int main (int argc, char* argv[]){
RegressionDataset dataset;
dataset.setName("IAM-sequenced10px");
fillImageVector(argv[1], 0, dataset, 10);
cout << dataset.getMean();
cout << dataset.getStandardDeviation();
dataset.save("../xml/IAM-10.xml");
return EXIT_SUCCESS;
}
int main(int argc, char **argv) {
RegressionDataset data;
importCSV(data, "blogData_train.csv", LAST_COLUMN,1,',','#', 2<<16);
LinearRegression trainer(100);
LinearModel<> model;
Timer time;
trainer.train(model, data);
double time_taken = time.stop();
SquaredLoss<> loss;
cout << "Residual sum of squares:" << loss(data.labels(),model(data.inputs()))<<std::endl;
cout << "Time:\n" << time_taken << endl;
cout << time_taken << endl;
}
//Train model with a regression dataset
void CARTTrainer::train(ModelType& model, RegressionDataset const& dataset)
{
//Store the number of input dimensions
m_inputDimension = inputDimension(dataset);
//Pass input dimension (i.e., number of attributes) to tree model
model.setInputDimension(m_inputDimension);
//Store the size of the labels
m_labelDimension = labelDimension(dataset);
// create cross-validation folds
RegressionDataset set=dataset;
CVFolds<RegressionDataset > folds = createCVSameSize(set, m_numberOfFolds);
double bestErrorRate = std::numeric_limits<double>::max();
CARTClassifier<RealVector>::TreeType bestTree;
for (unsigned fold = 0; fold < m_numberOfFolds; ++fold){
//Run through all the cross validation sets
RegressionDataset dataTrain = folds.training(fold);
RegressionDataset dataTest = folds.validation(fold);
std::size_t numTrainElements = dataTrain.numberOfElements();
AttributeTables tables = createAttributeTables(dataTrain.inputs());
std::vector < RealVector > labels(numTrainElements);
boost::copy(dataTrain.labels().elements(),labels.begin());
//Build tree form this fold
CARTClassifier<RealVector>::TreeType tree = buildTree(tables, dataTrain, labels, 0, dataTrain.numberOfElements());
//Add the tree to the model and prune
model.setTree(tree);
while(true){
//evaluate the error of current tree
SquaredLoss<> loss;
double error = loss.eval(dataTest.labels(), model(dataTest.inputs()));
if(error < bestErrorRate){
//We have found a subtree that has a smaller error rate when tested!
bestErrorRate = error;
bestTree = tree;
}
if(tree.size() == 1) break;
pruneTree(tree);
model.setTree(tree);
}
}
SHARK_CHECK(bestTree.size() > 0, "We should never set a tree that is empty.");
model.setTree(bestTree);
}
void fillImageVector(char* _directoryName,int _mode, RegressionDataset& _dataset, int _sectionLength){
DIR *dp;
struct dirent *ep;
dp = opendir (_directoryName);
if (dp != NULL){
while (ep = readdir (dp)){
//append an image to the vector only if it has a .png extension
if(strstr(ep->d_name,".png")!=NULL){
char str[200]="";
strcat(str,_directoryName);
strcat(str,"/");
strcat(str,ep->d_name);
Mat image = imread(str,_mode);
int subparts=floor(((float)image.cols)/((float)_sectionLength));
vector<FeatureVector> features;
for(int i=0;i<subparts;i++){
FeatureVector fv(_sectionLength*image.rows);
for(int j=0;j<_sectionLength;j++){
for(int k=0;k<image.rows;k++){
if((int)image.at<uchar>(k,i*_sectionLength+j)==255){
fv[j*image.rows+k]=1.0;
}
else{
fv[j*image.rows+k]=0.0;
}
}
}
features.push_back(fv);
}
_dataset.addSequence(features,features);
}
}
(void) closedir (dp);
}
else{
cerr << "ERROR in CreateImageVector : Couldn't open the directory";
exit(1);
}
}
// Regression
void RFTrainer::train(RFClassifier& model, RegressionDataset const& dataset)
{
model.clearModels(); // added by TG 23.02.2015
//TODO O.K.: i am just fixing these things for now so that they are working.
