@Test
public void train_setForestSize_randomForestHasCorrectSetting() {
// Arrange
// Act
GeneralTrainer randomForestTrainer =
createGeneralTrainer(
"weka.classifiers.trees.RandomForest -I 800"); // -I for number or trees
RandomForest randomForest = (RandomForest) randomForestTrainer.train(getTestInstances());
// Assert
assertEquals(800, randomForest.getNumTrees());
}
public static Double runClassify(String trainFile, String testFile) {
double predictOrder = 0.0;
double trueOrder = 0.0;
try {
String trainWekaFileName = trainFile;
String testWekaFileName = testFile;
Instances train = DataSource.read(trainWekaFileName);
Instances test = DataSource.read(testWekaFileName);
train.setClassIndex(0);
test.setClassIndex(0);
train.deleteAttributeAt(8);
test.deleteAttributeAt(8);
train.deleteAttributeAt(6);
test.deleteAttributeAt(6);
train.deleteAttributeAt(5);
test.deleteAttributeAt(5);
train.deleteAttributeAt(4);
test.deleteAttributeAt(4);
// AdditiveRegression classifier = new AdditiveRegression();
// NaiveBayes classifier = new NaiveBayes();
RandomForest classifier = new RandomForest();
// LibSVM classifier = new LibSVM();
classifier.buildClassifier(train);
Evaluation eval = new Evaluation(train);
eval.evaluateModel(classifier, test);
System.out.println(eval.toSummaryString("\nResults\n\n", true));
// System.out.println(eval.toClassDetailsString());
// System.out.println(eval.toMatrixString());
int k = 892;
for (int i = 0; i < test.numInstances(); i++) {
predictOrder = classifier.classifyInstance(test.instance(i));
trueOrder = test.instance(i).classValue();
System.out.println((k++) + "," + (int) predictOrder);
}
} catch (Exception e) {
e.printStackTrace();
}
return predictOrder;
}
public String classifyInstance(String newInst) {
File f = null;
String type = null;
try {
f = new File("/data/data/com.example.gpstracker/tmp.arff");
f.createNewFile();
FileWriter fw = new FileWriter(f);
BufferedWriter bw = new BufferedWriter(fw);
bw.write("@relation gps_tracking");
bw.newLine();
bw.newLine();
bw.write("@attribute Longtitude numeric");
bw.newLine();
bw.write("@attribute Latitude numeric");
bw.newLine();
bw.write("@attribute CurrentSpeed numeric");
bw.newLine();
bw.write("@attribute Timestamp date \"yyyy-MM-dd HH:mm:ss\"");
bw.newLine();
bw.write("@attribute MoveType {Walking,Running,Biking,Driving,Metro,Bus,Motionless}");
bw.newLine();
bw.write("@attribute IsGpsFixed {yes,no}");
bw.newLine();
bw.newLine();
bw.write("@data");
bw.newLine();
bw.write(newInst);
bw.close();
// load unlabeled data
Instances unlabeled =
new Instances(
new BufferedReader(new FileReader("/data/data/com.example.gpstracker/tmp.arff")));
// set class attribute
unlabeled.setClassIndex(unlabeled.numAttributes() - 2);
// label instances
double clsLabel = classifier.classifyInstance(unlabeled.instance(0));
type = unlabeled.classAttribute().value((int) clsLabel);
boolean deleted = f.delete();
} catch (FileNotFoundException e) {
e.printStackTrace();
} catch (IOException e) {
e.printStackTrace();
} catch (Exception e) {
e.printStackTrace();
}
return type;
}
public void trainSystem() {
BufferedReader reader = null;
Instances data = null;
try {
reader =
new BufferedReader(
new InputStreamReader(getResources().getAssets().open("training_data.arff")));
data = new Instances(reader);
reader.close();
data.setClassIndex(data.numAttributes() - 2);
classifier = new RandomForest();
classifier.buildClassifier(data);
} catch (FileNotFoundException e) {
Toast.makeText(getApplicationContext(), "File not Found.!", Toast.LENGTH_LONG).show();
Log.e("GPSTracker", "fnfexception", e);
} catch (IOException e) {
Toast.makeText(getApplicationContext(), "IO Exception.!", Toast.LENGTH_LONG).show();
Log.e("GPSTracker", "ioexception", e);
} catch (Exception e) {
Log.e("GPSTracker", "exception", e);
Toast.makeText(getApplicationContext(), "Exception.!", Toast.LENGTH_LONG).show();
}
}