/*************************************
* Function: update_feature_weight
* -------------------------------
* Given TrainingData and a WeakClassifier that has been weighted in
* get_best_classifier(), we recalculate the weights of all the features
*
* td: training data (set of features)
* wc: (weighted) weak classifier
*
* returns true if successful, false otherwise
*/
bool AdaBooster::update_feature_weight(TrainingData &td, WeakClassifier &wc){
// check that WeakClassifier has actually been weighted
if (wc.weight() < 0){
printf("Error in update_feature_weight: WeakClassifier has invalid weight\n");
return false;
}
// traverse features in feature set and adjust their weights
for (unsigned int i=0; i<num_features; i++){
FeatureVector* fv = td.feature(i);
// either 1 or -1 (used in weight below)
int is_correct = is_classifier_correct(wc, *fv) ? 1 : -1;
// calculate and update weight
// note M_E := 2.71828
float weight = pow(M_E, (double) -1 * wc.weight() * is_correct);
td.setWeight(i, td.weight(i)*weight);
}
// calculate normalization factor
float norm = 0;
for (unsigned int i=0; i<num_features; i++)
norm += td.weight(i);
// normalize feature weights
for (unsigned int i=0; i<num_features; i++)
td.setWeight(i, td.weight(i)/norm);
return true; // successful
}
void CascadeWriter::writeStages()
{
xml.writeStartElement("stages");
for (int i = 0; i < m_cascadeObj.stageNum(); i++)
{
QString stageStr = QString(" stage %1 ").arg(i);
xml.writeComment(stageStr);
Stage stage = m_cascadeObj.getStage(i);
xml.writeStartElement("_");
xml.writeTextElement("maxWeakCount", QString::number(stage.maxWeakCount()));
xml.writeTextElement("stageThreshold", QString::number(stage.stageThreshold()));
// write weak classifiers
xml.writeStartElement("weakClassifiers");
for (int j = 0; j < stage.maxWeakCount(); j++)
{
WeakClassifier wc = stage.getWeakClassifier(j);
xml.writeStartElement("_");
xml.writeTextElement("internalNodes", wc.internalNodes());
xml.writeTextElement("leafValues", wc.leafValues());
xml.writeEndElement();
}
xml.writeEndElement();
xml.writeEndElement();
}
xml.writeEndElement();
}
/**************************************
* Fucntion: is_classifier_correct
* -------------------------------
* returns true if weak classifier (wc) correctly identified the
* feature vector (fv), false otherwise.
*/
bool AdaBooster::is_classifier_correct(WeakClassifier &wc, FeatureVector &fv){
// check if threshold is greater than (or equal to) feature
bool guess = ( wc.threshold() >= fv.at(wc.dimension()) );
// if classifier is flipped, negate guess
guess = wc.isFlipped() ? !guess : guess;
// find actual value of point
bool real = ( fv.val() == POS );
// return if guess and real agree
return ( real == guess );
}
bool AdaBoost::addWeakClassifier(const WeakClassifier &weakClassifer){
WeakClassifier *weakClassiferPtr = weakClassifer.createNewInstance();
weakClassifiers.push_back( weakClassiferPtr );
return true;
}
bool AdaBoost::setWeakClassifier(const WeakClassifier &weakClassifer){
//Clear any previous weak classifers
clearWeakClassifiers();
WeakClassifier *weakClassiferPtr = weakClassifer.createNewInstance();
weakClassifiers.push_back( weakClassiferPtr );
return true;
}
float StrongClassifier::evaluate(const std::vector<float> &features) const { //feature
float decision = 0;
for (int i=0; i<m_weakClassifiers.size(); i++) {
WeakClassifier weak = m_weakClassifiers.at(i);
int sign;
if ( (weak.threshold() > features[weak.dimension()] && !weak.isFlipped()) ||
