Python sparse.dot方法代碼示例

# 需要導入模塊: from theano import sparse [as 別名]
# 或者: from theano.sparse import dot [as 別名]
def test_dot_sparse_sparse(self):
        # test dot for 2 input sparse matrix
        sparse_dtype = 'float64'
        sp_mat = {'csc': sp.csc_matrix,
                  'csr': sp.csr_matrix,
                  'bsr': sp.csr_matrix}

        for sparse_format_a in ['csc', 'csr', 'bsr']:
            for sparse_format_b in ['csc', 'csr', 'bsr']:
                a = SparseType(sparse_format_a, dtype=sparse_dtype)()
                b = SparseType(sparse_format_b, dtype=sparse_dtype)()
                d = theano.dot(a, b)
                f = theano.function([a, b], theano.Out(d, borrow=True))
                topo = f.maker.fgraph.toposort()
                for M, N, K, nnz in [(4, 3, 2, 3),
                                     (40, 30, 20, 3),
                                     (40, 30, 20, 30),
                                     (400, 3000, 200, 6000),
                                 ]:
                    a_val = sp_mat[sparse_format_a](
                        random_lil((M, N), sparse_dtype, nnz))
                    b_val = sp_mat[sparse_format_b](
                        random_lil((N, K), sparse_dtype, nnz))
                    f(a_val, b_val) 

# 需要導入模塊: from theano import sparse [as 別名]
# 或者: from theano.sparse import dot [as 別名]
def test_csr_dense(self):
        x = theano.sparse.csr_matrix('x')
        y = theano.tensor.matrix('y')
        v = theano.tensor.vector('v')

        for (x, y, x_v, y_v) in [(x, y, self.x_csr, self.y),
                                 (x, v, self.x_csr, self.v_100),
                                 (v, x, self.v_10, self.x_csr)]:
            f_a = theano.function([x, y], theano.sparse.dot(x, y))
            f_b = lambda x, y: x * y

            utt.assert_allclose(f_a(x_v, y_v), f_b(x_v, y_v))

            # Test infer_shape
            self._compile_and_check([x, y], [theano.sparse.dot(x, y)],
                                    [x_v, y_v],
                                    (Dot, Usmm, UsmmCscDense)) 

# 需要導入模塊: from theano import sparse [as 別名]
# 或者: from theano.sparse import dot [as 別名]
def test_csc_dense(self):
        x = theano.sparse.csc_matrix('x')
        y = theano.tensor.matrix('y')
        v = theano.tensor.vector('v')

        for (x, y, x_v, y_v) in [(x, y, self.x_csc, self.y),
                                 (x, v, self.x_csc, self.v_100),
                                 (v, x, self.v_10, self.x_csc)]:

            f_a = theano.function([x, y], theano.sparse.dot(x, y))
            f_b = lambda x, y: x * y

            utt.assert_allclose(f_a(x_v, y_v), f_b(x_v, y_v))

            # Test infer_shape
            self._compile_and_check([x, y], [theano.sparse.dot(x, y)],
                                    [x_v, y_v],
                                    (Dot, Usmm, UsmmCscDense)) 

# 需要導入模塊: from theano import sparse [as 別名]
# 或者: from theano.sparse import dot [as 別名]
def test_int32_dtype(self):
        # Reported on the theano-user mailing-list:
        # https://groups.google.com/d/msg/theano-users/MT9ui8LtTsY/rwatwEF9zWAJ
        size = 9
        intX = 'int32'

        C = tensor.matrix('C', dtype=intX)
        I = tensor.matrix('I', dtype=intX)

        fI = I.flatten()
        data = tensor.ones_like(fI)
        indptr = tensor.arange(data.shape[0] + 1, dtype='int32')

        m1 = sparse.CSR(data, fI, indptr, (8, size))
        m2 = sparse.dot(m1, C)
        y = m2.reshape(shape=(2, 4, 9), ndim=3)

        f = theano.function(inputs=[I, C], outputs=y)
        i = numpy.asarray([[4, 3, 7, 7], [2, 8, 4, 5]], dtype=intX)
        a = numpy.asarray(numpy.random.randint(0, 100, (size, size)),
                          dtype=intX)
        f(i, a) 

