CParam::CParam() {
min_Prob_A = 0.01;
accept_rate = RowVector(2) ; accept_rate = 0 ;
is_accept = RowVector(2) ; is_accept = 0;
Prob_A = 0;
K = -1; //to indicate no number of components has been set
msg_level = 0; //0 errors only; 1: errors and warnings; 2: errors, warnings and information
}
bool DESolver::Solve(int maxGenerations)
{
int generation;
int candidate;
bool bAtSolution;
bestEnergy = 1.0E20;
bAtSolution = false;
for (generation=0;(generation < maxGenerations) && !bAtSolution;generation++)
for (candidate=0; candidate < nPop; candidate++)
{
(this->*calcTrialSolution)(candidate);
trialEnergy = EnergyFunction(trialSolution,bAtSolution);
if (trialEnergy < popEnergy[candidate])
{
// New low for this candidate
popEnergy[candidate] = trialEnergy;
CopyVector(RowVector(population,candidate),trialSolution);
// Check if all-time low
if (trialEnergy < bestEnergy)
{
bestEnergy = trialEnergy;
CopyVector(bestSolution,trialSolution);
}
}
}
generations = generation;
return(bAtSolution);
}
bool SBVAR_symmetric_linear::SetParameters(const TDenseMatrix &A_0, const TDenseMatrix &A_plus)
{
if ((n_vars != A_0.rows) || (n_vars != A_0.cols) || (n_vars != A_plus.rows) || (n_predetermined != A_plus.cols))
throw dw_exception("VARToParameters() - invalid matrix dimensions");
parameters.UniqueMemory();
for (int i=n_vars-1; i >= 0; i--)
{
parameters.Insert(begin_b[i],Transpose(U[i])*RowVector(A_0,i));
parameters.Insert(begin_g[i],Transpose(V[i])*RowVector(A_plus,i));
}
A0.UniqueMemory(n_vars,n_vars,false);
Aplus.UniqueMemory(n_vars,n_predetermined,false);
for (int i=n_vars-1; i >= 0; i--)
{
A0.InsertRowMatrix(i,0,U[i]*parameters.SubVector(begin_b[i],begin_b[i]+dim_b[i]-1));
Aplus.InsertRowMatrix(i,0,V[i]*parameters.SubVector(begin_g[i],begin_g[i]+dim_g[i]-1));
}
return true;
}
void DESolver::RandToBest1Exp( int candidate )
{
int r1, r2;
int n;
SelectSamples(candidate, &r1, &r2);
n = (int)RandomUniform(0.0, (double)nDim);
CopyVector(trialSolution, RowVector(population, candidate));
for (int i = 0; (RandomUniform(0.0,1.0) < probability) && (i < nDim); i++) {
trialSolution[n] += scale * (bestSolution[n] - trialSolution[n])
+ scale * (Element(population,r1,n)
- Element(population,r2,n));
n = (n + 1) % nDim;
}
return;
}
void DESolver::Rand2Bin( int candidate )
{
int r1, r2, r3, r4, r5;
int n;
SelectSamples(candidate, &r1, &r2, &r3, &r4, &r5);
n = (int)RandomUniform(0.0, (double)nDim);
CopyVector(trialSolution, RowVector(population, candidate));
for (int i = 0; i < nDim; i++) {
if ((RandomUniform(0.0,1.0) < probability) || (i == (nDim - 1)))
trialSolution[n] = Element(population,r1,n)
+ scale * (Element(population,r2,n)
+ Element(population,r3,n)
- Element(population,r4,n)
- Element(population,r5,n));
n = (n + 1) % nDim;
}
}
void DESolver::Best1Bin( int candidate )
{
int r1, r2;
int n;
SelectSamples(candidate, &r1, &r2);
n = (int)RandomUniform(0.0, (double)nDim);
CopyVector(trialSolution, RowVector(population, candidate));
for (int i = 0; i < nDim; i++) {
if ((RandomUniform(0.0,1.0) < probability) || (i == (nDim - 1)))
trialSolution[n] = bestSolution[n]
+ scale * (Element(population,r1,n)
- Element(population,r2,n));
n = (n + 1) % nDim;
}
return;
}
int DESolver::Solve( int maxGenerations, int verbose )
{
int generation;
int candidate;
bool bAtSolution;
double relativeDeltas[3] = {100.0, 100.0, 100.0};
double lastBestEnergy;
bestEnergy = 1.0E20;
lastBestEnergy = bestEnergy;
bAtSolution = false;
for (generation = 0; (generation < maxGenerations) && !bAtSolution; generation++) {
for (candidate = 0; candidate < nPop; candidate++) {
// modified by PE
//(this->*calcTrialSolution)(candidate);
CalcTrialSolution(candidate);
// trialSolution now contains a newly generated parameter vector
// check for out-of-bounds values and generate random values w/in the bounds
CopyVector(oldValues, RowVector(population, candidate));
// oldValues is guaranteed to lie between minBounds and maxBounds
for (int j = 0; j < nDim; j++) {
if (trialSolution[j] < minBounds[j])
trialSolution[j] = minBounds[j] + RandomUniform(0.0,1.0)*(oldValues[j] - minBounds[j]);
if (trialSolution[j] > maxBounds[j])
trialSolution[j] = maxBounds[j] - RandomUniform(0.0,1.0)*(maxBounds[j] - oldValues[j]);
}
// Test our newly mutated/bred trial parameter vector
trialEnergy = EnergyFunction(trialSolution, bAtSolution);
if (trialEnergy < popEnergy[candidate]) {
// New low for this candidate
popEnergy[candidate] = trialEnergy;
CopyVector(RowVector(population,candidate), trialSolution);
// Check if all-time low
if (trialEnergy < bestEnergy) {
bestEnergy = trialEnergy;
CopyVector(bestSolution, trialSolution);
}
}
}
// Debugging printout code added by PE -- print an update every 10 generations
double relativeDeltaEnergy = 0.0;
if ((generation % 10) == 0) {
if (verbose > 0)
printf("\nGeneration %4d: bestEnergy = %12.10f", generation, bestEnergy);
if (generation == 20) {
relativeDeltaEnergy = fabs(1.0 - lastBestEnergy/bestEnergy);
relativeDeltas[0] = relativeDeltaEnergy;
if (verbose > 0)
printf(" (relative change = %e)", relativeDeltaEnergy);
}
else if (generation == 30) {
relativeDeltaEnergy = fabs(1.0 - lastBestEnergy/bestEnergy);
relativeDeltas[1] = relativeDeltas[0];
relativeDeltas[0] = relativeDeltaEnergy;
if (verbose > 0)
printf(" (relative change = %e)", relativeDeltaEnergy);
}
else if (generation >= 40) {
relativeDeltaEnergy = fabs(1.0 - lastBestEnergy/bestEnergy);
relativeDeltas[2] = relativeDeltas[1];
relativeDeltas[1] = relativeDeltas[0];
relativeDeltas[0] = relativeDeltaEnergy;
if (verbose > 0)
printf(" (relative change = %e)", relativeDeltaEnergy);
if (TestConverged(relativeDeltas, tolerance)) {
generations = generation;
bAtSolution = true;
return 1;
}
}
lastBestEnergy = bestEnergy;
}
if (isnan(bestEnergy)) {
// fprintf(stderr, "\n*** NaN-valued fit statistic detected (DE optimization)!\n");
printf("\n\tcandidate %d, bestEnergy = %f\n", candidate, bestEnergy);
}
}
generations = generation;
return 5;
}
