你能写出改进粒子群算法优化后的SVR代码吗

我可以提供改进粒子群算法优化后的SVR代码，以下是代码示例：

// 导入必要的库
#include <iostream>
#include <cmath>
#include <vector>
#include <random>

using namespace std;

// 定义粒子结构体
struct Particle {
    vector<double> position;
    vector<double> velocity;
    double fitness;
    vector<double> pBest;
    double pBestFitness;
};

// 定义SVR模型
class SVR {
public:
    SVR(double c, double gamma) : C(c), gamma(gamma) {}
    double predict(vector<double>& x) {
        double sum = .;
        for (int i = ; i < x.size(); i++) {
            sum += pow(x[i] - center[i], 2) / (2 * pow(sigma[i], 2));
        }
        return alpha * exp(-sum) + b;
    }
    void train(vector<vector<double>>& X, vector<double>& y) {
        // 初始化参数
        int n = X.size();
        int d = X[].size();
        center = vector<double>(d, .);
        sigma = vector<double>(d, .);
        alpha = vector<double>(n, .);
        b = .;
        // 计算中心和方差
        for (int i = ; i < d; i++) {
            double sum = .;
            for (int j = ; j < n; j++) {
                sum += X[j][i];
            }
            center[i] = sum / n;
            sum = .;
            for (int j = ; j < n; j++) {
                sum += pow(X[j][i] - center[i], 2);
            }
            sigma[i] = sqrt(sum / n);
        }
        // 计算核函数矩阵
        vector<vector<double>> K(n, vector<double>(n, .));
        for (int i = ; i < n; i++) {
            for (int j = ; j < n; j++) {
                double sum = .;
                for (int k = ; k < d; k++) {
                    sum += pow(X[i][k] - X[j][k], 2) / pow(sigma[k], 2);
                }
                K[i][j] = exp(-gamma * sum);
            }
        }
        // 求解alpha和b
        vector<vector<double>> H(n + 1, vector<double>(n + 1, .));
        for (int i = ; i < n; i++) {
            for (int j = ; j < n; j++) {
                H[i][j] = K[i][j];
            }
            H[i][n] = 1.;
            H[n][i] = 1.;
        }
        H[n][n] = .;
        vector<double> f(n + 1, .);
        for (int i = ; i < n; i++) {
            f[i] = y[i];
        }
        alpha = solve(H, f);
        b = alpha[n];
        alpha.pop_back();
    }
private:
    double C;
    double gamma;
    vector<double> center;
    vector<double> sigma;
    vector<double> alpha;
    double b;
    vector<double> solve(vector<vector<double>>& A, vector<double>& b) {
        int n = A.size();
        vector<vector<double>> L(n, vector<double>(n, .));
        vector<double> y(n, .);
        vector<double> x(n, .);
        // Cholesky分解
        for (int i = ; i < n; i++) {
            double sum = .;
            for (int k = ; k < i; k++) {
                sum += pow(L[i][k], 2);
            }
            L[i][i] = sqrt(A[i][i] - sum);
            for (int j = i + 1; j < n; j++) {
                sum = .;
                for (int k = ; k < i; k++) {
                    sum += L[i][k] * L[j][k];
                }
                L[j][i] = (A[j][i] - sum) / L[i][i];
            }
        }
        // 前向代入
        for (int i = ; i < n; i++) {
            double sum = .;
            for (int j = ; j < i; j++) {
                sum += L[i][j] * y[j];
            }
            y[i] = (b[i] - sum) / L[i][i];
        }
        // 后向代入
        for (int i = n - 1; i >= ; i--) {
            double sum = .;
            for (int j = i + 1; j < n; j++) {
                sum += L[j][i] * x[j];
            }
            x[i] = (y[i] - sum) / L[i][i];
        }
        return x;
    }
};

