C++ GestureRecognitionPipeline::getTestAccuracy方法代码示例

bool saveResults( const GestureRecognitionPipeline &pipeline, const string &filename ){
    
    infoLog << "Saving results to file: " << filename << endl;

    fstream file( filename.c_str(), fstream::out );

    if( !file.is_open() ){
        errorLog << "Failed to open results file: " << filename << endl;
        return false;
    }

    file << pipeline.getTestAccuracy() << endl;

    vector< UINT > classLabels = pipeline.getClassLabels();

    for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
        file << pipeline.getTestPrecision( classLabels[k] );
        if( k+1 < pipeline.getNumClassesInModel() ) file << "\t";
        else file << endl;
    }

    for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
        file << pipeline.getTestRecall( classLabels[k] );
        if( k+1 < pipeline.getNumClassesInModel() ) file << "\t";
        else file << endl;
    }

    for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
        file << pipeline.getTestFMeasure( classLabels[k] );
        if( k+1 < pipeline.getNumClassesInModel() ) file << "\t";
        else file << endl;
    }

    MatrixDouble confusionMatrix = pipeline.getTestConfusionMatrix();
    for(UINT i=0; i<confusionMatrix.getNumRows(); i++){
        for(UINT j=0; j<confusionMatrix.getNumCols(); j++){
            file << confusionMatrix[i][j];
            if( j+1 < confusionMatrix.getNumCols() ) file << "\t";
        }file << endl;
    }

    file.close();

    infoLog << "Results saved." << endl;

    return true;
}

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 segmented set\n");
		return 0;
	}

	printf("Successfully opened training data set ...\n");

    DTW dtw;
//    LowPassFilter lpf(0.1, 1, 1);
//    pipeline.setPreProcessingModule(lpf);

//    DoubleMovingAverageFilter filter( 1000, 3 );
//    pipeline.setPreProcessingModule(filter);

    //dtw.enableNullRejection( true );

    //Set the null rejection coefficient to 3, this controls the thresholds for the automatic null rejection
	//You can increase this value if you find that your real-time gestures are not being recognized
	//If you are getting too many false positives then you should decrease this value
    //dtw.setNullRejectionCoeff( 5 );
    dtw.enableTrimTrainingData(true, 0.1, 90);
//    dtw.setOffsetTimeseriesUsingFirstSample(true);

    pipeline.setClassifier( dtw );

    UINT KFolds = 5;

    /* Separate input dataset using KFold */
    KfoldTimeSeriesData* kFoldTS  = new KfoldTimeSeriesData(trainingData);
	if( !kFoldTS->spiltDataIntoKFolds(KFolds) ) {
		printf("BaseTGTestModel: Failed to spiltDataIntoKFolds!");
		return 0;
	}

	UINT maxTrainigSetSize = trainingData.getNumSamples() * (KFolds - 1) / (KFolds * trainingData.getNumClasses());


	// KFolds


	ofstream myfile;
	  myfile.open ("example.txt");
	Float acc = 0;
	for (GRT::UINT k = 1 ; k < KFolds; k++) {

		printf("Running tests for: %d fold", k);
		//  maxTrainigSetSize
//		for (UINT trainingSetSize = 1; trainingSetSize <= maxTrainigSetSize; trainingSetSize ++) {

			/* Set up training datasets for current fold */
			TimeSeriesClassificationData trainingDataset = kFoldTS->getTrainingFoldData(k, maxTrainigSetSize);

			/* Set up validation datasets for current fold */
			TimeSeriesClassificationDataStream testDataset = kFoldTS->getTestFoldData(k);

			/* Log test dataset size */
			//printf("Data set size: training %d; testing %d",
//					trainingDataset.getNumSamples(), testDataset.getNumSamples());

			/* Run test for current fold */
			pipeline.train(trainingDataset);
			pipeline.test(testDataset);
			myfile << pipeline.getTestAccuracy() << "\n";
//		}
	}

	  myfile.close();

	printf("Accuracy = %f ; %d\n", acc, maxTrainigSetSize);

}

int main (int argc, const char * argv[])
{
    //Load some training data from a file
    ClassificationData trainingData;
    
    if( !trainingData.loadDatasetFromFile("HelloWorldTrainingData.grt") ){
        cout << "ERROR: Failed to load training data from file\n";
        return EXIT_FAILURE;
    }
    
    cout << "Data Loaded\n";
    
    //Print out some stats about the training data
    trainingData.printStats();
    
    //Partition the training data into a training dataset and a test dataset. 80 means that 80%
    //of the data will be used for the training data and 20% will be returned as the test dataset
    ClassificationData testData = trainingData.partition(80);
    
    //Create a new Gesture Recognition Pipeline using an Adaptive Naive Bayes Classifier
    GestureRecognitionPipeline pipeline;
    pipeline.setClassifier( ANBC() );
    
    //Train the pipeline using the training data
    if( !pipeline.train( trainingData ) ){
        cout << "ERROR: Failed to train the pipeline!\n";
        return EXIT_FAILURE;
    }
    
    //Save the pipeline to a file
	if( !pipeline.savePipelineToFile( "HelloWorldPipeline.grt" ) ){
        cout << "ERROR: Failed to save the pipeline!\n";
        return EXIT_FAILURE;
    }
    
	//Load the pipeline from a file
	if( !pipeline.loadPipelineFromFile( "HelloWorldPipeline.grt" ) ){
        cout << "ERROR: Failed to load the pipeline!\n";
        return EXIT_FAILURE;
    }
    
    //Test the pipeline using the test data
    if( !pipeline.test( testData ) ){
        cout << "ERROR: Failed to test the pipeline!\n";
        return EXIT_FAILURE;
    }
    
