If there's no samples, returns an empty liss

These classes are supposed to follow scikit-learn's API, aren't they?

pca.transform([])
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-13-30a799240129> in <module>
----> 1 pca.transform([])

~/miniconda/envs/bob36/lib/python3.6/site-packages/sklearn/decomposition/_base.py in transform(self, X)
    125         check_is_fitted(self)
    126 
--> 127         X = check_array(X)
    128         if self.mean_ is not None:
    129             X = X - self.mean_

~/miniconda/envs/bob36/lib/python3.6/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    554                     "Reshape your data either using array.reshape(-1, 1) if "
    555                     "your data has a single feature or array.reshape(1, -1) "
--> 556                     "if it contains a single sample.".format(array))
    557 
    558         # in the future np.flexible dtypes will be handled like object dtypes

ValueError: Expected 2D array, got 1D array instead:
array=[].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

pca.transform([[]])
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-14-6a037ad0a10e> in <module>
----> 1 pca.transform([[]])

~/miniconda/envs/bob36/lib/python3.6/site-packages/sklearn/decomposition/_base.py in transform(self, X)
    125         check_is_fitted(self)
    126 
--> 127         X = check_array(X)
    128         if self.mean_ is not None:
    129             X = X - self.mean_

~/miniconda/envs/bob36/lib/python3.6/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    592                              " a minimum of %d is required%s."
    593                              % (n_features, array.shape, ensure_min_features,
--> 594                                 context))
    595 
    596     if warn_on_dtype and dtype_orig is not None and array.dtype != dtype_orig:

ValueError: Found array with 0 feature(s) (shape=(1, 0)) while a minimum of 1 is required.

Yeap. I'll push a better solution to that in a few minutes.

closed