Python networkx.floyd_warshall_numpy方法代码示例

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import floyd_warshall_numpy [as 别名]
def get_autocorrelation(self, atoms):
        """Return the autocorrelation fingerprint for a molecule."""
        connectivity = atoms.connectivity

        G = nx.Graph(connectivity)
        distance_matrix = nx.floyd_warshall_numpy(G)
        Bm = np.zeros(distance_matrix.shape)

        n = len(self.parameters)
        W = list_mendeleev_params(atoms.numbers, self.parameters).T

        fingerprint = np.zeros(n * (self.dstar + 1))
        for dd in range(self.dstar + 1):
            B = Bm.copy()
            B[distance_matrix == dd] = 1
            AC = np.dot(np.dot(W, B), W.T).diagonal()
            fingerprint[n * dd:n * (dd + 1)] = AC

        return fingerprint 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import floyd_warshall_numpy [as 别名]
def __init__(self, graph: nx.Graph, seed: Union[int, np.random.Generator] = None) -> None:
        """Construct an ApproximateTokenSwapping object.

        Args:
            graph (nx.Graph): Undirected graph represented a coupling map.
            seed (Union[int, np.random.default_rng]): Seed to use for random trials.
        """
        self.graph = graph
        # We need to fix the mapping from nodes in graph to nodes in shortest_paths.
        # The nodes in graph don't have to integer nor contiguous, but those in a NumPy array are.
        nodelist = list(graph.nodes())
        self.node_map = {node: i for i, node in enumerate(nodelist)}
        self.shortest_paths = nx.floyd_warshall_numpy(graph, nodelist=nodelist)
        if isinstance(seed, np.random.Generator):
            self.seed = seed
        else:
            self.seed = np.random.default_rng(seed) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import floyd_warshall_numpy [as 别名]
def autocorrelation(
        atoms=None,
        atoms_parameters=None,
        connectivity=None,
        d=0):
    """Autocorrelation convolution for systems without pbc."""
    G = nx.Graph(connectivity)
    D = nx.floyd_warshall_numpy(G)
    S = np.zeros_like(D)
    S[D == d] = 1

    AC = np.dot(np.dot(atoms_parameters, S), atoms_parameters.T).diagonal()

    return AC 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import floyd_warshall_numpy [as 别名]
def test_cycle_numpy(self):
        dist = nx.floyd_warshall_numpy(nx.cycle_graph(7))
        assert_equal(dist[0,3],3)
        assert_equal(dist[0,4],3) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import floyd_warshall_numpy [as 别名]
def test_weighted_numpy(self):
        XG3=nx.Graph()
        XG3.add_weighted_edges_from([ [0,1,2],[1,2,12],[2,3,1],
                                      [3,4,5],[4,5,1],[5,0,10] ])
        dist = nx.floyd_warshall_numpy(XG3)
        assert_equal(dist[0,3],15) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import floyd_warshall_numpy [as 别名]
def test_weight_parameter_numpy(self):
        XG4 = nx.Graph()
        XG4.add_edges_from([ (0, 1, {'heavy': 2}), (1, 2, {'heavy': 2}),
                             (2, 3, {'heavy': 1}), (3, 4, {'heavy': 1}),
                             (4, 5, {'heavy': 1}), (5, 6, {'heavy': 1}),
                             (6, 7, {'heavy': 1}), (7, 0, {'heavy': 1}) ])
        dist = nx.floyd_warshall_numpy(XG4, weight='heavy')
        assert_equal(dist[0, 2], 4) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import floyd_warshall_numpy [as 别名]
def test_directed_cycle_numpy(self):
        G = nx.DiGraph()
        G.add_cycle([0,1,2,3])
        pred,dist = nx.floyd_warshall_predecessor_and_distance(G)
        D = nx.utils.dict_to_numpy_array(dist)
        assert_equal(nx.floyd_warshall_numpy(G),D) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import floyd_warshall_numpy [as 别名]
def test_cycle_numpy(self):
        dist = nx.floyd_warshall_numpy(nx.cycle_graph(7))
        assert_equal(dist[0, 3], 3)
        assert_equal(dist[0, 4], 3) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import floyd_warshall_numpy [as 别名]
def test_weighted_numpy_three_edges(self):
        XG3 = nx.Graph()
        XG3.add_weighted_edges_from([[0, 1, 2], [1, 2, 12], [2, 3, 1],
                                     [3, 4, 5], [4, 5, 1], [5, 0, 10]])
        dist = nx.floyd_warshall_numpy(XG3)
        assert_equal(dist[0, 3], 15) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import floyd_warshall_numpy [as 别名]
def test_weighted_numpy_two_edges(self):
        XG4 = nx.Graph()
        XG4.add_weighted_edges_from([[0, 1, 2], [1, 2, 2], [2, 3, 1],
                                     [3, 4, 1], [4, 5, 1], [5, 6, 1],
                                     [6, 7, 1], [7, 0, 1]])
        dist = nx.floyd_warshall_numpy(XG4)
        assert_equal(dist[0, 2], 4) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import floyd_warshall_numpy [as 别名]
def test_weight_parameter_numpy(self):
        XG4 = nx.Graph()
        XG4.add_edges_from([(0, 1, {'heavy': 2}), (1, 2, {'heavy': 2}),
                            (2, 3, {'heavy': 1}), (3, 4, {'heavy': 1}),
                            (4, 5, {'heavy': 1}), (5, 6, {'heavy': 1}),
                            (6, 7, {'heavy': 1}), (7, 0, {'heavy': 1})])
        dist = nx.floyd_warshall_numpy(XG4, weight='heavy')
        assert_equal(dist[0, 2], 4) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import floyd_warshall_numpy [as 别名]
def test_directed_cycle_numpy(self):
        G = nx.DiGraph()
        nx.add_cycle(G, [0, 1, 2, 3])
        pred, dist = nx.floyd_warshall_predecessor_and_distance(G)
        D = nx.utils.dict_to_numpy_array(dist)
        assert_equal(nx.floyd_warshall_numpy(G), D) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import floyd_warshall_numpy [as 别名]
def test_directed_cycle_numpy(self):
        G = nx.DiGraph()
        nx.add_cycle(G, [0, 1, 2, 3])
        pred,dist = nx.floyd_warshall_predecessor_and_distance(G)
        D = nx.utils.dict_to_numpy_array(dist)
        assert_equal(nx.floyd_warshall_numpy(G),D) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import floyd_warshall_numpy [as 别名]
def compare(self, g_1, g_2, verbose=False):
        """Compute the kernel value (similarity) between two graphs.

