Python numpy.asmatrix方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import asmatrix [as 别名]
def add_pixels(self, uv_px, img1d, weight=None):
        # Lookup row & column for each in-bounds coordinate.
        mask = self.get_mask(uv_px)
        xx = uv_px[0,mask]
        yy = uv_px[1,mask]
        # Update matrix according to assigned weight.
        if weight is None:
            img1d[mask] = self.img[yy,xx]
        elif np.isscalar(weight):
            img1d[mask] += self.img[yy,xx] * weight
        else:
            w1 = np.asmatrix(weight, dtype='float32')
            w3 = w1.transpose() * np.ones((1,3))
            img1d[mask] += np.multiply(self.img[yy,xx], w3[mask])


# A panorama image made from several FisheyeImage sources.
# TODO: Add support for supersampled anti-aliasing filters. 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import asmatrix [as 别名]
def transform(info, sin, sout, sxtra, board, opts, vars): 
     
    if vars['loaded']:	

        sess = vars['sess']
        x = vars['x']
        y = vars['y']
        ph_n_shuffle = vars['ph_n_shuffle']
        ph_n_repeat = vars['ph_n_repeat']
        ph_n_batch = vars['ph_n_batch']
        init = vars['init']
        logits = vars['logits']

        input = np.asmatrix(sin).reshape(-1, x.shape[1]) 

        dummy = np.zeros((input.shape[0],), dtype=np.int32)
        sess.run(init, feed_dict = { x : input, y : dummy, ph_n_shuffle : 1, ph_n_repeat : 1, ph_n_batch : input.shape[0] })    
        output = sess.run(logits)    
        output = np.mean(output, axis=0)

        for i in range(sout.dim):
            sout[i] = output[i] 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import asmatrix [as 别名]
def label_relevance_score(self,
                              topic_models,
                              pmi_w2l):
        """
        Calculate the relevance scores between each label and each topic

        Parameters:
        ---------------
        topic_models: numpy.ndarray(#topics, #words)
           the topic models

        pmi_w2l: numpy.ndarray(#words, #labels)
           the Point-wise Mutual Information(PMI) table of
           the form, PMI(w, l | C)
        
        Returns;
        -------------
        numpy.ndarray, shape (#topics, #labels)
            the scores of each label on each topic
        """
        assert topic_models.shape[1] == pmi_w2l.shape[0]
        return np.asarray(np.asmatrix(topic_models) *
                          np.asmatrix(pmi_w2l)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import asmatrix [as 别名]
def test_return_type(self):
        a = np.ones([2, 2])
        m = np.asmatrix(a)
        assert_equal(type(kron(a, a)), np.ndarray)
        assert_equal(type(kron(m, m)), np.matrix)
        assert_equal(type(kron(a, m)), np.matrix)
        assert_equal(type(kron(m, a)), np.matrix)

        class myarray(np.ndarray):
            __array_priority__ = 0.0

        ma = myarray(a.shape, a.dtype, a.data)
        assert_equal(type(kron(a, a)), np.ndarray)
        assert_equal(type(kron(ma, ma)), myarray)
        assert_equal(type(kron(a, ma)), np.ndarray)
        assert_equal(type(kron(ma, a)), myarray) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import asmatrix [as 别名]
def coherence_of_columns(A):
    """Mutual coherence of columns of A.

    Parameters
    ----------
    A : array_like
        Input matrix.
    p : int, optional
        p-th norm.

    Returns
    -------
    array_like
        Mutual coherence of columns of A.
    """
    A = np.asmatrix(A)
    _, N = A.shape
    A = A * np.asmatrix(np.diag(1/norm_of_columns(A)))
    Gram_A = A.H*A
    for j in range(N):
        Gram_A[j, j] = 0
    return np.max(np.abs(Gram_A)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import asmatrix [as 别名]
def get_correlated_geometric_brownian_motions(params: ModelParameters,
                                              correlation_matrix: np.array,
                                              n: int):
    """
    Constructs a basket of correlated asset paths using the Cholesky
    decomposition method.

    Arguments:
        params : ModelParameters
            The parameters for the stochastic model.
        correlation_matrix : np.array
            An n x n correlation matrix.
        n : int
            Number of assets (number of paths to return)

    Returns:
        n correlated log return geometric brownian motion processes
    """
    decomposition = sp.linalg.cholesky(correlation_matrix, lower=False)
    uncorrelated_paths = []
    sqrt_delta_sigma = np.sqrt(params.all_delta) * params.all_sigma
    # Construct uncorrelated paths to convert into correlated paths
    for i in range(params.all_time):
        uncorrelated_random_numbers = []
        for j in range(n):
            uncorrelated_random_numbers.append(random.normalvariate(0, sqrt_delta_sigma))
        uncorrelated_paths.append(np.array(uncorrelated_random_numbers))
    uncorrelated_matrix = np.asmatrix(uncorrelated_paths)
    correlated_matrix = uncorrelated_matrix * decomposition
    assert isinstance(correlated_matrix, np.matrix)
    # The rest of this method just extracts paths from the matrix
    extracted_paths = []
    for i in range(1, n + 1):
        extracted_paths.append([])
    for j in range(0, len(correlated_matrix) * n - n, n):
        for i in range(n):
            extracted_paths[i].append(correlated_matrix.item(j + i))
    return extracted_paths 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import asmatrix [as 别名]
def test_fancy_indexing():
    # The matrix class messes with the shape. While this is always
    # weird (getitem is not used, it does not have setitem nor knows
    # about fancy indexing), this tests gh-3110
    # 2018-04-29: moved here from core.tests.test_index.
    m = np.matrix([[1, 2], [3, 4]])

    assert_(isinstance(m[[0, 1, 0], :], np.matrix))

