/// <summary>
/// Sample the visible (input), given the hidden neurons (output). Return the mean, and a sample, based on that
/// mean probability.
/// </summary>
/// <param name="rbm">The RBM to use.</param>
/// <param name="sampleH0">Hidden (h) samples.</param>
/// <param name="mean">Output: Visible (v) mean.</param>
/// <param name="sample">Output: Visible (v) sample.</param>
public void SampleVH(RestrictedBoltzmannMachine rbm, double[] sampleH0, double[] mean, double[] sample)
{
for (int i = 0; i < rbm.VisibleCount; i++)
{
mean[i] = PropDown(rbm, sampleH0, i, rbm.BiasV[i]);
sample[i] = rbm.binomial(1, mean[i]);
}
}
/// <summary>
/// Sample the hidden neurons (output), given the visible (input). Return the mean, and a sample, based on that
/// mean probability.
/// </summary>
/// <param name="rbm">The RBM to use.</param>
/// <param name="v0Sample">The input to the layer.</param>
/// <param name="mean">Output: mean value of each hidden neuron.</param>
/// <param name="sample">Output: sample, based on mean.</param>
public void SampleHV(RestrictedBoltzmannMachine rbm, double[] v0Sample, double[] mean, double[] sample)
{
for (int i = 0; i < rbm.HiddenCount; i++)
{
// Find the mean.
mean[i] = PropUp(rbm, v0Sample, rbm.Layer.Weights[i], rbm.BiasH[i]);
// Sample, based on that mean.
sample[i] = rbm.binomial(1, mean[i]);
}
}
/// <summary>
/// Sample the visible (input), given the hidden neurons (output). Return the mean, and a sample, based on that
/// mean probability.
/// </summary>
/// <param name="rbm">The RBM to use.</param>
/// <param name="sampleH0">Hidden (h) samples.</param>
/// <param name="mean">Output: Visible (v) mean.</param>
/// <param name="sample">Output: Visible (v) sample.</param>
public void SampleVH(RestrictedBoltzmannMachine rbm, double[] sampleH0, double[] mean, double[] sample)
{
for (int i = 0; i < rbm.VisibleCount; i++)
{
mean[i] = PropDown(rbm, sampleH0, i, rbm.BiasV[i]);
sample[i] = rbm.binomial(1, mean[i]);
}
}
/// <summary>
/// Sample the hidden neurons (output), given the visible (input). Return the mean, and a sample, based on that
/// mean probability.
/// </summary>
/// <param name="rbm">The RBM to use.</param>
/// <param name="v0Sample">The input to the layer.</param>
/// <param name="mean">Output: mean value of each hidden neuron.</param>
/// <param name="sample">Output: sample, based on mean.</param>
public void SampleHV(RestrictedBoltzmannMachine rbm, double[] v0Sample, double[] mean, double[] sample)
{
for (int i = 0; i < rbm.HiddenCount; i++)
{
// Find the mean.
mean[i] = PropUp(rbm, v0Sample, rbm.Layer.Weights[i], rbm.BiasH[i]);
// Sample, based on that mean.
sample[i] = rbm.binomial(1, mean[i]);
}
}