public static void main(String[] args) throws Exception {
/*
* First we load the test data from our ARFF file
*/
ArffLoader testLoader = new ArffLoader();
testLoader.setSource(new File("data/titanic/test.arff"));
testLoader.setRetrieval(Loader.BATCH);
Instances testDataSet = testLoader.getDataSet();
/*
* Now we tell the data set which attribute we want to classify, in our
* case, we want to classify the first column: survived
*/
Attribute testAttribute = testDataSet.attribute(0);
testDataSet.setClass(testAttribute);
testDataSet.deleteStringAttributes();
/*
* Now we read in the serialized model from disk
*/
Classifier classifier = (Classifier) SerializationHelper.read("data/titanic/titanic.model");
/*
* This part may be a little confusing. We load up the test data again
* so we have a prediction data set to populate. As we iterate over the
* first data set we also iterate over the second data set. After an
* instance is classified, we set the value of the prediction data set
* to be the value of the classification
*/
ArffLoader test1Loader = new ArffLoader();
test1Loader.setSource(new File("data/titanic/test.arff"));
Instances test1DataSet = test1Loader.getDataSet();
Attribute test1Attribute = test1DataSet.attribute(0);
test1DataSet.setClass(test1Attribute);
/*
* Now we iterate over the test data and classify each entry and set the
* value of the 'survived' column to the result of the classification
*/
Enumeration testInstances = testDataSet.enumerateInstances();
Enumeration test1Instances = test1DataSet.enumerateInstances();
while (testInstances.hasMoreElements()) {
Instance instance = (Instance) testInstances.nextElement();
Instance instance1 = (Instance) test1Instances.nextElement();
double classification = classifier.classifyInstance(instance);
instance1.setClassValue(classification);
}
/*
* Now we want to write out our predictions. The resulting file is in a
* format suitable to submit to Kaggle.
*/
CSVSaver predictedCsvSaver = new CSVSaver();
predictedCsvSaver.setFile(new File("data/titanic/predict.csv"));
predictedCsvSaver.setInstances(test1DataSet);
predictedCsvSaver.writeBatch();
System.out.println("Prediciton saved to predict.csv");
}
// 计算h1,h2分类器共同的分类错误率；
public double measureBothError(Classifier h1, Classifier h2, Instances test) {
int m = test.numInstances();
double value1, value2, value;
int error = 0, total = 0;
try {
for (int i = 0; i < m; i++) {
value = test.instance(i).classValue();
value1 = h1.classifyInstance(test.instance(i));
value2 = h2.classifyInstance(test.instance(i));
// 两分类器做出相同决策
if (value1 == value2) {
// 两分类器做出相同决策的样本数量
total++;
// 两分类器做出相同错误决策
if (value != value1) {
// 两分类器做出相同错误决策的样本数量
error++;
}
}
}
} catch (Exception e) {
System.out.println(e);
}
// System.out.println("m:=" + m);
// System.out.println("error:=" + error +"; total:=" + total);
// 两个分类器的分类错误率= 两分类器做出相同错误决策的样本数量/两分类器做出相同决策的样本数量
return (error * 1.0) / total;
}
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
Instances isTrainingSet = createSet(4);
Instance instance1 = createInstance(new double[] {1, 0.7, 0.1, 0.7}, "S1", isTrainingSet);
Instance instance2 = createInstance(new double[] {0.1, 0.2, 1, 0.3}, "S2", isTrainingSet);
Instance instance22 = createInstance(new double[] {0, 0, 0, 0}, "S3", isTrainingSet);
isTrainingSet.add(instance1);
isTrainingSet.add(instance2);
isTrainingSet.add(instance22);
Instances isTestingSet = createSet(4);
Instance instance3 = createInstance(new double[] {1, 0.7, 0.1, 0.7}, "S1", isTrainingSet);
Instance instance4 = createInstance(new double[] {0.1, 0.2, 1, 0.3}, "S2", isTrainingSet);
isTestingSet.add(instance3);
isTestingSet.add(instance4);
// Create a naïve bayes classifier
Classifier cModel = (Classifier) new BayesNet(); // M5P
cModel.buildClassifier(isTrainingSet);
// Test the model
Evaluation eTest = new Evaluation(isTrainingSet);
eTest.evaluateModel(cModel, isTestingSet);
// Print the result à la Weka explorer:
String strSummary = eTest.toSummaryString();
System.out.println(strSummary);
// Get the likelihood of each classes
// fDistribution[0] is the probability of being “positive”
// fDistribution[1] is the probability of being “negative”
double[] fDistribution = cModel.distributionForInstance(instance4);
for (int i = 0; i < fDistribution.length; i++) {
System.out.println(fDistribution[i]);
}
}
/**
* Gets the classifier specification string, which contains the class name of the classifier and
* any options to the classifier.
