Create regression_models.cpp

Author: mhjensen

doc/Programs/Regression/regression_models.cpp

@@ -0,0 +1,204 @@
+#include <iostream>
+#include <vector>
+#include <cmath>
+#include <random>
+#include <fstream>
+#include <numeric>
+#include <Eigen/Dense>
+
+using namespace std;
+using namespace Eigen;
+
+// Utility functions
+double mean_squared_error(const VectorXd& y_true, const VectorXd& y_pred) {
+    return (y_true - y_pred).squaredNorm() / y_true.size();
+}
+
+double r2_score(const VectorXd& y_true, const VectorXd& y_pred) {
+    double mean_y = y_true.mean();
+    double total = (y_true.array() - mean_y).square().sum();
+    double residual = (y_true - y_pred).squaredNorm();
+    return 1.0 - residual / total;
+}
+
+void save_csv(const string& filename, const MatrixXd& X, const VectorXd& y_true, const VectorXd& y_pred) {
+    ofstream file(filename);
+    file << "X,True Y,Predicted Y\n";
+    for (int i = 0; i < X.rows(); ++i) {
+        file << X(i, 0) << "," << y_true(i) << "," << y_pred(i) << "\n";
+    }
+    file.close();
+}
+
+// Linear Regression
+class LinearRegression {
+public:
+    VectorXd weights;
+
+    void fit(const MatrixXd& X, const VectorXd& y) {
+        MatrixXd X_bias(X.rows(), X.cols() + 1);
+        X_bias << MatrixXd::Ones(X.rows(), 1), X;
+        weights = (X_bias.transpose() * X_bias).ldlt().solve(X_bias.transpose() * y);
+    }
+
+    VectorXd predict(const MatrixXd& X) const {
+        MatrixXd X_bias(X.rows(), X.cols() + 1);
+        X_bias << MatrixXd::Ones(X.rows(), 1), X;
+        return X_bias * weights;
+    }
+};
+
+// Ridge Regression
+class RidgeRegression {
+public:
+    VectorXd weights;
+    double alpha;
+
+    RidgeRegression(double alpha = 1.0) : alpha(alpha) {}
+
+    void fit(const MatrixXd& X, const VectorXd& y) {
+        MatrixXd X_bias(X.rows(), X.cols() + 1);
+        X_bias << MatrixXd::Ones(X.rows(), 1), X;
+        MatrixXd I = MatrixXd::Identity(X_bias.cols(), X_bias.cols());
+        I(0, 0) = 0; // Don't regularize bias
+        weights = (X_bias.transpose() * X_bias + alpha * I).ldlt().solve(X_bias.transpose() * y);
+    }
+
+    VectorXd predict(const MatrixXd& X) const {
+        MatrixXd X_bias(X.rows(), X.cols() + 1);
+        X_bias << MatrixXd::Ones(X.rows(), 1), X;
+        return X_bias * weights;
+    }
+};
+
+// Kernel Ridge Regression (RBF kernel)
+class KernelRidgeRegression {
+public:
+    double alpha, gamma;
+    MatrixXd X_train;
+    VectorXd alpha_vec;
+
+    KernelRidgeRegression(double alpha = 1.0, double gamma = 1.0) : alpha(alpha), gamma(gamma) {}
+
+    MatrixXd rbf_kernel(const MatrixXd& A, const MatrixXd& B) const {
+        MatrixXd K(A.rows(), B.rows());
+        for (int i = 0; i < A.rows(); ++i) {
+            for (int j = 0; j < B.rows(); ++j) {
+                K(i, j) = exp(-gamma * (A.row(i) - B.row(j)).squaredNorm());
+            }
+        }
+        return K;
+    }
+
+    void fit(const MatrixXd& X, const VectorXd& y) {
+        X_train = X;
+        MatrixXd K = rbf_kernel(X, X);
+        alpha_vec = (K + alpha * MatrixXd::Identity(K.rows(), K.cols())).ldlt().solve(y);
+    }
+
+    VectorXd predict(const MatrixXd& X) const {
+        MatrixXd K = rbf_kernel(X, X_train);
+        return K * alpha_vec;
+    }
+};
+
+int main() {
+    // Generate synthetic data
+    int n_samples = 100;
+    MatrixXd X(n_samples, 1);
+    VectorXd y(n_samples);
+    std::mt19937 gen(42);