//Store the number of input dimensions
m_inputDimension = inputDimension(dataset);
//Store the size of the labels
m_labelDimension = labelDimension(dataset);
model.setInputDimension(m_inputDimension);
model.setLabelDimension(m_labelDimension);
m_regressionLearner = true;
setDefaults();
//we need direct element access sicne we need to generate elementwise subsets
std::size_t subsetSize = static_cast<std::size_t>(dataset.numberOfElements()*m_OOBratio);
DataView<RegressionDataset const> elements(dataset);
//Generate m_B trees
SHARK_PARALLEL_FOR(int i = 0; i < (int)m_B; ++i){
//For each tree generate a subset of the dataset
//generate indices of the dataset (pick k out of n elements)
std::vector<std::size_t> subsetIndices(dataset.numberOfElements());
boost::iota(subsetIndices,0);
boost::random_shuffle(subsetIndices);
// create oob indices
std::vector<std::size_t>::iterator oobStart = subsetIndices.begin() + subsetSize;
std::vector<std::size_t>::iterator oobEnd = subsetIndices.end();
//generate the dataset by copying (TODO: this is a quick fix!
subsetIndices.erase(oobStart, oobEnd);
RegressionDataset dataTrain = toDataset(subset(elements,subsetIndices));
AttributeTables tables;
createAttributeTables(dataTrain.inputs(), tables);
std::size_t dataTrainSize = dataTrain.numberOfElements();
std::vector<RealVector> labels;
for(std::size_t i = 0; i < dataTrainSize; i++){
labels.push_back(dataTrain.element(i).label);
}
CARTClassifier<RealVector>::TreeType tree = buildTree(tables, dataTrain, labels, 0);
CARTClassifier<RealVector> cart(tree, m_inputDimension);
// if oob error or importances have to be computed, create an oob sample
if(m_computeOOBerror || m_computeFeatureImportances){
std::vector<std::size_t> subsetIndicesOOB(oobStart, oobEnd);
RegressionDataset dataOOB = toDataset(subset(elements, subsetIndicesOOB));
// if importances should be computed, oob errors are computed implicitly
if(m_computeFeatureImportances){
cart.computeFeatureImportances(dataOOB);
} // if importances should not be computed, only compute the oob errors
else{
cart.computeOOBerror(dataOOB);
}
}
SHARK_CRITICAL_REGION{
model.addModel(cart);
}
}
if(m_computeOOBerror){
model.computeOOBerror();
}
if(m_computeFeatureImportances){
model.computeFeatureImportances();
}
}
CARTClassifier<RealVector>::TreeType RFTrainer::buildTree(AttributeTables& tables, RegressionDataset const& dataset, std::vector<RealVector> const& labels, std::size_t nodeId ){
//Construct tree
CARTClassifier<RealVector>::NodeInfo nodeInfo;
nodeInfo.nodeId = nodeId;
nodeInfo.attributeIndex = 0;
nodeInfo.attributeValue = 0.0;
nodeInfo.leftNodeId = 0;
nodeInfo.rightNodeId = 0;
nodeInfo.label = average(labels);
nodeInfo.misclassProp = 0.0;
nodeInfo.r = 0;
nodeInfo.g = 0.0;
CARTClassifier<RealVector>::TreeType tree, lTree, rTree;
//n = Total number of cases in the dataset
std::size_t n = tables[0].size();
bool isLeaf = false;
if(n <= m_nodeSize){
isLeaf = true;
}else{
//label vectors
std::vector<RealVector> bestLabels, tmpLabels;
RealVector labelSumAbove(m_labelDimension), labelSumBelow(m_labelDimension);
//Randomly select the attributes to test for split
set<std::size_t> tableIndicies;
generateRandomTableIndicies(tableIndicies);
//Index of attributes
std::size_t attributeIndex, bestAttributeIndex, bestAttributeValIndex;
