libsvm支援向量機演算法包的基本使用,此處演示的是支援向量回歸機
複製代碼代碼如下:
import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.util.ArrayList;
import java.util.List;
import libsvm.svm;
import libsvm.svm_model;
import libsvm.svm_node;
import libsvm.svm_parameter;
import libsvm.svm_problem;
public class SVM {
public static void main(String[] args) {
// 定義訓練集點a{10.0, 10.0} 和點b{-10.0, -10.0}，對應lable為{1.0, -1.0}
List<Double> label = new ArrayList<Double>();
List<svm_node[]> nodeSet = new ArrayList<svm_node[]>();
getData(nodeSet, label, "file/train.txt");
int dataRange=nodeSet.get(0).length;
svm_node[][] datas = new svm_node[nodeSet.size()][dataRange]; // 訓練集的向量表
for (int i = 0; i < datas.length; i++) {
for (int j = 0; j < dataRange; j++) {
datas[i][j] = nodeSet.get(i)[j];
}
}
double[] lables = new double[label.size()]; // a,b 對應的lable
for (int i = 0; i < lables.length; i++) {
lables[i] = label.get(i);
}
// 定義svm_problem對象
svm_problem problem = new svm_problem();
problem.l = nodeSet.size(); // 向量個數
problem.x = datas; // 訓練集向量表
problem.y = lables; // 對應的lable數組
// 定義svm_parameter對象
svm_parameter param = new svm_parameter();
param.svm_type = svm_parameter.EPSILON_SVR;
param.kernel_type = svm_parameter.LINEAR;
param.cache_size = 100;
param.eps = 0.00001;
param.C = 1.9;
//訓練SVM分類模型
System.out.println(svm.svm_check_parameter(problem, param));
// 如果參數沒有問題，則svm.svm_check_parameter()函數傳回null,否則傳回error描述。
svm_model model = svm.svm_train(problem, param);
// svm.svm_train()訓練出SVM分類模型
// 取得測試數據
List<Double> testlabel = new ArrayList<Double>();
List<svm_node[]> testnodeSet = new ArrayList<svm_node[]>();
getData(testnodeSet, testlabel, "file/test.txt");
svm_node[][] testdatas = new svm_node[testnodeSet.size()][dataRange]; // 訓練集的向量表
for (int i = 0; i < testdatas.length; i++) {
for (int j = 0; j < dataRange; j++) {
testdatas[i][j] = testnodeSet.get(i)[j];
}
}
double[] testlables = new double[testlabel.size()]; // a,b 對應的lable
for (int i = 0; i < testlables.length; i++) {
testlables[i] = testlabel.get(i);
}
// 預測測試資料的lable
double err = 0.0;
for (int i = 0; i < testdatas.length; i++) {
double truevalue = testlables[i];
System.out.print(truevalue + " ");
double predictValue = svm.svm_predict(model, testdatas[i]);
System.out.println(predictValue);
err += Math.abs(predictValue - truevalue);
}
System.out.println("err=" + err / datas.length);
}
public static void getData(List<svm_node[]> nodeSet, List<Double> label,
String filename) {
try {
FileReader fr = new FileReader(new File(filename));
BufferedReader br = new BufferedReader(fr);
String line = null;
while ((line = br.readLine()) != null) {
String[] datas = line.split(",");
svm_node[] vector = new svm_node[datas.length - 1];
for (int i = 0; i < datas.length - 1; i++) {
svm_node node = new svm_node();
node.index = i + 1;
node.value = Double.parseDouble(datas[i]);
vector[i] = node;
}
nodeSet.add(vector);
double lablevalue = Double.parseDouble(datas[datas.length - 1]);
label.add(lablevalue);
}
} catch (Exception e) {
e.printStackTrace();
}
}
}
訓練數據，最後一列為目標值
複製代碼代碼如下:
17.6,17.7,17.7,17.7,17.8
17.7,17.7,17.7,17.8,17.8
17.7,17.7,17.8,17.8,17.9
17.7,17.8,17.8,17.9,18
17.8,17.8,17.9,18,18.1
17.8,17.9,18,18.1,18.2
17.9,18,18.1,18.2,18.4
18,18.1,18.2,18.4,18.6
18.1,18.2,18.4,18.6,18.7
18.2,18.4,18.6,18.7,18.9
18.4,18.6,18.7,18.9,19.1
18.6,18.7,18.9,19.1,19.3
測試數據
複製代碼代碼如下:
18.7,18.9,19.1,19.3,19.6
18.9,19.1,19.3,19.6,19.9
19.1,19.3,19.6,19.9,20.2
19.3,19.6,19.9,20.2,20.6
19.6,19.9,20.2,20.6,21
19.9,20.2,20.6,21,21.5
20.2,20.6,21,21.5,22