Python numpy.tril_indices_from方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril_indices_from [as 別名]
def test_design_r(self):
        design = simple_mv_velocity_design(3)
        batch_design = design.for_batch(2, 1)

        cov = batch_design.R(0)[0]
        self.assertTupleEqual(cov.size(), (3, 3))

        self.assertTrue(cov.requires_grad)
        cholesky_log_diag = design.measure_covariance.param_dict()['cholesky_log_diag']
        cholesky_off_diag = design.measure_covariance.param_dict()['cholesky_off_diag']

        cov = cov.data.numpy()
        self.assertTrue(np.isclose(cov, cov.T).all(), msg="Covariance is not symmetric.")
        chol = cholesky(cov)

        for a, b in zip(torch.exp(cholesky_log_diag).tolist(), np.diag(chol).tolist()):
            self.assertAlmostEqual(a, b, places=4)

        for a, b in zip(cholesky_off_diag.tolist(), chol[np.tril_indices_from(chol, k=-1)].tolist()):
            self.assertAlmostEqual(a, b, places=4) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril_indices_from [as 別名]
def get_matrix(self):
        """Return the current internal matrix.

        Returns
        -------
        M : ndarray, shape (n, n)
            Dense matrix containing either the Hessian or its inverse
            (depending on how `approx_type` was defined).
        """
        if self.approx_type == 'hess':
            M = np.copy(self.B)
        else:
            M = np.copy(self.H)
        li = np.tril_indices_from(M, k=-1)
        M[li] = M.T[li]
        return M 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril_indices_from [as 別名]
def cho_invert(A):
    """ Returns the inverse of a positive definite matrix, using a Cholesky decomposition
        via calls to LAPACK dpotrf and dpotri in the F2PY module.

        :param A: Matrix (symmetric and positive definite, left-hand side).
        :type A: numpy array

        :return: The inverse matrix
        :rtype: numpy array
    """

    if len(A.shape) != 2 or A.shape[0] != A.shape[1]:
        raise ValueError('expected square matrix')

    I = np.asfortranarray(A)

    fcho_invert(I)

    # Matrix to store the inverse
    i_lower = np.tril_indices_from(A)

    # Copy lower triangle to upper
    I.T[i_lower] = I[i_lower]

    return I 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril_indices_from [as 別名]
def bkf_invert(A):
    """ Returns the inverse of a positive definite matrix, using a Cholesky decomposition
        via calls to LAPACK dpotrf and dpotri in the F2PY module.

        :param A: Matrix (symmetric and positive definite, left-hand side).
        :type A: numpy array

        :return: The inverse matrix
        :rtype: numpy array
    """

    if len(A.shape) != 2 or A.shape[0] != A.shape[1]:
        raise ValueError('expected square matrix')

    I = np.asfortranarray(A)

    fbkf_invert(I)

    # Matrix to store the inverse
    i_lower = np.tril_indices_from(A)

    # Copy lower triangle to upper
    I.T[i_lower] = I[i_lower]

    return I 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril_indices_from [as 別名]
def test_tensor_iterator():
    a = np.arange(16).reshape((4, 4))
    test_tensor = Tensor(tensor=a)
    assert np.allclose(test_tensor.data, a)
    assert test_tensor.size == 16
    assert isinstance(test_tensor.basis, Bijection)

    a_triu = a[np.triu_indices_from(a)]
    a_tril = a[np.tril_indices_from(a)]

    counter = 0
    for val, idx in test_tensor.utri_iterator():
        assert val == a[tuple(idx)]
        assert val == a_triu[counter]
        counter += 1
    assert counter == 4 * (4 + 1) / 2

    counter = 0
    for val, idx in test_tensor.ltri_iterator():
        assert val == a[tuple(idx)]
        assert val == a_tril[counter]
        counter += 1
    assert counter == 4 * (4 + 1) / 2

    counter = 0
    for val, idx in test_tensor.all_iterator():
        assert val == a[tuple(idx)]
        counter += 1