//Attribute values
double bestAttributeVal;
double bestImpurity = -1;
std::size_t prev;
bool doSplit = false;
for (set<std::size_t>::iterator it=tableIndicies.begin() ; it != tableIndicies.end(); it++ ){
attributeIndex = *it;
labelSumBelow.clear();
labelSumAbove.clear();
tmpLabels.clear();
//Create a labels table, that corresponds to the sorted attribute
for(std::size_t k=0; k<tables[attributeIndex].size(); k++){
tmpLabels.push_back(dataset.element(tables[attributeIndex][k].id).label);
labelSumBelow += dataset.element(tables[attributeIndex][k].id).label;
}
labelSumAbove += tmpLabels[0];
labelSumBelow -= tmpLabels[0];
for(std::size_t i=1; i<n; i++){
prev = i-1;
if(tables[attributeIndex][prev].value!=tables[attributeIndex][i].value){
std::size_t n1=i;
std::size_t n2 = n-n1;
//Calculate the squared error of the split
double impurity = (n1*totalSumOfSquares(tmpLabels,0,n1,labelSumAbove)+n2*totalSumOfSquares(tmpLabels,n1,n2,labelSumBelow))/(double)(n);
if(impurity<bestImpurity || bestImpurity<0){
//Found a more pure split, store the attribute index and value
doSplit = true;
bestImpurity = impurity;
bestAttributeIndex = attributeIndex;
bestAttributeValIndex = prev;
bestAttributeVal = tables[attributeIndex][bestAttributeValIndex].value;
bestLabels = tmpLabels;
}
}
labelSumAbove += tmpLabels[i];
labelSumBelow -= tmpLabels[i];
}
}
if(doSplit){
//Split the attribute tables
AttributeTables rTables, lTables;
splitAttributeTables(tables, bestAttributeIndex, bestAttributeValIndex, lTables, rTables);
tables.clear();//save memory
//Split the labels
std::vector<RealVector> lLabels, rLabels;
for(std::size_t i = 0; i <= bestAttributeValIndex; i++){
lLabels.push_back(bestLabels[i]);
}
for(std::size_t i = bestAttributeValIndex+1; i < bestLabels.size(); i++){
rLabels.push_back(bestLabels[i]);
}
//Continue recursively
nodeInfo.attributeIndex = bestAttributeIndex;
nodeInfo.attributeValue = bestAttributeVal;
nodeInfo.leftNodeId = 2*nodeId+1;
nodeInfo.rightNodeId = 2*nodeId+2;
lTree = buildTree(lTables, dataset, lLabels, nodeInfo.leftNodeId);
rTree = buildTree(rTables, dataset, rLabels, nodeInfo.rightNodeId);
}else{
//Leaf node
isLeaf = true;
}
}
if(isLeaf){
tree.push_back(nodeInfo);
return tree;
}
tree.push_back(nodeInfo);
tree.insert(tree.end(), lTree.begin(), lTree.end());
tree.insert(tree.end(), rTree.begin(), rTree.end());
//Store entry in the tree
return tree;
}
//Build CART tree in the regression case
CARTTrainer::TreeType CARTTrainer::buildTree(AttributeTables const& tables, RegressionDataset const& dataset, std::vector<RealVector> const& labels, std::size_t nodeId, std::size_t trainSize){
size_t nextId = 0;
std::queue<BuildData> bd;
bd.emplace(tables, labels, nextId++);
TreeType tree;
while(!bd.empty())
{
BuildData current(std::move(bd.front()));
bd.pop();
//Construct tree
CARTClassifier<RealVector>::NodeInfo nodeInfo;
nodeInfo.nodeId = current.nodeId;
nodeInfo.label = mean(current.labels);
nodeInfo.leftNodeId = 0;
nodeInfo.rightNodeId = 0;
//Store the Total Sum of Squares (TSS)
RealVector labelSum = current.labels[0];
for(std::size_t i=1; i< current.labels.size(); i++){
labelSum += current.labels[0];
}
nodeInfo.misclassProp = totalSumOfSquares(current.labels, 0, current.labels.size(), labelSum)*((double)dataset.numberOfElements()/trainSize);
