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neurospin.graph.BPmatch

Module: neurospin.graph.BPmatch

Routines for Matching of a graph to a cloud of points/tree structures through Bayesian networks (Belief propagation) algorithms

Author: Bertrand Thirion , 2006-2008.

Comment (2009/03/24)

Functions

nipy.neurospin.graph.BPmatch.BPmatch(c1, c2, graph, dmax)
nipy.neurospin.graph.BPmatch.BPmatch_slow(c1, c2, graph, dmax, imax=20, eps=9.9999999999999998e-13)
Matching the rows of c1 to those of c2 based on their relative positions graph is a matrix that yields a graph structure on the rows of c1 dmax is measure of the distance decay between points and correspondences for algorithmic details, see Thirion et al, MMBIA 2006
nipy.neurospin.graph.BPmatch.BPmatch_slow_asym(c1, c2, G1, G2, dmax)
New version which makes the differences between ascending and descending links - c1 and c2 are arrays of shape (n1,d) and (n2,d) that represent features or coordinates, where n1 and n2 are the number of things to be put in correpondence and d is the common dim - G1 and G2 are corresponding graphs (forests in fff sense) - dmax is a typical distance to compare positions
nipy.neurospin.graph.BPmatch.BPmatch_slow_asym_dep(c1, c2, G1, G2, dmax)
New version which makes the differences between ascending and descending links
nipy.neurospin.graph.BPmatch.BPmatch_slow_asym_dev(c1, c2, G1, G2, dmax)
New version which makes the differences between ascending and descending links INPUT: - c1 and c2 are arrays of shape (n1,d) and (n2,d) that represent features or coordinates, where n1 and n2 are the number of things to be put in correpondence and d is the common dim - G1 and G2 are corresponding graphs (forests in fff sense) - dmax is a typical distance to compare positions OUTPUT: - (i,j,k): sparse model of the probabilistic relationships, where k is the probability that i is associated with j
nipy.neurospin.graph.BPmatch.EDistance(X, Y)
Computation of the euclidian distances between all the columns of X and those of Y
nipy.neurospin.graph.BPmatch.leaves(G)
nipy.neurospin.graph.BPmatch.match_trivial(c1, c2, dmax, eps=9.9999999999999998e-13)
Matching the rows of c1 to those of c2 based on their relative positions
nipy.neurospin.graph.BPmatch.singles(G)