mixparams = mdnfwd(net, x) [mixparams, y, z] = mdnfwd(net, x) [mixparams, y, z, a] = mdnfwd(net, x)
mixparams = mdnfwd(net, x)takes a mixture density network data structure
netand a matrix
xof input vectors, and forward propagates the inputs through the network to generate a structure
mixparamswhich contains the parameters of several mixture models. Each row of
xrepresents one input vector and the corresponding row of the matrices in
mixparamsrepresents the parameters of a mixture model for the conditional probability of target vectors given the input vector. This is not represented as an array of
gmmstructures to improve the efficiency of MDN training.
The fields in
type = 'mdnmixes' ncentres = number of mixture components dimtarget = dimension of target space mixcoeffs = mixing coefficients centres = means of Gaussians: stored as one row per pattern covars = covariances of Gaussians nparams = number of parameters
[mixparams, y, z] = mdnfwd(net, x) also generates a matrix
the outputs of the MLP and a matrix
z of the hidden
unit activations where each row corresponds to one pattern.
[mixparams, y, z, a] = mlpfwd(net, x) also returns a matrix
giving the summed inputs to each output unit, where each row
corresponds to one pattern.
Copyright (c) Ian T Nabney (1996-9)
David J Evans (1998)