+    std::uniform_real_distribution<> dist(0, 2);
+    std::normal_distribution<> noise(0, 0.5);
+
+    for (int i = 0; i < n_samples; ++i) {
+        X(i, 0) = dist(gen);
+        y(i) = 4.0 + 3.0 * X(i, 0) + noise(gen);
+    }
+
+    // Train and evaluate models
+    LinearRegression linear;
+    RidgeRegression ridge(1.0);
+    KernelRidgeRegression kernel_ridge(1.0, 5.0);
+
+    linear.fit(X, y);
+    ridge.fit(X, y);
+    kernel_ridge.fit(X, y);
+
+    VectorXd y_pred_linear = linear.predict(X);
+    VectorXd y_pred_ridge = ridge.predict(X);
+    VectorXd y_pred_kernel = kernel_ridge.predict(X);
+
+    cout << "Linear -> MSE: " << mean_squared_error(y, y_pred_linear) << ", R2: " << r2_score(y, y_pred_linear) << endl;
+    cout << "Ridge  -> MSE: " << mean_squared_error(y, y_pred_ridge) << ", R2: " << r2_score(y, y_pred_ridge) << endl;
+    cout << "Kernel -> MSE: " << mean_squared_error(y, y_pred_kernel) << ", R2: " << r2_score(y, y_pred_kernel) << endl;
+
+    save_csv("predictions_linear.csv", X, y, y_pred_linear);
+    save_csv("predictions_ridge.csv", X, y, y_pred_ridge);
+    save_csv("predictions_kernel_ridge.csv", X, y, y_pred_kernel);
+
+    return 0;
+}
+
+
+// Lasso Regression (Coordinate Descent)
+class LassoRegression {
+public:
+    VectorXd weights;
+    double alpha;
+    int max_iter;
+    double tol;
+
+    LassoRegression(double alpha = 0.1, int max_iter = 1000, double tol = 1e-4)
+        : alpha(alpha), max_iter(max_iter), tol(tol) {}
+
+    void fit(const MatrixXd& X, const VectorXd& y) {
+        MatrixXd X_bias(X.rows(), X.cols() + 1);
+        X_bias << MatrixXd::Ones(X.rows(), 1), X;
+        int n_samples = X_bias.rows();
+        int n_features = X_bias.cols();
+        weights = VectorXd::Zero(n_features);
+
+        for (int iter = 0; iter < max_iter; ++iter) {
+            VectorXd weights_old = weights;
+            for (int j = 0; j < n_features; ++j) {
+                double tmp = 0.0;
+                for (int i = 0; i < n_samples; ++i) {
+                    double dot = 0.0;
+                    for (int k = 0; k < n_features; ++k) {
+                        if (k != j)
+                            dot += X_bias(i, k) * weights(k);
+                    }
+                    tmp += X_bias(i, j) * (y(i) - dot);
+                }
+                double rho = tmp;
+                double norm_sq = X_bias.col(j).squaredNorm();
+
+                if (j == 0) {
+                    weights(j) = rho / norm_sq;
+                } else {
+                    if (rho < -alpha / 2)
+                        weights(j) = (rho + alpha / 2) / norm_sq;
+                    else if (rho > alpha / 2)
+                        weights(j) = (rho - alpha / 2) / norm_sq;
+                    else
+                        weights(j) = 0.0;
+                }
+            }
+            if ((weights - weights_old).lpNorm<1>() < tol)
+                break;
+        }
+    }
+
+    VectorXd predict(const MatrixXd& X) const {
+        MatrixXd X_bias(X.rows(), X.cols() + 1);
+        X_bias << MatrixXd::Ones(X.rows(), 1), X;
+        return X_bias * weights;
+    }
+};
+
+    LassoRegression lasso(0.1);
+    lasso.fit(X, y);
+    VectorXd y_pred_lasso = lasso.predict(X);
+    cout << "Lasso  -> MSE: " << mean_squared_error(y, y_pred_lasso) << ", R2: " << r2_score(y, y_pred_lasso) << endl;
+    save_csv("predictions_lasso.csv", X, y, y_pred_lasso);