diff options
Diffstat (limited to 'ot/gromov.py')
| -rw-r--r-- | ot/gromov.py | 35 |
1 files changed, 22 insertions, 13 deletions
diff --git a/ot/gromov.py b/ot/gromov.py index ea667e4..2a70070 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -822,8 +822,12 @@ def GW_distance_estimation(C1, C2, p, q, loss_fun, T, index_k = np.zeros((nb_samples_p, nb_samples_q), dtype=int)
index_l = np.zeros((nb_samples_p, nb_samples_q), dtype=int)
- index_i = generator.choice(len_p, size=nb_samples_p, p=p, replace=False)
- index_j = generator.choice(len_p, size=nb_samples_p, p=p, replace=False)
+ index_i = generator.choice(
+ len_p, size=nb_samples_p, p=nx.to_numpy(p), replace=False
+ )
+ index_j = generator.choice(
+ len_p, size=nb_samples_p, p=nx.to_numpy(p), replace=False
+ )
for i in range(nb_samples_p):
if nx.issparse(T):
@@ -836,13 +840,13 @@ def GW_distance_estimation(C1, C2, p, q, loss_fun, T, index_k[i] = generator.choice(
len_q,
size=nb_samples_q,
- p=T_indexi / nx.sum(T_indexi),
+ p=nx.to_numpy(T_indexi / nx.sum(T_indexi)),
replace=True
)
index_l[i] = generator.choice(
len_q,
size=nb_samples_q,
- p=T_indexj / nx.sum(T_indexj),
+ p=nx.to_numpy(T_indexj / nx.sum(T_indexj)),
replace=True
)
@@ -934,15 +938,17 @@ def pointwise_gromov_wasserstein(C1, C2, p, q, loss_fun, index = np.zeros(2, dtype=int)
# Initialize with default marginal
- index[0] = generator.choice(len_p, size=1, p=p)
- index[1] = generator.choice(len_q, size=1, p=q)
+ index[0] = generator.choice(len_p, size=1, p=nx.to_numpy(p))
+ index[1] = generator.choice(len_q, size=1, p=nx.to_numpy(q))
T = nx.tocsr(emd_1d(C1[index[0]], C2[index[1]], a=p, b=q, dense=False))
best_gw_dist_estimated = np.inf
for cpt in range(max_iter):
- index[0] = generator.choice(len_p, size=1, p=p)
+ index[0] = generator.choice(len_p, size=1, p=nx.to_numpy(p))
T_index0 = nx.reshape(nx.todense(T[index[0], :]), (-1,))
- index[1] = generator.choice(len_q, size=1, p=T_index0 / T_index0.sum())
+ index[1] = generator.choice(
+ len_q, size=1, p=nx.to_numpy(T_index0 / T_index0.sum())
+ )
if alpha == 1:
T = nx.tocsr(
@@ -1071,13 +1077,16 @@ def sampled_gromov_wasserstein(C1, C2, p, q, loss_fun, C_are_symmetric = nx.allclose(C1, C1.T, rtol=1e-10, atol=1e-10) and nx.allclose(C2, C2.T, rtol=1e-10, atol=1e-10)
for cpt in range(max_iter):
- index0 = generator.choice(len_p, size=nb_samples_grad_p, p=p, replace=False)
+ index0 = generator.choice(
+ len_p, size=nb_samples_grad_p, p=nx.to_numpy(p), replace=False
+ )
Lik = 0
for i, index0_i in enumerate(index0):
- index1 = generator.choice(len_q,
- size=nb_samples_grad_q,
- p=T[index0_i, :] / nx.sum(T[index0_i, :]),
- replace=False)
+ index1 = generator.choice(
+ len_q, size=nb_samples_grad_q,
+ p=nx.to_numpy(T[index0_i, :] / nx.sum(T[index0_i, :])),
+ replace=False
+ )
# If the matrices C are not symmetric, the gradient has 2 terms, thus the term is chosen randomly.
if (not C_are_symmetric) and generator.rand(1) > 0.5:
Lik += nx.mean(loss_fun(
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