    # gh-3110. Note the transpose currently because matrices do *not*
    # support dimension fixing for fancy indexing correctly.
    x = np.asmatrix(np.arange(50).reshape(5, 10))
    assert_equal(x[:2, np.array(-1)], x[:2, -1].T) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import asmatrix [as 别名]
def test_asmatrix(self):
        A = np.arange(100).reshape(10, 10)
        mA = asmatrix(A)
        A[0, 0] = -10
        assert_(A[0, 0] == mA[0, 0]) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import asmatrix [as 别名]
def test_basic(self):
        x = asmatrix(np.zeros((3, 2), float))
        y = np.zeros((3, 1), float)
        y[:, 0] = [0.8, 0.2, 0.3]
        x[:, 1] = y > 0.5
        assert_equal(x, [[0, 1], [0, 0], [0, 0]]) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import asmatrix [as 别名]
def test_scalar_indexing(self):
        x = asmatrix(np.zeros((3, 2), float))
        assert_equal(x[0, 0], x[0][0]) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import asmatrix [as 别名]
def test_row_column_indexing(self):
        x = asmatrix(np.eye(2))
        assert_array_equal(x[0,:], [[1, 0]])
        assert_array_equal(x[1,:], [[0, 1]])
        assert_array_equal(x[:, 0], [[1], [0]])
        assert_array_equal(x[:, 1], [[0], [1]]) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import asmatrix [as 别名]
def test_boolean_indexing(self):
        A = np.arange(6)
        A.shape = (3, 2)
        x = asmatrix(A)
        assert_array_equal(x[:, np.array([True, False])], x[:, 0])
        assert_array_equal(x[np.array([True, False, False]),:], x[0,:]) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import asmatrix [as 别名]
def test_matrix_std_argmax(self):
        # Ticket #83
        x = np.asmatrix(np.random.uniform(0, 1, (3, 3)))
        assert_equal(x.std().shape, ())
        assert_equal(x.argmax().shape, ()) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import asmatrix [as 别名]
def improve_admm(x0, prob, *args, **kwargs):
    num_iters = kwargs.get('num_iters', 1000)
    viol_lim = kwargs.get('viol_lim', 1e4)
    tol = kwargs.get('tol', 1e-2)
    rho = kwargs.get('rho', None)
    phase1 = kwargs.get('phase1', True)

    if rho is not None:
        lmb0, P0Q = map(np.asmatrix, LA.eigh(prob.f0.P.todense()))
        lmb_min = np.min(lmb0)
        if lmb_min + prob.m*rho < 0:
            logging.error("rho parameter is too small, z-update not convex.")
            logging.error("Minimum possible value of rho: %.3f\n", -lmb_min/prob.m)
            logging.error("Given value of rho: %.3f\n", rho)
            raise Exception("rho parameter is too small, need at least %.3f." % rho)

    # TODO: find a reasonable auto parameter
    if rho is None:
        lmb0, P0Q = map(np.asmatrix, LA.eigh(prob.f0.P.todense()))
        lmb_min = np.min(lmb0)
        lmb_max = np.max(lmb0)
        if lmb_min < 0: rho = 2.*(1.-lmb_min)/prob.m
        else: rho = 1./prob.m
        rho *= 50.
        logging.warning("Automatically setting rho to %.3f", rho)

    if phase1:
        x1 = prob.better(x0, admm_phase1(x0, prob, tol, num_iters))
    else:
        x1 = x0
    x2 = prob.better(x1, admm_phase2(x1, prob, rho, tol, num_iters, viol_lim))
    return x2 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import asmatrix [as 别名]
def sum(self, axis=None, dtype=None, out=None):
        """Sum the matrix over the given axis.  If the axis is None, sum
        over both rows and columns, returning a scalar.
        """
        # The spmatrix base class already does axis=0 and axis=1 efficiently
        # so we only do the case axis=None here
        if (not hasattr(self, 'blocksize') and
                    axis in self._swap(((1, -1), (0, 2)))[0]):
            # faster than multiplication for large minor axis in CSC/CSR
            res_dtype = get_sum_dtype(self.dtype)
            ret = np.zeros(len(self.indptr) - 1, dtype=res_dtype)

            major_index, value = self._minor_reduce(np.add)
            ret[major_index] = value
            ret = np.asmatrix(ret)
            if axis % 2 == 1:
                ret = ret.T

            if out is not None and out.shape != ret.shape:
                raise ValueError('dimensions do not match')

            return ret.sum(axis=(), dtype=dtype, out=out)
        # spmatrix will handle the remaining situations when axis
        # is in {None, -1, 0, 1}
        else:
            return spmatrix.sum(self, axis=axis, dtype=dtype, out=out)