*
* @return the classifier string.
*/
protected String getClassifierSpec() {
Classifier c = getClassifier();
if (c instanceof OptionHandler) {
return c.getClass().getName() + " " + Utils.joinOptions(((OptionHandler) c).getOptions());
}
return c.getClass().getName();
}
public int SelectRow_KLDivergenceMisclassified(
Instances pool, Classifier myEstimator, int desiredAttr) {
// for each instance with unbought desiredAttr and label = desiredLabel
// measure KL-divergence (relative entropy between two prob distributions):
// KL(P||Q) = sum_i p_i log (p_i/q_i)
// withr respect to Q = Uniform, we have
// KL(P||U) = sum_i p_i log(p_i)
// choose (row) that is minimum (i.e. closest to uniform)
int numInstances = pool.numInstances();
double[] KLDivs = new double[numInstances];
boolean[] isValidInstance = new boolean[numInstances];
boolean misclassified = false;
double[] probs = null;
Instance inst;
for (int i = 0; i < numInstances; i++) {
inst = pool.instance(i);
try {
if (inst.classValue() != myEstimator.classifyInstance(inst)) misclassified = true;
else misclassified = false;
} catch (Exception e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
if (inst.isMissing(desiredAttr) && misclassified) {
try {
probs = myEstimator.distributionForInstance(inst);
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
for (int j = 0; j < probs.length; j++) KLDivs[i] += MyXLogX(probs[j]);
isValidInstance[i] = true;
} else {
KLDivs[i] = Double.MAX_VALUE;
isValidInstance[i] = false;
}
}
double leastDivergence = KLDivs[Utils.minIndex(KLDivs)];
int numLeastDivs = 0;
for (int i = 0; i < numInstances; i++)
if (isValidInstance[i] && KLDivs[i] == leastDivergence) numLeastDivs++;
int randomInstance = r.nextInt(numLeastDivs);
int index = 0;
for (int i = 0; i < numInstances; i++) {
if (isValidInstance[i] && KLDivs[i] == leastDivergence) {
if (index == randomInstance) return i;
else index++;
}
}
return -1;
}
/**
* Buildclassifier selects a classifier from the set of classifiers by minimising error on the
* training data.
*
* @param data the training data to be used for generating the boosted classifier.