//n = Total number of cases in the dataset
//n1 = Number of cases to the left child node
//n2 = number of cases to the right child node
std::size_t n, n1, n2;
n = current.tables[0].size();
size_t splitcount = n/m_numSplits;
splitcount = splitcount ? splitcount : 1; // Make sure splitcount is never 0
std::cout << labels.size() << " " << splitcount << " " << m_nodeSize << std::endl;
if(n > m_nodeSize){
//label vectors
std::vector<RealVector> bestLabels, tmpLabels;
RealVector labelSumAbove(m_labelDimension), labelSumBelow(m_labelDimension);
//Index of attributes
std::size_t bestAttributeIndex = 0;
std::size_t bestAttributeValIndex = m_nodeSize;
//Attribute values
double bestAttributeVal = current.tables[bestAttributeIndex][bestAttributeValIndex-1].value;
double impurity, fullImpurity, bestImpurity = -1;
bool doSplit = false;
for (size_t attributeIndex = 0; attributeIndex< m_inputDimension; attributeIndex++){
labelSumBelow.clear();
labelSumAbove.clear();
tmpLabels.clear();
//Create a labels table, that corresponds to the sorted attribute
for(std::size_t k=0; k<current.tables[attributeIndex].size(); k++){
tmpLabels.push_back(dataset.element(current.tables[attributeIndex][k].id).label);
noalias(labelSumBelow) += dataset.element(current.tables[attributeIndex][k].id).label;
}
for(std::size_t i=splitcount; i<n; i += splitcount){
// cerr << "Trying split at att: " << attributeIndex << " and point: " << i << endl;
for(std::size_t j = i-splitcount; j < i; j++)
{
noalias(labelSumAbove) += tmpLabels[j];
noalias(labelSumBelow) -= tmpLabels[j];
}
if(current.tables[attributeIndex][i-splitcount].value!=current.tables[attributeIndex][i].value){
n1=i;
n2 = n-n1;
//Calculate the squared error of the split
fullImpurity = totalSumOfSquares(tmpLabels,0,n,labelSumBelow + labelSumAbove);
impurity = (n1*totalSumOfSquares(tmpLabels,0,n1,labelSumAbove)+n2*totalSumOfSquares(tmpLabels,n1,n2,labelSumBelow))/(double)(n);
double improvement = (fullImpurity - impurity) / fullImpurity;
if(improvement*100 >= m_splitImpurityGain && (impurity<bestImpurity || bestImpurity<0)){
//Found a more pure split, store the attribute index and value
doSplit = true;
bestImpurity = impurity;
bestAttributeIndex = attributeIndex;
bestAttributeValIndex = i;
bestAttributeVal = current.tables[attributeIndex][bestAttributeValIndex-1].value;
bestLabels = tmpLabels;
}
}
}
}
if(doSplit){
BuildData leftNode;
BuildData rightNode;
//Split the attribute tables
splitAttributeTables(current.tables, bestAttributeIndex, bestAttributeValIndex-1, leftNode.tables, rightNode.tables);
//Split the labels
for(std::size_t i = 0; i < bestAttributeValIndex; i++){
leftNode.labels.push_back(bestLabels[i]);
}
for(std::size_t i = bestAttributeValIndex; i < bestLabels.size(); i++){
rightNode.labels.push_back(bestLabels[i]);
}
//Continue recursively
nodeInfo.attributeIndex = bestAttributeIndex;
nodeInfo.attributeValue = bestAttributeVal;
nodeInfo.leftNodeId = nextId++;
nodeInfo.rightNodeId = nextId++;
leftNode.nodeId = nodeInfo.leftNodeId;
rightNode.nodeId = nodeInfo.rightNodeId;
bd.push(std::move(leftNode));
bd.push(std::move(rightNode));
}
}
tree.push_back(nodeInfo);
}
std::cerr << "Tree size: " << tree.size() << std::endl;
cerr << "Will return\n";
return tree;
}
