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¶
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class
nipy.algorithms.graph.bipartite_graph.BipartiteGraph(V, W, edges=None, weights=None)[source]¶ Bases:
objectBipartite 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
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__init__(V, W, edges=None, weights=None)[source]¶ 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
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set_weights(weights)[source]¶ Set weights weights to edges
- Parameters
weights, array of shape(self.V): edges weights
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set_edges(edges)[source]¶ Set edges to graph
- sets self.edges=edges if
edges has a correct size
edges take values in [0..V-1]*[0..W-1]
- Parameters
edges: array of shape(self.E, 2): set of candidate edges
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subgraph_left(valid, renumb=True)[source]¶ Extraction of a subgraph
- Parameters
valid, boolean array of shape self.V
renumb, boolean: renumbering of the (left) edges
- Returns
G : None or
BipartiteGraphinstanceA new BipartiteGraph instance with only the left vertices that are True. If sum(valid)==0, None is returned
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subgraph_right(valid, renumb=True)[source]¶ Extraction of a subgraph
- Parameters
valid : bool array of shape self.V
renumb : bool, optional
renumbering of the (right) edges
- Returns
G : None or
BipartiteGraphinstance.A new BipartiteGraph instance with only the right vertices that are True. If sum(valid)==0, None is returned
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Functions¶
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nipy.algorithms.graph.bipartite_graph.bipartite_graph_from_adjacency(x)[source]¶ Instantiates a weighted graph from a square 2D array
- Parameters
x: 2D array instance, the input array
- Returns
wg: BipartiteGraph instance
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nipy.algorithms.graph.bipartite_graph.bipartite_graph_from_coo_matrix(x)[source]¶ Instantiates a weighted graph from a (sparse) coo_matrix
- Parameters
x: scipy.sparse.coo_matrix instance, the input matrix
- Returns
bg: BipartiteGraph instance
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nipy.algorithms.graph.bipartite_graph.check_feature_matrices(X, Y)[source]¶ 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
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nipy.algorithms.graph.bipartite_graph.cross_eps(X, Y, eps=1.0)[source]¶ Return the eps-neighbours 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 PCA-preprocessed matrices X and Y.
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nipy.algorithms.graph.bipartite_graph.cross_knn(X, Y, k=1)[source]¶ return the k-nearest-neighbours 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 PCA-transformed matrices X and Y.