GPR代码matlab
时间: 2023-09-04 17:07:56 浏览: 44
以下是一个简单的 Matlab GPR 代码示例:
```matlab
% Load data
load('mydata.mat');
% Set hyperparameters
sigma_f = 1;
l = 1;
% Compute covariance matrix
K = zeros(length(x), length(x));
for i = 1:length(x)
for j = 1:length(x)
K(i,j) = sigma_f^2 * exp(-(x(i) - x(j))^2 / (2*l^2));
end
end
% Add noise to covariance matrix
noise = 0.1;
K = K + noise^2 * eye(length(x));
% Compute mean and variance of predictive distribution
y_star = 0; % set to zero for simplicity
K_star = zeros(length(x_star), length(x));
for i = 1:length(x_star)
for j = 1:length(x)
K_star(i,j) = sigma_f^2 * exp(-(x_star(i) - x(j))^2 / (2*l^2));
end
end
K_star_star = sigma_f^2 * eye(length(x_star));
y_mean = y_star + K_star * inv(K) * (y - y_star);
y_var = K_star_star - K_star * inv(K) * K_star';
% Plot results
figure;
hold on;
plot(x, y, 'ko', 'MarkerSize', 10);
plot(x_star, y_mean, 'b-', 'LineWidth', 2);
plot(x_star, y_mean + 2*sqrt(diag(y_var)), 'r--', 'LineWidth', 2);
plot(x_star, y_mean - 2*sqrt(diag(y_var)), 'r--', 'LineWidth', 2);
legend('Training data', 'Mean prediction', '95% confidence interval');
```
此代码使用高斯过程回归(GPR)来预测数据。它首先计算训练数据的协方差矩阵,然后添加噪声并计算预测分布的均值和方差。最后,它绘制了训练数据和预测分布的图形。请注意,这只是一个简单的示例代码,实际应用中可能需要调整超参数和使用更复杂的技术。