From 4b546a43fe14178dcfb2b327e27a580fc9811499 Mon Sep 17 00:00:00 2001 From: tlacombe Date: Mon, 16 Mar 2020 13:16:04 +0100 Subject: update doc (indentation, mention of -1 for the diag) and added a few more tests --- src/python/gudhi/barycenter.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) (limited to 'src/python/gudhi') diff --git a/src/python/gudhi/barycenter.py b/src/python/gudhi/barycenter.py index a41b5906..3af12c14 100644 --- a/src/python/gudhi/barycenter.py +++ b/src/python/gudhi/barycenter.py @@ -96,9 +96,8 @@ def _optimal_matching(X, Y, withcost=False): def lagrangian_barycenter(pdiagset, init=None, verbose=False): ''' :param pdiagset: a list of size m containing numpy.array of shape (n x 2) - (n can variate), encoding a set of - persistence diagrams with only finite coordinates. - If empty, returns None. + (n can variate), encoding a set of + persistence diagrams with only finite coordinates. :param init: The initial value for barycenter estimate. If None, init is made on a random diagram from the dataset. Otherwise, it must be an int @@ -106,24 +105,25 @@ def lagrangian_barycenter(pdiagset, init=None, verbose=False): or a (n x 2) numpy.array enconding a persistence diagram with n points. :param verbose: if True, returns additional information about the - barycenter. + barycenter. :returns: If not verbose (default), a numpy.array encoding - the barycenter estimate + the barycenter estimate of pdiagset (local minima of the energy function). + If pdiagset is empty, returns None. If verbose, returns a couple (Y, log) where Y is the barycenter estimate, and log is a dict that contains additional informations: - groupings, a list of list of pairs (i,j), - That is, G[k] = [(i, j) ...], where (i,j) indicates - that X[i] is matched to Y[j] - if i > len(X) or j > len(Y), it means they - represent the diagonal. + That is, G[k] = [(i, j) ...], where (i,j) indicates + that X[i] is matched to Y[j] + if i = -1 or j = -1, it means they + represent the diagonal. - energy, a float representing the Frechet - energy value obtained, - that is the mean of squared distances - of observations to the output. + energy value obtained, + that is the mean of squared distances + of observations to the output. - nb_iter, integer representing the number of iterations - performed before convergence of the algorithm. + performed before convergence of the algorithm. ''' X = pdiagset # to shorten notations, not a copy m = len(X) # number of diagrams we are averaging @@ -136,7 +136,7 @@ def lagrangian_barycenter(pdiagset, init=None, verbose=False): # Initialisation of barycenter if init is None: i0 = np.random.randint(m) # Index of first state for the barycenter - Y = X[i0].copy() #copy() ensure that we do not modify X[i0] + Y = X[i0].copy() else: if type(init)==int: Y = X[init].copy() @@ -149,7 +149,7 @@ def lagrangian_barycenter(pdiagset, init=None, verbose=False): while not converged: nb_iter += 1 K = len(Y) # current nb of points in Y (some might be on diagonal) - G = np.zeros((K, m), dtype=int)-1 # will store for each j, the (index) + G = np.full((K, m), -1, dtype=int) # will store for each j, the (index) # point matched in each other diagram #(might be the diagonal). # that is G[j, i] = k <=> y_j is matched to -- cgit v1.2.3