void Toolbox::calculateGlobalMeanAndStd(DataSet &X,dVector& mean,dVector& stdDev)
{
calculateGlobalMean(X,mean);
int nbElements = 0;
//Calculate standard deviation
stdDev.set(0);
for(int i = 0;i < (int)X.size() ;i++)
{
double* pData = X.at(i)->getPrecomputedFeatures()->get();
int Width = X.at(i)->getPrecomputedFeatures()->getWidth();
int Height = X.at(i)->getPrecomputedFeatures()->getHeight();
for(int col=0; col < Width; col++)
{
double* pStdDev = stdDev.get();
double* pMean = mean.get();
for(int row = 0; row < Height;row++)
{
*pStdDev += (*pData-*pMean) * (*pData-*pMean);
pStdDev++;
pData++;
pMean++;
}
}
nbElements+=Width;
}
stdDev.multiply(1.0/(double)nbElements);
stdDev.eltSqrt();
}
double Gradient::computeGradient(dVector& vecGradient, Model* m, DataSet* X)
{
double ans = 0.0;
#ifdef _OPENMP
if( nbThreadsMP < 1 )
nbThreadsMP = omp_get_max_threads();
setMaxNumberThreads(nbThreadsMP);
pInfEngine->setMaxNumberThreads(nbThreadsMP);
pFeatureGen->setMaxNumberThreads(nbThreadsMP);
#endif
//Check the size of vecGradient
int nbFeatures = pFeatureGen->getNumberOfFeatures();
if(vecGradient.getLength() != nbFeatures)
vecGradient.create(nbFeatures);
else
vecGradient.set(0);
////////////////////////////////////////////////////////////
// Start of parallel Region
// Some weird stuff in gcc 4.1, with openmp 2.5 support
//
// Note 1: In OpenMP 2.5, the iteration variable in "for" must be
// a signed integer variable type. In OpenMP 3.0 (_OPENMP>=200805),
// it may also be an unsigned integer variable type, a pointer type,
// or a constant-time random access iterator type.
//
// Note 2: schedule(static | dynamic): In the dynamic schedule, there
// is no predictable order in which the loop items are assigned to
// different threads. Each thread asks the OpenMP runtime library for
// an iteration number, then handles it, then asks for the next one.
// It is thus useful when different iterations in the loop may take
// different time to execute.
#pragma omp parallel default(none) \
shared(vecGradient, X, m, ans, nbFeatures, std::cout)
{
// code inside this region runs in parallel
dVector g(nbFeatures, COLVECTOR, 0.0);
#pragma omp for schedule(dynamic) reduction(+:ans)
for(int i=0; (int)i<X->size(); i++) {
DataSequence* x = X->at(i);
if( m->isWeightSequence() && x->getWeightSequence() != 1.0) {
dVector tmp(nbFeatures, COLVECTOR, 0.0);
ans += computeGradient(tmp, m, x) * x->getWeightSequence();
tmp.multiply(x->getWeightSequence());
g.add(tmp);
}
else {
ans += computeGradient(g, m, x);
}
}
// We now put togheter the gradients
// No two threads can execute a critical directive of the same name at the same time
#pragma omp critical (reduce_sum)
{
vecGradient.add(g);
}
}
// End of parallel Region
////////////////////////////////////////////////////////////
vecGradient.negate();
// MaxMargin objective: min L = 0.5*\L2sigma*W*W + Loss()
// MLE objective: min L = 0.5*1/(\L2sigma*\L2sigma)*W*W - log p(y|x)
// Add the regularization term
double scale = (m->isMaxMargin())
? m->getRegL2Sigma()
: 1/(double)(m->getRegL2Sigma()*m->getRegL2Sigma());
if( m->isMaxMargin() )
