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author | MathieuCarriere <mathieu.carriere3@gmail.com> | 2020-01-31 14:59:32 -0500 |
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committer | MathieuCarriere <mathieu.carriere3@gmail.com> | 2020-01-31 14:59:32 -0500 |
commit | 1dd1c554a962db70809eadb470eb2eaa733970d4 (patch) | |
tree | 320cb22c2ec633b56f701191aa0f2a75102db92f /src/python/gudhi | |
parent | fc3ca9579418b7436ea435be2dd6aeb16869aee5 (diff) |
revert first commit
Diffstat (limited to 'src/python/gudhi')
-rw-r--r-- | src/python/gudhi/representations/metrics.py | 59 |
1 files changed, 0 insertions, 59 deletions
diff --git a/src/python/gudhi/representations/metrics.py b/src/python/gudhi/representations/metrics.py index 290c1d07..5f9ec6ab 100644 --- a/src/python/gudhi/representations/metrics.py +++ b/src/python/gudhi/representations/metrics.py @@ -10,7 +10,6 @@ import numpy as np from sklearn.base import BaseEstimator, TransformerMixin from sklearn.metrics import pairwise_distances -from gudhi.wasserstein import wasserstein_distance try: from .. import bottleneck_distance USE_GUDHI = True @@ -146,64 +145,6 @@ class BottleneckDistance(BaseEstimator, TransformerMixin): return Xfit -class WassersteinDistance(BaseEstimator, TransformerMixin): - """ - This is a class for computing the Wasserstein distance matrix from a list of persistence diagrams. - """ - def __init__(self, order=2, internal_p=2): - """ - Constructor for the WassersteinDistance class. - - Parameters: - order (int): exponent for Wasserstein, default value is 2., see :func:`gudhi.wasserstein.wasserstein_distance`. - internal_p (int): ground metric on the (upper-half) plane (i.e. norm l_p in R^2), default value is 2 (euclidean norm), see :func:`gudhi.wasserstein.wasserstein_distance`. - """ - self.order, self.internal_p = order, internal_p - - def fit(self, X, y=None): - """ - Fit the WassersteinDistance class on a list of persistence diagrams: persistence diagrams are stored in a numpy array called **diagrams**. - - Parameters: - X (list of n x 2 numpy arrays): input persistence diagrams. - y (n x 1 array): persistence diagram labels (unused). - """ - self.diagrams_ = X - return self - - def transform(self, X): - """ - Compute all Wasserstein distances between the persistence diagrams that were stored after calling the fit() method, and a given list of (possibly different) persistence diagrams. - - Parameters: - X (list of n x 2 numpy arrays): input persistence diagrams. - - Returns: - numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise Wasserstein distances. - """ - num_diag1 = len(X) - - #if len(self.diagrams_) == len(X) and np.all([np.array_equal(self.diagrams_[i], X[i]) for i in range(len(X))]): - if X is self.diagrams_: - matrix = np.zeros((num_diag1, num_diag1)) - - for i in range(num_diag1): - for j in range(i+1, num_diag1): - matrix[i,j] = wasserstein_distance(X[i], X[j], self.order, self.internal_p) - matrix[j,i] = matrix[i,j] - - else: - num_diag2 = len(self.diagrams_) - matrix = np.zeros((num_diag1, num_diag2)) - - for i in range(num_diag1): - for j in range(num_diag2): - matrix[i,j] = wasserstein_distance(X[i], self.diagrams_[j], self.order, self.internal_p) - - Xfit = matrix - - return Xfit - class PersistenceFisherDistance(BaseEstimator, TransformerMixin): """ This is a class for computing the persistence Fisher distance matrix from a list of persistence diagrams. The persistence Fisher distance is obtained by computing the original Fisher distance between the probability distributions associated to the persistence diagrams given by convolving them with a Gaussian kernel. See http://papers.nips.cc/paper/8205-persistence-fisher-kernel-a-riemannian-manifold-kernel-for-persistence-diagrams for more details. |