diff options
Diffstat (limited to 'src')
| -rwxr-xr-x | src/python/example/diagram_vectorizations_distances_kernels.py | 7 | ||||
| -rw-r--r-- | src/python/gudhi/representations/metrics.py | 59 |
2 files changed, 1 insertions, 65 deletions
diff --git a/src/python/example/diagram_vectorizations_distances_kernels.py b/src/python/example/diagram_vectorizations_distances_kernels.py index 66c32cc2..119072eb 100755 --- a/src/python/example/diagram_vectorizations_distances_kernels.py +++ b/src/python/example/diagram_vectorizations_distances_kernels.py @@ -9,7 +9,7 @@ from gudhi.representations import DiagramSelector, Clamping, Landscape, Silhouet TopologicalVector, DiagramScaler, BirthPersistenceTransform,\ PersistenceImage, PersistenceWeightedGaussianKernel, Entropy, \ PersistenceScaleSpaceKernel, SlicedWassersteinDistance,\ - SlicedWassersteinKernel, BottleneckDistance, WassersteinDistance, PersistenceFisherKernel + SlicedWassersteinKernel, BottleneckDistance, PersistenceFisherKernel D = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.], [0., np.inf], [5., np.inf]]) diags = [D] @@ -117,11 +117,6 @@ X = SW.fit(diags) Y = SW.transform(diags2) print("SW kernel is " + str(Y[0][0])) -W = WassersteinDistance(order=2, internal_p=2) -X = W.fit(diags) -Y = W.transform(diags2) -print("Wasserstein distance is " + str(Y[0][0])) - W = BottleneckDistance(epsilon=.001) X = W.fit(diags) Y = W.transform(diags2) 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. |