/** recognize each Shorthand samples in words (arraylist) using the WordRecognizerClass */
private void recognizeTestingData() throws ClassNotFoundException, IOException, SQLException {
for (int i = 0; i < testingSamples.size(); i++) {
try {
Shorthand word = testingSamples.get(i);
r.recognize(word);
System.out.println(word.annRes + " " + word.svmRes + " " + word.bnRes);
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
}
@Override
public Void doInBackground() {
BufferedReader reader;
publish("Computing features...");
int testingSamples = p.getAllFeatures2(path, "testing_data");
try {
publish("Reading data...");
reader = new BufferedReader(new FileReader("testing_data.arff"));
final Instances testingdata = new Instances(reader);
reader.close();
// setting class attribute
testingdata.setClassIndex(13);
testingdata.randomize(new Random(1));
long startTime = System.nanoTime();
Classifier ann = (Classifier) weka.core.SerializationHelper.read("mlp.model");
publish("Evaluating ANN...");
evalANN = new Evaluation(testingdata);
startTime = System.nanoTime();
evalANN.evaluateModel(ann, testingdata);
long runningTimeANN = (System.nanoTime() - startTime) / 1000000;
// runningTimeANN /= 100;
publish("Done evaluating ANN");
publish("Evaluating SVM...");
Classifier svm = (Classifier) weka.core.SerializationHelper.read("svm.model");
evalSVM = new Evaluation(testingdata);
startTime = System.nanoTime();
evalSVM.evaluateModel(svm, testingdata);
long runningTimeSVM = (System.nanoTime() - startTime) / 1000000;
// runningTimeSVM /= 100;
publish("Done evaluating SVM");
publish("Evaluating NB...");
Classifier nb = (Classifier) weka.core.SerializationHelper.read("naivebayes.model");
evalNB = new Evaluation(testingdata);
startTime = System.nanoTime();
evalNB.evaluateModel(nb, testingdata);
long runningTimeNB = (System.nanoTime() - startTime) / 1000000;
// runningTimeNB /= 100;
publish("Done evaluating ANN");
Platform.runLater(
new Runnable() {
@Override
public void run() {
bc.getData()
.get(0)
.getData()
.get(0)
.setYValue(evalANN.correct() / testingdata.size() * 100);
bc.getData()
.get(0)
.getData()
.get(1)
.setYValue(evalSVM.correct() / testingdata.size() * 100);
bc.getData()
.get(0)
.getData()
.get(2)
.setYValue(evalNB.correct() / testingdata.size() * 100);
for (int i = 0; i < NUM_CLASSES; i++) {
lineChart.getData().get(0).getData().get(i).setYValue(evalANN.recall(i) * 100);
lineChart.getData().get(1).getData().get(i).setYValue(evalSVM.recall(i) * 100);
lineChart.getData().get(2).getData().get(i).setYValue(evalNB.recall(i) * 100);
}
}
});
panel.fillConfTable(evalSVM.confusionMatrix());
summaryTable.setValueAt(evalANN.correct() / testingdata.size() * 100., 0, 1);
summaryTable.setValueAt(evalSVM.correct() / testingdata.size() * 100, 0, 2);
summaryTable.setValueAt(evalNB.correct() / testingdata.size() * 100, 0, 3);
summaryTable.setValueAt(runningTimeANN, 1, 1);
summaryTable.setValueAt(runningTimeSVM, 1, 2);
summaryTable.setValueAt(runningTimeNB, 1, 3);
} catch (Exception e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
return null;
}
@Override
public Void doInBackground() {
BufferedReader reader;
try {
publish("Reading data...");
reader = new BufferedReader(new FileReader("cross_validation_data.arff"));
final Instances trainingdata = new Instances(reader);
reader.close();
// setting class attribute
trainingdata.setClassIndex(13);
trainingdata.randomize(new Random(1));
long startTime = System.nanoTime();
publish("Training Naive Bayes Classifier...");
NaiveBayes nb = new NaiveBayes();
startTime = System.nanoTime();
nb.buildClassifier(trainingdata);
double runningTimeNB = (System.nanoTime() - startTime) / 1000000;
runningTimeNB /= 1000;
// saving the naive bayes model
weka.core.SerializationHelper.write("naivebayes.model", nb);
System.out.println("running time" + runningTimeNB);
publish("Done training NB.\nEvaluating NB using 10-fold cross-validation...");
evalNB = new Evaluation(trainingdata);
evalNB.crossValidateModel(nb, trainingdata, 10, new Random(1));
publish("Done evaluating NB.");
// System.out.println(evalNB.toSummaryString("\nResults for Naive Bayes\n======\n", false));
MultilayerPerceptron mlp = new MultilayerPerceptron();
mlp.setOptions(Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a"));
publish("Training ANN...");
startTime = System.nanoTime();
mlp.buildClassifier(trainingdata);
long runningTimeANN = (System.nanoTime() - startTime) / 1000000;
runningTimeANN /= 1000;
// saving the MLP model
weka.core.SerializationHelper.write("mlp.model", mlp);
publish("Done training ANN.\nEvaluating ANN using 10-fold cross-validation...");
evalANN = new Evaluation(trainingdata);
evalANN.evaluateModel(mlp, trainingdata);
// evalMLP.crossValidateModel(mlp, trainingdata, 10, new Random(1));
publish("Done evaluating ANN.");
publish("Training SVM...");
SMO svm = new SMO();
startTime = System.nanoTime();
svm.buildClassifier(trainingdata);
long runningTimeSVM = (System.nanoTime() - startTime) / 1000000;
runningTimeSVM /= 1000;
weka.core.SerializationHelper.write("svm.model", svm);
publish("Done training SVM.\nEvaluating SVM using 10-fold cross-validation...");
evalSVM = new Evaluation(trainingdata);
evalSVM.evaluateModel(svm, trainingdata);
publish("Done evaluating SVM.");
Platform.runLater(
new Runnable() {
@Override
public void run() {
bc.getData()
.get(0)
.getData()
.get(0)
.setYValue(evalANN.correct() / trainingdata.size() * 100);
bc.getData()
.get(0)
.getData()
.get(1)
.setYValue(evalSVM.correct() / trainingdata.size() * 100);
bc.getData()
.get(0)
.getData()
.get(2)
.setYValue(evalNB.correct() / trainingdata.size() * 100);
for (int i = 0; i < NUM_CLASSES; i++) {
lineChart.getData().get(0).getData().get(i).setYValue(evalANN.recall(i) * 100);
lineChart.getData().get(1).getData().get(i).setYValue(evalSVM.recall(i) * 100);
lineChart.getData().get(2).getData().get(i).setYValue(evalNB.recall(i) * 100);
}
}
});
panel.fillConfTable(evalSVM.confusionMatrix());
summaryTable.setValueAt(evalANN.correct() / trainingdata.size() * 100., 0, 1);
summaryTable.setValueAt(evalSVM.correct() / trainingdata.size() * 100, 0, 2);
summaryTable.setValueAt(evalNB.correct() / trainingdata.size() * 100, 0, 3);
summaryTable.setValueAt(runningTimeANN, 1, 1);
summaryTable.setValueAt(runningTimeSVM, 1, 2);
summaryTable.setValueAt(runningTimeNB, 1, 3);
} catch (Exception e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
return null;
}