# 需要導入模塊: from theano import sparse [as 別名]
# 或者: from theano.sparse import dot [as 別名]
def test_op_ss(self):
        for format in sparse.sparse_formats:
            for dtype in sparse.all_dtypes:
                variable, data = sparse_random_inputs(format,
                                                      shape=(10, 10),
                                                      out_dtype=dtype,
                                                      n=2,
                                                      p=0.1)

                f = theano.function(variable, self.op(*variable))

                tested = f(*data)

                x, y = [m.toarray() for m in data]
                expected = numpy.dot(x, y)

                assert tested.format == format
                assert tested.dtype == expected.dtype
                tested = tested.toarray()
                utt.assert_allclose(tested, expected) 

# 需要導入模塊: from theano import sparse [as 別名]
# 或者: from theano.sparse import dot [as 別名]
def test_op_sd(self):
        for format in sparse.sparse_formats:
            for dtype in sparse.all_dtypes:
                variable, data = sparse_random_inputs(format,
                                                      shape=(10, 10),
                                                      out_dtype=dtype,
                                                      n=2,
                                                      p=0.1)
                variable[1] = tensor.TensorType(dtype=dtype,
                                                broadcastable=(False, False))()
                data[1] = data[1].toarray()

                f = theano.function(variable, self.op(*variable))

                tested = f(*data)
                expected = numpy.dot(data[0].toarray(), data[1])

                assert tested.format == format
                assert tested.dtype == expected.dtype
                tested = tested.toarray()
                utt.assert_allclose(tested, expected) 

# 需要導入模塊: from theano import sparse [as 別名]
# 或者: from theano.sparse import dot [as 別名]
def squared_euclidean_distances(x_1, x_2):
        """
        Compute the euclidian distances in 3D between all the points in x_1 and x_2

        Args:
            x_1 (theano.tensor.matrix): shape n_points x number dimension
            x_2 (theano.tensor.matrix): shape n_points x number dimension

        Returns:
            theano.tensor.matrix: Distancse matrix. shape n_points x n_points
        """

        # T.maximum avoid negative numbers increasing stability
        sqd = T.sqrt(T.maximum(
            (x_1 ** 2).sum(1).reshape((x_1.shape[0], 1)) +
            (x_2 ** 2).sum(1).reshape((1, x_2.shape[0])) -
            2 * x_1.dot(x_2.T), 1e-12
        ))
        return sqd 

# 需要導入模塊: from theano import sparse [as 別名]
# 或者: from theano.sparse import dot [as 別名]
def extend_dual_kriging(self, weights, grid_shape):
        # TODO Think what object is worth to save to speed up computation
        """
        Tile the dual kriging vector to cover all the points to interpolate.So far I just make a matrix with the
        dimensions len(DK)x(grid) but in the future maybe I have to try to loop all this part so consume less memory

        Returns:
            theano.tensor.matrix: Matrix with the Dk parameters repeated for all the points to interpolate
        """
        DK_parameters = weights
        # Creation of a matrix of dimensions equal to the grid with the weights for every point (big 4D matrix in
        # ravel form)
        # TODO IMP: Change the tile by a simple dot op -> The DOT version in gpu is slower
        DK_weights = T.tile(DK_parameters, (grid_shape, 1)).T

        return DK_weights
    # endregion

    # region Evaluate Geology 

# 需要導入模塊: from theano import sparse [as 別名]
# 或者: from theano.sparse import dot [as 別名]
def select_finite_faults(self, grid):
        fault_points = T.vertical_stack(T.stack([self.ref_layer_points[0]], axis=0), self.rest_layer_points).T
        ctr = T.mean(fault_points, axis=1)
        x = fault_points - ctr.reshape((-1, 1))
        M = T.dot(x, x.T)
        U, D, V = T.nlinalg.svd(M)
        rotated_x = T.dot(T.dot(grid, U), V)
        rotated_fault_points = T.dot(T.dot(fault_points.T, U), V)
        rotated_ctr = T.mean(rotated_fault_points, axis=0)
        a_radius = (rotated_fault_points[:, 0].max() - rotated_fault_points[:, 0].min()) / 2
        b_radius = (rotated_fault_points[:, 1].max() - rotated_fault_points[:, 1].min()) / 2