(weak.threshold() < features[weak.dimension()] && weak.isFlipped()) )
sign = 1;
else
sign = -1;
decision += weak.weight() * sign;
}
return decision;
}
void AdaBoostClassifier::AddOneSimpleClassifier(bool* used)
{
int i;
REAL alpha,beta;
WeakClassifier minsc;
REAL minerror;
int minindex;
minerror = REAL(1.01); minindex = -1;
for(i=0;i<gTotalCount;i++) gLabels[i] = gTrainSet[i].m_iLabel;
NormalizeWeight();
TRACE("%f %f %f %f\n",*min_element(gWeights,gWeights+gFaceCount)*10000,*max_element(gWeights,gWeights+gFaceCount)*10000,
*min_element(gWeights+gFaceCount,gWeights+gTotalCount)*10000,*max_element(gWeights+gFaceCount,gWeights+gTotalCount)*10000);
for(i=0;i<gTotalFeatures;i++)
{
if(used[i]) continue;
SingleFeatureClassifier(gLabels,gClassifiers[i],gTable[i]);
if(gClassifiers[i].m_rError<minerror)
{
minerror = gClassifiers[i].m_rError;
minsc = gClassifiers[i];
minindex = i;
}
}
used[minindex] = true;
beta = minsc.m_rError / (REAL(1.0)-minsc.m_rError);
for(i=0;i<gTotalCount;i++)
if(minsc.Apply(gTrainSet[i]) == gTrainSet[i].m_iLabel)
gWeights[i] *= beta;
if(beta<REAL(1e-8)) beta = REAL(1e-8);
alpha = -log(beta);
m_WeakClassifiers[m_iCount] = minsc;
m_rAlphas[m_iCount] = alpha;
m_rThreshold += (REAL(0.5)*alpha);
m_iCount++;
}
void SingleFeatureClassifier(const int* const labels,WeakClassifier& sc,const int* const indexes)
{
int i;
REAL min1; REAL min2;
REAL e1,e2;
int pos1,pos2;
// compute errors1, suppose parity is 1, that is f(x)<thresh ==> h(x) = 1
// compute errors2, suppose parity is 0, that is f(x)>thresh ==> h(x) = 1
e1 = 0.0; for(i=0;i<gTotalCount;i++) if(labels[i]!=0) e1+=gWeights[i];
e2 = REAL(1.0)-e1;
min1 = e1; pos1 = 0;
min2 = e2; pos2 = 0;
for(i=0;i<gTotalCount;i++)
{
if(labels[indexes[i]]!=0) e1-=gWeights[indexes[i]]; else e1+=gWeights[indexes[i]];
if(e1<min1) { min1=e1; pos1=i; }
e2 = 1 - e1;
if(e2<min2) { min2=e2; pos2=i; }
}
pos1++; if(pos1==gTotalCount) pos1--;
pos2++; if(pos2==gTotalCount) pos2--;
if(min1<min2)
{
sc.m_iParity = 1;
sc.m_rError = min1;
sc.m_rThreshold = sc.GetOneFeature(gTrainSet[indexes[pos1]]);
}
else
{
sc.m_iParity = 0;
sc.m_rError = min2;
sc.m_rThreshold = sc.GetOneFeature(gTrainSet[indexes[pos2]]);
}
}
bool AdaBoost::train_(ClassificationData &trainingData){
//Clear any previous model
clear();
if( trainingData.getNumSamples() <= 1 ){
errorLog << "train_(ClassificationData &trainingData) - There are not enough training samples to train a model! Number of samples: " << trainingData.getNumSamples() << endl;
return false;
}
numInputDimensions = trainingData.getNumDimensions();
numClasses = trainingData.getNumClasses();
const UINT M = trainingData.getNumSamples();
const UINT POSITIVE_LABEL = WEAK_CLASSIFIER_POSITIVE_CLASS_LABEL;
const UINT NEGATIVE_LABEL = WEAK_CLASSIFIER_NEGATIVE_CLASS_LABEL;
double alpha = 0;
const double beta = 0.001;
double epsilon = 0;
TrainingResult trainingResult;
const UINT K = (UINT)weakClassifiers.size();
if( K == 0 ){
errorLog << "train_(ClassificationData &trainingData) - No weakClassifiers have been set. You need to set at least one weak classifier first." << endl;
return false;
}
classLabels.resize(numClasses);
models.resize(numClasses);
ranges = trainingData.getRanges();
//Scale the training data if needed
if( useScaling ){
trainingData.scale(ranges,0,1);
}
//Create the weights vector
VectorDouble weights(M);
//Create the error matrix
MatrixDouble errorMatrix(K,M);
for(UINT classIter=0; classIter<numClasses; classIter++){
//Get the class label for the current class
classLabels[classIter] = trainingData.getClassLabels()[classIter];
