本文整理汇总了C++中TimeSeriesClassificationData::getClassData方法的典型用法代码示例。如果您正苦于以下问题：C++ TimeSeriesClassificationData::getClassData方法的具体用法？C++ TimeSeriesClassificationData::getClassData怎么用？C++ TimeSeriesClassificationData::getClassData使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类TimeSeriesClassificationData的用法示例。
在下文中一共展示了TimeSeriesClassificationData::getClassData方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: train_discrete
bool HMM::train_discrete(TimeSeriesClassificationData &trainingData){
clear();
if( trainingData.getNumSamples() == 0 ){
errorLog << "train_discrete(TimeSeriesClassificationData &trainingData) - There are no training samples to train the HMM classifer!" << endl;
return false;
}
if( trainingData.getNumDimensions() != 1 ){
errorLog << "train_discrete(TimeSeriesClassificationData &trainingData) - The number of dimensions in the training data must be 1. If your training data is not 1 dimensional then you must quantize the training data using one of the GRT quantization algorithms" << endl;
return false;
}
//Reset the HMM
numInputDimensions = trainingData.getNumDimensions();
numClasses = trainingData.getNumClasses();
discreteModels.resize( numClasses );
classLabels.resize( numClasses );
//Init the models
for(UINT k=0; k<numClasses; k++){
discreteModels[k].resetModel(numStates,numSymbols,modelType,delta);
discreteModels[k].setMaxNumEpochs( maxNumEpochs );
discreteModels[k].setMinChange( minChange );
}
//Train each of the models
for(UINT k=0; k<numClasses; k++){
//Get the class ID of this gesture
UINT classID = trainingData.getClassTracker()[k].classLabel;
classLabels[k] = classID;
//Convert this classes training data into a list of observation sequences
TimeSeriesClassificationData classData = trainingData.getClassData( classID );
vector< vector< UINT > > observationSequences;
if( !convertDataToObservationSequence( classData, observationSequences ) ){
return false;
}
//Train the model
if( !discreteModels[k].train( observationSequences ) ){
errorLog << "train_discrete(TimeSeriesClassificationData &trainingData) - Failed to train HMM for class " << classID << endl;
return false;
}
}
//Compute the rejection thresholds
nullRejectionThresholds.resize(numClasses);
for(UINT k=0; k<numClasses; k++){
//Get the class ID of this gesture
UINT classID = trainingData.getClassTracker()[k].classLabel;
classLabels[k] = classID;
//Convert this classes training data into a list of observation sequences
TimeSeriesClassificationData classData = trainingData.getClassData( classID );
vector< vector< UINT > > observationSequences;
if( !convertDataToObservationSequence( classData, observationSequences ) ){
return false;
}
//Test the model
double loglikelihood = 0;
double avgLoglikelihood = 0;
for(UINT i=0; i<observationSequences.size(); i++){
loglikelihood = discreteModels[k].predict( observationSequences[i] );
avgLoglikelihood += fabs( loglikelihood );
}
nullRejectionThresholds[k] = -( avgLoglikelihood / double( observationSequences.size() ) );
}
//Flag that the model has been trained
trained = true;
return true;
}