高斯过程的偏导数wrt特征

Question

给定一个具有多维特征和标量观测的高斯过程模型，我如何在GPyTorch或GPFlow(或scikit learn(中计算输出wrt对每个输入的导数？

Answer 1

如果我正确理解你的问题，下面的内容应该能让你在使用TensorFlow的GPflow中得到你想要的：

import numpy as np
import tensorflow as tf
import gpflow
### Set up toy data & model -- change as appropriate:
X = np.linspace(0, 10, 5)[:, None]
Y = np.random.randn(5, 1)
data = (X, Y)
kernel = gpflow.kernels.SquaredExponential()
model = gpflow.models.GPR(data, kernel)
Xtest = np.linspace(-1, 11, 7)[:, None]  # where you want to predict
### Compute gradient of prediction with respect to input:
# TensorFlow can only compute gradients with respect to tensor objects,
# so let's convert the inputs to a tensor:
Xtest_tensor = tf.convert_to_tensor(Xtest)  
with tf.GradientTape(
persistent=True  # this allows us to compute different gradients below
) as tape:
# By default, only Variables are watched. For gradients with respect to tensors,
# we need to explicitly watch them:
tape.watch(Xtest_tensor)
mean, var = model.predict_f(Xtest_tensor)  # or any other predict function
grad_mean = tape.gradient(mean, Xtest_tensor)
grad_var = tape.gradient(var, Xtest_tensor)