C++ TimeSeriesClassificationData::partition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[])
{
    //Create a new instance of the TimeSeriesClassificationData
    TimeSeriesClassificationData trainingData;
    
    //Set the dimensionality of the data (you need to do this before you can add any samples)
    trainingData.setNumDimensions( 3 );
    
    //You can also give the dataset a name (the name should have no spaces)
    trainingData.setDatasetName("DummyData");
    
    //You can also add some info text about the data
    trainingData.setInfoText("This data contains some dummy timeseries data");
    
    //Here you would record a time series, when you have finished recording the time series then add the training sample to the training data
    UINT gestureLabel = 1;
    MatrixDouble trainingSample;
    
    //For now we will just add 10 x 20 random walk data timeseries
    Random random;
    for(UINT k=0; k<10; k++){//For the number of classes
        gestureLabel = k+1;
        
        //Get the init random walk position for this gesture
        VectorDouble startPos( trainingData.getNumDimensions() );
        for(UINT j=0; j<startPos.size(); j++){
            startPos[j] = random.getRandomNumberUniform(-1.0,1.0);
        }
                
        //Generate the 20 time series
        for(UINT x=0; x<20; x++){
            
            //Clear any previous timeseries
            trainingSample.clear();
            
            //Generate the random walk
            UINT randomWalkLength = random.getRandomNumberInt(90, 110);
            VectorDouble sample = startPos;
            for(UINT i=0; i<randomWalkLength; i++){
                for(UINT j=0; j<startPos.size(); j++){
                    sample[j] += random.getRandomNumberUniform(-0.1,0.1);
                }
                
                //Add the sample to the training sample
                trainingSample.push_back( sample );
            }
            
            //Add the training sample to the dataset
            trainingData.addSample( gestureLabel, trainingSample );
            
        }
    }
    
    //After recording your training data you can then save it to a file
    if( !trainingData.saveDatasetToFile( "TrainingData.txt" ) ){
	    cout << "Failed to save dataset to file!\n";
	    return EXIT_FAILURE;
	}
    
    //This can then be loaded later
    if( !trainingData.loadDatasetFromFile( "TrainingData.txt" ) ){
		cout << "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;
    }
    
    //If you want to partition the dataset into a training dataset and a test dataset then you can use the partition function
    //A value of 80 means that 80% of the original data will remain in the training dataset and 20% will be returned as the test dataset
    TimeSeriesClassificationData testData = trainingData.partition( 80 );
    
    //If you have multiple datasets that you want to merge together then use the merge function
    if( !trainingData.merge( testData ) ){
		cout << "Failed to merge datasets!\n";
		return EXIT_FAILURE;
	}
    
    //If you want to run K-Fold cross validation using the dataset then you should first spilt the dataset into K-Folds
    //A value of 10 splits the dataset into 10 folds and the true parameter signals that stratified sampling should be used
    if( !trainingData.spiltDataIntoKFolds( 10, true ) ){
		cout << "Failed to spiltDataIntoKFolds!\n";
//.........这里部分代码省略.........

int main(int argc, const char * argv[]){
    
    //Load the training data
    TimeSeriesClassificationData trainingData;
    
    if( !trainingData.loadDatasetFromFile("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().getRowVector(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 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( HiddenMarkovModel::LEFTRIGHT );
    hmm.setDelta( 1 );
    
    //Set the training parameters
    hmm.setMinImprovement( 1.0e-5 );
    hmm.setMaxNumIterations( 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().getRowVector(j) );
            
            quantizedSample.push_back( quantizer.getFeatureVector() );
        }
        
        if( !quantizedTestData.addSample(classLabel, quantizedSample) ){
            cout << "ERROR: Failed to quantize training data!\n";
            return false;
        }
    }
//.........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;
}