void NeuralNetwork::trainOn(arma::mat& input, const arma::mat& output, int numIterations, int iterationsBetweenReport)
{
if (input.n_cols != static_cast<unsigned int>(m_numNeuronsOnLayer[0]) ||
output.n_cols != static_cast<unsigned int>(m_numNeuronsOnLayer[m_numLayers - 1]))
throw InvalidInputException("File's input / output length doesn't match with the"
"number of neurons on input / output layer.");
if (m_featureNormalization)
normalizeFeatures(input);
double prevCost = computeCost(input, output, m_theta);
double crtCost = prevCost;
for (int iteration = 0; iteration < numIterations; ++iteration)
{
if (iterationsBetweenReport)
if (iteration % iterationsBetweenReport == 0 || iteration + 1 == numIterations)
std::cout << "Iteration: " << iteration << " | Cost: " << crtCost << std::endl;
if (crtCost > prevCost)
{
std::cout << "The cost is increasing. Choose a smaller learning rate." << std::endl;
return;
}
backprop(input, output);
prevCost = crtCost;
crtCost = computeCost(input, output, m_theta);
}
}
void SegmentList::prepare() {
foreach (Segment * const segment, *this) {
segment->relativizePosition();
segment->calculateSpatialFeatures();
segment->calculateColorFeatures();
segment->calculateContour();
}
normalizeFeatures();
calculateFeatureVariances();
}
void CFeatureOperatorAnd::getModifiedState(CStateCollection *stateCol, CState *featState)
{
int featureOffset = 1;
std::list<CStateModifier *>::iterator it = getStateModifiers()->begin();
std::list<CState *>::iterator stateIt = states->begin();
CState *stateBuf;
for (unsigned int i = 0; i < getNumDiscreteStates();i ++)
{
featState->setDiscreteState(i, 0);
featState->setContinuousState(i, 1.0);
}
int repetitions = getNumDiscreteStates();
for (int j = 0; it != getStateModifiers()->end(); it ++, stateIt ++, j ++)
{
repetitions /= (*it)->getNumDiscreteStates();
stateBuf = NULL;
if (stateCol->isMember(*it))
{
stateBuf = stateCol->getState(*it);
}
else
{
stateBuf = *stateIt;
(*it)->getModifiedState(stateCol, stateBuf);
}
if (stateBuf->getStateProperties()->isType(FEATURESTATE))
{
for (unsigned int i = 0; i < getNumDiscreteStates(); i++)
{
unsigned int singleStateFeatureNum = (i / repetitions) % stateBuf->getNumDiscreteStates();
featState->setDiscreteState(i, featState->getDiscreteState(i) + featureOffset * stateBuf->getDiscreteState(singleStateFeatureNum));
featState->setContinuousState(i, featState->getContinuousState(i) * stateBuf->getContinuousState(singleStateFeatureNum));
}
}
else
{
for (unsigned int i = 0; i < getNumDiscreteStates(); i++)
{
featState->setDiscreteState(i, featState->getDiscreteState(i) + featureOffset * stateBuf->getDiscreteState(0));
}
}
featureOffset = featureOffset * (*it)->getDiscreteStateSize();
}
normalizeFeatures(featState);
}
/**
* Function used to compute the features of the whole image
* @return the sequence of vectors of features
*/
vector< VectorXd > FeatureExtractor::getFeatures(){
int cols = im_bw.cols;
vector< vector <double> > sequenceOfFeatures;
vector< VectorXd > result;
for(int j = 0; j < cols; ++j)
{
vector<double> winFeatures = computeFeaturesPerWindow(j);
sequenceOfFeatures.push_back(winFeatures);
result.push_back(convertToVectorXd(sequenceOfFeatures[j]));
}
return normalizeFeatures(result);
}
void CFeatureOperatorOr::getModifiedState(CStateCollection *stateCol, CState *state)
{
assert(bInit);
std::list<CStateModifier *>::iterator it = getStateModifiers()->begin();
std::list<CState *>::iterator stateIt = states->begin();
CState *stateBuf;
int i = 0;
int numFeatures = 0;
for (; it != getStateModifiers()->end(); it++, stateIt++)
{
if (stateCol->isMember(*it))
{
stateBuf = stateCol->getState(*it);
}
else
{
stateBuf = *stateIt;
(*it)->getModifiedState(stateCol, stateBuf);
}
double featureStateFactor = (*this->featureFactors)[*it];
if (stateBuf->getStateProperties()->isType(FEATURESTATE))
{
for (unsigned int j = 0; j < stateBuf->getNumDiscreteStates(); j++)
{
state->setDiscreteState(i, stateBuf->getDiscreteState(j) + numFeatures);
state->setContinuousState(i, stateBuf->getContinuousState(j) * featureStateFactor);
i ++;
}
}
else
{
if (stateBuf->getStateProperties()->isType(DISCRETESTATE))
{
state->setDiscreteState(i, stateBuf->getDiscreteState(0) + numFeatures);
state->setContinuousState(i, featureStateFactor);
i ++;
}
}
numFeatures += (*it)->getDiscreteStateSize();
}
normalizeFeatures(state);
}
svmModel SMO::train()
{
int passes = 0;
int maxPasses = 25;
int numChanged = 0;
int examineAll = 1;
alpha.resize(points.size(), 0);
errorCache.resize(points.size(), 0);
w.resize(points.size(), 0);
threshold = 0;
// Normalize the input points
normalizeFeatures();
// SMO outer loop:
// Every iteration altranates between sweep through all points examineAll = 1 and sweep through non-boundary points examineAll = 0.