* @exception Exception if the classifier could not be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
if (m_Classifiers.length == 0) {
throw new Exception("No base classifiers have been set!");
}
Instances newData = new Instances(data);
newData.deleteWithMissingClass();
newData.randomize(new Random(m_Seed));
if (newData.classAttribute().isNominal() && (m_NumXValFolds > 1))
newData.stratify(m_NumXValFolds);
Instances train = newData; // train on all data by default
Instances test = newData; // test on training data by default
Classifier bestClassifier = null;
int bestIndex = -1;
double bestPerformance = Double.NaN;
int numClassifiers = m_Classifiers.length;
for (int i = 0; i < numClassifiers; i++) {
Classifier currentClassifier = getClassifier(i);
Evaluation evaluation;
if (m_NumXValFolds > 1) {
evaluation = new Evaluation(newData);
for (int j = 0; j < m_NumXValFolds; j++) {
train = newData.trainCV(m_NumXValFolds, j);
test = newData.testCV(m_NumXValFolds, j);
currentClassifier.buildClassifier(train);
evaluation.setPriors(train);
evaluation.evaluateModel(currentClassifier, test);
}
} else {
currentClassifier.buildClassifier(train);
evaluation = new Evaluation(train);
evaluation.evaluateModel(currentClassifier, test);
}
double error = evaluation.errorRate();
if (m_Debug) {
System.err.println(
"Error rate: "
+ Utils.doubleToString(error, 6, 4)
+ " for classifier "
+ currentClassifier.getClass().getName());
}
if ((i == 0) || (error < bestPerformance)) {
bestClassifier = currentClassifier;
bestPerformance = error;
bestIndex = i;
}
}
m_ClassifierIndex = bestIndex;
m_Classifier = bestClassifier;
if (m_NumXValFolds > 1) {
m_Classifier.buildClassifier(newData);
}
}
public void batchPredict() {
// load all test set
String modelFile =
"data\\AcquireValueShopper\\decisionTable_bayes_trees.model".replace("\\", File.separator);
String pathTest = "data/AcquireValueShopper/test_new.csv";
String pathPredict = "data/AcquireValueShopper/submission.csv";
Scanner scanner;
String line = "";
String[] partsOfLine = null;
String id = "";
PrintWriter output;
Map<String, String> testSet = new HashMap<String, String>();
try {
scanner = new Scanner(new File(pathTest));
while (scanner.hasNext()) {
line = scanner.nextLine().trim();
partsOfLine = line.split(",");
id = partsOfLine[0];
testSet.put(id, line);
}
scanner.close();
} catch (FileNotFoundException e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
double[] returnProb;
double prob = 0.0;
// predict
try {
// load model
Classifier classifier = (Classifier) SerializationHelper.read(modelFile);
output = new PrintWriter(pathPredict);
output.append("id,repeatProbability" + "\n");
Iterator<String> idIterator = testSet.keySet().iterator();
while (idIterator.hasNext()) {
id = idIterator.next();
line = testSet.get(id);
Instances instances = buildInstance(line);
Instance instance = instances.instance(0);
returnProb = classifier.distributionForInstance(instance);
prob = returnProb[1];
// prob = classifier.classifyInstance(instance);
output.append(id + "," + prob + "\n");
}
output.close();
} catch (FileNotFoundException e) {
// TODO Auto-generated catch block
e.printStackTrace();
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
/**
* Gets the classifier specification string, which contains the class name of the classifier and
* any options to the classifier
*
* @param index the index of the classifier string to retrieve, starting from 0.
* @return the classifier string, or the empty string if no classifier has been assigned (or the
* index given is out of range).
*/
protected String getClassifierSpec(int index) {
if (m_Classifiers.length < index) {
return "";
}
Classifier c = getClassifier(index);
if (c instanceof OptionHandler) {
return c.getClass().getName() + " " + Utils.joinOptions(((OptionHandler) c).getOptions());
}
return c.getClass().getName();
}
@Override
public String toString() {
StringBuffer buffer = new StringBuffer();
buffer.append("MultiClassifier\n");
buffer.append("-----------------\n");
for (Classifier classifier : classifiers) {
buffer.append(classifier.toString());
buffer.append("\n-----------------\n");
}
return buffer.toString();
}
@Override
public double classifyInstance(Instance instance) throws Exception {
double sum = 0.0;
for (Classifier classifier : classifiers) {
double classification = classifier.classifyInstance(instance);
sum += classification;
}
if (sum >= classifiers.size() / 2) {
return 1.0;
}
return 0.0;
}
/**
* Boosting method. Boosts any classifier that can handle weighted instances.
*
* @param data the training data to be used for generating the boosted classifier.
