pytorch_backend.py 文件源码

def partial_svd(matrix, n_eigenvecs=None):
    """Computes a fast partial SVD on `matrix`

        if `n_eigenvecs` is specified, sparse eigendecomposition
        is used on either matrix.dot(matrix.T) or matrix.T.dot(matrix)

    Parameters
    ----------
    matrix : 2D-array
    n_eigenvecs : int, optional, default is None
        if specified, number of eigen[vectors-values] to return

    Returns
    -------
    U : 2D-array
        of shape (matrix.shape[0], n_eigenvecs)
        contains the right singular vectors
    S : 1D-array
        of shape (n_eigenvecs, )
        contains the singular values of `matrix`
    V : 2D-array
        of shape (n_eigenvecs, matrix.shape[1])
        contains the left singular vectors
    """
    # Check that matrix is... a matrix!
    if ndim(matrix) != 2:
        raise ValueError('matrix be a matrix. matrix.ndim is {} != 2'.format(
            ndim(matrix)))

    U, S, V = torch.svd(matrix, some=False)
    U, S, V = U[:, :n_eigenvecs], S[:n_eigenvecs], V.t()[:n_eigenvecs, :]
    return U, S, V