// 定义改进粒子群算法
class ImprovedPSO {
public:
    ImprovedPSO(int n, int m, int maxIter, double w, double c1, double c2, double vMax, double tol) :
        n(n), m(m), maxIter(maxIter), w(w), c1(c1), c2(c2), vMax(vMax), tol(tol) {}
    void optimize(SVR& svr, vector<vector<double>>& X, vector<double>& y) {
        // 初始化粒子群
        vector<Particle> particles(n);
        for (int i = ; i < n; i++) {
            particles[i].position = vector<double>(m, .);
            particles[i].velocity = vector<double>(m, .);
            particles[i].fitness = 1e10;
            particles[i].pBest = particles[i].position;
            particles[i].pBestFitness = particles[i].fitness;
            for (int j = ; j < m; j++) {
                particles[i].position[j] = rand() / double(RAND_MAX);
                particles[i].velocity[j] = rand() / double(RAND_MAX);
            }
        }
        // 迭代优化
        for (int iter = ; iter < maxIter; iter++) {
            for (int i = ; i < n; i++) {
                // 更新速度和位置
                for (int j = ; j < m; j++) {
                    particles[i].velocity[j] = w * particles[i].velocity[j] +
                        c1 * rand() / double(RAND_MAX) * (particles[i].pBest[j] - particles[i].position[j]) +
                        c2 * rand() / double(RAND_MAX) * (globalBest[j] - particles[i].position[j]);
                    if (particles[i].velocity[j] > vMax) {
                        particles[i].velocity[j] = vMax;
                    }
                    if (particles[i].velocity[j] < -vMax) {
                        particles[i].velocity[j] = -vMax;
                    }
                    particles[i].position[j] += particles[i].velocity[j];
                    if (particles[i].position[j] > 1.) {
                        particles[i].position[j] = 1.;
                    }
                    if (particles[i].position[j] < .) {
                        particles[i].position[j] = .;
                    }
                }
                // 计算适应度
                svr.train(X, y);
                double fitness = .;
                for (int j = ; j < X.size(); j++) {
                    double yPred = svr.predict(X[j]);
                    fitness += pow(yPred - y[j], 2);
                }
                if (fitness < particles[i].fitness) {
                    particles[i].fitness = fitness;
                    particles[i].pBest = particles[i].position;
                    particles[i].pBestFitness = fitness;
                }
                if (fitness < globalBestFitness) {
                    globalBest = particles[i].position;
                    globalBestFitness = fitness;
                }
            }
            // 判断是否收敛
            if (globalBestFitness < tol) {
                break;
            }
        }
    }
private:
    int n;
    int m;
    int maxIter;
    double w;
    double c1;
    double c2;
    double vMax;
    double tol;
    vector<double> globalBest;
    double globalBestFitness = 1e10;
};

// 测试代码
int main() {
    // 生成随机数据
    int n = 100;
    int d = 5;
    vector<vector<double>> X(n, vector<double>(d, .));
    vector<double> y(n, .);
    default_random_engine generator;
    normal_distribution<double> distribution(., 1.);
    for (int i = ; i < n; i++) {
        for (int j = ; j < d; j++) {
            X[i][j] = distribution(generator);
        }
        y[i] = sin(X[i][]) + cos(X[i][1]) + X[i][2] * X[i][3] - X[i][4] * X[i][4];
    }
    // 初始化SVR和PSO
    double c = 1.;
    double gamma = .1;
    SVR svr(c, gamma);
    int maxIter = 100;
    double w = .8;
    double c1 = 2.;
    double c2 = 2.;
    double vMax = .1;
    double tol = 1e-6;
    ImprovedPSO pso(50, d, maxIter, w, c1, c2, vMax, tol);
    // 进行优化
    pso.optimize(svr, X, y);
    // 输出结果
    cout << "Global best fitness: " << globalBestFitness << endl;
    cout << "Global best position: ";
    for (int i = ; i < d; i++) {
        cout << globalBest[i] << " ";
    }
    cout << endl;
    return ;
}