From 967ceab26b09ad74e0cff0d84429a766af267f6b Mon Sep 17 00:00:00 2001 From: tlacombe Date: Tue, 10 Mar 2020 16:47:09 +0100 Subject: removed _clean_match and changed matching format, it is now a (n x 2) numpy array --- src/python/gudhi/wasserstein.py | 31 ++++++------------------------- 1 file changed, 6 insertions(+), 25 deletions(-) (limited to 'src/python') diff --git a/src/python/gudhi/wasserstein.py b/src/python/gudhi/wasserstein.py index e28c63e6..9efa946e 100644 --- a/src/python/gudhi/wasserstein.py +++ b/src/python/gudhi/wasserstein.py @@ -64,34 +64,13 @@ def _perstot(X, order, internal_p): return (np.sum(np.linalg.norm(X - Xdiag, ord=internal_p, axis=1)**order))**(1./order) -def _clean_match(match, n, m): - ''' - :param match: a list of the form [(i,j) ...] - :param n: int, size of the first dgm - :param m: int, size of the second dgm - :return: a modified version of match where indices greater than n, m are replaced by -1, encoding the diagonal. - and (-1, -1) are removed - ''' - new_match = [] - for i,j in match: - if i >= n: - if j < m: - new_match.append((-1, j)) - elif j >= m: - if i < n: - new_match.append((i,-1)) - else: - new_match.append((i,j)) - return new_match - - def wasserstein_distance(X, Y, matching=False, order=2., internal_p=2.): ''' :param X: (n x 2) numpy.array encoding the (finite points of the) first diagram. Must not contain essential points (i.e. with infinite coordinate). :param Y: (m x 2) numpy.array encoding the second diagram. :param matching: if True, computes and returns the optimal matching between X and Y, encoded as - a list of tuple [...(i,j)...], meaning the i-th point in X is matched to + a (n x 2) np.array [...[i,j]...], meaning the i-th point in X is matched to the j-th point in Y, with the convention (-1) represents the diagonal. :param order: exponent for Wasserstein; Default value is 2. :param internal_p: Ground metric on the (upper-half) plane (i.e. norm L^p in R^2); @@ -114,12 +93,12 @@ def wasserstein_distance(X, Y, matching=False, order=2., internal_p=2.): if not matching: return _perstot(Y, order, internal_p) else: - return _perstot(Y, order, internal_p), [(-1, j) for j in range(m)] + return _perstot(Y, order, internal_p), np.array([[-1, j] for j in range(m)]) elif Y.size == 0: if not matching: return _perstot(X, order, internal_p) else: - return _perstot(X, order, internal_p), [(i, -1) for i in range(n)] + return _perstot(X, order, internal_p), np.array([[i, -1] for i in range(n)]) M = _build_dist_matrix(X, Y, order=order, internal_p=internal_p) a = np.ones(n+1) # weight vector of the input diagram. Uniform here. @@ -130,9 +109,11 @@ def wasserstein_distance(X, Y, matching=False, order=2., internal_p=2.): if matching: P = ot.emd(a=a,b=b,M=M, numItermax=2000000) ot_cost = np.sum(np.multiply(P,M)) + P[-1, -1] = 0 # Remove matching corresponding to the diagonal match = np.argwhere(P) # Now we turn to -1 points encoding the diagonal - match = _clean_match(match, n, m) + match[:,0][match[:,0] >= n] = -1 + match[:,1][match[:,1] >= m] = -1 return ot_cost ** (1./order) , match # Comptuation of the otcost using the ot.emd2 library. -- cgit v1.2.3