    assert np.allclose(test_tensor.vectorize(), a.reshape((-1, 1), order='C'))

    with pytest.raises(TypeError):
        list(test_tensor._iterator('blah')) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril_indices_from [as 別名]
def read_self(self):
        logger.log(5, 'READING: {}'.format(self.filename))
        stuff = re.search(
            'Atomic numbers\s+I\s+N=\s+(?P<num_atoms>\d+)'
            '\n\s+(?P<anums>.*?)'
            'Nuclear charges.*?Current cartesian coordinates.*?\n(?P<coords>.*?)'
            'Force Field'
            '.*?Real atomic weights.*?\n(?P<masses>.*?)'
            'Atom fragment info.*?Cartesian Gradient.*?\n(?P<evals>.*?)'
            'Cartesian Force Constants.*?\n(?P<hess>.*?)'
            'Dipole Moment',
            open(self.path, 'r').read(), flags=re.DOTALL)
        anums = [int(x) for x in stuff.group('anums').split()]
        masses = [float(x) for x in stuff.group('masses').split()]
        coords = [float(x) for x in stuff.group('coords').split()]
        coords = [coords[i:i+3] for i in range(0, len(coords), 3)]
        for anum, mass, coord in zip(anums, masses, coords):
            self.atoms.append(
                Atom(
                    atomic_num = anum,
                    coords = coord,
                    exact_mass = mass)
                )
        logger.log(5, '  -- Read {} atoms.'.format(len(self.atoms)))
        self.evals = np.array(
            [float(x) for x in stuff.group('evals').split()], dtype=float)
        logger.log(5, '  -- Read {} eigenvectors.'.format(len(self.evals)))
        self.low_tri = np.array(
            [float(x) for x in stuff.group('hess').split()], dtype=float)
        one_dim = len(anums) * 3
        self._hess = np.empty([one_dim, one_dim], dtype=float)
        self._hess[np.tril_indices_from(self._hess)] = self.low_tri
        self._hess += np.tril(self._hess, -1).T
        # Convert to MacroModel units.
        self._hess *= co.HESSIAN_CONVERSION
        logger.log(5, '  -- Read {} Hessian.'.format(self._hess.shape)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril_indices_from [as 別名]
def _1d_to_2d(values, n):
    from numpy import zeros, tril_indices_from, tril

    K = zeros((n, n))
    K[tril_indices_from(K)] = values
    K = K + tril(K, -1).T
    return K 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril_indices_from [as 別名]
def cho_solve(A, y):
    """ Solves the equation

            :math:`A x = y`

        for x using a Cholesky decomposition  via calls to LAPACK dpotrf and dpotrs in the F2PY module. Preserves the input matrix A.

        :param A: Matrix (symmetric and positive definite, left-hand side).
        :type A: numpy array
        :param y: Vector (right-hand side of the equation).
        :type y: numpy array

        :return: The solution vector.
        :rtype: numpy array
        """

    if len(A.shape) != 2 or A.shape[0] != A.shape[1]:
        raise ValueError('expected square matrix')

    if len(y.shape) != 1 or y.shape[0] != A.shape[1]:
        raise ValueError('expected matrix and vector of same stride size')

    n = A.shape[0]

    # Backup diagonal before Cholesky-decomposition
    A_diag = A[np.diag_indices_from(A)]

    x = np.zeros(n)
    fcho_solve(A, y, x)

    # Reset diagonal after Cholesky-decomposition
    A[np.diag_indices_from(A)] = A_diag

    # Copy lower triangle to upper
    i_lower = np.tril_indices_from(A)
    A.T[i_lower] = A[i_lower]

    return x 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril_indices_from [as 別名]
def bkf_solve(A, y):
    """ Solves the equation

            :math:`A x = y`

        for x using a Cholesky decomposition  via calls to LAPACK dpotrf and dpotrs in the F2PY module. Preserves the input matrix A.

