update default value

Author: fegounna

ot/low_rank/_factor_relaxation.py

@@ -215,42 +215,3 @@ def solve_balanced_FRLC(
             return nx.dot(nx.dot(Q_new, X_new), R_new.T)  # Shape (n, m)
 
         Q, R, T, X = Q_new, R_new, T_new, X_new
-
-
-if __name__ == "__main__":
-    import torch
-
-    grid_size = 4
-    torch.manual_seed(42)
-    x_vals = torch.linspace(0, 3, grid_size)
-    y_vals = torch.linspace(0, 3, grid_size)
-    X, Y = torch.meshgrid(x_vals, y_vals, indexing="ij")
-    source_points = torch.stack([X.ravel(), Y.ravel()], dim=-1)  # (16, 2)
-    a = torch.ones(len(source_points)) / len(source_points)  # Uniform distribution
-
-    # Generate Target Distribution (Gaussian Samples)
-    mean = torch.tensor([2.0, 2.0])
-    cov = torch.tensor([[1.0, 0.5], [0.5, 1.0]])
-    target_points = torch.distributions.MultivariateNormal(
-        mean, covariance_matrix=cov
-    ).sample((len(source_points),))  # (16, 2)
-    b = torch.ones(len(target_points)) / len(target_points)  # Uniform distribution
-
-    # Compute Cost Matrix (Squared Euclidean Distance)
-    C = torch.cdist(source_points, target_points, p=2) ** 2
-
-    # Solve OT problem (assuming you have PyTorch versions of these functions)
-    print(type(a.numpy()))
-    P = solve_balanced_FRLC(
-        a.to(torch.float64),
-        b.to(torch.float64),
-        C.to(torch.float64),
-        10,
-        tau=1e2,
-        gamma=1e2,
-        stopThr=1e-7,
-        numItermax=100,
-        log=True,
-    )
-    P = sinkhorn(a, b, C, reg=1)
-    print(torch.sum(P * C))