DoubleMovingAverageFilter.cpp

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#include "DoubleMovingAverageFilter.h"

GRT_BEGIN_NAMESPACE
 
//Register the DoubleMovingAverageFilter module with the PreProcessing base class
RegisterPreProcessingModule< DoubleMovingAverageFilter > DoubleMovingAverageFilter::registerModule("DoubleMovingAverageFilter");

DoubleMovingAverageFilter::DoubleMovingAverageFilter(UINT filterSize,UINT numDimensions){
 classType = "DoubleMovingAverageFilter";
 preProcessingType = classType;
 debugLog.setProceedingText("[DEBUG DoubleMovingAverageFilter]");
 errorLog.setProceedingText("[ERROR DoubleMovingAverageFilter]");
 warningLog.setProceedingText("[WARNING DoubleMovingAverageFilter]");
 init(filterSize,numDimensions);
}
 
DoubleMovingAverageFilter::DoubleMovingAverageFilter(const DoubleMovingAverageFilter &rhs){
 
 this->filterSize = rhs.filterSize;
 this->filter1 = rhs.filter1;
 this->filter2 = rhs.filter2;
 classType = "DoubleMovingAverageFilter";
 preProcessingType = classType;
 debugLog.setProceedingText("[DEBUG DoubleMovingAverageFilter]");
 errorLog.setProceedingText("[ERROR DoubleMovingAverageFilter]");
 warningLog.setProceedingText("[WARNING DoubleMovingAverageFilter]");
 
 //Copy the base variables
 copyBaseVariables( (PreProcessing*)&rhs );
}
 
DoubleMovingAverageFilter::~DoubleMovingAverageFilter(){

}
 
DoubleMovingAverageFilter& DoubleMovingAverageFilter::operator=(const DoubleMovingAverageFilter &rhs){
 if(this!=&rhs){
 this->filterSize = rhs.filterSize;
 this->filter1 = rhs.filter1;
 this->filter2 = rhs.filter2;
 
 //Copy the base variables
 copyBaseVariables( (PreProcessing*)&rhs );
 }
 return *this;
}
 
bool DoubleMovingAverageFilter::deepCopyFrom(const PreProcessing *preProcessing){
 
 if( preProcessing == NULL ) return false;
 
 if( this->getPreProcessingType() == preProcessing->getPreProcessingType() ){
 
 DoubleMovingAverageFilter *ptr = (DoubleMovingAverageFilter*)preProcessing;
 
 //Clone the classLabelTimeoutFilter values 
 this->filterSize = ptr->filterSize;
 this->filter1 = ptr->filter1;
 this->filter2 = ptr->filter2;
 
 //Clone the preprocessing base variables
 return copyBaseVariables( preProcessing );
 }
 
 errorLog << "clone(const PreProcessing *preProcessing) - PreProcessing Types Do Not Match!" << std::endl;
 
 return false;
}

 
bool DoubleMovingAverageFilter::process(const VectorFloat &inputVector){
 
 if( !initialized ){
 errorLog << "process(const VectorFloat &inputVector) - The filter has not been initialized!" << std::endl;
 return false;
 }

 if( inputVector.size() != numInputDimensions ){
 errorLog << "process(const VectorFloat &inputVector) - The size of the inputVector (" << inputVector.getSize() << ") does not match that of the filter (" << numInputDimensions << ")!" << std::endl;
 return false;
 }
 
 processedData = filter( inputVector );
 
 if( processedData.size() == numOutputDimensions ) return true;
 return false;
}

bool DoubleMovingAverageFilter::reset(){
 if( initialized ) return init(filterSize,numInputDimensions);
 return false;
}
 
bool DoubleMovingAverageFilter::saveModelToFile( std::string filename ) const{
 
 if( !initialized ){
 errorLog << "saveModelToFile(string filename) - The DoubleMovingAverageFilter has not been initialized" << std::endl;
 return false;
 }
 
 std::fstream file; 
 file.open(filename.c_str(), std::ios::out);
 
 if( !saveModelToFile( file ) ){
 file.close();
 return false;
 }
 
 file.close();
 
 return true;
}
 
bool DoubleMovingAverageFilter::saveModelToFile(std::fstream &file) const{
 
 if( !file.is_open() ){
 errorLog << "saveModelToFile(std::fstream &file) - The file is not open!" << std::endl;
 return false;
 }
 
 file << "GRT_DOUBLE_MOVING_AVERAGE_FILTER_FILE_V1.0" << std::endl;
 
 file << "NumInputDimensions: " << numInputDimensions << std::endl;
 file << "NumOutputDimensions: " << numOutputDimensions << std::endl;
 file << "FilterSize: " << filterSize << std::endl;
 
 return true;
}

bool DoubleMovingAverageFilter::loadModelFromFile( std::string filename ){
 
 std::fstream file; 
 file.open(filename.c_str(), std::ios::in);
 
 if( !loadModelFromFile( file ) ){
 file.close();
 initialized = false;
 return false;
 }
 
 file.close();
 
 return true;
}

bool DoubleMovingAverageFilter::loadModelFromFile( std::fstream &file ){
 
 if( !file.is_open() ){
 errorLog << "loadModelFromFile(std::fstream &file) - The file is not open!" << std::endl;
 return false;
 }
 
 std::string word;
 
