把上面的代码用c++写出来

以下是使用C++编写的可转债多因子择券的模型：

#include <iostream>
#include <fstream>
#include <vector>
#include <string>
#include <algorithm>
#include <cmath>

using namespace std;

// 定义可转债结构体
struct ConvertibleBond {
    int id;
    double market_cap;
    double pb_ratio;
    double roe;
    double debt_to_equity_ratio;
    int market_cap_score;
    int pb_ratio_score;
    int roe_score;
    int debt_to_equity_ratio_score;
    double composite_score;
};

// 定义因子和权重
vector<string> factors = {"market_cap", "pb_ratio", "roe", "debt_to_equity_ratio"};
vector<double> weights = {0.25, 0.25, 0.25, 0.25};

// 定义函数：计算分位数
double percentile(vector<double>& v, double p) {
    int n = v.size();
    if (n == 0) {
        return 0.0;
    }
    sort(v.begin(), v.end());
    int rank = (int) ceil(p * n) - 1;
    rank = max(0, rank);
    rank = min(n - 1, rank);
    return v[rank];
}

// 定义函数：计算因子得分
int score(double x, vector<double>& v) {
    double p = percentile(v, x);
    int s = (int) floor(p * 5);
    return s;
}

// 定义函数：计算综合得分
double composite_score(ConvertibleBond& bond) {
    double score = 0.0;
    score += bond.market_cap_score * weights[0];
    score += bond.pb_ratio_score * weights[1];
    score += bond.roe_score * weights[2];
    score += bond.debt_to_equity_ratio_score * weights[3];
    return score;
}

int main() {
    // 读取数据
    vector<ConvertibleBond> data;
    ifstream fin("convertible_bond_data.csv");
    string line;
    getline(fin, line); // 跳过首行
    while (getline(fin, line)) {
        ConvertibleBond bond;
        sscanf(line.c_str(), "%d,%lf,%lf,%lf,%lf", 
               &(bond.id), &(bond.market_cap), &(bond.pb_ratio), &(bond.roe), &(bond.debt_to_equity_ratio));
        data.push_back(bond);
    }
    fin.close();

    // 计算因子得分
    vector<vector<double>> values(4);
    for (int i = 0; i < data.size(); i++) {
        for (int j = 0; j < 4; j++) {
            double x = 0.0;
            switch (j) {
            case 0:
                x = data[i].market_cap;
                break;
            case 1:
                x = data[i].pb_ratio;
                break;
            case 2:
                x = data[i].roe;
                break;
            case 3:
                x = data[i].debt_to_equity_ratio;
                break;
            }
            values[j].push_back(x);
        }
    }
    for (int i = 0; i < data.size(); i++) {
        for (int j = 0; j < 4; j++) {
            double x = 0.0;
            switch (j) {
            case 0:
                x = data[i].market_cap;
                break;
            case 1:
                x = data[i].pb_ratio;
                break;
            case 2:
                x = data[i].roe;
                break;
            case 3:
                x = data[i].debt_to_equity_ratio;
                break;
            }
            data[i].market_cap_score = score(x, values[0]);
            data[i].pb_ratio_score = score(x, values[1]);
            data[i].roe_score = score(x, values[2]);
            data[i].debt_to_equity_ratio_score = score(x, values[3]);
        }
    }

    // 计算综合得分
    for (int i = 0; i < data.size(); i++) {
        data[i].composite_score = composite_score(data[i]);
    }

    // 建立回归模型
    vector<vector<double>> X(data.size(), vector<double>(4, 0.0));
    vector<double> y(data.size(), 0.0);
    for (int i = 0; i < data.size(); i++) {
        X[i][0] = 1.0;
        X[i][1] = data[i].market_cap;
        X[i][2] = data[i].pb_ratio;
        X[i][3] = data[i].roe;
        y[i] = data[i].composite_score;
    }
    vector<double> beta(4, 0.0);
    for (int j = 0; j < 4; j++) {
        vector<double> xi;
        for (int i = 0; i < data.size(); i++) {
            xi.push_back(X[i][j]);
        }
        double sum_xi = accumulate(xi.begin(), xi.end(), 0.0);
        double mean_xi = sum_xi / data.size();
        double ss_xi = 0.0;
        for (int i = 0; i < data.size(); i++) {
            ss_xi += (X[i][j] - mean_xi) * (X[i][j] - mean_xi);
        }
        double cov_xy = 0.0;
        for (int i = 0; i < data.size(); i++) {
            cov_xy += (X[i][j] - mean_xi) * (y[i] - mean(y));
        }
        beta[j] = cov_xy / ss_xi;
    }

    // 预测得分
    vector<ConvertibleBond> new_data;
    ifstream fin_new("new_data.csv");
    getline(fin_new, line); // 跳过首行
    while (getline(fin_new, line)) {
        ConvertibleBond bond;
        sscanf(line.c_str(), "%d,%lf,%lf,%lf,%lf", 
               &(bond.id), &(bond.market_cap), &(bond.pb_ratio), &(bond.roe), &(bond.debt_to_equity_ratio));
        new_data.push_back(bond);
    }
    fin_new.close();
    vector<double> predicted_score(new_data.size(), 0.0);
    for (int i = 0; i < new_data.size(); i++) {
        new_data[i].market_cap_score = score(new_data[i].market_cap, values[0]);
        new_data[i].pb_ratio_score = score(new_data[i].pb_ratio, values[1]);
        new_data[i].roe_score = score(new_data[i].roe, values[2]);
        new_data[i].debt_to_equity_ratio_score = score(new_data[i].debt_to_equity_ratio, values[3]);
        new_data[i].composite_score = composite_score(new_data[i]);
        predicted_score[i] = beta[0] + beta[1] * new_data[i].market_cap + beta[2] * new_data[i].pb_ratio + beta[3] * new_data[i].roe;
    }

    // 选取得分最高的可转债
    int idx = max_element(predicted_score.begin(), predicted_score.end()) - predicted_score.begin();
    ConvertibleBond target_bond = new_data[idx];

    return 0;
}

这个C++代码的逻辑和上面的Python和MATLAB代码基本一致，只是用了C++的语法和STL库来实现。首先定义因子和权重，然后计算因子得分和综合得分。接着，使用线性回归模型拟合历史数据，然后使用新数据来预测综合得分，并选取得分最高的可转债作为目标。在这个例子中，我们使用了四个因子，即市值、市净率、净资产收益率和负债比率。