int main(int argc, char* argv[]) {
RegressionDataset dataset;
dataset.load(argv[2]);
RegressionDataset dataset2;
dataset2.load(argv[3]);
cout << "Dataset loaded, total elements : " << dataset.getNumSamples() << endl;
Mask mask;
LearningParams params;
PBDNN pop;
ifstream inStream(argv[1]);
inStream >> pop;
inStream >> params;
vector<Vec3b> colors = createColorRepartition(pop.getPopulation().size());
ofstream log("training.log");
PopulationClusterBP pbp(pop, dataset, params, dataset2,mask, mask,log);
AEMeasurer mae;
DiversityMeasurer diversity(pop, dataset, mae);
diversity.measurePerformance();
cout << "Starting diversity" << endl << diversity.getDisagreementMatrix() << endl;
cout << "Starting overall diversity" << endl << diversity.getDisagreementScalar() << endl;
double t = (double) getTickCount();
pbp.train();
t = ((double) getTickCount() - t) / getTickFrequency();
log << "Time :" << t << endl;
cout << endl << "Saving network" << endl;
ofstream outStream("IAMpop.txt");
outStream << pop;
DiversityMeasurer diversity2(pop, dataset2, mae);
vector<NeuralNetworkPtr> population = pop.getPopulation();
vector<vector<int> > assignedTo = diversity2.findBestNetwork();
vector<vector<FeatureVector> > recomposed = diversity2.buildBestOutput();
vector<int> pngParams = vector<int>();
pngParams.push_back(CV_IMWRITE_PNG_COMPRESSION);
pngParams.push_back(3);
cout << "Recording Data" << endl;
for (uint i = 0; i < population.size(); i++) {
ostringstream dir;
dir << "network" << i;
if (mkdir(dir.str().c_str(), S_IRWXU) == 0) {
for (uint j = 0; j < dataset2.getNumSequences(); j++) {
ostringstream name;
name << "network" << i << "\/neuralNet" << i << "sample" << j << ".png";
vector<FeatureVector> features;
for (uint k = 0; k < dataset2[j].size(); k++) {
population[i]->forward(dataset2[j][k]);
features.push_back(population[i]->getOutputSignal());
}
vector<int> color = vector<int>(features.size(), i);
Mat image = buildColorMapImage(features, 3, color, colors);
imwrite(name.str(), image, pngParams);
}
} else {
throw invalid_argument("pbdnnCluster : could not create directory");
}
}
ostringstream dirR;
dirR << "recomposed";
if (mkdir(dirR.str().c_str(), S_IRWXU) == 0) {
for (uint j = 0; j < recomposed.size(); j++) {
ostringstream name;
name << "recomposed\/recomposedSample" << j << ".png";
vector<FeatureVector> features;
Mat image = buildColorMapImage(recomposed[j], 3, assignedTo[j], colors);
imwrite(name.str(), image, pngParams);
}
}
log.close();
return EXIT_SUCCESS;
}
int main(int argc, char* argv[]) {
vector<string> arguments;
arguments.push_back("population size");
arguments.push_back("number of hidden units");
arguments.push_back("number of iterations");
arguments.push_back("learning dataset");
arguments.push_back("validation dataset");
arguments.push_back("simple load mode");
cout << helper("Pbdnn cluster", "Train a population of neural networks on a regression task.", arguments) << endl;
if (argc != arguments.size() + 1) {
cerr << "Not enough arguments, " << argc - 1 << " given and " << arguments.size() << " required" << endl;
return EXIT_FAILURE;
}
int simpleMode = atoi(argv[6]);
RegressionDataset dataset;
RegressionDataset dataset2;
if(simpleMode!=0) {
dataset.simpleLoad(argv[4]);
dataset2.simpleLoad(argv[5]);
}
else {
dataset.load(argv[4]);