* @throws Exception if the classifier could not be built successfully
*/
protected void buildClassifierWithWeights(Instances data) throws Exception {
Instances trainData, training, trainingWeightsNotNormalized;
int numInstances = data.numInstances();
Random randomInstance = new Random(m_Seed);
double minLoss = Double.MAX_VALUE;
// Create a copy of the data so that when the weights are diddled
// with it doesn't mess up the weights for anyone else
trainingWeightsNotNormalized = new Instances(data, 0, numInstances);
// Do boostrap iterations
for (m_NumIterationsPerformed = -1;
m_NumIterationsPerformed < m_Classifiers.length;
m_NumIterationsPerformed++) {
if (m_Debug) {
System.err.println("Training classifier " + (m_NumIterationsPerformed + 1));
}
training = new Instances(trainingWeightsNotNormalized);
normalizeWeights(training, m_SumOfWeights);
// Select instances to train the classifier on
if (m_WeightThreshold < 100) {
trainData = selectWeightQuantile(training, (double) m_WeightThreshold / 100);
} else {
trainData = new Instances(training, 0, numInstances);
}
// Build classifier
if (m_NumIterationsPerformed == -1) {
m_ZeroR = new weka.classifiers.rules.ZeroR();
m_ZeroR.buildClassifier(data);
} else {
if (m_Classifiers[m_NumIterationsPerformed] instanceof Randomizable)
((Randomizable) m_Classifiers[m_NumIterationsPerformed])
.setSeed(randomInstance.nextInt());
m_Classifiers[m_NumIterationsPerformed].buildClassifier(trainData);
}
// Update instance weights
setWeights(trainingWeightsNotNormalized, m_NumIterationsPerformed);
// Has progress been made?
double loss = 0;
for (Instance inst : trainingWeightsNotNormalized) {
loss += Math.log(inst.weight());
}
if (m_Debug) {
System.err.println("Current loss on log scale: " + loss);
}
if ((m_NumIterationsPerformed > -1) && (loss > minLoss)) {
if (m_Debug) {
System.err.println("Loss has increased: bailing out.");
}
break;
}
minLoss = loss;
}
}
private void jButton3ActionPerformed(
java.awt.event.ActionEvent evt) { // GEN-FIRST:event_jButton3ActionPerformed
// TODO add your handling code here:
switch (jComboBox1.getSelectedIndex()) {
case 0:
model = new NaiveBayes();
jTextArea1.append("Building NaiveBayes model from training data ...\n");
break;
case 1:
model = new Id3();
jTextArea1.append("Building ID3 model from training data ...\n");
break;
case 2:
model = new J48();
jTextArea1.append("Building J48 model from training data ...\n");
break;
}
try {
model.buildClassifier(training);
jTextArea1.append("Model building is complete ...\n");
jButton4.setEnabled(true);
jButton6.setEnabled(true);
} catch (Exception ex) {
jTextArea1.append("Model building failed ...\n");
jTextArea1.append(ex.getMessage());
jTextArea1.append("\n");
jButton4.setEnabled(true);
jButton6.setEnabled(false);
model = null;
}
} // GEN-LAST:event_jButton3ActionPerformed
public static Instances getKnowledgeBase() {
if (knowledgeBase == null) {
try {
// load knowledgebase from file
CreateAppInsertIntoVm.knowledgeBase =
Action.loadKnowledge(Configuration.getInstance().getKBCreateAppInsertIntoVm());
// prediction is also performed therefore the classifier and the evaluator must be
// instantiated
if (!isOnlyLearning()) {
System.out.println("Classify data CreateAppInsertInto");
if (knowledgeBase.numInstances() > 0) {
classifier = new MultilayerPerceptron();
classifier.buildClassifier(knowledgeBase);
evaluation = new Evaluation(knowledgeBase);
evaluation.crossValidateModel(
classifier,
knowledgeBase,
10,
knowledgeBase.getRandomNumberGenerator(randomData.nextLong(1, 1000)));
System.out.println("Classified data CreateAppInsertInto");
} else {
System.out.println("No Instancedata for classifier CreateAppInsertIntoVm");
}
}
} catch (Exception e) {
e.printStackTrace();
}
}
return knowledgeBase;
}
/**
* Gets the raw output from the classifier
*
* @return the raw output from the classifier
*/
public String getRawResultOutput() {
StringBuffer result = new StringBuffer();
if (m_Classifier == null) {
return "<null> classifier";
}
result.append(toString());
result.append("Classifier model: \n" + m_Classifier.toString() + '\n');
// append the performance statistics
if (m_result != null) {
result.append(m_result);
if (m_doesProduce != null) {
for (int i = 0; i < m_doesProduce.length; i++) {
if (m_doesProduce[i]) {
try {
double dv =
((AdditionalMeasureProducer) m_Classifier).getMeasure(m_AdditionalMeasures[i]);
if (!Utils.isMissingValue(dv)) {
Double value = new Double(dv);
result.append(m_AdditionalMeasures[i] + " : " + value + '\n');
} else {
result.append(m_AdditionalMeasures[i] + " : " + '?' + '\n');
}
} catch (Exception ex) {
System.err.println(ex);
}
}
}
}
}
return result.toString();
}
/**
* Returns an enumeration describing the available options..