        ellipse_factor = (rotated_x[:, 0] - rotated_ctr[0])**2 / a_radius**2 + \
            (rotated_x[:, 1] - rotated_ctr[1])**2 / b_radius**2

        if "select_finite_faults" in self.verbose:
            ellipse_factor = theano.printing.Print("h")(ellipse_factor)

        return ellipse_factor 

# 需要導入模塊: from theano import sparse [as 別名]
# 或者: from theano.sparse import dot [as 別名]
def P(self, lat, lon):
        """Compute the pixelization matrix, no filters or illumination."""
        # Get the Cartesian points
        xpt, ypt, zpt = self.latlon_to_xyz(lat, lon)

        # Compute the polynomial basis at the point
        pT = self.pT(xpt, ypt, zpt)[:, : (self.ydeg + 1) ** 2]

        # Transform to the Ylm basis
        pTA1 = ts.dot(pT, self.A1)

        # NOTE: The factor of `pi` ensures the correct normalization.
        # This is *different* from the derivation in the paper, but it's
        # due to the fact that the in starry we normalize the spherical
        # harmonics in a slightly strange way (they're normalized so that
        # the integral of Y_{0,0} over the unit sphere is 4, not 4pi).
        # This is useful for thermal light maps, where the flux from a map
        # with Y_{0,0} = 1 is *unity*. But it messes up things for reflected
        # light maps, so we need to account for that here.
        if self._reflected:
            pTA1 *= np.pi

        # We're done
        return pTA1 

# 需要導入模塊: from theano import sparse [as 別名]
# 或者: from theano.sparse import dot [as 別名]
def compute_ortho_grid_inc_obl(self, res, inc, obl):
        """Compute the polynomial basis on the plane of the sky, accounting
        for the map inclination and obliquity."""
        # See NOTE on tt.mgrid bug in `compute_ortho_grid`
        dx = 2.0 / (res - 0.01)
        y, x = tt.mgrid[-1:1:dx, -1:1:dx]
        z = tt.sqrt(1 - x ** 2 - y ** 2)
        y = tt.set_subtensor(y[tt.isnan(z)], np.nan)
        x = tt.reshape(x, [1, -1])
        y = tt.reshape(y, [1, -1])
        z = tt.reshape(z, [1, -1])
        Robl = self.RAxisAngle(tt.as_tensor_variable([0.0, 0.0, 1.0]), -obl)
        Rinc = self.RAxisAngle(
            tt.as_tensor_variable([tt.cos(obl), tt.sin(obl), 0.0]),
            -(0.5 * np.pi - inc),
        )
        R = tt.dot(Robl, Rinc)
        xyz = tt.dot(R, tt.concatenate((x, y, z)))
        x = tt.reshape(xyz[0], [1, -1])
        y = tt.reshape(xyz[1], [1, -1])
        z = tt.reshape(xyz[2], [1, -1])
        lat = tt.reshape(0.5 * np.pi - tt.arccos(y), [1, -1])
        lon = tt.reshape(tt.arctan2(x, z), [1, -1])
        return tt.concatenate((lat, lon)), tt.concatenate((x, y, z)) 

# 需要導入模塊: from theano import sparse [as 別名]
# 或者: from theano.sparse import dot [as 別名]
def test_csr_correct_output_faster_than_scipy(self):