while ((numChanged > 0 || examineAll) && (passes < maxPasses)) {
numChanged = 0;
if (examineAll) {
for (unsigned int i = 0; i < points.size(); i++)
{
if (plugin->isAborted() == true)
{
plugin->progress.report("User Aborted", 0, ABORT, true);
return svmModel();
}
plugin->progress.report("Training SVM for class " + className, passes*100/maxPasses + (i+1)*100/maxPasses/points.size(), NORMAL);
numChanged += examineExample (i);
}
}
else {
for (unsigned int i = 0; i < points.size(); i++)
if (alpha[i] != 0 && alpha[i] != C)
{
if (plugin->isAborted() == true)
{
plugin->progress.report("User Aborted", 0, ABORT, true);
return svmModel();
}
plugin->progress.report("Training SVM for class " + className, passes*100/maxPasses + (i+1)*100/maxPasses/points.size(), NORMAL);
numChanged += examineExample (i);
}
}
if (examineAll == 1)
examineAll = 0;
else if (numChanged == 0)
examineAll = 1;
/*
double s = 0.0;
for (unsigned int i=0; i<points.size(); i++)
s += alpha[i];
double t = 0.;
for (unsigned int i=0; i<points.size(); i++)
for (unsigned int j=0; j<points.size(); j++)
t += alpha[i]*alpha[j]*target[i]*target[j]*kernel(points[i],points[j]);
double objFunc = (s - t/2.0);
plugin->progress.report(QString("The value of objective function should increase with each iteration.\n The value of objective function = %1").arg(objFunc).toStdString(), (passes*100)/maxPasses, NORMAL);
*/
passes++;
}
plugin->progress.report("Finished training SVM for class " + className, 100, NORMAL, true);
// Get the model for this class
vector<double> m_alpha;
vector<int> m_target;
int numberOfsupportVectors = 0;
vector<point> supportVectors;
int attributes = points[0].size();
for (unsigned int i = 0; i < alpha.size() ; i++)
{
if (alpha[i] > 0)
{
m_alpha.push_back(alpha[i]);
}
}
for (unsigned int i = 0; i < alpha.size(); i++)
{
if (alpha[i] > 0)
{
numberOfsupportVectors++;
supportVectors.push_back(points[i]);
m_target.push_back(target[i]);
}
}
svmModel model = svmModel(className, kernelType, threshold, attributes, w, sigma, numberOfsupportVectors, m_alpha, supportVectors, m_target, mu, stdv);
// Compute the error rates.
plugin->progress.report("Computing Error Rates using the model for class " + className, 0, NORMAL, true);
double trainErrorRate = 0;
double testErrorRate = 0;
double crossValidationErrorRate = 0;
for (unsigned int i = 0; i < points.size(); i++)
{
plugin->progress.report("Comuting Error Rates using the model for class " + className, (i+1)*60.0/points.size(), NORMAL, true);
if (model.predict(points[i]) > 0 != target[i] > 0)
trainErrorRate++;
}
trainErrorRate = trainErrorRate*100/points.size();
for (unsigned int i = 0; i < testSet.size(); i++)
{
plugin->progress.report("Comuting Error Rates using the model for class " + className, 60 + (i+1)*20.0/testSet.size(), NORMAL, true);
if (model.predict(testSet[i]) > 0 != yTest[i] > 0)
testErrorRate++;
}
testErrorRate = testErrorRate*100/points.size();
for (unsigned int i = 0; i < crossValidationSet.size(); i++)
{
plugin->progress.report("Comuting Error Rates using the model for class " + className, 80 + (i+1)*20/crossValidationSet.size(), NORMAL, true);
if (model.predict(crossValidationSet[i]) > 0 != yCV[i] > 0)
crossValidationErrorRate++;
}
crossValidationErrorRate = crossValidationErrorRate*100/crossValidationSet.size();
if (testSet.size() && crossValidationSet.size())
plugin->progress.report(QString("%1\nTrain error = %2\nCrossValidation error = %3\nTest error = %4\n").arg(className.c_str()).arg(trainErrorRate).arg(crossValidationErrorRate).arg(testErrorRate).toStdString(), 100, WARNING, true);
else
plugin->progress.report(QString("%1\nTrain error = %2").arg(className.c_str()).arg(trainErrorRate).toStdString(), 100, WARNING, true);
return model;
}
