本文整理汇总了C++中GestureRecognitionPipeline::getCrossValidationAccuracy方法的典型用法代码示例。如果您正苦于以下问题：C++ GestureRecognitionPipeline::getCrossValidationAccuracy方法的具体用法？C++ GestureRecognitionPipeline::getCrossValidationAccuracy怎么用？C++ GestureRecognitionPipeline::getCrossValidationAccuracy使用的例子？那么, 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GestureRecognitionPipeline的用法示例。
在下文中一共展示了GestureRecognitionPipeline::getCrossValidationAccuracy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: main
int main (int argc, const char * argv[])
{
TimeSeriesClassificationData trainingData; //This will store our training data
GestureRecognitionPipeline pipeline; //This is a wrapper for our classifier and any pre/post processing modules
string dirPath = "/home/vlad/AndroidStudioProjects/DataCapture/dataSetGenerator/build";
if (!trainingData.loadDatasetFromFile(dirPath + "/acc-training-set-segmented.data")) {
printf("Cannot open training set\n");
return 0;
}
printf("Successfully opened training data set ...\n");
HMM hmm;
hmm.setHMMType( HMM_CONTINUOUS );
hmm.setDownsampleFactor( 5 );
hmm.setAutoEstimateSigma( true );
hmm.setSigma( 20.0 );
hmm.setModelType( HMM_LEFTRIGHT );
hmm.setDelta( 1 );
// LowPassFilter lpf(0.1, 1, 3);
// pipeline.setPreProcessingModule(lpf);
pipeline.setClassifier( hmm );
pipeline.train(trainingData, 20);
//You can then get then get the accuracy of how well the pipeline performed during the k-fold cross validation testing
double accuracy = pipeline.getCrossValidationAccuracy();
printf("Accuracy: %f\n", accuracy);
}