ac92dbec0facc37d2efa9e208c107055e081fe24,tensorly/backend/core.py,Backend,truncated_svd,#Backend#Any#Any#,798

Before Change

        else:
            min_dim = dim_2

        if n_eigenvecs is None or n_eigenvecs > min_dim:
            full_matrices = True
        else:
            full_matrices = False

        U, S, V = self.svd(matrix)
        U, S, V = U[:, :n_eigenvecs], S[:n_eigenvecs], V[:n_eigenvecs, :]
        return U, S, V

After Change

        full_matrices = (n_eigenvecs is None) or (n_eigenvecs > min_dim)

        U, S, V = self.svd(matrix, full_matrices=full_matrices)
        U, S, V = U[:, :n_eigenvecs], S[:n_eigenvecs], V[:n_eigenvecs, :]
        return U, S, V

    index = Index()

Instances