ans = (1/(double)X->size()) * ans;
if(m->getRegL2Sigma()!=0.0f)
{
for(int f=0; f<nbFeatures; f++)
vecGradient[f] += (*m->getWeights())[f]*scale;
ans += 0.5*scale*m->getWeights()->l2Norm(false);
}
return ans;
}
double Gradient::computeGradient(dVector& vecGradient, Model* m, DataSet* X)
{
//Check the size of vecGradient
int nbFeatures = pFeatureGen->getNumberOfFeatures();
double ans = 0.0;
int TID = 0;
if(vecGradient.getLength() != nbFeatures)
vecGradient.create(nbFeatures);
else
vecGradient.set(0);
// Initialize the buffers (vecFeaturesMP) for each thread
#ifdef _OPENMP
setMaxNumberThreads(omp_get_max_threads());
pInfEngine->setMaxNumberThreads(omp_get_max_threads());
pFeatureGen->setMaxNumberThreads(omp_get_max_threads());
#endif
for(int t=0;t<nbThreadsMP;t++)
{
if(localGrads[t].getLength() != nbFeatures)
localGrads[t].resize(1,nbFeatures,0);
else
localGrads[t].set(0);
}
////////////////////////////////////////////////////////////
// Start of parallel Region
// Some weird stuff in gcc 4.1, with openmp 2.5 support
#if ((_OPENMP == 200505) && __GNUG__)
#pragma omp parallel \
shared(X, m, ans, nbFeatures, std::cout) \
private(TID) \
default(none)
#else
#pragma omp parallel \
shared(vecGradient, X, m, ans, nbFeatures, std::cout) \
private(TID) \
default(none)
#endif
{
#ifdef _OPENMP
TID = omp_get_thread_num();
#endif
// Create a temporary gradient
double localSum = 0;
#ifdef WITH_SEQUENCE_WEIGHTS
dVector tmpVecGradient(nbFeatures);
#endif
#pragma omp for
// we can use unsigned if we have openmp 3.0 support (_OPENMP>=200805).
#ifdef _OPENMP
#if _OPENMP >= 200805
for(unsigned int i = 0; i< X->size(); i++){
#else
for(int i = 0; i< X->size(); i++){
#endif
#else
for(unsigned int i = 0; i< X->size(); i++){
#endif
if (m->getDebugLevel() >=2){
#pragma omp critical(output)
std::cout << "Thread "<<TID<<" computes gradient for sequence "
<< i <<" out of " << (int)X->size()
<< " (Size: " << X->at(i)->length() << ")" << std::endl;
}
DataSequence* x = X->at(i);
#ifdef WITH_SEQUENCE_WEIGHTS
tmpVecGradient.set(0);
localSum += computeGradient(tmpVecGradient, m, x) * x->getWeightSequence();
if(x->getWeightSequence() != 1.0)
tmpVecGradient.multiply(x->getWeightSequence());
localGrads[TID].add(tmpVecGradient);
#else
localSum += computeGradient(localGrads[TID], m, x);// * x->getWeightSequence();
#endif
}
#pragma omp critical (reduce_sum)
// We now put togheter the sums
{
if( m->getDebugLevel() >= 2){
std::cout<<"Thread "<<TID<<" update sums"<<std::endl;
}
ans += localSum;
vecGradient.add(localGrads[TID]);
}
}
// End of parallel Region
////////////////////////////////////////////////////////////
// because we are minimizing -LogP
vecGradient.negate();
// Add the regularization term
double sigmaL2Square = m->getRegL2Sigma()*m->getRegL2Sigma();
if(sigmaL2Square != 0.0f) {
if (m->getDebugLevel() >= 2){
std::cout << "Adding L2 norm gradient\n";
}
for(int f = 0; f < nbFeatures; f++) {
vecGradient[f] += (*m->getWeights())[f]/sigmaL2Square;
}
double weightNorm = m->getWeights()->l2Norm(false);
ans += weightNorm / (2.0*m->getRegL2Sigma()*m->getRegL2Sigma());
}
return ans;
}
