Java OpenCV-4.x 人工智能01 机器学习 支持向量机 SVM

OpenCV 中的机器学习(Machine Learning，ML)算法。

序号	中文名称	英文名称
01	决策树	Decision Tree
02	EM算法	Expectation - Maximization
03	贝叶斯分类	Normal Bayes Classifier
04	K-邻近算法	K-Nearest Neighbour Classifier
05	支持向量机	Support Vector Machine
06	Boost树算法	Boosted Tree Classifier
07	随机森林算法	Random Trees Classifier
08	人工神经网络	Artificial Neural Networks
09	梯度Boost树算法	Gradient Boosted Trees
10	绝对随机森林算法	Extremely Randomized Trees Classifier
11	SGD支持向量机	Stochastic Gradient Descent Support Vector Machine

Support Vector Machine SVM 介绍

package com.xu.opencv;

import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.TermCriteria;
import org.opencv.ml.Ml;
import org.opencv.ml.SVM;
import org.opencv.ml.TrainData;

/**  
 * 
 * @Author: hyacinth
 * @Title: ML.java   
 * @Package com.xu.opencv   
 * @Description:
 * @Date: 2019年11月22日20:23:23 
 * @Version V-1.0 
 * @Copyright: 2019 hyacinth
 * 
 */
public class ML {

	static {
		System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
	}

	public static void main(String[] args) {
		// 训练数据 体重,身高
		float[] train_data = { 186, 80, 185, 81, 165, 60, 168, 61, 160, 50, 161, 48 };
		// 测试数据 身高,体重
		float[] test = { 184, 79, 160, 62, 159, 50 };
		// 训练数据 0=男 ,1=女
		int[] label = { 0, 0, 0, 0, 1, 1 };     
		
		Mat train_mat = new Mat(6, 2, CvType.CV_32FC1);  
		train_mat.put(0, 0, train_data);  

		Mat train_lable = new Mat(6, 1, CvType.CV_32SC1);  
		train_lable.put(0, 0, label);   

		Mat test_lable = new Mat(3, 2, CvType.CV_32FC1);  
		test_lable.put(0, 0, test);  

		SVM(train_mat,train_lable,test_lable);
	}

	/**
	 * OpenCV-4.1.0 SVM 支持向量机
	 * @Author: hyacinth
	 * @Title: SVM
	 * @param : tarin 训练数据
	 * @param : lable 训练标签
	 * @param : test  测试数据
	 * @Description: TODO       
	 * @return void  
	 * @date: 2019年11月22日20:23:23
	 */
	public static void SVM(Mat tarin,Mat lable,Mat test) {
		SVM svm = SVM.create();
		svm.setC(1);
		svm.setP(0);
		svm.setNu(0);
		svm.setCoef0(0);
		svm.setGamma(1);
		svm.setDegree(0);
		svm.setType(SVM.C_SVC);
		svm.setKernel(SVM.LINEAR);
		TermCriteria criteria=new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER,1000,0);
		svm.setTermCriteria(criteria);

		TrainData trainData = TrainData.create(tarin,Ml.ROW_SAMPLE,lable);
		svm.train(trainData.getSamples(), Ml.ROW_SAMPLE,trainData.getResponses());
		svm.save("C:\\Users\\Administrator\\Desktop\\number.xml");

		Mat response = new Mat();
		svm.predict(test, response, 0);

		System.out.println(response.dump());

		for (int i = 0; i < response.height(); i++) {
			if (response.get(i, 0)[0] == 0)
				System.out.println("男");
			if (response.get(i, 0)[0] == 1)
				System.out.println("女");
		}

	}

}