/**
* Computes the dot product of each basis vector against the sample. Can be used as a measure
* for membership in the training sample set. High values correspond to a better fit.
*
* @param sample Sample of original data.
* @return Higher value indicates it is more likely to be a member of input dataset.
*/
public double response(double[] sample)
{
if (sample.Length != A.numCols)
{
throw new ArgumentException("Expected input vector to be in sample space");
}
DMatrixRMaj dots = new DMatrixRMaj(numComponents, 1);
DMatrixRMaj s = DMatrixRMaj.wrap(A.numCols, 1, sample);
CommonOps_DDRM.mult(V_t, s, dots);
return(NormOps_DDRM.normF(dots));
}
/**
* Converts a vector from sample space into eigen space.
*
* @param sampleData Sample space data.
* @return Eigen space projection.
*/
public double[] sampleToEigenSpace(double[] sampleData)
{
if (sampleData.Length != A.getNumCols())
{
throw new ArgumentException("Unexpected sample length");
}
DMatrixRMaj mean = DMatrixRMaj.wrap(A.getNumCols(), 1, this.mean);
DMatrixRMaj s = new DMatrixRMaj(A.getNumCols(), 1, true, sampleData);
DMatrixRMaj r = new DMatrixRMaj(numComponents, 1);
CommonOps_DDRM.subtract(s, mean, s);
CommonOps_DDRM.mult(V_t, s, r);
return(r.data);
}
/**
* Converts a vector from eigen space into sample space.
*
* @param eigenData Eigen space data.
* @return Sample space projection.
*/
public double[] eigenToSampleSpace(double[] eigenData)
{
if (eigenData.Length != numComponents)
{
throw new ArgumentException("Unexpected sample length");
}
DMatrixRMaj s = new DMatrixRMaj(A.getNumCols(), 1);
DMatrixRMaj r = DMatrixRMaj.wrap(numComponents, 1, eigenData);
CommonOps_DDRM.multTransA(V_t, r, s);
DMatrixRMaj mean = DMatrixRMaj.wrap(A.getNumCols(), 1, this.mean);
CommonOps_DDRM.add(s, mean, s);
return(s.data);
}
public override Individual NewIndividual(IEvolutionState state, int thread)
{
Individual newind = base.NewIndividual(state, thread);
IMersenneTwister random = state.Random[thread];
if (!(newind is DoubleVectorIndividual)) // uh oh
{
state.Output.Fatal(
"To use CMAESSpecies, the species must be initialized with a DoubleVectorIndividual. But it contains a " +
newind);
}
DoubleVectorIndividual dvind = (DoubleVectorIndividual)(newind);
DMatrixRMaj genome = DMatrixRMaj.wrap(GenomeSize, 1, dvind.genome);
DMatrixRMaj temp = new DMatrixRMaj(GenomeSize, 1);
// arz(:,k) = randn(N,1); % standard normally distributed vector
// arx(:,k) = xmean + sigma*(B*D*arz(:,k));
int tries = 0;
while (true)
{
for (int i = 0; i < GenomeSize; i++)
{
dvind.genome[i] = random.NextGaussian();
}
CommonOps_DDRM.mult(sbd, genome, temp); // temp = sigma*b*d*genome;
CommonOps_DDRM.add(temp, xmean.getMatrix(), genome); // genome = temp + xmean;
bool invalid_value = false;
for (int i = 0; i < GenomeSize; i++)
{
if (dvind.genome[i] < MinGenes[i] || dvind.genome[i] > MaxGenes[i])
{
if (useAltGenerator && tries > altGeneratorTries)
{
// instead of just failing, we're going to select uniformly from
// possible values for this particular gene.
dvind.genome[i] = state.Random[thread].NextDouble() * (MaxGenes[i] - MinGenes[i]) +
MinGenes[i];
}
else
{
invalid_value = true;
break;
}
}
}
if (invalid_value)
{
if (++tries > MAX_TRIES_BEFORE_WARNING)
{
state.Output.WarnOnce(
"CMA-ES may be slow because many individuals are being generated which\n" +
"are outside the min/max gene bounds. If an individual violates a single\n" +
"gene bounds, it is rejected, so as the number of genes grows, the\n" +
"probability of this happens increases exponentially. You can deal\n" +
"with this by decreasing sigma. Alternatively you can use set\n" +
"pop.subpop.0.alternative-generation=true (see the manual).\n" +
"Finally, if this is happening during initialization, you might also\n" +
"change pop.subpop.0.species.covariance=scaled.\n");
}
continue;
}
return(newind);
}
}