 //Load the header
 file >> word;
 
 if( word != "GRT_DOUBLE_MOVING_AVERAGE_FILTER_FILE_V1.0" ){
 errorLog << "loadModelFromFile(std::fstream &file) - Invalid file format!" << std::endl;
 return false; 
 }
 
 //Load the number of input dimensions
 file >> word;
 if( word != "NumInputDimensions:" ){
 errorLog << "loadModelFromFile(std::fstream &file) - Failed to read NumInputDimensions header!" << std::endl;
 return false; 
 }
 file >> numInputDimensions;
 
 //Load the number of output dimensions
 file >> word;
 if( word != "NumOutputDimensions:" ){
 errorLog << "loadModelFromFile(std::fstream &file) - Failed to read NumOutputDimensions header!" << std::endl;
 return false; 
 }
 file >> numOutputDimensions;
 
 //Load the filter size
 file >> word;
 if( word != "FilterSize:" ){
 errorLog << "loadModelFromFile(std::fstream &file) - Failed to read FilterSize header!" << std::endl;
 return false; 
 }
 file >> filterSize;
 
 //Init the filter module to ensure everything is initialized correctly
 return init(filterSize,numInputDimensions); 
}

bool DoubleMovingAverageFilter::init(UINT filterSize,UINT numDimensions){
 
 //Cleanup the old memory
 initialized = false;
 
 if( filterSize == 0 ){
 errorLog << "init(UINT filterSize,UINT numDimensions) - Filter size can not be zero!" << std::endl;
 return false;
 }
 
 if( numDimensions == 0 ){
 errorLog << "init(UINT filterSize,UINT numDimensions) - The number of dimensions must be greater than zero!" << std::endl;
 return false;
 }
 
 //Resize the filter
 this->filterSize = filterSize;
 numInputDimensions = numDimensions;
 numOutputDimensions = numDimensions;
 processedData.clear();
 processedData.resize(numDimensions,0 );
 
 if( !filter1.init(filterSize, numDimensions) ){
 errorLog << "init(UINT filterSize,UINT numDimensions) - Failed to initialize filter 1!" << std::endl;
 return false;
 }
 
 if( !filter2.init(filterSize, numDimensions) ){
 errorLog << "init(UINT filterSize,UINT numDimensions) - Failed to initialize filter 1!" << std::endl;
 return false;
 }
 
 //Flag that the filter has been initialized
 initialized = true;
 
 return true;
}

Float DoubleMovingAverageFilter::filter(const Float x){
 
 //If the filter has not been initialised then return 0, otherwise filter x and return y
 if( !initialized ){
 errorLog << "filter(const Float x) - The filter has not been initialized!" << std::endl;
 return 0;
 }
 
 VectorFloat y = filter(VectorFloat(1,x));
 
 if( y.getSize() == 0 ) return 0;
 return y[0];
}
 
VectorFloat DoubleMovingAverageFilter::filter(const VectorFloat &x){
 
 //If the filter has not been initialised then return 0, otherwise filter x and return y
 if( !initialized ){
 errorLog << "filter(const VectorFloat &x) - The filter has not been initialized!" << std::endl;
 return VectorFloat();
 }
 
 if( x.getSize() != numInputDimensions ){
 errorLog << "filter(const VectorFloat &x) - The size of the input vector (" << x.getSize() << ") does not match that of the number of dimensions of the filter (" << numInputDimensions << ")!" << std::endl;
 return VectorFloat();
 }
 
 //Perform the first filter
 VectorFloat y = filter1.filter( x );
 
 if( y.size() == 0 ) return y;
 
 //Perform the second filter
 VectorFloat yy = filter2.filter( y );
 
 if( yy.size() == 0 ) return y;
 
 //Account for the filter lag
 const UINT N = y.getSize();
 for(UINT i=0; i<N; i++){
 yy[i] = y[i] + (y[i] - yy[i]); 
 processedData[i] = yy[i];
 }
 
 return yy;
}

GRT_END_NAMESPACE