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Revert "[WIP] Update bregman.py : barycenter_sinkhorn function" #196

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Merged
merged 1 commit into from
Jun 22, 2020
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Revert "[WIP] Update bregman.py : barycenter_sinkhorn function" #196

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2 changes: 1 addition & 1 deletion ot/bregman.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
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6 changes: 3 additions & 3 deletions ot/optim.py
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Expand Up @@ -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:
Expand Down Expand Up @@ -275,7 +275,7 @@ def cost(G):

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:
Expand Down Expand Up @@ -413,7 +413,7 @@ def cost(G):


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
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