demmdn1
x and one target variable t with data generated by
sampling t at equal intervals and then generating target data by
computing t + 0.3*sin(2*pi*t) and adding Gaussian noise. A
Mixture Density Network with 3 centres in the mixture model is trained
by minimizing a negative log likelihood error function using the scaled
conjugate gradient optimizer.
The conditional means, mixing coefficients and variances are plotted
as a function of x, and a contour plot of the full conditional
density is also generated.
mdn, mdnerr, mdngrad, scgCopyright (c) Ian T Nabney (1996-9)