#机器学习
public static Mat MyAnn(Mat trainingData, Mat labels, Mat testData) {
// train data using aNN
TrainData td = TrainData.create(trainingData, Ml.ROW_SAMPLE, labels);
Mat layerSizes = new Mat(1, 4, CvType.CV_32FC1);
// 含有两个隐含层的网络结构，输入、输出层各两个节点，每个隐含层含两个节点
layerSizes.put(0, 0, new float[] { 2, 2, 2, 2 });
ANN_MLP ann = ANN_MLP.create();
ann.setLayerSizes(layerSizes);
ann.setTrainMethod(ANN_MLP.BACKPROP);
ann.setBackpropWeightScale(0.1);
ann.setBackpropMomentumScale(0.1);
ann.setActivationFunction(ANN_MLP.SIGMOID_SYM, 1, 1);
ann.setTermCriteria(new TermCriteria(TermCriteria.MAX_ITER + TermCriteria.EPS, 300, 0.0));
boolean success = ann.train(td.getSamples(), Ml.ROW_SAMPLE, td.getResponses());
System.out.println("Ann training result: " + success);
// ann.save("D:/bp.xml");//存储模型
// ann.load("D:/bp.xml");//读取模型
// 测试数据
Mat responseMat = new Mat();
ann.predict(testData, responseMat, 0);
System.out.println("Ann responseMat:\n" + responseMat.dump());
for (int i = 0; i < responseMat.size().height; i++) {
if (responseMat.get(i, 0)[0] + responseMat.get(i, i)[0] >= 1)
System.out.println("Girl\n");
if (responseMat.get(i, 0)[0] + responseMat.get(i, i)[0] < 1)
System.out.println("Boy\n");
}
return responseMat;
}
public static Mat MyBoost(Mat trainingData, Mat labels, Mat testData) {
Boost boost = Boost.create();
// boost.setBoostType(Boost.DISCRETE);
boost.setBoostType(Boost.GENTLE);
boost.setWeakCount(2);
boost.setWeightTrimRate(0.95);
boost.setMaxDepth(2);
boost.setUseSurrogates(false);
boost.setPriors(new Mat());
TrainData td = TrainData.create(trainingData, Ml.ROW_SAMPLE, labels);
boolean success = boost.train(td.getSamples(), Ml.ROW_SAMPLE, td.getResponses());
System.out.println("Boost training result: " + success);
// boost.save("D:/bp.xml");//存储模型
Mat responseMat = new Mat();
float response = boost.predict(testData, responseMat, 0);
System.out.println("Boost responseMat:\n" + responseMat.dump());
for (int i = 0; i < responseMat.height(); i++) {
if (responseMat.get(i, 0)[0] == 0)
System.out.println("Boy\n");
if (responseMat.get(i, 0)[0] == 1)
System.out.println("Girl\n");
}
return responseMat;
}
public static Mat MyDtrees(Mat trainingData, Mat labels, Mat testData) {
DTrees dtree = DTrees.create(); // 创建分类器
dtree.setMaxDepth(8); // 设置最大深度
dtree.setMinSampleCount(2);
dtree.setUseSurrogates(false);
dtree.setCVFolds(0); // 交叉验证
dtree.setUse1SERule(false);
dtree.setTruncatePrunedTree(false);
TrainData td = TrainData.create(trainingData, Ml.ROW_SAMPLE, labels);
boolean success = dtree.train(td.getSamples(), Ml.ROW_SAMPLE, td.getResponses());
System.out.println("Dtrees training result: " + success);
// dtree.save("D:/bp.xml");//存储模型
Mat responseMat = new Mat();
float response = dtree.predict(testData, responseMat, 0);
System.out.println("Dtrees responseMat:\n" + responseMat.dump());
for (int i = 0; i < responseMat.height(); i++) {
if (responseMat.get(i, 0)[0] == 0)
System.out.println("Boy\n");
if (responseMat.get(i, 0)[0] == 1)
System.out.println("Girl\n");
}
return responseMat;
}
public static Mat MyKnn(Mat trainingData, Mat labels, Mat testData) {
final int K = 2;
TrainData td = TrainData.create(trainingData, Ml.ROW_SAMPLE, labels);
KNearest knn = KNearest.create();
boolean success = knn.train(trainingData, Ml.ROW_SAMPLE, labels);
System.out.println("Knn training result: " + success);
// knn.save("D:/bp.xml");//存储模型
// find the nearest neighbours of test data
Mat results = new Mat();
Mat neighborResponses = new Mat();
Mat dists = new Mat();
knn.findNearest(testData, K, results, neighborResponses, dists);
System.out.println("results:\n" + results.dump());
System.out.println("Knn neighborResponses:\n" + neighborResponses.dump());
System.out.println("dists:\n" + dists.dump());
for (int i = 0; i < results.height(); i++) {
if (results.get(i, 0)[0] == 0)
System.out.println("Boy\n");
if (results.get(i, 0)[0] == 1)
System.out.println("Girl\n");
}
return results;
}
public static Mat MyLogisticRegression(Mat trainingData, Mat labels, Mat testData) {
LogisticRegression lr = LogisticRegression.create();
