예제 #1
0
// Tests the MatrixFloat type
TEST(DynamicType, MatrixFloatTest) {
  DynamicType type;
  MatrixFloat a(3,1);
  a[0][0] = 1.1; a[1][0] = 1.2; a[2][0] = 1.3;
  EXPECT_TRUE( type.set( a ) );
  MatrixFloat b = type.get< MatrixFloat >();
  EXPECT_EQ( a.getSize(), b.getSize() );
  EXPECT_EQ( a.getNumRows(), b.getNumRows() );
  EXPECT_EQ( a.getNumCols(), b.getNumCols() );
  for(unsigned int i=0; i<a.getNumRows(); i++){
    for(unsigned int j=0; j<a.getNumCols(); j++){
      EXPECT_EQ( a[i][j], b[i][j] );
    }
  }
}
bool TimeSeriesClassificationData::loadDatasetFromCSVFile(const std::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 std::string &filename) - Failed to parse CSV file!" << std::endl;
        return false;
    }
    
    if( !parser.getConsistentColumnSize() ){
        errorLog << "loadDatasetFromCSVFile(const std::string &filename) - The CSV file does not have a consistent number of columns!" << std::endl;
        return false;
    }
    
    if( parser.getColumnSize() <= 2 ){
        errorLog << "loadDatasetFromCSVFile(const std::string &filename) - The CSV file does not have enough columns! It should contain at least three columns!" << std::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;
    VectorFloat sample(numDimensions);
    MatrixFloat timeseries;
    for(UINT i=0; i<parser.getRowSize(); i++){
        
        sampleCounter = grt_from_str< UINT >( 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 std::string &filename,const UINT classLabelColumnIndex) - Could not add sample " << i << " to the dataset!" << std::endl;
            }
            timeseries.clear();
        }
        lastSampleCounter = sampleCounter;
        
        //Get the class label
        classLabel = grt_from_str< UINT >( parser[i][1] );
        
        //Get the sample data
        j=0;
        n=2;
        while( j != numDimensions ){
            sample[j++] = grt_from_str< Float >( 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 std::string &filename,const UINT classLabelColumnIndex) - Could not add sample " << parser.getRowSize()-1 << " to the dataset!" << std::endl;
        }
    
    return true;
}