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(1);
newVector.addElement(
new Option(
"\tSkips the determination of sizes (train/test/classifier)\n"
+ "\t(default: sizes are determined)",
"no-size",
0,
"-no-size"));
newVector.addElement(
new Option(
"\tThe full class name of the classifier.\n"
+ "\teg: weka.classifiers.bayes.NaiveBayes",
"W",
1,
"-W <class name>"));
if ((m_Template != null) && (m_Template instanceof OptionHandler)) {
newVector.addElement(
new Option(
"",
"",
0,
"\nOptions specific to classifier " + m_Template.getClass().getName() + ":"));
Enumeration enu = ((OptionHandler) m_Template).listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
}
return newVector.elements();
}
/**
* Generates the classifier.
*
* @param instances set of instances serving as training data
* @throws Exception if the classifier has not been generated successfully
*/
public void buildClassifier(Instances instances) throws Exception {
if (!(m_Classifier instanceof WeightedInstancesHandler)) {
throw new IllegalArgumentException("Classifier must be a " + "WeightedInstancesHandler!");
}
// can classifier handle the data?
getCapabilities().testWithFail(instances);
// remove instances with missing class
instances = new Instances(instances);
instances.deleteWithMissingClass();
// only class? -> build ZeroR model
if (instances.numAttributes() == 1) {
System.err.println(
"Cannot build model (only class attribute present in data!), "
+ "using ZeroR model instead!");
m_ZeroR = new weka.classifiers.rules.ZeroR();
m_ZeroR.buildClassifier(instances);
return;
} else {
m_ZeroR = null;
}
m_Train = new Instances(instances, 0, instances.numInstances());
m_NNSearch.setInstances(m_Train);
}
/**
* 用分类器测试
*
* @param trainFileName
* @param testFileName
*/
public static void classify(String trainFileName, String testFileName) {
try {
File inputFile = new File(fileName + trainFileName); // 训练语料文件
ArffLoader atf = new ArffLoader();
atf.setFile(inputFile);
Instances instancesTrain = atf.getDataSet(); // 读入训练文件
// 设置类标签类
inputFile = new File(fileName + testFileName); // 测试语料文件
atf.setFile(inputFile);
Instances instancesTest = atf.getDataSet(); // 读入测试文件
instancesTest.setClassIndex(instancesTest.numAttributes() - 1);
instancesTrain.setClassIndex(instancesTrain.numAttributes() - 1);
classifier = (Classifier) Class.forName(CLASSIFIERNAME).newInstance();
classifier.buildClassifier(instancesTrain);
Evaluation eval = new Evaluation(instancesTrain);
// 第一个为一个训练过的分类器，第二个参数是在某个数据集上评价的数据集
eval.evaluateModel(classifier, instancesTest);
System.out.println(eval.toClassDetailsString());
System.out.println(eval.toSummaryString());
System.out.println(eval.toMatrixString());
System.out.println("precision is :" + (1 - eval.errorRate()));
} catch (Exception e) {
e.printStackTrace();
}
}
/**
* Analyses the given list of decision points according to the context specified. Furthermore, the
* context is provided with some visualization of the analysis result.
