修改旧的GaussianProcessor示例以与GaussianProcessRegressor一起运行

问题描述

我有一个要从Jupyter笔记本中运行的数据科学书中的示例.代码段看起来像这样

I have an example from a data science book I am trying to run in a Jupyter notebook. The code sippet looks like this

from sklearn.gaussian_process import GaussianProcess

# define the model and draw some data
model = lambda x: x * np.sin(x)
xdata = np.array([1, 3, 5, 6, 8])
ydata = model(xdata)

# Compute the Gaussian process fit
gp = GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4, thetaU=1E-1,
                    random_start=100)
gp.fit(xdata[:, np.newaxis], ydata)

xfit = np.linspace(0, 10, 1000)
yfit, MSE = gp.predict(xfit[:, np.newaxis], eval_MSE=True)
dyfit = 2 * np.sqrt(MSE)  # 2*sigma ~ 95% confidence region

因为GaussianProcess已被弃用并由GaussianProcessRegressor代替.我试图将代码段修复为这样

since GaussianProcess has been deprecated and replaced with GaussianProcessRegressor. I tried to fix the code snippet to look like this

from sklearn.gaussian_process import GaussianProcessRegressor

# define the model and draw some data
model = lambda x: x * np.sin(x)
xdata = np.array([1, 3, 5, 6, 8])
ydata = model(xdata)

# Compute the Gaussian process fit
gp = GaussianProcessRegressor(random_state=100)
gp.fit(xdata[:, np.newaxis], ydata)

xfit = np.linspace(0, 10, 1000)
yfit, MSE = gp.predict(xfit[:, np.newaxis])
dyfit = 2 * np.sqrt(MSE)  # 2*sigma ~ 95% confidence region

但我收到ValueError

but I get a ValueError

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-47-c04ac57d1897> in <module>
     11 
     12 xfit = np.linspace(0, 10, 1000)
---> 13 yfit, MSE = gp.predict(xfit[:, np.newaxis])
     14 dyfit = 2 * np.sqrt(MSE)  # 2*sigma ~ 95% confidence region

ValueError: too many values to unpack (expected 2)

不确定为什么预测函数会在这里抱怨吗?

bit unsure why the predict function complains here?

推荐答案

错误有答案.

在yfit, MSE = gp.predict(xfit[:, np.newaxis])中，您尝试将预测结果分配给两个变量，而预测只返回单个numpy.ndarray.

At yfit, MSE = gp.predict(xfit[:, np.newaxis]) you are trying to assign the result of predict to two variables while the predict only returns a single numpy.ndarray.

要解决此问题，请运行

yfit = gp.predict(xfit[:, np.newaxis])

这篇关于修改旧的GaussianProcessor示例以与GaussianProcessRegressor一起运行的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！