TrainData td = TrainData.create(trainingData, Ml.ROW_SAMPLE, labels);
boolean success = lr.train(td.getSamples(), Ml.ROW_SAMPLE, td.getResponses());
System.out.println("LogisticRegression training result: " + success);
// lr.save("D:/bp.xml");//存储模型
Mat responseMat = new Mat();
float response = lr.predict(testData, responseMat, 0);
System.out.println("LogisticRegression responseMat:\n" + responseMat.dump());
for (int i = 0; i < responseMat.height(); i++) {
if (responseMat.get(i, 0)[0] == 0)
System.out.println("Boy\n");
if (responseMat.get(i, 0)[0] == 1)
System.out.println("Girl\n");
}
return responseMat;
}
public static Mat MyNormalBayes(Mat trainingData, Mat labels, Mat testData) {
NormalBayesClassifier nb = NormalBayesClassifier.create();
TrainData td = TrainData.create(trainingData, Ml.ROW_SAMPLE, labels);
boolean success = nb.train(td.getSamples(), Ml.ROW_SAMPLE, td.getResponses());
System.out.println("NormalBayes training result: " + success);
// nb.save("D:/bp.xml");//存储模型
Mat responseMat = new Mat();
float response = nb.predict(testData, responseMat, 0);
System.out.println("NormalBayes responseMat:\n" + responseMat.dump());
for (int i = 0; i < responseMat.height(); i++) {
if (responseMat.get(i, 0)[0] == 0)
System.out.println("Boy\n");
if (responseMat.get(i, 0)[0] == 1)
System.out.println("Girl\n");
}
return responseMat;
}
public static Mat MyRTrees(Mat trainingData, Mat labels, Mat testData) {
RTrees rtrees = RTrees.create();
rtrees.setMaxDepth(4);
rtrees.setMinSampleCount(2);
rtrees.setRegressionAccuracy(0.f);
rtrees.setUseSurrogates(false);
rtrees.setMaxCategories(16);
rtrees.setPriors(new Mat());
rtrees.setCalculateVarImportance(false);
rtrees.setActiveVarCount(1);
rtrees.setTermCriteria(new TermCriteria(TermCriteria.MAX_ITER, 5, 0));
TrainData tData = TrainData.create(trainingData, Ml.ROW_SAMPLE, labels);
boolean success = rtrees.train(tData.getSamples(), Ml.ROW_SAMPLE, tData.getResponses());
System.out.println("Rtrees training result: " + success);
// rtrees.save("D:/bp.xml");//存储模型
Mat responseMat = new Mat();
rtrees.predict(testData, responseMat, 0);
System.out.println("Rtrees responseMat:\n" + responseMat.dump());
for (int i = 0; i < responseMat.height(); i++) {
if (responseMat.get(i, 0)[0] == 0)
System.out.println("Boy\n");
if (responseMat.get(i, 0)[0] == 1)
System.out.println("Girl\n");
}
return responseMat;
}
public static Mat MySvm(Mat trainingData, Mat labels, Mat testData) {
SVM svm = SVM.create();
svm.setKernel(SVM.LINEAR);
svm.setType(SVM.C_SVC);
TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 1000, 0);
svm.setTermCriteria(criteria);
svm.setGamma(0.5);
svm.setNu(0.5);
svm.setC(1);
TrainData td = TrainData.create(trainingData, Ml.ROW_SAMPLE, labels);
boolean success = svm.train(td.getSamples(), Ml.ROW_SAMPLE, td.getResponses());
System.out.println("Svm training result: " + success);
// svm.save("D:/bp.xml");//存储模型
// svm.load("D:/bp.xml");//读取模型
Mat responseMat = new Mat();
svm.predict(testData, responseMat, 0);
System.out.println("SVM responseMat:\n" + responseMat.dump());
for (int i = 0; i < responseMat.height(); i++) {
if (responseMat.get(i, 0)[0] == 0)
System.out.println("Boy\n");
if (responseMat.get(i, 0)[0] == 1)
System.out.println("Girl\n");
}
return responseMat;
}
public static Mat MySvmsgd(Mat trainingData, Mat labels, Mat testData) {
SVMSGD Svmsgd = SVMSGD.create();
TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 1000, 0);
Svmsgd.setTermCriteria(criteria);
Svmsgd.setInitialStepSize(2);
Svmsgd.setSvmsgdType(SVMSGD.SGD);
Svmsgd.setMarginRegularization(0.5f);
boolean success = Svmsgd.train(trainingData, Ml.ROW_SAMPLE, labels);
System.out.println("SVMSGD training result: " + success);
// svm.save("D:/bp.xml");//存储模型
// svm.load("D:/bp.xml");//读取模型
Mat responseMat = new Mat();
Svmsgd.predict(testData, responseMat, 0);
System.out.println("SVMSGD responseMat:\n" + responseMat.dump());
for (int i = 0; i < responseMat.height(); i++) {
if (responseMat.get(i, 0)[0] == 0)
System.out.println("Boy\n");
if (responseMat.get(i, 0)[0] == 1)
System.out.println("Girl\n");
}
return responseMat;
}