//Set the class label of the current model
models[ classIter ].setClassLabel( classLabels[classIter] );
//Setup the labels for this class, POSITIVE_LABEL == 1, NEGATIVE_LABEL == 2
ClassificationData classData;
classData.setNumDimensions(trainingData.getNumDimensions());
for(UINT i=0; i<M; i++){
UINT label = trainingData[i].getClassLabel()==classLabels[classIter] ? POSITIVE_LABEL : NEGATIVE_LABEL;
VectorDouble trainingSample = trainingData[i].getSample();
classData.addSample(label,trainingSample);
}
//Setup the initial training sample weights
std::fill(weights.begin(),weights.end(),1.0/M);
//Run the boosting loop
bool keepBoosting = true;
UINT t = 0;
while( keepBoosting ){
//Pick the classifier from the family of classifiers that minimizes the total error
UINT bestClassifierIndex = 0;
double minError = numeric_limits<double>::max();
for(UINT k=0; k<K; k++){
//Get the k'th possible classifier
WeakClassifier *weakLearner = weakClassifiers[k];
//Train the current classifier
if( !weakLearner->train(classData,weights) ){
errorLog << "Failed to train weakLearner!" << endl;
return false;
}
//Compute the weighted error for this clasifier
double e = 0;
double positiveLabel = weakLearner->getPositiveClassLabel();
double numCorrect = 0;
double numIncorrect = 0;
for(UINT i=0; i<M; i++){
//Only penalize errors
double prediction = weakLearner->predict( classData[i].getSample() );
if( (prediction == positiveLabel && classData[i].getClassLabel() != POSITIVE_LABEL) || //False positive
(prediction != positiveLabel && classData[i].getClassLabel() == POSITIVE_LABEL) ){ //False negative
e += weights[i]; //Increase the error proportional to the weight of the example
errorMatrix[k][i] = 1; //Flag that there was an error
numIncorrect++;
}else{
errorMatrix[k][i] = 0; //Flag that there was no error
numCorrect++;
}
}
trainingLog << "PositiveClass: " << classLabels[classIter] << " Boosting Iter: " << t << " Classifier: " << k << " WeightedError: " << e << " NumCorrect: " << numCorrect/M << " NumIncorrect: " <<numIncorrect/M << endl;
if( e < minError ){
minError = e;
bestClassifierIndex = k;
}
}
epsilon = minError;
//Set alpha, using the M1 weight value, small weights (close to 0) will receive a strong weight in the final classifier
alpha = 0.5 * log( (1.0-epsilon)/epsilon );
trainingLog << "PositiveClass: " << classLabels[classIter] << " Boosting Iter: " << t << " Best Classifier Index: " << bestClassifierIndex << " MinError: " << minError << " Alpha: " << alpha << endl;
if( isinf(alpha) ){ keepBoosting = false; trainingLog << "Alpha is INF. Stopping boosting for current class" << endl; }
if( 0.5 - epsilon <= beta ){ keepBoosting = false; trainingLog << "Epsilon <= Beta. Stopping boosting for current class" << endl; }
if( ++t >= numBoostingIterations ) keepBoosting = false;
trainingResult.setClassificationResult(t, minError, this);
trainingResults.push_back(trainingResult);
trainingResultsObserverManager.notifyObservers( trainingResult );
if( keepBoosting ){
//Add the best weak classifier to the committee
models[ classIter ].addClassifierToCommitee( weakClassifiers[bestClassifierIndex], alpha );
//Update the weights for the next boosting iteration
double reWeight = (1.0 - epsilon) / epsilon;
double oldSum = 0;
double newSum = 0;
for(UINT i=0; i<M; i++){
oldSum += weights[i];
//Only update the weights that resulted in an incorrect prediction
if( errorMatrix[bestClassifierIndex][i] == 1 ) weights[i] *= reWeight;
newSum += weights[i];
}
//Normalize all the weights
