Python scipy.sparse方法代碼示例

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import sparse [as 別名]
def getOffsets(width, coords=None):
    """Get the offset and slices for a sparse band diagonal array
    For an operator that interacts with its neighbors we want a band diagonal matrix,
    where each row describes the 8 pixels that are neighbors for the reference pixel
    (the diagonal). Regardless of the operator, these 8 bands are always the same,
    so we make a utility function that returns the offsets (passed to scipy.sparse.diags).
    See `diagonalizeArray` for more on the slices and format of the array used to create
    NxN operators that act on a data vector.
    """
    # Use the neighboring pixels by default
    if coords is None:
        coords = [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)]
    offsets = [width * y + x for y, x in coords]
    slices = [slice(None, s) if s < 0 else slice(s, None) for s in offsets]
    slicesInv = [slice(-s, None) if s < 0 else slice(None, -s) for s in offsets]
    return offsets, slices, slicesInv 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import sparse [as 別名]
def comp_aos_csr(self, coords, tol=1e-8, ram=160e6):
    """ 
          Compute the atomic orbitals for a given set of (Cartesian) coordinates.
        The sparse format CSR is used for output and the computation is organized block-wise.
        Thence, larger molecules can be tackled right away
          coords :: set of Cartesian coordinates
          tol :: tolerance for dropping the values 
          ram :: size of the allowed block (in bytes)
        Returns 
          co2v :: CSR matrix of shape (coordinate, atomic orbital) 
    """
    from pyscf.nao.m_aos_libnao import aos_libnao
    from pyscf import lib
    from scipy.sparse import csr_matrix
    if not self.init_sv_libnao_orbs : raise RuntimeError('not self.init_sv_libnao')
    assert coords.shape[-1] == 3
    nc,no = len(coords), self.norbs
    bsize = int(min(max(ram / (no*8.0), 1), nc))
    co2v = csr_matrix((nc,no))
    for s,f in lib.prange(0,nc,bsize):
      ca2o = aos_libnao(coords[s:f], no) # compute values of atomic orbitals
      ab = np.where(abs(ca2o)>tol)
      co2v += csr_matrix((ca2o[ab].reshape(-1), (ab[0]+s, ab[1])), shape=(nc,no))
    return co2v 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import sparse [as 別名]
def _sparse_series_to_coo(ss, row_levels=(0, ), column_levels=(1, ),
                          sort_labels=False):
    """ Convert a SparseSeries to a scipy.sparse.coo_matrix using index
    levels row_levels, column_levels as the row and column
    labels respectively. Returns the sparse_matrix, row and column labels.
    """

    import scipy.sparse

    if ss.index.nlevels < 2:
        raise ValueError('to_coo requires MultiIndex with nlevels > 2')
    if not ss.index.is_unique:
        raise ValueError('Duplicate index entries are not allowed in to_coo '
                         'transformation.')

    # to keep things simple, only rely on integer indexing (not labels)
    row_levels = [ss.index._get_level_number(x) for x in row_levels]
    column_levels = [ss.index._get_level_number(x) for x in column_levels]

    v, i, j, rows, columns = _to_ijv(ss, row_levels=row_levels,
                                     column_levels=column_levels,
                                     sort_labels=sort_labels)
    sparse_matrix = scipy.sparse.coo_matrix(
        (v, (i, j)), shape=(len(rows), len(columns)))
    return sparse_matrix, rows, columns 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import sparse [as 別名]
def __init__(self, i, j, row_start, column_start, inverse, matrix):
        """
        Parameters:
            i(int): The row-block index of the covariance matrix block.
            j(int): The column-block index of the covariance matrix block.
            row_start(int): Row index of the left- and uppermost element in
                in the block.
            column_start(int): Column index of the left- and uppermost element
                in the block
            inverse(bool): Flag indicating whether the block is part of the
                inverse of the covariance matrix or not.
            matrix(np.ndarray or sp.sparse): The matrix of which the block
                consists.

        """
        self._i = i
        self._j = j
        self._row_start    = row_start
        self._column_start = column_start
        self._inverse = inverse
        self._matrix = matrix

    #
    # Read-only properties
    # 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import sparse [as 別名]
def to_dense(self):
        """Conversion to dense representation.

        Converts the covariance matrix to a 2-dimensional numpy.ndarray.

        Returns:
            The covariance matrix as dense matrix.

