C++ TimeSeriesClassificationData::load方法代码示例

int main (int argc, const char * argv[])
{
	//Create a new DTW instance, using the default parameters
	DTW dtw;
    
	//Load some training data to train the classifier - the DTW uses TimeSeriesClassificationData
	TimeSeriesClassificationData trainingData;
    
	if( !trainingData.load("DTWTrainingData.grt") ){
		cout << "Failed to load training data!\n";
		return EXIT_FAILURE;
	}
    
	//Use 20% of the training dataset to create a test dataset
	TimeSeriesClassificationData testData = trainingData.partition( 80 );

	//Trim the training data for any sections of non-movement at the start or end of the recordings
	dtw.enableTrimTrainingData(true,0.1,90);
    
	//Train the classifier
	if( !dtw.train( trainingData ) ){
		cout << "Failed to train classifier!\n";
		return EXIT_FAILURE;
	}	
    
	//Save the DTW model to a file
	if( !dtw.save("DTWModel.grt") ){
		cout << "Failed to save the classifier model!\n";
		return EXIT_FAILURE;
	}
    
	//Load the DTW model from a file
	if( !dtw.load("DTWModel.grt") ){
		cout << "Failed to load the classifier model!\n";
		return EXIT_FAILURE;
	}
    
	//Use the test dataset to test the DTW model
	double accuracy = 0;
	for(UINT i=0; i<testData.getNumSamples(); i++){
		//Get the i'th test sample - this is a timeseries
		UINT classLabel = testData[i].getClassLabel();
		MatrixDouble timeseries = testData[i].getData();
        
		//Perform a prediction using the classifier
		if( !dtw.predict( timeseries ) ){
			cout << "Failed to perform prediction for test sampel: " << i <<"\n";
			return EXIT_FAILURE;
		}
        
		//Get the predicted class label
		UINT predictedClassLabel = dtw.getPredictedClassLabel();
		double maximumLikelihood = dtw.getMaximumLikelihood();
		VectorDouble classLikelihoods = dtw.getClassLikelihoods();
		VectorDouble classDistances = dtw.getClassDistances();
        
		//Update the accuracy
		if( classLabel == predictedClassLabel ) accuracy++;
        
        cout << "TestSample: " << i <<  "\tClassLabel: " << classLabel << "\tPredictedClassLabel: " << predictedClassLabel << "\tMaximumLikelihood: " << maximumLikelihood << endl;
	}
    
	cout << "Test Accuracy: " << accuracy/double(testData.getNumSamples())*100.0 << "%" << endl;
    
	return EXIT_SUCCESS;
}

int main(int argc, const char * argv[]){
    
    //Load the training data
    TimeSeriesClassificationData trainingData;
    
    if( !trainingData.load("HMMTrainingData.grt") ){
        cout << "ERROR: Failed to load training data!\n";
        return false;
    }
    
    //Remove 20% of the training data to use as test data
    TimeSeriesClassificationData testData = trainingData.partition( 80 );
    
    //The input to the HMM must be a quantized discrete value
    //We therefore use a KMeansQuantizer to covert the N-dimensional continuous data into 1-dimensional discrete data
    const UINT NUM_SYMBOLS = 10;
    KMeansQuantizer quantizer( NUM_SYMBOLS );
    
    //Train the quantizer using the training data
    if( !quantizer.train( trainingData ) ){
        cout << "ERROR: Failed to train quantizer!\n";
        return false;
    }
    
    //Quantize the training data
    TimeSeriesClassificationData quantizedTrainingData( 1 );
    
    for(UINT i=0; i<trainingData.getNumSamples(); i++){
        
        UINT classLabel = trainingData[i].getClassLabel();
        MatrixDouble quantizedSample;
        
        for(UINT j=0; j<trainingData[i].getLength(); j++){
            quantizer.quantize( trainingData[i].getData().getRow(j) );
            
            quantizedSample.push_back( quantizer.getFeatureVector() );
        }
        
        if( !quantizedTrainingData.addSample(classLabel, quantizedSample) ){
            cout << "ERROR: Failed to quantize training data!\n";
            return false;
        }
        
    }
    
    //Create a new HMM instance
    HMM hmm;
    
    //Set the HMM as a Discrete HMM
    hmm.setHMMType( HMM_DISCRETE );
    
    //Set the number of states in each model
    hmm.setNumStates( 4 );
    
