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 net and a matrix x of input vectors, and forward
propagates the inputs through the network to generate a structure
mixparams which contains the parameters of several mixture models.
Each row of x represents
one input vector and the corresponding row of the matrices in mixparams
represents 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 gmm structures to improve the efficiency of MDN training.
The fields in mixparams are
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 y of
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 a
giving the summed inputs to each output unit, where each row
corresponds to one pattern.
mdn, mdn2gmm, mdnerr, mdngrad, mlpfwdCopyright (c) Ian T Nabney (1996-9)
David J Evans (1998)