平稳高斯随机过程pytorch代码

我可以回答这个问题。以下是一个平稳高斯随机过程的 PyTorch 代码示例：

import torch
import numpy as np

def gaussian_process(x, mean, cov):
    """
    x: input tensor of shape (batch_size, input_dim)
    mean: mean function
    cov: covariance function
    """
    n = x.shape[0]
    # Compute mean vector
    mu = mean(x)
    # Compute covariance matrix
    K = cov(x)
    # Generate samples from multivariate normal distribution
    eps = torch.randn(n, 1)
    samples = mu + torch.mm(torch.cholesky(K), eps)
    return samples

# Example usage
x = torch.tensor(np.random.rand(10, 1))
mean = lambda x: torch.zeros(x.shape[0], 1)
cov = lambda x: torch.exp(-0.5 * torch.cdist(x, x) ** 2)
samples = gaussian_process(x, mean, cov)
print(samples)

这个代码可以生成一个平稳高斯随机过程的样本。