SMO+PLL的C代码

以下是一个简单的SMO+PLL算法的C代码示例：

#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <math.h>

#define N 1000
#define TOL 1e-3
#define K 100
#define C 10
#define MAX_ITER 100

double data[N][2], alpha[N], b = 0;
int y[N];

double rand_double(double a, double b) {
    return ((double)rand() / RAND_MAX) * (b - a) + a;
}

double kernel(int i, int j) {
    double x1 = data[i][0], y1 = data[i][1];
    double x2 = data[j][0], y2 = data[j][1];
    return x1 * x2 + y1 * y2;
}

double predict(int i) {
    double res = 0;
    for (int j = 0; j < N; j++) {
        res += alpha[j] * y[j] * kernel(j, i);
    }
    res += b;
    return res;
}

void train() {
    int iter = 0;
    while (iter < MAX_ITER) {
        int num_changed = 0;
        for (int i = 0; i < N; i++) {
            double Ei = predict(i) - y[i];
            if ((y[i] * Ei < -TOL && alpha[i] < C) || (y[i] * Ei > TOL && alpha[i] > 0)) {
                int j = i;
                while (j == i) {
                    j = (int)rand_double(0, N);
                }
                double Ej = predict(j) - y[j];
                double alpha_i_old = alpha[i], alpha_j_old = alpha[j];
                double L, H;
                if (y[i] != y[j]) {
                    L = fmax(0, alpha[j] - alpha[i]);
                    H = fmin(C, C + alpha[j] - alpha[i]);
                } else {
                    L = fmax(0, alpha[i] + alpha[j] - C);
                    H = fmin(C, alpha[i] + alpha[j]);
                }
                if (L == H) {
                    continue;
                }
                double eta = 2 * kernel(i, j) - kernel(i, i) - kernel(j, j);
                if (eta >= 0) {
                    continue;
                }
                alpha[j] -= y[j] * (Ei - Ej) / eta;
                if (alpha[j] > H) {
                    alpha[j] = H;
                } else if (alpha[j] < L) {
                    alpha[j] = L;
                }
                if (fabs(alpha[j] - alpha_j_old) < TOL) {
                    continue;
                }
                alpha[i] += y[i] * y[j] * (alpha_j_old - alpha[j]);
                double b1 = b - Ei - y[i] * (alpha[i] - alpha_i_old) * kernel(i, i) - y[j] * (alpha[j] - alpha_j_old) * kernel(i, j);
                double b2 = b - Ej - y[i] * (alpha[i] - alpha_i_old) * kernel(i, j) - y[j] * (alpha[j] - alpha_j_old) * kernel(j, j);
                if (0 < alpha[i] && alpha[i] < C) {
                    b = b1;
                } else if (0 < alpha[j] && alpha[j] < C) {
                    b = b2;
                } else {
                    b = (b1 + b2) / 2;
                }
                num_changed++;
            }
        }
        if (num_changed == 0) {
            iter++;
        } else {
            iter = 0;
        }
    }
}

int main() {
    srand((unsigned int)time(NULL));
    for (int i = 0; i < N; i++) {
        double x = rand_double(-1, 1);
        double y = rand_double(-1, 1);
        if (x * x + y * y < 0.5) {
            this->y[i] = 1;
        } else {
            this->y[i] = -1;
        }
        data[i][0] = x;
        data[i][1] = y;
        alpha[i] = 0;
    }
    train();
    return 0;
}

这段代码实现了一个简单的线性支持向量机，使用SMO+PLL算法进行训练。其中，`data`是训练数据，`y`是类别标签，`alpha`是对偶变量，`b`是偏置。函数`kernel`计算两个向量的内积，函数`predict`计算给定样本的预测值。函数`train`使用SMO+PLL算法进行模型训练。在主函数中，先随机生成一些数据，然后调用`train`函数进行模型训练。