dataset2.load(argv[5]);
}
cout << "Learning dataset loaded, total elements : " << dataset.getNumSamples() << endl;
cout << "Validation dataset loaded, total elements : " << dataset2.getNumSamples() << endl;
int populationSize = atoi(argv[1]);
int numberOfHiddenUnits = atoi(argv[2]);
int iterations = atoi(argv[3]);
vector<Vec3b> colors = createColorRepartition(populationSize);
AEMeasurer mae;
PBDNN pop = PBDNN(populationSize, dataset.getFeatureVectorLength(), numberOfHiddenUnits, dataset.getMean(), dataset.getStandardDeviation());
DiversityMeasurer diversity(pop, dataset2, mae,0.01);
// 07/02/13 : Not sure if useful or not so stop doing it
/*do {
pop = PBDNN(populationSize, dataset.getFeatureVectorLength(), numberOfHiddenUnits, dataset.getMean(), dataset.getStandardDeviation());
diversity.measurePerformance();
} while (diversity.getDisagreementScalar() < 0.17);*/
Mask mask;
LearningParams params;
params.setActualIteration(0);
params.setMaxIterations(iterations);
params.setLearningRate(0.001);
params.setMaxTrainedPercentage(0.1);
params.setSavedDuringProcess(true);
params.setValidateEveryNIteration(100);
ofstream log("training.log");
PopulationClusterBP pbp(pop, dataset, params, dataset2, mask, mask, log);
// 07/02/13 : Not sure if useful or not so stop doing it
/*cout << "Starting diversity" << endl << diversity.getDisagreementMatrix() << endl;
cout << "Starting overall diversity : " << diversity.getDisagreementScalar() << endl;*/
cout << "Training" << endl;
double t = (double) getTickCount();
pbp.train();
t = ((double) getTickCount() - t) / getTickFrequency();
cout << "Time :" << t << endl;
cout << endl << "Saving network" << endl;
ofstream outStream("IAMpop.pop");
outStream << pop;
if(simpleMode == 0) {
cout << "Recording Data" << endl;
vector<NeuralNetworkPtr> population = pop.getPopulation();
vector<vector<int> > assignedTo = diversity.findBestNetwork();
vector<vector<FeatureVector> > recomposed = diversity.buildBestOutput();
vector<int> pngParams = vector<int>();
pngParams.push_back(CV_IMWRITE_PNG_COMPRESSION);
pngParams.push_back(3);
for (uint i = 0; i < population.size(); i++) {
ostringstream dir;
dir << "network" << i;
if (mkdir(dir.str().c_str(), S_IRWXU) == 0) {
for (uint j = 0; j < dataset2.getNumSequences(); j++) {
ostringstream name;
name << "network" << i << "\/neuralNet" << i << "sample" << j << ".png";
vector<FeatureVector> features;
for (uint k = 0; k < dataset2[j].size(); k++) {
population[i]->forward(dataset2[j][k]);
features.push_back(population[i]->getOutputSignal());
}
vector<int> color = vector<int>(features.size(), i);
Mat image = buildColorMapImage(features, 3, color, colors);
imwrite(name.str(), image, pngParams);
}
}
else {
throw invalid_argument("pbdnnCluster : could not create directory");
}
}
ostringstream dirR;
dirR << "recomposed";
if (mkdir(dirR.str().c_str(), S_IRWXU) == 0) {
for (uint j = 0; j < recomposed.size(); j++) {
ostringstream name;
name << "recomposed\/recomposedSample" << j << ".png";
vector<FeatureVector> features;
Mat image = buildColorMapImage(recomposed[j], 3, assignedTo[j], colors);
imwrite(name.str(), image, pngParams);
}
}
}
return EXIT_SUCCESS;
}
//Build CART tree in the regression case