*
* @param decisionPoints the list of decision points to be analysed
* @param log the log to be analysed
* @param highLevelPN the simulation model to export discovered data dependencies
*/
public void analyse(ClusterDecisionAnalyzer cda) {
clusterDecisionAnalyzer = cda;
// create empty data set with attribute information
Instances data = cda.getDataInfo();
// in case no single learning instance can be provided (as decision
// point is never
// reached, or decision classes cannot specified properly) --> do not
// call algorithm
if (data.numInstances() == 0) {
System.out.println("No learning instances available");
}
// actually solve the classification problem
else {
try {
myClassifier.buildClassifier(data);
// build up result visualization
cda.setResultVisualization(createResultVisualization());
cda.setEvaluationVisualization(createEvaluationVisualization(data));
} catch (Exception ex) {
ex.printStackTrace();
cda.setResultVisualization(
createMessagePanel("Error while solving the classification problem"));
}
}
}
/** trains the classifier */
@Override
public void train() throws Exception {
if (_train.classIndex() == -1) _train.setClassIndex(_train.numAttributes() - 1);
_cl.buildClassifier(_train);
// evaluate classifier and print some statistics
evaluate();
}
private static void writePredictedDistributions(
Classifier c, Instances data, int idIndex, Writer out) throws Exception {
// header
out.write("id");
for (int i = 0; i < data.numClasses(); i++) {
out.write(",\"");
out.write(data.classAttribute().value(i).replaceAll("[\"\\\\]", "_"));
out.write("\"");
}
out.write("\n");
// data
for (int i = 0; i < data.numInstances(); i++) {
final String id = data.instance(i).stringValue(idIndex);
double[] distribution = c.distributionForInstance(data.instance(i));
// final String label = data.attribute(classIndex).value();
out.write(id);
for (double probability : distribution) {
out.write(",");
out.write(String.valueOf(probability > 1e-5 ? (float) probability : 0f));
}
out.write("\n");
}
}
public void run() throws Exception {
BufferedReader datafileclassificationpickup =
readDataFile(Config.outputPath() + "DaysPickUpClassification.txt");
BufferedReader datafileclassificationdropoff =
readDataFile(Config.outputPath() + "DaysDropOffClassification.txt");
BufferedReader datafileregresssionpickup =
readDataFile(Config.outputPath() + "DaysPickUpRegression.txt");
BufferedReader datafileregresssiondropoff =
readDataFile(Config.outputPath() + "DaysDropOffRegression.txt");
dataclassificationpickup = new Instances(datafileclassificationpickup);
dataclassificationpickup.setClassIndex(dataclassificationpickup.numAttributes() - 1);
dataclassificationdropoff = new Instances(datafileclassificationdropoff);
dataclassificationdropoff.setClassIndex(dataclassificationdropoff.numAttributes() - 1);
dataregressionpickup = new Instances(datafileregresssionpickup);
dataregressionpickup.setClassIndex(dataregressionpickup.numAttributes() - 1);
dataregressiondropoff = new Instances(datafileregresssiondropoff);
dataregressiondropoff.setClassIndex(dataregressiondropoff.numAttributes() - 1);
System.out.println("KNN classification model");
ibkclassificationpickup = new IBk(10);
ibkclassificationpickup.buildClassifier(dataclassificationpickup);
ibkclassificationdropoff = new IBk(10);
ibkclassificationdropoff.buildClassifier(dataclassificationdropoff);
System.out.println("Classification Model Ready");
System.out.println("KNN regression model");
ibkregressionpickup = new IBk(10);
ibkregressionpickup.buildClassifier(dataregressionpickup);
ibkregressiondropoff = new IBk(10);
ibkregressiondropoff.buildClassifier(dataregressiondropoff);
System.out.println("Regression Model Ready");
instclassificationpickup = new DenseInstance(9);
instclassificationpickup.setDataset(dataclassificationpickup);
instclassificationdropoff = new DenseInstance(9);
instclassificationdropoff.setDataset(dataclassificationdropoff);
instregressionpickup = new DenseInstance(9);
instregressionpickup.setDataset(dataregressionpickup);
instregressiondropoff = new DenseInstance(9);
instregressiondropoff.setDataset(dataregressiondropoff);
System.out.println("Models ready");
}
/**
* Gets the key describing the current SplitEvaluator. For example This may contain the name of
* the classifier used for classifier predictive evaluation. The number of key fields must be
* constant for a given SplitEvaluator.
*
* @return an array of objects containing the key.