        :param A: Matrix (symmetric and positive definite, left-hand side).
        :type A: numpy array
        :param y: Vector (right-hand side of the equation).
        :type y: numpy array

        :return: The solution vector.
        :rtype: numpy array
        """

    if len(A.shape) != 2 or A.shape[0] != A.shape[1]:
        raise ValueError('expected square matrix')

    if len(y.shape) != 1 or y.shape[0] != A.shape[1]:
        raise ValueError('expected matrix and vector of same stride size')

    n = A.shape[0]

    # Backup diagonal before Cholesky-decomposition
    A_diag = A[np.diag_indices_from(A)]

    x = np.zeros(n)
    fbkf_solve(A, y, x)

    # Reset diagonal after Cholesky-decomposition
    A[np.diag_indices_from(A)] = A_diag

    # Copy lower triangle to upper
    i_lower = np.tril_indices_from(A)
    A.T[i_lower] = A[i_lower]

    return x 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril_indices_from [as 別名]
def _test_score(estimator, p_mat, graph):
    np.random.seed(8888)
    graph = graph.copy()
    p_mat = p_mat.copy()
    estimator.fit(graph)
    estimator.p_mat_ = p_mat  # hack just for testing likelihood

    if is_symmetric(graph):
        inds = np.triu_indices_from(graph, k=1)
    else:
        xu, yu = np.triu_indices_from(graph, k=1)
        xl, yl = np.tril_indices_from(graph, k=-1)
        x = np.concatenate((xl, xu))
        y = np.concatenate((yl, yu))
        inds = (x, y)

    p_rav = p_mat[inds]
    g_rav = graph[inds]

    lik = np.zeros(g_rav.shape)
    c = 1 / p_mat.size
    for i, (g, p) in enumerate(zip(g_rav, p_rav)):
        if p < c:
            p = c
        if p > 1 - c:
            p = 1 - c
        if g == 1:
            lik[i] = p
        else:
            lik[i] = 1 - p

    # lik = np.reshape(lik_rav, p_mat.shape)
    lik[lik < 1e-10] = 1
    lik = np.log(lik)
    assert_allclose(lik, estimator.score_samples(graph))
    assert np.sum(lik) == estimator.score(graph) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril_indices_from [as 別名]
def test_tril_indices_from(self):
        self.check(np.tril_indices_from) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril_indices_from [as 別名]
def slotted_autocorrelation(
        self, data, time, T, K, second_round=False, K1=100
    ):

        slots, i = np.zeros((K, 1)), 1

        # make time start from 0
        time = time - np.min(time)

        # subtract mean from mag values
        m = np.mean(data)
        data = data - m

        prod = np.zeros((K, 1))
        pairs = np.subtract.outer(time, time)
        pairs[np.tril_indices_from(pairs)] = 10000000

        ks = np.int64(np.floor(np.abs(pairs) / T + 0.5))

        # We calculate the slotted autocorrelation for k=0 separately
        idx = np.where(ks == 0)
        prod[0] = (sum(data ** 2) + sum(data[idx[0]] * data[idx[1]])) / (
            len(idx[0]) + len(data)
        )
        slots[0] = 0

        # We calculate it for the rest of the ks
        if second_round is False:
            for k in np.arange(1, K):
                idx = np.where(ks == k)
                if len(idx[0]) != 0:
                    prod[k] = sum(data[idx[0]] * data[idx[1]]) / (len(idx[0]))
                    slots[i] = k
                    i = i + 1
                else:
                    prod[k] = np.infty
        else:
            for k in np.arange(K1, K):
                idx = np.where(ks == k)
                if len(idx[0]) != 0:
                    prod[k] = sum(data[idx[0]] * data[idx[1]]) / (len(idx[0]))
                    slots[i - 1] = k
                    i = i + 1
                else:
                    prod[k] = np.infty
            np.trim_zeros(prod, trim="b")

        slots = np.trim_zeros(slots, trim="b")
        return prod / prod[0], np.int64(slots).flatten() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril_indices_from [as 別名]
def score_samples(self, graph, clip=None):
        """
        Compute the weighted log probabilities for each potential edge.

