gtm_pci {gtm} | R Documentation |
The returned weight matrix maps the mean of the latent variable to the mean of the target variable, and the L-dimensional latent variable variance to the variance of the target data along its L first principal components.
W = gtm_pci(T, X, FI)
T |
target distribution sample; one data point per row; N-by-D |
X |
the latent distribution sample, K-by-L |
FI |
basis functions' activation when fed the latent data, X, plus a bias, K-by-(M+1) |
W
- the initialised weight matrix, (K+1)-by-D
The first dimension (column) of X will map to the first principal component, the second dimension (column) of X will map to the second principal component, and so on. This may be of importance for the choice of sampling density along the different dimensions of X, if it differs between different dimensions.
MU = matrix(c(0,1), ncol = 1) sigma = 1 X = gtm_pts(8) FI = gtm_gbf(MU, sigma, X) t = matrix((1:40)/5, ncol=1) T = cbind(t, sin(t)) gtm_pci(T, X, FI)