// 构造一个tri-trainer分类器。
public Tritrainer(
String classifier, String trainingIns_File, String testIns_File, double precentage) {
try {
this.classifier1 = (Classifier) Class.forName(classifier).newInstance();
this.classifier2 = (Classifier) Class.forName(classifier).newInstance();
this.classifier3 = (Classifier) Class.forName(classifier).newInstance();
Instances trainingInstances = Util.getInstances(trainingIns_File);
// 将trainIns_File按照precentage和(1-precentage)的比例切割成labeledIns和unlabeledIns;
int length = trainingInstances.numInstances();
int i = new Double(length * precentage).intValue();
labeledIns = new Instances(trainingInstances, 0);
for (int j = 0; j < i; j++) {
labeledIns.add(trainingInstances.firstInstance());
trainingInstances.delete(0);
}
unlabeledIns = trainingInstances;
testIns = Util.getInstances(testIns_File);
Init();
} catch (Exception e) {
}
}
public static ArrayList<Integer> getProfiles(Instances inst, List<Integer> marks)
throws Exception {
// Instances inst = Utils.prepareProfileMatcherData(schoolNo, grade, term, subjects);
// ReplaceMissingValues rmv = new ReplaceMissingValues();
// rmv.setInputFormat(inst);
// inst = Filter.useFilter(inst, rmv);
for (int i = 0; i < inst.numAttributes(); i++) {
inst.deleteWithMissing(i);
}
KDTree tree = new KDTree();
tree.setMeasurePerformance(true);
try {
tree.setInstances(inst);
EuclideanDistance df = new EuclideanDistance(inst);
df.setDontNormalize(true);
df.setAttributeIndices("2-last");
tree.setDistanceFunction(df);
} catch (Exception e) {
e.printStackTrace();
}
Instances neighbors = null;
Instances test = CFilter.createInstance(112121, (ArrayList<Integer>) marks);
Instance p = test.firstInstance();
try {
neighbors = tree.kNearestNeighbours(p, 50);
} catch (Exception e) {
e.printStackTrace();
}
// System.out.println(tree.getPerformanceStats().getTotalPointsVisited());
// System.out.println(nn1 + " is the nearest neigbor for " + p);
// System.out.println(nn2 + " is the second nearest neigbor for " + p);
ArrayList<Integer> profiles = new ArrayList<Integer>();
for (int i = 0; i < neighbors.numInstances(); i++) {
System.out.println(neighbors.instance(i));
profiles.add(Integer.valueOf(neighbors.instance(i).toString(0)));
}
// Now we can also easily compute the distances as the KDTree does it
DistanceFunction df = tree.getDistanceFunction();
// System.out.println("The distance between" + nn1 + " and " + p + " is " + df.distance(nn1,
// p));
// System.out.println("The distance between" + nn2 + " and " + p + " is " + df.distance(nn2,
// p));
return profiles;
}
public void dataAvailable(String inputStreamName, StreamElement data) {
// mapping the input stream to an instance and setting its dataset
String[] dfn = data.getFieldNames().clone();
Byte[] dft = data.getFieldTypes().clone();
Serializable[] da = data.getData().clone();
data = new StreamElement(dfn, dft, da, data.getTimeStamp());
Instance i = instanceFromStream(data);
if (att.size() == 0) {
att = attFromStream(data);
}
dataset = new Instances("input", att, 0);
dataset.setClassIndex(classIndex);
if (i != null) {
dataset.add(i);
i = dataset.firstInstance();
boolean success = true;
// extracting latitude/longitude
Double center_lat = i.value(1);
Double center_long = i.value(2);
// filling the grid with predictions/extrapolations
Double[][] rawData = new Double[gridSize][gridSize];
for (int j = 0; j < gridSize; j++) {
for (int k = 0; k < gridSize; k++) {
i.setValue(1, center_lat - (cellSize * gridSize / 2) + cellSize * j);
i.setValue(2, center_long - (cellSize * gridSize / 2) + cellSize * k);
rawData[j][k] = ms.predict(i);
success = success && (rawData[j][k] != null);
}
}
// preparing the output
Serializable[] stream = new Serializable[7];
try {
ByteArrayOutputStream bos = new ByteArrayOutputStream();
ObjectOutputStream oos = new ObjectOutputStream(bos);
oos.writeObject(rawData);
oos.flush();
oos.close();
bos.close();
stream[0] = new Integer(gridSize);
stream[1] = new Integer(gridSize);
stream[2] = new Double(center_lat - (cellSize * gridSize / 2));
stream[3] = new Double(center_long - (cellSize * gridSize / 2));
stream[4] = new Double(cellSize);
stream[5] = new Double(0);
stream[6] = bos.toByteArray();
} catch (IOException e) {
logger.warn(e.getMessage(), e);
success = false;
}
if (success) {
StreamElement se = new StreamElement(getOutputFormat(), stream, data.getTimeStamp());
dataProduced(se);
} else {
logger.warn("Prediction error. Something get wrong with the prediction.");
}
} else {
logger.warn(
"Predicting instance has wrong attibutes, please check the model and the inputs.");
}
}