diff options
| -rw-r--r-- | ot/bregman.py | 2 | ||||
| -rw-r--r-- | ot/optim.py | 6 |
2 files changed, 4 insertions, 4 deletions
diff --git a/ot/bregman.py b/ot/bregman.py index 457bdd4..f1f8437 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1116,7 +1116,7 @@ def barycenter_sinkhorn(A, M, reg, weights=None, numItermax=1000, err = 1 UKv = np.dot(K, np.divide(A.T, np.sum(K, axis=0)).T) - u = (geometricBar(weights, UKv) / UKv.T).T + u = (geometricMean(UKv) / UKv.T).T while (err > stopThr and cpt < numItermax): cpt = cpt + 1 diff --git a/ot/optim.py b/ot/optim.py index 8e546d0..e7e6e65 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -136,7 +136,7 @@ def solve_linesearch(cost, G, deltaG, Mi, f_val, def cg(a, b, M, reg, f, df, G0=None, numItermax=200, numItermaxEmd=100000, stopThr=1e-9, stopThr2=1e-9, verbose=False, log=False, **kwargs): - r""" + """ Solve the general regularized OT problem with conditional gradient The function solves the following optimization problem: @@ -275,7 +275,7 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, numItermaxEmd=100000, def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, numInnerItermax=200, stopThr=1e-9, stopThr2=1e-9, verbose=False, log=False): - r""" + """ Solve the general regularized OT problem with the generalized conditional gradient The function solves the following optimization problem: @@ -413,7 +413,7 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, def solve_1d_linesearch_quad(a, b, c): - r""" + """ For any convex or non-convex 1d quadratic function f, solve on [0,1] the following problem: .. math:: \argmin f(x)=a*x^{2}+b*x+c |