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author | MathieuCarriere <mathieu.carriere3@gmail.com> | 2019-12-08 21:22:09 -0500 |
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committer | MathieuCarriere <mathieu.carriere3@gmail.com> | 2019-12-08 21:22:09 -0500 |
commit | ce58cc97866605fe64df479e96d455e90f56f8e2 (patch) | |
tree | 7c541ef8266956ca68930bf657d4d0b76cfae68f /src/python/doc/representations.rst | |
parent | 3e829fd6f4a3a122da9df35a88e5c51122860bf6 (diff) |
fixed useless coordinates in Landscape if min and max are computed from data
Diffstat (limited to 'src/python/doc/representations.rst')
-rw-r--r-- | src/python/doc/representations.rst | 25 |
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. ]] |