*/
public Object[] getKey() {
Object[] key = new Object[KEY_SIZE];
key[0] = m_Template.getClass().getName();
key[1] = m_ClassifierOptions;
key[2] = m_ClassifierVersion;
return key;
}
private static int calculatenpeople(Classifier ibkregression, Instance instregression) {
Double predictValue = null;
try {
predictValue = ibkregression.classifyInstance(instregression);
} catch (Exception e) {
e.printStackTrace();
}
return predictValue.intValue();
}
// 通过h1,h2分类器学习样本集，将h1,h2分类决策相同的样本放入L中，得到标记集合；
public void updateL(Classifier h1, Classifier h2, Instances L, Instances test) {
int length = unlabeledIns.numInstances();
double value1 = 0.0, value2 = 0.0;
try {
for (int i = 0; i < length; i++) {
value1 = h1.classifyInstance(test.instance(i));
value2 = h2.classifyInstance(test.instance(i));
if (value1 == value2) {
// 当两个分类器做出相同决策时重新标记样本的类别；
test.instance(i).setClassValue(value1);
L.add(test.instance(i));
}
}
} catch (Exception e) {
System.out.println(e);
}
// return false;
}
@Override
public final void run() {
try {
Classifier copiedClassifier = AbstractClassifier.makeCopy(classifier);
copiedClassifier.buildClassifier(train);
// log.print("The " + threadId + "th classifier is built!!!");
// accuracy = getAccuracy(copiedClassifier, test);
// classifier = AbstractClassifier.makeCopy(classifier);
// classifier.buildClassifier(train);
log.print("The " + threadId + "th classifier is built!!!");
accuracy = getAccuracy(copiedClassifier, test);
} catch (Exception e) {
log.print(e.getStackTrace().toString());
log.print(e.toString());
}
multiThreadEval.finishOneThreads();
log.print("The " + threadId + "th thread is finshed! accuracy = " + accuracy);
}
/**
* Calculates the class membership probabilities for the given test instance.
*
* @param instance the instance to be classified
* @return predicted class probability distribution
* @exception Exception if distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance instance) throws Exception {
if (instance.classAttribute().isNumeric()) {
throw new UnsupportedClassTypeException("Decorate can't handle a numeric class!");
}
double[] sums = new double[instance.numClasses()], newProbs;
Classifier curr;
for (int i = 0; i < m_Committee.size(); i++) {
curr = (Classifier) m_Committee.get(i);
newProbs = curr.distributionForInstance(instance);
for (int j = 0; j < newProbs.length; j++) sums[j] += newProbs[j];
}
if (Utils.eq(Utils.sum(sums), 0)) {
return sums;
} else {
Utils.normalize(sums);
return sums;
}
}
/** 分类过程 */
public double classifyMessage(String message) throws Exception {
filter.input(makeInstance(message, instances.stringFreeStructure()));
Instance filteredInstance = filter.output(); // 必须使用原来的filter
double predicted = classifier.classifyInstance(filteredInstance); // (int)predicted是类标索引
// System.out.println("Message classified as : "
// + instances.classAttribute().value((int) predicted));
return predicted;
}
public double getAccuracy(Classifier classifier, Instances test) throws Exception {
for (Instance instance : test) {
int predClass = (int) classifier.classifyInstance(instance);
int realClass = (int) instance.classValue();
if (predClass == realClass) {
corrCnt++;
}
}
return (double) corrCnt / totCnt;
}
/**
* Generates the classifier.
*
* @param instances set of instances serving as training data
* @throws Exception if the classifier has not been generated successfully
*/
public void buildClassifier(Instances instances) throws Exception {
if (!Groovy.isPresent()) throw new Exception("Groovy classes not in CLASSPATH!");
// try loading the module
initGroovyObject();
// build the model
if (m_GroovyObject != null) m_GroovyObject.buildClassifier(instances);
else System.err.println("buildClassifier: No Groovy object present!");
}
private static boolean calcultateifhotspot(
Classifier ibkclassification, Instance instclassification) {
Double predictValue = null;
try {
predictValue = ibkclassification.classifyInstance(instclassification);
} catch (Exception e) {
e.printStackTrace();
}
if (predictValue == 0.0) return true;
else return false;
}