bool LabelledTimeSeriesClassificationSampleTrimmer::trimTimeSeries(LabelledTimeSeriesClassificationSample &timeSeries){
const UINT M = timeSeries.getLength();
const UINT N = timeSeries.getNumDimensions();
if( M == 0 ){
warningLog << "trimTimeSeries(LabelledTimeSeriesClassificationSample &timeSeries,LabelledTimeSeriesClassificationSample &trimmedTimeSeries) - can't trim data, the length of the input time series is 0!" << endl;
return false;
}
if( N == 0 ){
warningLog << "trimTimeSeries(LabelledTimeSeriesClassificationSample &timeSeries,LabelledTimeSeriesClassificationSample &trimmedTimeSeries) - can't trim data, the number of dimensions in the input time series is 0!" << endl;
return false;
}
//Compute the derivative of the time series
double maxValue = 0;
vector< double > x(M,0);
for(UINT i=1; i<M; i++){
for(UINT j=0; j<N; j++){
x[i] += timeSeries[i][j]-timeSeries[i-1][j];
}
x[i]/=N;
if( x[i] > maxValue ) maxValue = x[i];
}
//Normalize x and at the same time search for the first time x[i] passes the trim threshold
UINT firstIndex = 0;
for(UINT i=1; i<M; i++){
x[i] /= maxValue;
if( x[i] > trimThreshold && firstIndex == 0 ){
firstIndex = i;
}
}
//Search for the last time x[i] passes the trim threshold
UINT lastIndex = 0;
for(UINT i=M; i>1; i--){
if( x[i] < trimThreshold && lastIndex == 0 ){
lastIndex = i;
break;
}
}
if( firstIndex == 0 && lastIndex == 0 ){
warningLog << "Failed to find either the first index or the last index!";
return false;
}
if( lastIndex == 0 ){
warningLog << "Failed to find the last index!";
lastIndex = M-1;
}
//Compute how long the new time series would be if we trimmed it
UINT newM = lastIndex-firstIndex;
if( (double(newM) / double(M)) * 100.0 > 100.0-maximumTrimPercentage ){
Matrix< double > newTimeSeries(newM,N);
UINT index = 0;
for(UINT i=firstIndex; i<lastIndex; i++){
for(UINT j=0; j<N; j++){
newTimeSeries[index][j] = timeSeries[i][j];
}
index++;
}
timeSeries.setTrainingSample(timeSeries.getClassLabel(), newTimeSeries);
}else{
debugLog << "MAXIMUM TRIM PERCENTAGE EXCEDDED - NOT TRIMMING TIME SERIES\n";
return false;
}
return true;
}
bool TimeSeriesClassificationSampleTrimmer::trimTimeSeries(TimeSeriesClassificationSample &timeSeries) {
const UINT M = timeSeries.getLength();
const UINT N = timeSeries.getNumDimensions();
if( M == 0 ) {
warningLog << "trimTimeSeries(TimeSeriesClassificationSample &timeSeries) - can't trim data, the length of the input time series is 0!" << endl;
return false;
}
if( N == 0 ) {
warningLog << "trimTimeSeries(TimeSeriesClassificationSample &timeSeries) - can't trim data, the number of dimensions in the input time series is 0!" << endl;
return false;
}
//Compute the energy of the time series
double maxValue = 0;
VectorDouble x(M,0);
for(UINT i=1; i<M; i++) {
for(UINT j=0; j<N; j++) {
x[i] += fabs(timeSeries[i][j]-timeSeries[i-1][j]);
}
x[i] /= N;
if( x[i] > maxValue ) maxValue = x[i];
}
//Normalize x so that the maximum energy has a value of 1
//At the same time search for the first time x[i] passes the trim threshold
UINT firstIndex = 0;
for(UINT i=1; i<M; i++) {
x[i] /= maxValue;
if( x[i] > trimThreshold && firstIndex == 0 ) {
firstIndex = i;
}
}
//Search for the last time x[i] passes the trim threshold
UINT lastIndex = 0;
for(UINT i=M-1; i>firstIndex; i--) {
if( x[i] > trimThreshold && lastIndex == 0 ) {
lastIndex = i;
break;
}
}
if( firstIndex == 0 && lastIndex == 0 ) {
warningLog << "Failed to find either the first index or the last index!";
return false;
}
if( firstIndex == lastIndex ) {
warningLog << "The first index and last index are the same!";
return false;
}
if( firstIndex > lastIndex ) {
warningLog << "The first index is greater than the last index!";
return false;
}
if( lastIndex == 0 ) {
warningLog << "Failed to find the last index!";
lastIndex = M-1;
}
//Compute how long the new time series would be if we trimmed it
UINT newM = lastIndex-firstIndex;
double trimPercentage = (double(newM) / double(M)) * 100.0;
if( 100 - trimPercentage <= maximumTrimPercentage ) {
MatrixDouble newTimeSeries(newM,N);
UINT index = 0;
for(UINT i=firstIndex; i<lastIndex; i++) {
for(UINT j=0; j<N; j++) {
newTimeSeries[index][j] = timeSeries[i][j];
}
index++;
}
timeSeries.setTrainingSample(timeSeries.getClassLabel(), newTimeSeries);
return true;
}
warningLog << "Maximum Trim Percentage Excedded, Can't Trim Sample!";
warningLog << " Original Timeseries Length: " << M << " Trimmed Timeseries Length: " << newM;
warningLog << " Percentage: " << (100-trimPercentage) << " MaximumTrimPercentage: " << maximumTrimPercentage << endl;
return false;
}
