Python sparse.tril方法代碼示例

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import tril [as 別名]
def get_graph_tool_from_adjacency(adjacency, directed=None):
    """Get graph_tool graph from adjacency matrix."""
    import graph_tool as gt
    adjacency_edge_list = adjacency
    if not directed:
        from scipy.sparse import tril
        adjacency_edge_list = tril(adjacency)
    g = gt.Graph(directed=directed)
    g.add_vertex(adjacency.shape[0])  # this adds adjacency.shap[0] vertices
    g.add_edge_list(np.transpose(adjacency_edge_list.nonzero()))
    weights = g.new_edge_property('double')
    for e in g.edges():
        # graph_tool uses the following convention,
        # which is opposite to the rest of scanpy
        weights[e] = adjacency[int(e.source()), int(e.target())]
    g.edge_properties['weight'] = weights
    return g 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import tril [as 別名]
def test_topological_nodes(index_dtype, n=100):
    g = dgl.DGLGraph()
    a = sp.random(n, n, 3 / n, data_rvs=lambda n: np.ones(n))
    b = sp.tril(a, -1).tocoo()
    g.from_scipy_sparse_matrix(b)
    if index_dtype == 'int32':
        g = dgl.graph(g.edges()).int()
    else:
        g = dgl.graph(g.edges()).long()

    layers_dgl = dgl.topological_nodes_generator(g)

    adjmat = g.adjacency_matrix()
    def tensor_topo_traverse():
        n = g.number_of_nodes()
        mask = F.copy_to(F.ones((n, 1)), F.cpu())
        degree = F.spmm(adjmat, mask)
        while F.reduce_sum(mask) != 0.:
            v = F.astype((degree == 0.), F.float32)
            v = v * mask
            mask = mask - v
            frontier = F.copy_to(F.nonzero_1d(F.squeeze(v, 1)), F.cpu())
            yield frontier
            degree -= F.spmm(adjmat, v)

    layers_spmv = list(tensor_topo_traverse())

    assert len(layers_dgl) == len(layers_spmv)
    assert all(toset(x) == toset(y) for x, y in zip(layers_dgl, layers_spmv)) 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import tril [as 別名]
def _triangle_matrix(mat: dok_matrix) -> dok_matrix:
        lower = tril(mat, -1, format='dok')
        # `todok` is necessary because subtraction results in other format
        return (mat + lower.transpose() - lower).todok() 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import tril [as 別名]
def test_triul(shape, k):
    s = sparse.random(shape, density=0.5)
    x = s.todense()

    assert_eq(np.triu(x, k), sparse.triu(s, k))
    assert_eq(np.tril(x, k), sparse.tril(s, k)) 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import tril [as 別名]
def tril(x, k=0):
    """
    Returns an array with all elements above the k-th diagonal set to zero.

    Parameters
    ----------
    x : COO
        The input array.
    k : int, optional
        The diagonal above which elements are set to zero. The default is
        zero, which corresponds to the main diagonal.

    Returns
    -------
    COO
        The output lower-triangular matrix.

    Raises
    ------
    ValueError
        If :code:`x` doesn't have zero fill-values.

    See Also
    --------
    numpy.tril : NumPy equivalent function
    """
    from .core import COO

    check_zero_fill_value(x)

    if not x.ndim >= 2:
        raise NotImplementedError(
            "sparse.tril is not implemented for scalars or 1-D arrays."
        )

    mask = x.coords[-2] + k >= x.coords[-1]

    coords = x.coords[:, mask]
    data = x.data[mask]

    return COO(coords, data, shape=x.shape, has_duplicates=False, sorted=True) 

# 需要導入模塊: from scipy import sparse [as 別名]
# 或者: from scipy.sparse import tril [as 別名]
def get_adjacency_sparse(self, attribute=None):
        """Returns the adjacency matrix of a graph as scipy csr matrix.
        @param attribute: if C{None}, returns the ordinary adjacency
          matrix. When the name of a valid edge attribute is given
          here, the matrix returned will contain the default value
          at the places where there is no edge or the value of the
          given attribute where there is an edge.
        @return: the adjacency matrix as a L{scipy.sparse.csr_matrix}."""
        try:
            from scipy import sparse
        except ImportError:
            raise ImportError('You should install scipy package in order to use this function')
        import numpy as np

        edges = self.get_edgelist()
        if attribute is None:
            weights = [1] * len(edges)
        else:
            if attribute not in self.es.attribute_names():
                raise ValueError("Attribute does not exist")

            weights = self.es[attribute]

        N = self.vcount()
        mtx = sparse.csr_matrix((weights, zip(*edges)), shape=(N, N))

        if not self.is_directed():
            mtx = mtx + sparse.triu(mtx, 1).T + sparse.tril(mtx, -1).T
        return mtx