30 classType =
"WeightedAverageFilter";
31 preProcessingType = classType;
32 debugLog.setProceedingText(
"[DEBUG WeightedAverageFilter]");
33 errorLog.setProceedingText(
"[ERROR WeightedAverageFilter]");
34 warningLog.setProceedingText(
"[WARNING WeightedAverageFilter]");
35 init(filterSize,numDimensions);
40 classType =
"WeightedAverageFilter";
41 preProcessingType = classType;
42 debugLog.setProceedingText(
"[DEBUG WeightedAverageFilter]");
43 errorLog.setProceedingText(
"[ERROR WeightedAverageFilter]");
44 warningLog.setProceedingText(
"[WARNING WeightedAverageFilter]");
67 if( rhs.initialized ){
80 if( preProcessing == NULL )
return false;
90 errorLog <<
"clone(const PreProcessing *preProcessing) - PreProcessing Types Do Not Match!" << std::endl;
99 errorLog <<
"process(const VectorFloat &inputVector) - The filter has not been initialized!" << std::endl;
103 if( inputVector.size() != numInputDimensions ){
104 errorLog <<
"process(const VectorFloat &inputVector) - The size of the inputVector (" << inputVector.size() <<
") does not match that of the filter (" << numInputDimensions <<
")!" << std::endl;
110 if( processedData.size() == numOutputDimensions )
return true;
122 if( !file.is_open() ){
123 errorLog <<
"saveModelToFile(fstream &file) - The file is not open!" << std::endl;
127 file <<
"GRT_MOVING_AVERAGE_FILTER_FILE_V1.0" << std::endl;
129 file <<
"NumInputDimensions: " << numInputDimensions << std::endl;
130 file <<
"NumOutputDimensions: " << numOutputDimensions << std::endl;
131 file <<
"FilterSize: " <<
filterSize << std::endl;
138 if( !file.is_open() ){
139 errorLog <<
"loadModelFromFile(fstream &file) - The file is not open!" << std::endl;
148 if( word !=
"GRT_MOVING_AVERAGE_FILTER_FILE_V1.0" ){
149 errorLog <<
"loadModelFromFile(fstream &file) - Invalid file format!" << std::endl;
155 if( word !=
"NumInputDimensions:" ){
156 errorLog <<
"loadModelFromFile(fstream &file) - Failed to read NumInputDimensions header!" << std::endl;
159 file >> numInputDimensions;
163 if( word !=
"NumOutputDimensions:" ){
164 errorLog <<
"loadModelFromFile(fstream &file) - Failed to read NumOutputDimensions header!" << std::endl;
167 file >> numOutputDimensions;
171 if( word !=
"FilterSize:" ){
172 errorLog <<
"loadModelFromFile(fstream &file) - Failed to read FilterSize header!" << std::endl;
178 return init(filterSize,numInputDimensions);
187 if( filterSize == 0 ){
188 errorLog <<
"init(UINT filterSize,UINT numDimensions) - Filter size can not be zero!" << std::endl;
192 if( numDimensions == 0 ){
193 errorLog <<
"init(UINT filterSize,UINT numDimensions) - The number of dimensions must be greater than zero!" << std::endl;
199 this->numInputDimensions = numDimensions;
200 this->numOutputDimensions = numDimensions;
201 processedData.clear();
203 processedData.
resize(numDimensions,0);
213 errorLog <<
"init(UINT filterSize,UINT numDimensions) - Failed to resize dataBuffer!" << std::endl;
223 errorLog <<
"filter(const Float x) - The filter has not been initialized!" << std::endl;
229 if( y.size() == 0 )
return 0;
237 errorLog <<
"filter(const VectorFloat &x) - The filter has not been initialized!" << std::endl;
241 if( x.size() != numInputDimensions ){
242 errorLog <<
"filter(const VectorFloat &x) - The size of the input vector (" << x.size() <<
") does not match that of the number of dimensions of the filter (" << numInputDimensions <<
")!" << std::endl;
252 for(
unsigned int j=0; j<numInputDimensions; j++){
253 processedData[j] = 0;
257 weightSum += weights[i];
259 if( weightSum != 0.0 ) processedData[j] /= weightSum;
262 return processedData;
bool push_back(const T &value)
virtual ~WeightedAverageFilter()
UINT inputSampleCounter
A counter to keep track of the number of input samples.
virtual bool resize(const unsigned int size)
WeightedAverageFilter & operator=(const WeightedAverageFilter &rhs)
virtual bool loadModelFromFile(std::fstream &file)
CircularBuffer< VectorFloat > dataBuffer
A buffer to store the previous N values, N = filterSize.
virtual bool deepCopyFrom(const PreProcessing *preProcessing)
The WeightedAverageFilter implements a weighted average filter that gives a larger weight to more rec...
WeightedAverageFilter(UINT filterSize=5, UINT numDimensions=1)
std::string getPreProcessingType() const
virtual bool process(const VectorFloat &inputVector)
UINT filterSize
The size of the filter.
virtual bool saveModelToFile(std::fstream &file) const
Float filter(const Float x)
bool copyBaseVariables(const PreProcessing *preProcessingModule)
VectorFloat weights
Stores the weights for each sample in the buffer, the size of this vector will match the filterSize...
bool resize(const unsigned int newBufferSize)