diff options
| -rw-r--r-- | ot/bregman.py | 11 |
1 files changed, 6 insertions, 5 deletions
diff --git a/ot/bregman.py b/ot/bregman.py index f844f03..5327dbc 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -920,8 +920,8 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1e-9, verbose=False, log=False): - """Compute the entropic regularized wasserstein barycenter of distributions A - where A is a collection of 2D images. + """Compute the entropic regularized wasserstein barycenter of distributions A + where A is a collection of 2D images. The function solves the following optimization problem: @@ -966,8 +966,8 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 ---------- .. [21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). - Convolutional wasserstein distances: Efficient optimal transportation on geometric domains - ACM Transactions on Graphics (TOG), 34(4), 66 + Convolutional wasserstein distances: Efficient optimal transportation on geometric domains + ACM Transactions on Graphics (TOG), 34(4), 66 """ @@ -993,7 +993,8 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 [Y, X] = np.meshgrid(t, t) xi1 = np.exp(-(X - Y)**2 / reg) - def K(x): return np.dot(np.dot(xi1, x), xi1) + def K(x): + return np.dot(np.dot(xi1, x), xi1) while (err > stopThr and cpt < numItermax): |