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| author | tlacombe <lacombe1993@gmail.com> | 2020-02-24 10:14:31 +0100 |
|---|---|---|
| committer | tlacombe <lacombe1993@gmail.com> | 2020-02-24 10:14:31 +0100 |
| commit | 3e15e9fe5bffb0ffcf8f7f3a0dac1c331646630a (patch) | |
| tree | d78b2373e15a0274930a213b5534b80dc33e05d9 /src/python | |
| parent | 59f046cd0f405b124a6e08f26ca7b0248f707374 (diff) | |
changed double quote into simple quote to be consistent with wasserstein.py
Diffstat (limited to 'src/python')
| -rw-r--r-- | src/python/gudhi/barycenter.py | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/src/python/gudhi/barycenter.py b/src/python/gudhi/barycenter.py index dc9e8241..4e132c23 100644 --- a/src/python/gudhi/barycenter.py +++ b/src/python/gudhi/barycenter.py @@ -17,12 +17,12 @@ from gudhi.wasserstein import _build_dist_matrix, _perstot def _mean(x, m): - """ + ''' :param x: a list of 2D-points, off diagonal, x_0... x_{k-1} :param m: total amount of points taken into account, that is we have (m-k) copies of diagonal :returns: the weighted mean of x with (m-k) copies of the diagonal - """ + ''' k = len(x) if k > 0: w = np.mean(x, axis=0) @@ -33,7 +33,7 @@ def _mean(x, m): def _optimal_matching(X, Y, withcost=False): - """ + ''' :param X: numpy.array of size (n x 2) :param Y: numpy.array of size (m x 2) :param withcost: returns also the cost corresponding to the optimal matching @@ -44,7 +44,7 @@ def _optimal_matching(X, Y, withcost=False): if i >= len(X) or j >= len(Y), it means they represent the diagonal. They will be encoded by -1 afterwards. - """ + ''' n = len(X) m = len(Y) @@ -94,7 +94,7 @@ def _optimal_matching(X, Y, withcost=False): def lagrangian_barycenter(pdiagset, init=None, verbose=False): - """ + ''' Returns the estimated barycenter computed with the algorithm provided by Turner et al (2014). As the algorithm is not convex, the output depends on initialization. @@ -129,7 +129,7 @@ def lagrangian_barycenter(pdiagset, init=None, verbose=False): of observations to the output. - nb_iter, integer representing the number of iterations performed before convergence of the algorithm. - """ + ''' X = pdiagset # to shorten notations, not a copy m = len(X) # number of diagrams we are averaging if m == 0: |