int main (int argc, const char * argv[]) { //Create a new DTW instance, using the default parameters DTW dtw; //Load some training data to train the classifier - the DTW uses TimeSeriesClassificationData TimeSeriesClassificationData trainingData; if( !trainingData.load("DTWTrainingData.grt") ){ cout << "Failed to load training data!\n"; return EXIT_FAILURE; } //Use 20% of the training dataset to create a test dataset TimeSeriesClassificationData testData = trainingData.partition( 80 ); //Trim the training data for any sections of non-movement at the start or end of the recordings dtw.enableTrimTrainingData(true,0.1,90); //Train the classifier if( !dtw.train( trainingData ) ){ cout << "Failed to train classifier!\n"; return EXIT_FAILURE; } //Save the DTW model to a file if( !dtw.save("DTWModel.grt") ){ cout << "Failed to save the classifier model!\n"; return EXIT_FAILURE; } //Load the DTW model from a file if( !dtw.load("DTWModel.grt") ){ cout << "Failed to load the classifier model!\n"; return EXIT_FAILURE; } //Use the test dataset to test the DTW model double accuracy = 0; for(UINT i=0; i<testData.getNumSamples(); i++){ //Get the i'th test sample - this is a timeseries UINT classLabel = testData[i].getClassLabel(); MatrixDouble timeseries = testData[i].getData(); //Perform a prediction using the classifier if( !dtw.predict( timeseries ) ){ cout << "Failed to perform prediction for test sampel: " << i <<"\n"; return EXIT_FAILURE; } //Get the predicted class label UINT predictedClassLabel = dtw.getPredictedClassLabel(); double maximumLikelihood = dtw.getMaximumLikelihood(); VectorDouble classLikelihoods = dtw.getClassLikelihoods(); VectorDouble classDistances = dtw.getClassDistances(); //Update the accuracy if( classLabel == predictedClassLabel ) accuracy++; cout << "TestSample: " << i << "\tClassLabel: " << classLabel << "\tPredictedClassLabel: " << predictedClassLabel << "\tMaximumLikelihood: " << maximumLikelihood << endl; } cout << "Test Accuracy: " << accuracy/double(testData.getNumSamples())*100.0 << "%" << endl; return EXIT_SUCCESS; }
int main() { vector<string> gestures(0,""); GetFilesInDirectory(gestures, "rawdata"); CreateDirectory("processed", NULL); sort(gestures.begin(), gestures.end()); data = vector<vector<vector<double > > >(gestures.size(), vector<vector<double > >(0,vector<double>(0,0))); for(size_t i = 0; i < gestures.size(); i++) { ifstream fin(gestures[i]); int n; fin >> n; // cerr << gestures[i] << endl; // cerr << n << endl; data[i] = vector<vector<double> >(n, vector<double>(NUMPARAM, 0)); for(int j = 0; j < n; j++) { for(int k = 0; k < NUMPARAM; k++) { fin >> data[i][j][k]; } } fin.close(); } //Create a new instance of the TimeSeriesClassificationDataStream TimeSeriesClassificationData trainingData; // ax, ay, az trainingData.setNumDimensions(3); trainingData.setDatasetName("processed\\GestureTrainingData.txt"); ofstream labelfile("processed\\GestureTrainingDataLabels.txt"); UINT currLabel = 1; Random random; map<string, int> gesturenames; for(size_t overall = 0; overall < gestures.size(); overall++) { string nam = gestures[overall].substr(8,gestures[overall].find_first_of('_')-8); if(gesturenames.count(nam)) currLabel = gesturenames[nam]; else { currLabel = gesturenames.size()+1; gesturenames[nam] = currLabel; labelfile << currLabel << " " << nam << endl; } MatrixDouble trainingSample; VectorDouble currVec( trainingData.getNumDimensions() ); for(size_t k = 1; k < data[overall].size(); k++) { for(UINT j=0; j<currVec.size(); j++){ currVec[j] = data[overall][k][j]; } trainingSample.push_back(currVec); } trainingData.addSample(currLabel, trainingSample); } for(size_t i = 0; i < gestures.size(); i++) { MatrixDouble trainingSample; VectorDouble currVec(trainingData.getNumDimensions()); for(UINT j = 0; j < currVec.size(); j++) { currVec[j] = random.getRandomNumberUniform(-1.0, 1.0); } for(size_t k = 0; k < 100; k++) { trainingSample.push_back(currVec); } trainingData.addSample(0, trainingSample); } //After recording your training data you can then save it to a file if( !trainingData.save( "processed\\TrainingData.grt" ) ){ cout << "ERROR: Failed to save dataset to file!\n"; return EXIT_FAILURE; } //This can then be loaded later if( !trainingData.load( "processed\\TrainingData.grt" ) ){ cout << "ERROR: 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; } DTW dtw; if( !dtw.train( trainingData ) ){ cerr << "Failed to train classifier!\n"; exit(EXIT_FAILURE); } dtw.enableNullRejection(true); dtw.setNullRejectionCoeff(4); dtw.enableTrimTrainingData(true, 0.1, 90); //Save the DTW model to a file if( !dtw.saveModelToFile("processed\\DTWModel.txt") ){ cerr << "Failed to save the classifier model!\n"; exit(EXIT_FAILURE); } trainingData.clear(); return EXIT_SUCCESS; }