//This results to increasing the weights of the samples that were incorrectly labelled
//While decreasing the weights of the samples that were correctly classified
reWeight = oldSum/newSum;
for(UINT i=0; i<M; i++){
weights[i] *= reWeight;
}
}else{
trainingLog << "Stopping boosting training at iteration : " << t-1 << " with an error of " << epsilon << endl;
if( t-1 == 0 ){
//Add the best weak classifier to the committee (we have to add it as this is the first iteration)
if( isinf(alpha) ){ alpha = 1; } //If alpha is infinite then the first classifier got everything correct
models[ classIter ].addClassifierToCommitee( weakClassifiers[bestClassifierIndex], alpha );
}
}
}
}
//Normalize the weights
for(UINT k=0; k<numClasses; k++){
models[k].normalizeWeights();
}
//Flag that the model has been trained
trained = true;
//Setup the data for prediction
predictedClassLabel = 0;
maxLikelihood = 0;
classLikelihoods.resize(numClasses);
classDistances.resize(numClasses);
return true;
}
void WriteSimpleClassifiers(void)
{
int x1,x2,x3,x4,y1,y2,y3,y4;
WeakClassifier sc;
int index;
ofstream f;
int pickup=9;
f.open("classifiers.txt");
index = 0;
for(x1=0;x1<gSx;x1+=1)
for(x3=x1+2;x3<=gSx;x3+=2)
for(y1=0;y1<gSy;y1+=1)
for(y3=y1+1;y3<=gSy;y3+=1)
{
x2 = (x1+x3)/2;
y2 = y4 = x4 = -1;
sc.m_iType = 0; sc.m_rError = 0.0;
sc.x1 = x1; sc.x2 = x2; sc.x3 = x3; sc.x4 = x4;
sc.y1 = y1; sc.y2 = y2; sc.y3 = y3; sc.y4 = y4;
sc.m_iParity = 0;
sc.m_rThreshold = 0.0;
if(index%10==pickup) sc.WriteToFile(f);
index++;
}
for(x1=0;x1<gSx;x1+=1)
for(x3=x1+1;x3<=gSx;x3+=1)
for(y1=0;y1<gSy;y1+=1)
for(y3=y1+2;y3<=gSy;y3+=2)
{
y2 = (y1+y3)/2;
x2 = x4 = y4 = -1;
sc.m_iType = 1; sc.m_rError = 0.0;
sc.x1 = x1; sc.x2 = x2; sc.x3 = x3; sc.x4 = x4;
sc.y1 = y1; sc.y2 = y2; sc.y3 = y3; sc.y4 = y4;
sc.m_iParity = 0;
sc.m_rThreshold = 0.0;
if(index%10==pickup) sc.WriteToFile(f);
index++;
}
for(x1=0;x1<gSx;x1++)
for(x4=x1+3;x4<=gSx;x4+=3)
for(y1=0;y1<gSy;y1+=1)
for(y3=y1+1;y3<=gSy;y3+=1)
{
x2 = x1 + (x4-x1)/3;
x3 = x2 + (x4-x1)/3;
y2 = y4 = -1;
sc.m_iType = 2; sc.m_rError = 0.0;
sc.x1 = x1; sc.x2 = x2; sc.x3 = x3; sc.x4 = x4;
sc.y1 = y1; sc.y2 = y2; sc.y3 = y3; sc.y4 = y4;
sc.m_iParity = 0;
sc.m_rThreshold = 0.0;
if(index%10==pickup) sc.WriteToFile(f);
index++;
}
for(x1=0;x1<gSx;x1++)
for(x3=x1+1;x3<=gSx;x3+=1)
for(y1=0;y1<gSy;y1++)
for(y4=y1+3;y4<=gSy;y4+=3)
{
y2 = y1 + (y4-y1)/3;
y3 = y2 + (y4-y1)/3;
x2 = x4 = -1;
sc.m_iType = 3; sc.m_rError = 0.0;
sc.x1 = x1; sc.x2 = x2; sc.x3 = x3; sc.x4 = x4;
sc.y1 = y1; sc.y2 = y2; sc.y3 = y3; sc.y4 = y4;
sc.m_iParity = 0;
sc.m_rThreshold = 0.0;
if(index%10==pickup) sc.WriteToFile(f);
index++;
}
for(x1=0;x1<gSx;x1+=1)
for(x3=x1+2;x3<=gSx;x3+=2)
for(y1=0;y1<gSy;y1+=1)
for(y3=y1+2;y3<=gSy;y3+=2)
{
x2 = (x1+x3)/2;
y2 = (y1+y3)/2;
x4 = y4 = -1;
sc.m_iType = 4; sc.m_rError = 0.0;
sc.x1 = x1; sc.x2 = x2; sc.x3 = x3; sc.x4 = x4;
sc.y1 = y1; sc.y2 = y2; sc.y3 = y3; sc.y4 = y4;
sc.m_iParity = 0;
sc.m_rThreshold = 0.0;
if(index%10==pickup) sc.WriteToFile(f);
index++;
}
f.close();
}
/**********************************************
* Function: classifierGuess
* -------------------------
* Returns true if classifier decided a feature was POS, otherwise return false
*/
bool ImageTester::classifierGuess(WeakClassifier &wc, float value){
return ( (wc.threshold() > value && !wc.isFlipped()) ||
(wc.threshold() < value && wc.isFlipped()) );
}
WeakClassifier::WeakClassifier(const WeakClassifier &other)
{
m_internalNodes = other.internalNodes();
m_leafValues = other.leafValues();
}