        Parameters
        ----------
        g1 : networkx.Graph
            First graph.
        g2 : networkx.Graph
            Second graph.

        Returns
        -------
        k : The similarity value between g1 and g2.
        """
        # Diagonal superior matrix of the floyd warshall shortest
        # paths:
        fwm1 = np.array(nx.floyd_warshall_numpy(g_1))
        fwm1 = np.where(fwm1 == np.inf, 0, fwm1)
        fwm1 = np.where(fwm1 == np.nan, 0, fwm1)
        fwm1 = np.triu(fwm1, k=1)
        bc1 = np.bincount(fwm1.reshape(-1).astype(int))

        fwm2 = np.array(nx.floyd_warshall_numpy(g_2))
        fwm2 = np.where(fwm2 == np.inf, 0, fwm2)
        fwm2 = np.where(fwm2 == np.nan, 0, fwm2)
        fwm2 = np.triu(fwm2, k=1)
        bc2 = np.bincount(fwm2.reshape(-1).astype(int))

        # Copy into arrays with the same length the non-zero shortests
        # paths:
        v1 = np.zeros(max(len(bc1), len(bc2)) - 1)
        v1[range(0, len(bc1)-1)] = bc1[1:]

        v2 = np.zeros(max(len(bc1), len(bc2)) - 1)
        v2[range(0, len(bc2)-1)] = bc2[1:]

        return np.sum(v1 * v2) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import floyd_warshall_numpy [as 别名]
def test_database(self):
        """Test a general use case for the fingerprint database."""
        dstar = 8
        with FingerprintDB('tmp-fingerprints.db') as fpd:
            for i in range(dstar + 1):
                fpd.parameter_entry('Z{}'.format(i),
                                    'Atomic charge: depth {}'.format(i))
                fpd.parameter_entry(
                    'r{}'.format(i),
                    'Cordero covalent radius: depth {}'.format(i))
                fpd.parameter_entry(
                    'x{}'.format(i),
                    'Pauling electronegetivity: depth {}'.format(i))
                fpd.parameter_entry('T{}'.format(i),
                                    'Coordination number: depth {}'.format(i))
                fpd.parameter_entry(
                    '1{}'.format(i), 'Unity: depth {}'.format(i))
            fpd.parameter_entry('Ef', 'Formation energy')
            fpd.parameter_entry('Et', 'Total energy')

            par = fpd.get_parameters()

            for d in db.select():
                fpd.image_entry(d)

                atoms = d.toatoms()

                edges = [tuple([u, v]) for u, v in d.data['edges']]
                G = nx.Graph()
                G.add_nodes_from(range(len(atoms)))
                G.add_edges_from(edges)
                distance_matrix = nx.floyd_warshall_numpy(G)
                Bm = np.zeros(distance_matrix.shape)

                W = np.ones((5, len(atoms)))
                W[0] = atoms.numbers

                for i, n in enumerate(W[0]):
                    W[1][i] = properties['covalent_radius_cordero'][int(n)]
                    W[2][i] = properties['en_pauling'][int(n)]
                    W[3][i] = len(G[i])

                for dd in range(dstar + 1):
                    B = Bm.copy()
                    B[distance_matrix == dd] = 1
                    AC = np.dot(np.dot(W, B), W.T).diagonal()

                    for j, v in enumerate(AC):
                        ind = j + dd * len(AC)
                        fpd.fingerprint_entry(d.id, par[ind], v)

                fpd.fingerprint_entry(d.id, 46, d.Uref)
                fpd.fingerprint_entry(d.id, 47, atoms.get_potential_energy())