java实现smo算法（附完整源码）

以下是Java实现SMO算法的完整源代码：

import java.util.ArrayList;
import java.util.List;

public class SMO {

    private double[][] data; // 数据集
    private double[] labels; // 数据标签
    private double[] alphas; // 拉格朗日乘子
    private double b; // 偏置量
    private int m; // 数据集大小
    private double C = 1.0; // 软间隔常数
    private double toler = 0.001; // 容错率
    private int maxIter = 50; // 最大迭代次数

    public void train(double[][] data, double[] labels) {
        this.data = data;
        this.labels = labels;
        this.m = data.length;
        this.alphas = new double[m];
        this.b = 0.0;

        int iter = 0;
        int alphaPairsChanged = 0;
        while (iter < maxIter && alphaPairsChanged > 0) {
            alphaPairsChanged = 0;
            for (int i = 0; i < m; i++) {
                double fXi = predict(i);
                double Ei = fXi - labels[i];
                if ((labels[i] * Ei < -toler && alphas[i] < C)
                        || (labels[i] * Ei > toler && alphas[i] > 0)) {
                    int j = selectRandom(i);
                    double fXj = predict(j);
                    double Ej = fXj - labels[j];
                    double alphaIold = alphas[i];
                    double alphaJold = alphas[j];
                    double L, H;
                    if (labels[i] != labels[j]) {
                        L = Math.max(0, alphas[j] - alphas[i]);
                        H = Math.min(C, C + alphas[j] - alphas[i]);
                    } else {
                        L = Math.max(0, alphas[j] + alphas[i] - C);
                        H = Math.min(C, alphas[j] + alphas[i]);
                    }
                    if (L == H) {
                        continue;
                    }
                    double eta = 2.0 * data[i][j] - data[i][i] - data[j][j];
                    if (eta >= 0) {
                        continue;
                    }
                    alphas[j] -= labels[j] * (Ei - Ej) / eta;
                    alphas[j] = Math.max(L, Math.min(H, alphas[j]));
                    if (Math.abs(alphas[j] - alphaJold) < toler) {
                        continue;
                    }
                    alphas[i] += labels[i] * labels[j] * (alphaJold - alphas[j]);
                    double b1 = b - Ei - labels[i] * (alphas[i] - alphaIold) * data[i][i]
                            - labels[j] * (alphas[j] - alphaJold) * data[i][j];
                    double b2 = b - Ej - labels[i] * (alphas[i] - alphaIold) * data[i][j]
                            - labels[j] * (alphas[j] - alphaJold) * data[j][j];
                    if (alphas[i] > 0 && alphas[i] < C) {
                        b = b1;
                    } else if (alphas[j] > 0 && alphas[j] < C) {
                        b = b2;
                    } else {
                        b = (b1 + b2) / 2.0;
                    }
                    alphaPairsChanged++;
                }
            }
            if (alphaPairsChanged == 0) {
                iter++;
            } else {
                iter = 0;
            }
        }
    }

    public double predict(double[] x) {
        double res = b;
        for (int i = 0; i < m; i++) {
            res += alphas[i] * labels[i] * kernel(data[i], x);
        }
        return res;
    }

    private double predict(int i) {
        double res = b;
        for (int j = 0; j < m; j++) {
            res += alphas[j] * labels[j] * kernel(data[j], data[i]);
        }
        return res;
    }

    private double kernel(double[] xi, double[] xj) {
        double res = 0;
        for (int i = 0; i < xi.length; i++) {
            res += xi[i] * xj[i];
        }
        return res;
    }

    private int selectRandom(int i) {
        int j = i;
        while (j == i) {
            j = (int) (Math.random() * m);
        }
        return j;
    }

    public static void main(String[] args) {
        // 构造数据
        double[][] data = { { 1.0, 2.0 }, { 2.0, 3.0 }, { 3.0, 4.0 }, { 4.0, 5.0 }, { 5.0, 6.0 }, { 6.0, 7.0 } };
        double[] labels = { -1, -1, -1, 1, 1, 1 };
        // 训练模型
        SMO smo = new SMO();
        smo.train(data, labels);
        // 预测新数据
        double[] x = { 2.0, 2.0 };
        System.out.println(smo.predict(x));
    }
}

这里实现了SMO算法的train()方法来训练模型，predict()方法来进行预测，kernel()方法来计算核函数，selectRandom()方法来随机选择第二个变量。在main()方法中，我们构造了一个简单的数据集，对模型进行训练，并对新数据进行预测。