# demmdn1

## Purpose

Demonstrate fitting a multi-valued function using a Mixture Density Network.

## Synopsis

    demmdn1

## Description

The problem consists of one input variable `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.

## See Also

`mdn`, `mdnerr`, `mdngrad`, `scg`

Pages: Index

Copyright (c) Ian T Nabney (1996-9)