void Util::sampleGaussianMat(MatrixXf& mat){
for (int i = 0; i < mat.rows(); ++i){
int j = 0;
for ( ; j+1 < mat.cols(); j += 2){
float f1, f2;
sampleTwoGaussian(f1, f2);
mat(i,j ) = f1;
mat(i,j+1) = f2;
}
for (; j < mat.cols(); j ++){
float f1, f2;
sampleTwoGaussian(f1, f2);
mat(i, j) = f1;
}
}
}
void
GRBM<VIS_DIM, HID_DIM>::computeVisibleProb()
{
// before we actually compute anything here we need to ensure
// that the hiddenProbs are binary
for (int i = 0; i < HID_DIM; ++i)
{
hiddenProb(i) = hiddenProb(i) > RAND_0_1() ? 1. : 0.;
}
// we have several cases here for which the visible probabilities need to be computed:
// 1) visible gaussian with learned stdev
// 2) visible gaussian with added gaussian noise and fixed stdev
// 3) visible gaussian withoud gaussian noise and fixed stdev
// 4) visible binary
if (!isVisibleBinary && isVisStdevLearned)
{
visibleProb = weights * hiddenProb;
if (addGaussianNoise)
{
// add some gaussian noise to the visible units here
g_float gn1, gn2;
int i = 0;
for (; i + 1 < visibleProb.size(); i += 2)
{
sampleTwoGaussian(gn1, gn2);
visibleProb(i) += gn1;
visibleProb(i+1) += gn2;
}
for (; i < visibleProb.size(); ++i)
{
sampleTwoGaussian(gn1, gn2);
visibleProb(i) += gn1;
}
}
visibleProb.array() = visibleProb.array() * visStdevs.array();
}
else
{
if (!isVisibleBinary && addGaussianNoise)
{
sampleGaussianMat<VisVType>( visibleProb);
// scale sampled matrix
//visibleProb *= (1. / visStdev); // << done in next row
visibleProb.array() += (visibleProb.array() / visStdev)
+ (weights * hiddenProb).array() / visStdev;
}
else
{
visibleProb = weights * hiddenProb;
if (!isVisibleBinary)
visibleProb *= (1. / visStdev);
}
}
// add bias / prior
visibleProb += visiblebias;
//std::cout << "vProb: " << std::endl << visibleProb.transpose() << std::endl << std::endl;
// if visible should be binary threshold via logistic activation
if (isVisibleBinary)
{
for (int i = 0; i < visibleProb.size(); ++i)
visibleProb(i) = 1. / (1 + exp(-visibleProb(i)));
}
}
