多元 Huber Regression 带截距和预测的C++带类的完整正确实现,要保证预测结果的正确,及可以得到正确结果的案例 非其它库支持,要保证结果的正确性
时间: 2024-03-17 21:40:19 浏览: 19
以下是一个简单的多元 Huber Regression 带截距和预测的 C++ 类的实现:
```cpp
#include <iostream>
#include <vector>
#include <cmath>
class HuberRegression {
private:
std::vector<std::vector<double>> x; // independent variables
std::vector<double> y; // dependent variable
std::vector<double> w; // weight
double alpha; // tuning parameter
double b; // intercept
std::vector<double> beta; // coefficients
int num_vars; // number of independent variables
int num_obs; // number of observations
public:
HuberRegression(double a) : alpha(a), b(0.0) {}
void fit(std::vector<std::vector<double>> X, std::vector<double> Y) {
x = X;
y = Y;
num_vars = X[0].size();
num_obs = X.size();
w = std::vector<double>(num_obs, 1.0);
// initialize coefficients to 0
beta = std::vector<double>(num_vars, 0.0);
// set maximum number of iterations
int max_iter = 1000;
int iter = 0;
// set convergence threshold
double tol = 1e-6;
while (iter < max_iter) {
// update weights
for (int i = 0; i < num_obs; i++) {
double pred = b;
for (int j = 0; j < num_vars; j++) {
pred += beta[j] * x[i][j];
}
double resid = y[i] - pred;
if (std::abs(resid) > alpha) {
w[i] = alpha / std::abs(resid);
}
else {
w[i] = 1.0;
}
}
// update intercept
double b_new = 0.0;
for (int i = 0; i < num_obs; i++) {
double pred = 0.0;
for (int j = 0; j < num_vars; j++) {
pred += beta[j] * x[i][j];
}
b_new += w[i] * (y[i] - pred);
}
b_new /= w.size();
// update coefficients
std::vector<double> beta_new(num_vars, 0.0);
for (int j = 0; j < num_vars; j++) {
double num = 0.0;
double den = 0.0;
for (int i = 0; i < num_obs; i++) {
num += w[i] * x[i][j] * (y[i] - (b_new + beta[j] * x[i][j]));
den += w[i] * x[i][j] * x[i][j];
}
if (den == 0.0) {
beta_new[j] = 0.0;
}
else {
beta_new[j] = num / den;
}
}
// check for convergence
double b_diff = std::abs(b_new - b);
double beta_diff = 0.0;
for (int j = 0; j < num_vars; j++) {
beta_diff += std::abs(beta_new[j] - beta[j]);
}
if (b_diff < tol && beta_diff < tol) {
break;
}
// update intercept and coefficients
b = b_new;
beta = beta_new;
iter++;
}
}
double predict(std::vector<double> x_new) {
double pred = b;
for (int j = 0; j < num_vars; j++) {
pred += beta[j] * x_new[j];
}
return pred;
}
};
```
该类具有 `fit` 和 `predict` 两个主要方法。 `fit` 方法接收一个矩阵 `X` 和一个向量 `Y`,其中 `X` 包含所有的自变量,而 `Y` 包含因变量。该方法使用 huber 回归算法来拟合数据,并计算出截距 `b` 和系数向量 `beta`。 `predict` 方法接收一个自变量向量,并返回相应的因变量预测值。
以下是一个简单的使用案例:
```cpp
int main() {
// create data
std::vector<std::vector<double>> X{ {1.0, 2.0}, {2.0, 3.0}, {3.0, 4.0}, {4.0, 5.0}, {5.0, 6.0} };
std::vector<double> Y{ 2.0, 3.0, 4.0, 5.0, 6.0 };
// create HuberRegression object
HuberRegression hr(1.0);
// fit data
hr.fit(X, Y);
// predict new data
std::vector<double> x_new{ 6.0, 7.0 };
double y_pred = hr.predict(x_new);
// print result
std::cout << "Predicted value: " << y_pred << std::endl;
return 0;
}
```
在这个案例中,我们创建了一个包含 5 个观测值和 2 个自变量的数据集。我们使用 `HuberRegression` 类来拟合数据,并预测一个新的自变量向量的因变量值。
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