C++ MatrixDouble::clear方法代码示例

bool TimeSeriesClassificationData::loadDatasetFromCSVFile(const string &filename){
    
    numDimensions = 0;
    datasetName = "NOT_SET";
    infoText = "";
    
    //Clear any previous data
    clear();
    
    //Parse the CSV file
    FileParser parser;
    
    if( !parser.parseCSVFile(filename,true) ){
        errorLog << "loadDatasetFromCSVFile(const string &filename) - Failed to parse CSV file!" << endl;
        return false;
    }
    
    if( !parser.getConsistentColumnSize() ){
        errorLog << "loadDatasetFromCSVFile(const string &filename) - The CSV file does not have a consistent number of columns!" << endl;
        return false;
    }
    
    if( parser.getColumnSize() <= 2 ){
        errorLog << "loadDatasetFromCSVFile(const string &filename) - The CSV file does not have enough columns! It should contain at least three columns!" << endl;
        return false;
    }
    
    //Set the number of dimensions
    numDimensions = parser.getColumnSize()-2;
    
    //Reserve the memory for the data
    data.reserve( parser.getRowSize() );
    
    UINT sampleCounter = 0;
    UINT lastSampleCounter = 0;
    UINT classLabel = 0;
    UINT j = 0;
    UINT n = 0;
    VectorDouble sample(numDimensions);
    MatrixDouble timeseries;
    for(UINT i=0; i<parser.getRowSize(); i++){
        
        sampleCounter = Util::stringToInt( parser[i][0] );
        
        //Check to see if a new timeseries has started, if so then add the previous time series as a sample and start recording the new time series
        if( sampleCounter != lastSampleCounter && i != 0 ){
            //Add the labelled sample to the dataset
            if( !addSample(classLabel, timeseries) ){
                warningLog << "loadDatasetFromCSVFile(const string &filename,const UINT classLabelColumnIndex) - Could not add sample " << i << " to the dataset!" << endl;
            }
            timeseries.clear();
        }
        lastSampleCounter = sampleCounter;
        
        //Get the class label
        classLabel = Util::stringToInt( parser[i][1] );
        
        //Get the sample data
        j=0;
        n=2;
        while( j != numDimensions ){
            sample[j++] = Util::stringToDouble( parser[i][n] );
            n++;
        }
        
        //Add the sample to the timeseries
        timeseries.push_back( sample );
    }
	if ( timeseries.getSize() > 0 )
        //Add the labelled sample to the dataset
        if( !addSample(classLabel, timeseries) ){
            warningLog << "loadDatasetFromCSVFile(const string &filename,const UINT classLabelColumnIndex) - Could not add sample " << parser.getRowSize()-1 << " to the dataset!" << endl;
        }
    
    return true;
}

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";
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