        Note that this implicitly assumes the input graph is indexed like the 
        fit model.

        Parameters
        ----------
        graph : np.ndarray
            Input graph. Must be same shape as model's ``p_mat_`` attribute
        
        clip : scalar or None, optional (default=None)
            Values for which to clip probability matrix, entries less than c or more
            than 1 - c are set to c or 1 - c, respectively.
            If None, values will not be clipped in the likelihood calculation, which may
            result in poorly behaved likelihoods depending on the model. 
        
        Returns
        -------
        sample_scores : np.ndarray (size of ``graph``)
            log-likelihood per potential edge in the graph
        """
        check_is_fitted(self, "p_mat_")
        # P.ravel() <dot> graph * (1 - P.ravel()) <dot> (1 - graph)
        graph = import_graph(graph)
        if not is_unweighted(graph):
            raise ValueError("Model only implemented for unweighted graphs")
        p_mat = self.p_mat_.copy()

        if np.shape(p_mat) != np.shape(graph):
            raise ValueError("Input graph size must be the same size as P matrix")

        inds = None
        if not self.directed and self.loops:
            inds = np.triu_indices_from(graph)  # ignore lower half of graph, symmetric
        elif not self.directed and not self.loops:
            inds = np.triu_indices_from(graph, k=1)  # ignore the diagonal
        elif self.directed and not self.loops:
            xu, yu = np.triu_indices_from(graph, k=1)
            xl, yl = np.tril_indices_from(graph, k=-1)
            x = np.concatenate((xl, xu))
            y = np.concatenate((yl, yu))
            inds = (x, y)
        if inds is not None:
            p_mat = p_mat[inds]
            graph = graph[inds]

        # clip the probabilities that are degenerate
        if clip is not None:
            p_mat[p_mat < clip] = clip
            p_mat[p_mat > 1 - clip] = 1 - clip

        # TODO: use nonzero inds here will be faster
        successes = np.multiply(p_mat, graph)
        failures = np.multiply((1 - p_mat), (1 - graph))
        likelihood = successes + failures
        return np.log(likelihood) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril_indices_from [as 別名]
def slotted_autocorrelation(self, data, time, T, K,
                                second_round=False, K1=100):

        slots = np.zeros((K, 1))
        i = 1

        # make time start from 0
        time = time - np.min(time)

        # subtract mean from mag values
        m = np.mean(data)
        data = data - m

        prod = np.zeros((K, 1))
        pairs = np.subtract.outer(time, time)
        pairs[np.tril_indices_from(pairs)] = 10000000

        ks = np.int64(np.floor(np.abs(pairs) / T + 0.5))

        # We calculate the slotted autocorrelation for k=0 separately
        idx = np.where(ks == 0)
        prod[0] = ((sum(data ** 2) + sum(data[idx[0]] *
                   data[idx[1]])) / (len(idx[0]) + len(data)))
        slots[0] = 0

        # We calculate it for the rest of the ks
        if second_round is False:
            for k in np.arange(1, K):
                idx = np.where(ks == k)
                if len(idx[0]) != 0:
                    prod[k] = sum(data[idx[0]] * data[idx[1]]) / (len(idx[0]))
                    slots[i] = k
                    i = i + 1
                else:
                    prod[k] = np.infty
        else:
            for k in np.arange(K1, K):
                idx = np.where(ks == k)
                if len(idx[0]) != 0:
                    prod[k] = sum(data[idx[0]] * data[idx[1]]) / (len(idx[0]))
                    slots[i - 1] = k
                    i = i + 1
                else:
                    prod[k] = np.infty
            np.trim_zeros(prod, trim='b')

        slots = np.trim_zeros(slots, trim='b')
        return prod / prod[0], np.int64(slots).flatten()