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+:orphan:
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+.. To get rid of WARNING: document isn't included in any toctree
+
+======================
+Representations manual
+======================
+
+.. include:: representations_sum.inc
+
+This module, originally available at https://github.com/MathieuCarriere/sklearn-tda and named sklearn_tda, aims at bridging the gap between persistence diagrams and machine learning, by providing implementations of most of the vector representations for persistence diagrams in the literature, in a scikit-learn format. More specifically, it provides tools, using the scikit-learn standard interface, to compute distances and kernels on persistence diagrams, and to convert these diagrams into vectors in Euclidean space.
+
+A diagram is represented as a numpy array of shape (n,2), as can be obtained from :func:`~gudhi.SimplexTree.persistence_intervals_in_dimension` for instance. Points at infinity are represented as a numpy array of shape (n,1), storing only the birth time.
+
+A small example is provided
+
+.. only:: builder_html
+
+    * :download:`diagram_vectorizations_distances_kernels.py <../example/diagram_vectorizations_distances_kernels.py>`
+
+
+Preprocessing
+-------------
+.. automodule:: gudhi.representations.preprocessing
+   :members:
+   :special-members:
+   :show-inheritance:
+
+Vector methods
+--------------
+.. automodule:: gudhi.representations.vector_methods
+   :members:
+   :special-members:
+   :show-inheritance:
+
+Kernel methods
+--------------
+.. automodule:: gudhi.representations.kernel_methods
+   :members:
+   :special-members:
+   :show-inheritance:
+
+Metrics
+-------
+.. automodule:: gudhi.representations.metrics
+   :members:
+   :special-members:
+   :show-inheritance:
+
+Basic example
+-------------
+
+This example computes the first two Landscapes associated to a persistence diagram with four points. The landscapes are evaluated on ten samples, leading to two vectors with ten coordinates each, that are eventually concatenated in order to produce a single vector representation.
+
+.. testcode::
+
+    import numpy as np
+    from gudhi.representations import Landscape
+    # A single diagram with 4 points
+    D = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])
+    diags = [D]
+    l=Landscape(num_landscapes=2,resolution=10).fit_transform(diags)
+    print(l) 
+
+The output is:
+
+.. testoutput::
+
+    [[1.02851895 2.05703791 2.57129739 1.54277843 0.89995409 1.92847304
+      2.95699199 3.08555686 2.05703791 1.02851895 0.         0.64282435
+      0.         0.         0.51425948 0.         0.         0.
+      0.77138922 1.02851895]]