sparse_coding.py 文件源码

def _omp(x, D, Gram, alpha, n_nonzero_coefs=None, tol=None):
    _, n_atoms = D.shape
    # the dict indexes of the atoms this datapoint uses
    Dx = np.array([]).astype(int)
    z = np.zeros(n_atoms)
    # the residual
    r = np.copy(x)
    i = 0
    if n_nonzero_coefs is not None:
        tol = 1e-10
        def cont_criterion():
            not_reached_sparsity = i < n_nonzero_coefs
            return (not_reached_sparsity and norm(r) > tol)
    else:
        cont_criterion = lambda: norm(r) >= tol

    while (cont_criterion()):

        # find the atom that correlates the
        # most with the residual
        k = np.argmax(np.abs(alpha))
        if k in Dx:
            break
        Dx = np.append(Dx, k)
        # solve the Least Squares problem
        # to find the coefs z
        DI = D[:, Dx]
        G = Gram[Dx, :][:, Dx]
        G = np.atleast_2d(G)
        try:
            G_inv = inv(G)
        except LinAlgError:
            print gram_singular_msg
            break

        z[Dx] = np.dot(G_inv, np.dot(D.T, x)[Dx])
        r = x - np.dot(D[:, Dx], z[Dx])
        alpha = np.dot(D.T, r)
        i += 1

    return z