CARTTrainer::TreeType CARTTrainer::buildTree(AttributeTables const& tables, RegressionDataset const& dataset, std::vector<RealVector> const& labels, std::size_t nodeId, std::size_t trainSize){
//Construct tree
CARTClassifier<RealVector>::NodeInfo nodeInfo;
nodeInfo.nodeId = nodeId;
nodeInfo.label = mean(labels);
nodeInfo.leftNodeId = 0;
nodeInfo.rightNodeId = 0;
//Store the Total Sum of Squares (TSS)
RealVector labelSum = labels[0];
for(std::size_t i=1; i< labels.size(); i++){
labelSum += labels[0];
}
nodeInfo.misclassProp = totalSumOfSquares(labels, 0, labels.size(), labelSum)*((double)dataset.numberOfElements()/trainSize);
TreeType tree, lTree, rTree;
//n = Total number of cases in the dataset
//n1 = Number of cases to the left child node
//n2 = number of cases to the right child node
std::size_t n, n1, n2;
n = tables[0].size();
if(n > m_nodeSize){
//label vectors
std::vector<RealVector> bestLabels, tmpLabels;
RealVector labelSumAbove(m_labelDimension), labelSumBelow(m_labelDimension);
//Index of attributes
std::size_t attributeIndex, bestAttributeIndex, bestAttributeValIndex;
//Attribute values
double bestAttributeVal;
double impurity, bestImpurity = -1;
std::size_t prev;
bool doSplit = false;
for ( attributeIndex = 0; attributeIndex< m_inputDimension; attributeIndex++){
labelSumBelow.clear();
labelSumAbove.clear();
tmpLabels.clear();
//Create a labels table, that corresponds to the sorted attribute
for(std::size_t k=0; k<tables[attributeIndex].size(); k++){
tmpLabels.push_back(dataset.element(tables[attributeIndex][k].id).label);
labelSumBelow += dataset.element(tables[attributeIndex][k].id).label;
}
labelSumAbove += tmpLabels[0];
labelSumBelow -= tmpLabels[0];
for(std::size_t i=1; i<n; i++){
prev = i-1;
if(tables[attributeIndex][prev].value!=tables[attributeIndex][i].value){
n1=i;
n2 = n-n1;
//Calculate the squared error of the split
impurity = (n1*totalSumOfSquares(tmpLabels,0,n1,labelSumAbove)+n2*totalSumOfSquares(tmpLabels,n1,n2,labelSumBelow))/(double)(n);
if(impurity<bestImpurity || bestImpurity<0){
//Found a more pure split, store the attribute index and value
doSplit = true;
bestImpurity = impurity;
bestAttributeIndex = attributeIndex;
bestAttributeValIndex = prev;
bestAttributeVal = tables[attributeIndex][bestAttributeValIndex].value;
bestLabels = tmpLabels;
}
}
labelSumAbove += tmpLabels[i];
labelSumBelow -= tmpLabels[i];
}
}
if(doSplit){
//Split the attribute tables
AttributeTables rTables, lTables;
splitAttributeTables(tables, bestAttributeIndex, bestAttributeValIndex, lTables, rTables);
//Split the labels
std::vector<RealVector> lLabels, rLabels;
for(std::size_t i = 0; i <= bestAttributeValIndex; i++){
lLabels.push_back(bestLabels[i]);
}
for(std::size_t i = bestAttributeValIndex+1; i < bestLabels.size(); i++){
rLabels.push_back(bestLabels[i]);
}
//Continue recursively
nodeInfo.attributeIndex = bestAttributeIndex;
nodeInfo.attributeValue = bestAttributeVal;
nodeInfo.leftNodeId = nodeId+1;
lTree = buildTree(lTables, dataset, lLabels, nodeInfo.leftNodeId, trainSize);
nodeInfo.rightNodeId = nodeInfo.leftNodeId + lTree.size();
rTree = buildTree(rTables, dataset, rLabels, nodeInfo.rightNodeId, trainSize);
}
}
tree.push_back(nodeInfo);
tree.insert(tree.end(), lTree.begin(), lTree.end());
tree.insert(tree.end(), rTree.begin(), rTree.end());
//Store entry in the tree
return tree;
}