algorithms.graph.bipartite_graph¶
Module: algorithms.graph.bipartite_graph
¶
Inheritance diagram for nipy.algorithms.graph.bipartite_graph
:
This module implements the BipartiteGraph class, used to represent weighted bipartite graph: it contains two types of vertices, say ‘left’ and ‘right’; then edges can only exist between ‘left’ and ‘right’ vertices. For simplicity the vertices of either side are labeled [1..V] and [1..W] respectively.
Author: Bertrand Thirion, 2006–2011
Class¶
BipartiteGraph
¶

class
nipy.algorithms.graph.bipartite_graph.
BipartiteGraph
(V, W, edges=None, weights=None)¶ Bases:
object
Bipartite graph class
A graph for which there are two types of nodes, such that edges can exist only between nodes of type 1 and type 2 (not within) fields of this class: V (int, > 0) the number of type 1 vertices W (int, > 0) the number of type 2 vertices E: (int) the number of edges edges: array of shape (self.E, 2) reprensenting pairwise neighbors weights, array of shape (self.E), +1/1 for scending/descending links

__init__
(V, W, edges=None, weights=None)¶ Constructor
Parameters: V (int), the number of vertices of subset 1
W (int), the number of vertices of subset 2
edges=None: array of shape (self.E, 2)
the edge array of the graph
weights=None: array of shape (self.E)
the asociated weights array

copy
()¶ returns a copy of self

set_edges
(edges)¶ Set edges to graph
 sets self.edges=edges if
 edges has a correct size
 edges take values in [0..V1]*[0..W1]
Parameters: edges: array of shape(self.E, 2): set of candidate edges

set_weights
(weights)¶ Set weights weights to edges
Parameters: weights, array of shape(self.V): edges weights

subgraph_left
(valid, renumb=True)¶ Extraction of a subgraph
Parameters: valid, boolean array of shape self.V
renumb, boolean: renumbering of the (left) edges
Returns: G : None or
BipartiteGraph
instanceA new BipartiteGraph instance with only the left vertices that are True. If sum(valid)==0, None is returned

subgraph_right
(valid, renumb=True)¶ Extraction of a subgraph
Parameters: valid : bool array of shape self.V
renumb : bool, optional
renumbering of the (right) edges
Returns: G : None or
BipartiteGraph
instance.A new BipartiteGraph instance with only the right vertices that are True. If sum(valid)==0, None is returned

Functions¶

nipy.algorithms.graph.bipartite_graph.
bipartite_graph_from_adjacency
(x)¶ Instantiates a weighted graph from a square 2D array
Parameters: x: 2D array instance, the input array Returns: wg: BipartiteGraph instance

nipy.algorithms.graph.bipartite_graph.
bipartite_graph_from_coo_matrix
(x)¶ Instantiates a weighted graph from a (sparse) coo_matrix
Parameters: x: scipy.sparse.coo_matrix instance, the input matrix Returns: bg: BipartiteGraph instance

nipy.algorithms.graph.bipartite_graph.
check_feature_matrices
(X, Y)¶ checks wether the dismension of X and Y are consistent
Parameters: X, Y arrays of shape (n1, p) and (n2, p)
where p = common dimension of the features

nipy.algorithms.graph.bipartite_graph.
cross_eps
(X, Y, eps=1.0)¶ Return the epsneighbours graph of from X to Y
Parameters: X, Y arrays of shape (n1, p) and (n2, p)
where p = common dimension of the features
eps=1, float: the neighbourhood size considered
Returns: the resulting bipartite graph instance
Notes
for the sake of speed it is advisable to give PCApreprocessed matrices X and Y.

nipy.algorithms.graph.bipartite_graph.
cross_knn
(X, Y, k=1)¶ return the knearestneighbours graph of from X to Y
Parameters: X, Y arrays of shape (n1, p) and (n2, p)
where p = common dimension of the features
eps=1, float: the neighbourhood size considered
Returns: BipartiteGraph instance
Notes
For the sake of speed it is advised to give PCAtransformed matrices X and Y.