问题
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
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
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)
bit unsure why the predict function complains here?
回答1:
The error has the answer.
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.
To solve this issue, run
yfit = gp.predict(xfit[:, np.newaxis])
来源：https://stackoverflow.com/questions/57221972/modifying-old-gaussianprocessor-example-to-run-with-gaussianprocessregressor