        """
        m = max([b.row_start + b.matrix.shape[0] for b in self.blocks])
        n = max([b.column_start + b.matrix.shape[1] for b in self.blocks])
        mat = np.zeros((m, n))
        for b in self.blocks:
            m0 = b.row_start
            n0 = b.column_start
            dm = b.matrix.shape[0]
            dn = b.matrix.shape[1]
            if sp.sparse.issparse(b.matrix):
                mat[m0 : m0 + dm, n0 : n0 + dn] = b.matrix.toarray()
            else:
                mat[m0 : m0 + dm, n0 : n0 + dn] = b.matrix
        return mat 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import sparse [as 別名]
def to_scipy_sparse_matrix(edge_index, edge_attr=None, num_nodes=None):
    r"""Converts a graph given by edge indices and edge attributes to a scipy
    sparse matrix.

    Args:
        edge_index (LongTensor): The edge indices.
        edge_attr (Tensor, optional): Edge weights or multi-dimensional
            edge features. (default: :obj:`None`)
        num_nodes (int, optional): The number of nodes, *i.e.*
            :obj:`max_val + 1` of :attr:`index`. (default: :obj:`None`)
    """
    row, col = edge_index.cpu()

    if edge_attr is None:
        edge_attr = torch.ones(row.size(0))
    else:
        edge_attr = edge_attr.view(-1).cpu()
        assert edge_attr.size(0) == row.size(0)

    N = maybe_num_nodes(edge_index, num_nodes)
    out = scipy.sparse.coo_matrix((edge_attr, (row, col)), (N, N))
    return out 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import sparse [as 別名]
def get_grad_operator(mask):
    """Returns sparse matrix computing horizontal, vertical, and two diagonal gradients."""
    horizontal_left = np.ravel_multi_index(np.nonzero(mask[:, :-1] | mask[:, 1:]), mask.shape)
    horizontal_right = horizontal_left + 1

    vertical_top = np.ravel_multi_index(np.nonzero(mask[:-1, :] | mask[1:, :]), mask.shape)
    vertical_bottom = vertical_top + mask.shape[1]

    diag_main_1 = np.ravel_multi_index(np.nonzero(mask[:-1, :-1] | mask[1:, 1:]), mask.shape)
    diag_main_2 = diag_main_1 + mask.shape[1] + 1

    diag_sub_1 = np.ravel_multi_index(np.nonzero(mask[:-1, 1:] | mask[1:, :-1]), mask.shape) + 1
    diag_sub_2 = diag_sub_1 + mask.shape[1] - 1

    indices = np.stack((
        np.concatenate((horizontal_left, vertical_top, diag_main_1, diag_sub_1)),
        np.concatenate((horizontal_right, vertical_bottom, diag_main_2, diag_sub_2))
    ), axis=-1)
    return scipy.sparse.coo_matrix(
        (np.tile([-1, 1], len(indices)), (np.arange(indices.size) // 2, indices.flatten())),
        shape=(len(indices), mask.size)) 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import sparse [as 別名]
def testGammalnExecution(self):
        raw = np.random.rand(10, 8, 6)
        a = tensor(raw, chunk_size=3)

        r = gammaln(a)

        result = self.executor.execute_tensor(r, concat=True)[0]
        expected = scipy_gammaln(raw)

        np.testing.assert_array_equal(result, expected)

        # test sparse
        raw = sps.csr_matrix(np.array([0, 1.0, 1.01, np.nan]))
        a = tensor(raw, chunk_size=3)

        r = gammaln(a)

        result = self.executor.execute_tensor(r, concat=True)[0]

        data = scipy_gammaln(raw.data)
        expected = sps.csr_matrix((data, raw.indices, raw.indptr), raw.shape)

        np.testing.assert_array_equal(result.toarray(), expected.toarray()) 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import sparse [as 別名]
def testErfExecution(self):
        raw = np.random.rand(10, 8, 6)
        a = tensor(raw, chunk_size=3)

        r = erf(a)

        result = self.executor.execute_tensor(r, concat=True)[0]
        expected = scipy_erf(raw)

        np.testing.assert_array_equal(result, expected)

        # test sparse
        raw = sps.csr_matrix(np.array([0, 1.0, 1.01, np.nan]))
        a = tensor(raw, chunk_size=3)

        r = erf(a)

        result = self.executor.execute_tensor(r, concat=True)[0]

        data = scipy_erf(raw.data)
        expected = sps.csr_matrix((data, raw.indices, raw.indptr), raw.shape)

        np.testing.assert_array_equal(result.toarray(), expected.toarray()) 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import sparse [as 別名]
def testEntrExecution(self):
        raw = np.random.rand(10, 8, 6)
        a = tensor(raw, chunk_size=3)

        r = entr(a)

        result = self.executor.execute_tensor(r, concat=True)[0]
        expected = scipy_entr(raw)

        np.testing.assert_array_equal(result, expected)

