fixed useless coordinates in Landscape if min and max are computed from data

1 files changed, 23 insertions, 2 deletions

diff --git a/src/python/doc/representations.rst b/src/python/doc/representations.rst
index b3131a25..b338f7f0 100644
--- a/src/python/doc/representations.rst
+++ b/src/python/doc/representations.rst
@@ -8,9 +8,9 @@ Representations manual
 
 .. include:: representations_sum.inc
 
-This module, originally named sklearn_tda, aims at bridging the gap between persistence diagrams and machine learning tools, in particular scikit-learn. It provides tools, using the scikit-learn standard interface, to compute distances and kernels on diagrams, and to convert diagrams into vectors.
+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 diagram is represented as a numpy array of shape (n,2), as can be obtained from `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
 
@@ -46,3 +46,24 @@ 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::
+
+    [[0.         1.25707872 2.51415744 1.88561808 0.7856742  2.04275292 3.29983165 2.51415744 1.25707872 0.         0.         0.         0.31426968 0.         0.62853936 0.         0.         0.31426968 1.25707872 0.        ]]