static double Predict(MultiVariateSet *variateSet, float x)
{
double slope = 0.f, intercept = 0.f;
double sdx = 0.f, sdy = 0.f, cor;
double y_ = 0.f, x_ = 0.f, totalx = 0.f, totaly = 0.f;
EType *bgn1 = variateSet->elem[0], *end1 = variateSet->elem[0]+variateSet->cols, *bgn2 = variateSet->elem[1], *end2 = variateSet->elem[1] + variateSet->cols;
cor = (double)ComputeCorrelation(variateSet);
for (; bgn1 != end1; bgn1++) totalx += *bgn1;
for (; bgn2 != end2; bgn2++) totaly += *bgn2;
x_ = totalx / (double)variateSet->cols;
y_ = totaly / (double)variateSet->cols;
totalx = 0.f; totaly = 0.f;
bgn1 = variateSet->elem[0]; end1 = variateSet->elem[0] + variateSet->cols;
bgn2 = variateSet->elem[1]; end2 = variateSet->elem[1] + variateSet->cols;
for (; bgn1 != end1; bgn1++) totalx += Square(*bgn1 - x_);
for (; bgn2 != end2; bgn2++) totaly += Square(*bgn2 - y_);
sdx = sqrt((totalx / (double)variateSet->cols));
sdy = sqrt((totaly / (double)variateSet->cols));
slope = cor * sdy / sdx;
intercept = y_ - (slope*x_);
return intercept + slope*x;
}
int main(int argc, char *argv[])
{
MultiVariateSet *setVariate = NULL;
int i;
float* x;
setVariate = CreateMultiVariateSet("data.dat");
PrintMultiVariateSet(setVariate);
printf("\n");
printf("Covariance = %6.3f\n", ComputeCovariance(setVariate));
printf("Correlation = %6.3f\n", ComputeCorrelation(setVariate));
x = setVariate->elem[0];
for (i = 0; i < setVariate->cols; i++,x++)
{
printf("Predict value : %6.3f", Predict(setVariate, *x));
printf(" ei = %6.3f\n", *(setVariate->elem[1] + i) - Predict(setVariate, *x));
}
DestoryMultiVariateSet(setVariate);
return 0;
}
void NetworkTemporal3BCPNN::TrainLayer(const vector<vector<float> >& trainingData, PopulationColumns* inputLayer, StructureMIMDSVQ* structure, int iterationsCorrs, int iterationsMDS, int iterationsVQ, int iterationsFeatures)
{
// Training phase
int nrTrainImages = trainingData.size();
structure->MDSHypercolumns()->SwitchOnOff(false);
structure->MDS()->SwitchOnOff(false);
structure->VQ()->SwitchOnOff(false);
structure->CSLLearn()->SwitchOnOff(false);
structure->GetLayer(1)->SwitchOnOff(false);
structure->SetRecording(false);
// Semi-sequential version
// 1. Training phase
// 1A. Patches creation
structure->CSLLearn()->SetMaxPatterns(nrTrainImages);
structure->CSLLearn()->SetEta(0.001);
// turn of response in 2nd layer during initial training phase for speed
structure->GetLayer(1)->SwitchOnOff(false);
int j=0;
while(j<iterationsCorrs) {
//if(j==(int)iterationsPatches*0.8)
// break;
//for(int i=0;i<trainingData.size();i++) {
ComputeCorrelation(trainingData,inputLayer,structure,j);
j++;
}
j=0;
while(j<iterationsMDS) {
//if(j==(int)iterationsPatches*0.9)
// break;
//for(int i=0;i<trainingData.size();i++) {
if(!ComputeMDS(trainingData,inputLayer,structure,j))
break;
j++;
}
structure->GetLayer(1)->SwitchOnOff(true);
j=0;
while(j<iterationsVQ) {
//for(int i=0;i<trainingData.size();i++) {
if(m_verbose && this->MPIGetNodeId() == 0)
cout << "DataPoint (patches): " << j <<endl;
ComputeVQ(trainingData,inputLayer,structure,j);
j++;
}
structure->CSLLearn()->SetMaxPatterns(nrTrainImages);
j=0;
while(j<iterationsFeatures) {
//for(int i=0;i<trainingData.size();i++) {
if(m_verbose && this->MPIGetNodeId() == 0)
cout << "DataPoint (features): " << j <<endl;
if(!ExtractFeatures(trainingData,inputLayer,structure))
break;
//}
j++;
}
this->RecordAll();
}