        # test sparse
        raw = sps.csr_matrix(np.array([0, 1.0, 1.01, np.nan]))
        a = tensor(raw, chunk_size=3)

        r = entr(a)

        result = self.executor.execute_tensor(r, concat=True)[0]

        data = scipy_entr(raw.data)
        expected = sps.csr_matrix((data, raw.indices, raw.indptr), raw.shape)

        np.testing.assert_array_equal(result.toarray(), expected.toarray()) 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import sparse [as 別名]
def testRelEntrExecution(self):
        raw1 = np.random.rand(4, 3, 2)
        raw2 = np.random.rand(4, 3, 2)
        a = tensor(raw1, chunk_size=3)
        b = tensor(raw2, chunk_size=3)

        r = rel_entr(a, b)

        result = self.executor.execute_tensor(r, concat=True)[0]
        expected = scipy_rel_entr(raw1, raw2)

        np.testing.assert_array_equal(result, expected)

        # test sparse
        raw1 = sps.csr_matrix(np.array([0, 1.0, 1.01, np.nan] * 3).reshape(4, 3))
        a = tensor(raw1, chunk_size=3)
        raw2 = np.random.rand(4, 3)
        b = tensor(raw2, chunk_size=3)

        r = rel_entr(a, b)

        result = self.executor.execute_tensor(r, concat=True)[0]

        expected = scipy_rel_entr(raw1.toarray(), raw2)
        np.testing.assert_array_equal(result.toarray(), expected) 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import sparse [as 別名]
def laplacian(W, normalized=True):
    """Return graph Laplacian"""

    # Degree matrix.
    d = W.sum(axis=0)

    # Laplacian matrix.
    if not normalized:
        D = scipy.sparse.diags(d.A.squeeze(), 0)
        L = D - W
    else:
        d += np.spacing(np.array(0, W.dtype))
        d = 1 / np.sqrt(d)
        D = scipy.sparse.diags(d.A.squeeze(), 0)
        I = scipy.sparse.identity(d.size, dtype=W.dtype)
        L = I - D * W * D

    assert np.abs(L - L.T).mean() < 1e-9
    assert type(L) is scipy.sparse.csr.csr_matrix
    return L 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import sparse [as 別名]
def setup(self, x, f, func):
        Jacobian.setup(self, x, f, func)
        self.x0 = x
        self.f0 = f
        self.op = scipy.sparse.linalg.aslinearoperator(self)

        if self.rdiff is None:
            self.rdiff = np.finfo(x.dtype).eps ** (1./2)

        self._update_diff_step()

        # Setup also the preconditioner, if possible
        if self.preconditioner is not None:
            if hasattr(self.preconditioner, 'setup'):
                self.preconditioner.setup(x, f, func)


#------------------------------------------------------------------------------
# Wrapper functions
#------------------------------------------------------------------------------ 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import sparse [as 別名]
def array_from_header(self, hdr, process=True):
        mclass = hdr.mclass
        if mclass == mxFULL_CLASS:
            arr = self.read_full_array(hdr)
        elif mclass == mxCHAR_CLASS:
            arr = self.read_char_array(hdr)
            if process and self.chars_as_strings:
                arr = chars_to_strings(arr)
        elif mclass == mxSPARSE_CLASS:
            # no current processing (below) makes sense for sparse
            return self.read_sparse_array(hdr)
        else:
            raise TypeError('No reader for class code %s' % mclass)
        if process and self.squeeze_me:
            return squeeze_element(arr)
        return arr 

# 需要導入模塊: import scipy [as 別名]
# 或者: from scipy import sparse [as 別名]
def read_full_array(self, hdr):
        ''' Full (rather than sparse) matrix getter

        Read matrix (array) can be real or complex

        Parameters
        ----------
        hdr : ``VarHeader4`` instance

        Returns
        -------
        arr : ndarray
            complex array if ``hdr.is_complex`` is True, otherwise a real
            numeric array
        '''
        if hdr.is_complex:
            # avoid array copy to save memory
            res = self.read_sub_array(hdr, copy=False)
            res_j = self.read_sub_array(hdr, copy=False)
            return res + (res_j * 1j)
        return self.read_sub_array(hdr)