I'm using scikit learn to fit a Gaussian process regressor to some data. Ideally I want to do this for data with multiple targets, however the prediction doesn't seem return the std for multiple targets. As an example here I train a Gaussian process on 3 target statistics and predict at 100 sampled positions
gpr = GaussianProcessRegressor(kernel=kernel)
gpr.fit(x.reshape(-1,1), y_obs)
y,y_err=gpr.predict(x_sample.reshape(-1,1),return_std=True)
Where the training data has shape x.shape=(20,) and y_obs.shape=(20,3). The predicted mean and errors (y,y_sample) then do not have the same shape.
print(y.shape)
print(y_err.shape)
returns
(100,3)
(100,)
The mean, y, is the shape I expect as I requested the 3 targets at 100 sampled positions. However y_err doesn't seem be predicted for each target statistic.
This doesn't seem to be working as the documentation describes as both the mean and std should have shape (n_samples,) or (n_samples, n_targets)
Is this a bug, or am I missing something?