    //Set the number of symbols in each model, this must match the number of symbols in the quantizer
    hmm.setNumSymbols( NUM_SYMBOLS );
    
    //Set the HMM model type to LEFTRIGHT with a delta of 1
    hmm.setModelType( HMM_LEFTRIGHT );
    hmm.setDelta( 1 );
    
    //Set the training parameters
    hmm.setMinChange( 1.0e-5 );
    hmm.setMaxNumEpochs( 100 );
    hmm.setNumRandomTrainingIterations( 20 );
    
    //Train the HMM model
    if( !hmm.train( quantizedTrainingData ) ){
        cout << "ERROR: Failed to train the HMM model!\n";
        return false;
    }
    
    //Save the HMM model to a file
    if( !hmm.save( "HMMModel.grt" ) ){
        cout << "ERROR: Failed to save the model to a file!\n";
        return false;
    }
    
    //Load the HMM model from a file
    if( !hmm.load( "HMMModel.grt" ) ){
        cout << "ERROR: Failed to load the model from a file!\n";
        return false;
    }
    
    //Quantize the test data
    TimeSeriesClassificationData quantizedTestData( 1 );
    
    for(UINT i=0; i<testData.getNumSamples(); i++){
        
        UINT classLabel = testData[i].getClassLabel();
        MatrixDouble quantizedSample;
        
        for(UINT j=0; j<testData[i].getLength(); j++){
            quantizer.quantize( testData[i].getData().getRow(j) );
            
            quantizedSample.push_back( quantizer.getFeatureVector() );
        }
        
        if( !quantizedTestData.addSample(classLabel, quantizedSample) ){
            cout << "ERROR: Failed to quantize training data!\n";
//.........这里部分代码省略.........

int main() {
    vector<string> gestures(0,"");
    GetFilesInDirectory(gestures, "rawdata");
    CreateDirectory("processed", NULL);
    sort(gestures.begin(), gestures.end());
    data = vector<vector<vector<double > > >(gestures.size(), vector<vector<double > >(0,vector<double>(0,0)));
    for(size_t i = 0; i < gestures.size(); i++) {
        ifstream fin(gestures[i]);
        int n; fin >> n;
       // cerr << gestures[i] << endl;
       // cerr << n << endl;
        data[i] = vector<vector<double> >(n, vector<double>(NUMPARAM, 0));
        for(int j = 0; j < n; j++) {
            for(int k = 0; k < NUMPARAM; k++) {
                fin >> data[i][j][k];
            }
        }
        fin.close();
    }


    //Create a new instance of the TimeSeriesClassificationDataStream
    TimeSeriesClassificationData trainingData;

    // ax, ay, az
    trainingData.setNumDimensions(3);
    trainingData.setDatasetName("processed\\GestureTrainingData.txt");
    ofstream labelfile("processed\\GestureTrainingDataLabels.txt");
    UINT currLabel = 1;
    Random random;
    map<string, int> gesturenames;
    for(size_t overall = 0; overall < gestures.size(); overall++) {

        string nam = gestures[overall].substr(8,gestures[overall].find_first_of('_')-8);
        if(gesturenames.count(nam)) currLabel = gesturenames[nam];
        else {
            currLabel = gesturenames.size()+1;
            gesturenames[nam] = currLabel;
            labelfile << currLabel << " " << nam << endl;
        }
        MatrixDouble trainingSample;
        VectorDouble currVec( trainingData.getNumDimensions() );
        for(size_t k = 1; k < data[overall].size(); k++) {
            for(UINT j=0; j<currVec.size(); j++){
                currVec[j] = data[overall][k][j];
            }
            trainingSample.push_back(currVec);
        }
        trainingData.addSample(currLabel, trainingSample);

    }
    for(size_t i = 0; i < gestures.size(); i++) {
        MatrixDouble trainingSample;
        VectorDouble currVec(trainingData.getNumDimensions());
        for(UINT j = 0; j < currVec.size(); j++) {
            currVec[j] = random.getRandomNumberUniform(-1.0, 1.0);
        }
        for(size_t k = 0; k < 100; k++) {
            trainingSample.push_back(currVec);
        }
        trainingData.addSample(0, trainingSample);
    }

    //After recording your training data you can then save it to a file
    if( !trainingData.save( "processed\\TrainingData.grt" ) ){
        cout << "ERROR: Failed to save dataset to file!\n";
        return EXIT_FAILURE;
    }