        # contrast with test_grad, we put csr in float32, csc in float64

        sparse_dtype = 'float32'
        dense_dtype = 'float32'

        a = SparseType('csr', dtype=sparse_dtype)()
        b = tensor.matrix(dtype=dense_dtype)
        d = theano.dot(a, b)
        f = theano.function([a, b], d)

        for M, N, K, nnz in [(4, 3, 2, 3),
                             (40, 30, 20, 3),
                             (40, 30, 20, 30),
                             (400, 3000, 200, 6000),
                         ]:
            spmat = sp.csr_matrix(random_lil((M, N), sparse_dtype, nnz))
            mat = numpy.asarray(numpy.random.randn(N, K), dense_dtype)
            t0 = time.time()
            theano_result = f(spmat, mat)
            t1 = time.time()
            scipy_result = spmat * mat
            t2 = time.time()

            theano_time = t1 - t0
            scipy_time = t2 - t1
            # print 'theano took', theano_time,
            # print 'scipy took', scipy_time
            overhead_tol = 0.002  # seconds
            overhead_rtol = 1.1  # times as long
            utt.assert_allclose(scipy_result, theano_result)
            if (not theano.config.mode in ["DebugMode", "DEBUG_MODE"] and
                theano.config.cxx):
                    self.assertFalse(
                        theano_time > overhead_rtol * scipy_time + overhead_tol,
                        (theano_time,
                         overhead_rtol * scipy_time + overhead_tol,
                         scipy_time, overhead_rtol, overhead_tol)) 

# 需要導入模塊: from theano import sparse [as 別名]
# 或者: from theano.sparse import dot [as 別名]
def test_cuda(self):
        import theano.sandbox.cuda as cuda
        if not cuda.cuda_available:
            raise SkipTest("Optional package cuda not available")

        a = sparse.csr_matrix('a', dtype='float32')
        b = cuda.float32_shared_constructor(
            numpy.random.rand(3, 4).astype('float32'))
        d = sparse.dot(a, b)
        f = theano.function([a], d)

        a_val = scipy.sparse.csr_matrix(random_lil((5, 3), 'float32', 5))
        d_theano = f(a_val)
        d_numpy = a_val * b.get_value()
        utt.assert_allclose(d_numpy, d_theano) 

# 需要導入模塊: from theano import sparse [as 別名]
# 或者: from theano.sparse import dot [as 別名]
def multivariate_normal(datasets, weights, hyperparams, residuals):
    """
    Calculate posterior Likelihood of a Multivariate Normal distribution.
    Uses plain inverse of the covariances.
    DEPRECATED! Is currently not being used in beat.
    Can only be executed in a `with model context`.

    Parameters
    ----------
    datasets : list
        of :class:`heart.SeismicDataset` or :class:`heart.GeodeticDataset`
    weights : list
        of :class:`theano.shared`
        Square matrix of the inverse of the covariance matrix as weights
    hyperparams : dict
        of :class:`theano.`
    residual : list or array of model residuals

    Returns
    -------
    array_like
    """
    n_t = len(datasets)

    logpts = tt.zeros((n_t), tconfig.floatX)

    for l, data in enumerate(datasets):
        M = tt.cast(shared(
            data.samples, name='nsamples', borrow=True), 'int16')
        hp_name = get_hyper_name(data)
        norm = (M * (2 * hyperparams[hp_name] + log_2pi))
        logpts = tt.set_subtensor(
            logpts[l:l + 1],
            (-0.5) * (
                data.covariance.slog_pdet +
                norm +
                (1 / tt.exp(hyperparams[hp_name] * 2)) *
                (residuals[l].dot(weights[l]).dot(residuals[l].T))))

    return logpts 

# 需要導入模塊: from theano import sparse [as 別名]
# 或者: from theano.sparse import dot [as 別名]
def compRelationProbsFunc(self, xFeats):
        #  xFeats [l, h] matrix
        # xFeats = theano.printing.Print("xFeats")(xFeats)
        # self.Wb = theano.printing.Print("Wb ") (self.Wb)
        # self.W = theano.printing.Print("W ") (self.W)
        # scores of each role by a classifier
        relationScores = sparse.dot(xFeats, self.W) + self.Wb   # [l, h] x [h, r] => [l, r]
        #relationScores = theano.printing.Print("relationScores=")(relationScores)

        # convert it to probabilities
        relationProbs = T.nnet.softmax(relationScores)
        #relationProbs = theano.printing.Print("relationProbs = ")(relationProbs)


        return relationProbs  # [l, r]