void TouchTracker::Calibrator::getAverageTemplateDistance()
{
MLSignal temp(mWidth, mHeight);
MLSignal tempSample(kTemplateSize, kTemplateSize);
float sum = 0.f;
int samples = 0;
for(int j=0; j<mHeight; ++j)
{
for(int i=0; i<mWidth; ++i)
{
int idx = j*mWidth + i;
// put mean of input samples into temp signal at i, j
temp.clear();
tempSample.copy(mDataSum[idx]);
tempSample.scale(1.f / (float)mSampleCount[idx]);
temp.add2D(tempSample, i - kTemplateRadius, j - kTemplateRadius);
float diff = differenceFromTemplateTouch(temp, Vec2(i, j));
sum += diff;
samples++;
// debug() << "template diff [" << i << ", " << j << "] : " << diff << "\n";
}
}
mAvgDistance = sum / (float)samples;
}
void SampleSet::swapSamples(const int samIndexToMoveToBack, const int samIndexToMoveFromBack) {
//trainSet[samIndexToMoveToBack].swap(trainSet[samIndexToMoveFromBack]);
if (samIndexToMoveToBack != samIndexToMoveFromBack) {
Sample tempSample(samples[samIndexToMoveToBack]);
samples[samIndexToMoveToBack] = samples[samIndexToMoveFromBack];
samples[samIndexToMoveFromBack] = tempSample;
}
}
bool ClassificationData::loadDatasetFromFile(const std::string &filename){
std::fstream file;
file.open(filename.c_str(), std::ios::in);
UINT numClasses = 0;
clear();
if( !file.is_open() ){
errorLog << "loadDatasetFromFile(const std::string &filename) - could not open file!" << std::endl;
return false;
}
std::string word;
//Check to make sure this is a file with the Training File Format
file >> word;
if(word != "GRT_LABELLED_CLASSIFICATION_DATA_FILE_V1.0"){
errorLog << "loadDatasetFromFile(const std::string &filename) - could not find file header!" << std::endl;
file.close();
return false;
}
//Get the name of the dataset
file >> word;
if(word != "DatasetName:"){
errorLog << "loadDatasetFromFile(const std::string &filename) - failed to find DatasetName header!" << std::endl;
errorLog << word << std::endl;
file.close();
return false;
}
file >> datasetName;
file >> word;
if(word != "InfoText:"){
errorLog << "loadDatasetFromFile(const std::string &filename) - failed to find InfoText header!" << std::endl;
file.close();
return false;
}
//Load the info text
file >> word;
infoText = "";
while( word != "NumDimensions:" ){
infoText += word + " ";
file >> word;
}
//Get the number of dimensions in the training data
if( word != "NumDimensions:" ){
errorLog << "loadDatasetFromFile(const std::string &filename) - failed to find NumDimensions header!" << std::endl;
file.close();
return false;
}
file >> numDimensions;
//Get the total number of training examples in the training data
file >> word;
if( word != "TotalNumTrainingExamples:" && word != "TotalNumExamples:" ){
errorLog << "loadDatasetFromFile(const std::string &filename) - failed to find TotalNumTrainingExamples header!" << std::endl;
file.close();
return false;
}
file >> totalNumSamples;
//Get the total number of classes in the training data
file >> word;
if(word != "NumberOfClasses:"){
errorLog << "loadDatasetFromFile(string filename) - failed to find NumberOfClasses header!" << std::endl;
file.close();
return false;
}
file >> numClasses;
//Resize the class counter buffer and load the counters
classTracker.resize(numClasses);
//Get the total number of classes in the training data
file >> word;
if(word != "ClassIDsAndCounters:"){
errorLog << "loadDatasetFromFile(const std::string &filename) - failed to find ClassIDsAndCounters header!" << std::endl;
file.close();
return false;
}
for(UINT i=0; i<classTracker.getSize(); i++){
file >> classTracker[i].classLabel;
file >> classTracker[i].counter;
file >> classTracker[i].className;
}
//Check if the dataset should be scaled using external ranges
file >> word;
if(word != "UseExternalRanges:"){
errorLog << "loadDatasetFromFile(const std::string &filename) - failed to find UseExternalRanges header!" << std::endl;
file.close();
return false;
}
file >> useExternalRanges;
//If we are using external ranges then load them
if( useExternalRanges ){
externalRanges.resize(numDimensions);
for(UINT i=0; i<externalRanges.getSize(); i++){
file >> externalRanges[i].minValue;
file >> externalRanges[i].maxValue;
}
}
//Get the main training data
file >> word;
if( word != "LabelledTrainingData:" && word != "Data:"){
errorLog << "loadDatasetFromFile(const std::string &filename) - failed to find LabelledTrainingData header!" << std::endl;
file.close();
return false;
}
ClassificationSample tempSample( numDimensions );
data.resize( totalNumSamples, tempSample );
for(UINT i=0; i<totalNumSamples; i++){
UINT classLabel = 0;
VectorFloat sample(numDimensions,0);
file >> classLabel;
for(UINT j=0; j<numDimensions; j++){
file >> sample[j];
}
data[i].set(classLabel, sample);
}
file.close();
//Sort the class labels
sortClassLabels();
return true;
}
void swapSamples(Sample& s1, Sample& s2) {
Sample tempSample(s1);
s1 = s2;
s2 = tempSample;
}
