst

@@ -3,7 +3,7 @@
 Computation times
 =================
 
-total execution time for **tutorial_auto_examples** files:
+Total execution time for **tutorial_auto_examples** files:
 
 
 .. toctree::