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

TimeSeriesClassificationData TimeSeriesClassificationData::getTrainingFoldData(const UINT foldIndex) const {

    TimeSeriesClassificationData trainingData;

    if( !crossValidationSetup ){
        errorLog << "getTrainingFoldData(UINT foldIndex) - Cross Validation has not been setup! You need to call the spiltDataIntoKFolds(UINT K,bool useStratifiedSampling) function first before calling this function!" << std::endl;
        return trainingData;
    }

    if( foldIndex >= kFoldValue ) return trainingData;

    trainingData.setNumDimensions( numDimensions );

    //Add the data to the training set, this will consist of all the data that is NOT in the foldIndex
    UINT index = 0;
    for(UINT k=0; k<kFoldValue; k++){
        if( k != foldIndex ){
            for(UINT i=0; i<crossValidationIndexs[k].size(); i++){

                index = crossValidationIndexs[k][i];
                trainingData.addSample( data[ index ].getClassLabel(), data[ index ].getData() );
            }
        }
    }

    return trainingData;
}

TimeSeriesClassificationData TimeSeriesClassificationDataStream::getTimeSeriesClassificationData( const bool includeNullGestures ) const {
    
    TimeSeriesClassificationData tsData;
    
    tsData.setNumDimensions( getNumDimensions() );
    tsData.setAllowNullGestureClass( includeNullGestures );
    
    bool addSample = false;
    const UINT numTimeseries = (UINT)timeSeriesPositionTracker.size();
    for(UINT i=0; i<numTimeseries; i++){
        addSample = includeNullGestures ? true : timeSeriesPositionTracker[i].getClassLabel() != GRT_DEFAULT_NULL_CLASS_LABEL;
        if( addSample ){
            tsData.addSample(timeSeriesPositionTracker[i].getClassLabel(), getTimeSeriesData( timeSeriesPositionTracker[i] ) );
        }
    }
    
    return tsData;
}

TimeSeriesClassificationData TimeSeriesClassificationData::getTestFoldData(const UINT foldIndex) const {
    TimeSeriesClassificationData testData;

    if( !crossValidationSetup ) return testData;

    if( foldIndex >= kFoldValue ) return testData;

    //Add the data to the training
    testData.setNumDimensions( numDimensions );

    UINT index = 0;
    for(UINT i=0; i<crossValidationIndexs[ foldIndex ].size(); i++){

        index = crossValidationIndexs[ foldIndex ][i];
        testData.addSample( data[ index ].getClassLabel(), data[ index ].getData() );
    }

    return testData;
}

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[])
{
    //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.........