    //Print some stats about the testing
    cout << "Test Accuracy: " << pipeline.getTestAccuracy() << endl;
    
    cout << "Precision: ";
    for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
        UINT classLabel = pipeline.getClassLabels()[k];
        cout << "\t" << pipeline.getTestPrecision(classLabel);
    }cout << endl;
    
    cout << "Recall: ";
    for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
        UINT classLabel = pipeline.getClassLabels()[k];
        cout << "\t" << pipeline.getTestRecall(classLabel);
    }cout << endl;
    
    cout << "FMeasure: ";
    for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
        UINT classLabel = pipeline.getClassLabels()[k];
        cout << "\t" << pipeline.getTestFMeasure(classLabel);
    }cout << endl;
    
    MatrixDouble confusionMatrix = pipeline.getTestConfusionMatrix();
    cout << "ConfusionMatrix: \n";
    for(UINT i=0; i<confusionMatrix.getNumRows(); i++){
        for(UINT j=0; j<confusionMatrix.getNumCols(); j++){
            cout << confusionMatrix[i][j] << "\t";
        }cout << endl;
    }
    
    return EXIT_SUCCESS;
}

int main (int argc, const char * argv[])
{

    //We are going to use the Iris dataset, you can find more about the orginal dataset at: http://en.wikipedia.org/wiki/Iris_flower_data_set
    
    //Create a new instance of LabelledClassificationData to hold the training data
    LabelledClassificationData trainingData;
    
    //Load the training dataset from a file, the file should be in the same directory as this program
    if( !trainingData.loadDatasetFromFile("IrisData.txt") ){
        cout << "Failed to load Iris data from file!\n";
        return EXIT_FAILURE;
    }
    
    //Print some basic stats about the dataset we have loaded
    trainingData.printStats();
    
    //Partition the training dataset into a training dataset and test dataset
    //We will use 60% of the data to train the algorithm and 40% of the data to test it
    //The true parameter flags that we want to use stratified sampling, which means there 
    //should be an equal class distribution between the training and test datasets
    LabelledClassificationData testData = trainingData.partition( 60, true );
    
    //Setup the gesture recognition pipeline
    GestureRecognitionPipeline pipeline;
    
    //Add a KNN classification algorithm as the main classifier with a K value of 10
    pipeline.setClassifier( KNN(10) );
    
    //Train the KNN algorithm using the training dataset
    if( !pipeline.train( trainingData ) ){
        cout << "Failed to train the pipeline!\n";
        return EXIT_FAILURE;
    }
    
    //Test the KNN model using the test dataset
    if( !pipeline.test( testData ) ){
        cout << "Failed to test the pipeline!\n";
        return EXIT_FAILURE;
    }
    
    //Print some metrics about how successful the classification was
    //Print the accuracy
    cout << "The classification accuracy was: " << pipeline.getTestAccuracy() << "%\n" << endl;
    
    //Print the precision for each class
    for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
        UINT classLabel = pipeline.getClassLabels()[k];
        double classPrecision = pipeline.getTestPrecision( classLabel );
        cout << "The precision for class " << classLabel << " was " << classPrecision << endl;
    }
    cout << endl;
    
    //Print the recall for each class
    for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
        UINT classLabel = pipeline.getClassLabels()[k];
        double classRecall = pipeline.getTestRecall( classLabel );
        cout << "The recall for class " << classLabel << " was " << classRecall << endl;
    }
    cout << endl;
    
    //Print the confusion matrix
    Matrix< double > confusionMatrix = pipeline.getTestConfusionMatrix();
    cout << "Confusion Matrix: \n";
    for(UINT i=0; i<confusionMatrix.getNumRows(); i++){
        for(UINT j=0; j<confusionMatrix.getNumCols(); j++){
            cout << confusionMatrix[i][j] << "\t";
        }
        cout << endl;
        
    }
    cout << endl;

    return EXIT_SUCCESS;
}

bool test( CommandLineParser &parser ){

    infoLog << "Testing model..." << endl;

    string datasetFilename = "";
    string modelFilename = "";
    string resultsFilename = "";

    //Get the model filename
    if( !parser.get("model-filename",modelFilename) ){
        errorLog << "Failed to parse model filename from command line! You can set the model filename using the -m." << endl;
        printUsage();
        return false;
    }

    //Get the filename
    if( !parser.get("dataset-filename",datasetFilename) ){
        errorLog << "Failed to parse dataset filename from command line! You can set the dataset filename using the -f." << endl;
        printUsage();
        return false;
    }

    //Get the model filename
    parser.get("results-filename",resultsFilename,string("results.txt"));

    //Load the pipeline from a file
    GestureRecognitionPipeline pipeline;

    infoLog << "- Loading model..." << endl;

    if( !pipeline.load( modelFilename ) ){
        errorLog << "Failed to load model from file: " << modelFilename << endl;
        printUsage();
        return false;
    }

    infoLog << "- Model loaded!" << endl;

    //Load the data to test the classifier
    ClassificationData data;

    infoLog << "- Loading Training Data..." << endl;
    if( !data.load( datasetFilename ) ){
        errorLog << "Failed to load data!\n";
        return false;
    }

    const unsigned int N = data.getNumDimensions();
    infoLog << "- Num training samples: " << data.getNumSamples() << endl;
    infoLog << "- Num dimensions: " << N << endl;
    infoLog << "- Num classes: " << data.getNumClasses() << endl;

    //Test the classifier
    if( !pipeline.test( data ) ){
        errorLog << "Failed to test pipeline!" << endl;
        return false;
    }

    infoLog << "- Test complete in " << pipeline.getTestTime()/1000.0 << " seconds with and accuracy of: " << pipeline.getTestAccuracy() << endl;

    return saveResults( pipeline, resultsFilename );
}