    //This can then be loaded later
    if( !trainingData.load( "processed\\TrainingData.grt" ) ){
        cout << "ERROR: Failed to load dataset from file!\n";
        return EXIT_FAILURE;
    }

    //This is how you can get some stats from the training data
    string datasetName = trainingData.getDatasetName();
    string infoText = trainingData.getInfoText();
    UINT numSamples = trainingData.getNumSamples();
    UINT numDimensions = trainingData.getNumDimensions();
    UINT numClasses = trainingData.getNumClasses();

    cout << "Dataset Name: " << datasetName << endl;
    cout << "InfoText: " << infoText << endl;
    cout << "NumberOfSamples: " << numSamples << endl;
    cout << "NumberOfDimensions: " << numDimensions << endl;
    cout << "NumberOfClasses: " << numClasses << endl;

    //You can also get the minimum and maximum ranges of the data
    vector< MinMax > ranges = trainingData.getRanges();

    cout << "The ranges of the dataset are: \n";
    for(UINT j=0; j<ranges.size(); j++){
        cout << "Dimension: " << j << " Min: " << ranges[j].minValue << " Max: " << ranges[j].maxValue << endl;
    }

    DTW dtw;

    if( !dtw.train( trainingData ) ){
        cerr << "Failed to train classifier!\n";
//.........这里部分代码省略.........

int main(int argc, const char * argv[]){
    
    //Load the training data
    TimeSeriesClassificationData trainingData;
    
    if( !trainingData.load("HMMTrainingData.grt") ){
        cout << "ERROR: Failed to load training data!\n";
        return false;
    }
    
    //Remove 20% of the training data to use as test data
    TimeSeriesClassificationData testData = trainingData.partition( 80 );
    
    //Create a new HMM instance
    HMM hmm;
    
    //Set the HMM as a Continuous HMM
    hmm.setHMMType( HMM_CONTINUOUS );
    
    //Set the downsample factor, a higher downsample factor will speed up the prediction time, but might reduce the classification accuracy
    hmm.setDownsampleFactor( 5 );
    
    //Set the committee size, this sets the (top) number of models that will be used to make a prediction
    hmm.setCommitteeSize( 10 );
    
    //Tell the hmm algorithm that we want it to estimate sigma from the training data
    hmm.setAutoEstimateSigma( true );
    
    //Set the minimum value for sigma, you might need to adjust this based on the range of your data
    //If you set setAutoEstimateSigma to false, then all sigma values will use the value below
    hmm.setSigma( 20.0 );
    
    //Set the HMM model type to LEFTRIGHT with a delta of 1, this means the HMM can only move from the left-most state to the right-most state
    //in steps of 1
    hmm.setModelType( HMM_LEFTRIGHT );
    hmm.setDelta( 1 );
    
    //Train the HMM model
    if( !hmm.train( trainingData ) ){
        cout << "ERROR: Failed to train the HMM model!\n";
        return false;
    }
    
    //Save the HMM model to a file
    if( !hmm.save( "HMMModel.grt" ) ){
        cout << "ERROR: Failed to save the model to a file!\n";
        return false;
    }
    
    //Load the HMM model from a file
    if( !hmm.load( "HMMModel.grt" ) ){
        cout << "ERROR: Failed to load the model from a file!\n";
        return false;
    }

    //Compute the accuracy of the HMM models using the test data
    double numCorrect = 0;
    double numTests = 0;
    for(UINT i=0; i<testData.getNumSamples(); i++){
        
        UINT classLabel = testData[i].getClassLabel();
        hmm.predict( testData[i].getData() );
        
        if( classLabel == hmm.getPredictedClassLabel() ) numCorrect++;
        numTests++;
        
        VectorFloat classLikelihoods = hmm.getClassLikelihoods();
        VectorFloat classDistances = hmm.getClassDistances();
        
        cout << "ClassLabel: " << classLabel;
        cout << " PredictedClassLabel: " << hmm.getPredictedClassLabel();
        cout << " MaxLikelihood: " << hmm.getMaximumLikelihood();
        
        cout << "  ClassLikelihoods: ";
        for(UINT k=0; k<classLikelihoods.size(); k++){
            cout << classLikelihoods[k] << "\t";
        }
        
        cout << "ClassDistances: ";
        for(UINT k=0; k<classDistances.size(); k++){
            cout << classDistances[k] << "\t";
        }
        cout << endl;
    }
    
    cout << "Test Accuracy: " << numCorrect/numTests*100.0 << endl;
    
    return true;
}