def __init__(self, samples, sa=None, fa=None, a=None):
"""
A Dataset might have an arbitrary number of attributes for samples,
features, or the dataset as a whole. However, only the data samples
themselves are required.
Parameters
----------
samples : ndarray
Data samples. This has to be a two-dimensional (samples x features)
array. If the samples are not in that format, please consider one of
the `AttrDataset.from_*` classmethods.
sa : SampleAttributesCollection
Samples attributes collection.
fa : FeatureAttributesCollection
Features attributes collection.
a : DatasetAttributesCollection
Dataset attributes collection.
"""
# conversions
if isinstance(samples, list):
samples = np.array(samples)
# Check all conditions we need to have for `samples` dtypes
if not hasattr(samples, 'dtype'):
raise ValueError(
"AttrDataset only supports dtypes as samples that have a "
"`dtype` attribute that behaves similar to the one of an "
"array-like.")
if not hasattr(samples, 'shape'):
raise ValueError(
"AttrDataset only supports dtypes as samples that have a "
"`shape` attribute that behaves similar to the one of an "
"array-like.")
if not len(samples.shape):
raise ValueError("Only `samples` with at least one axis are "
"supported (got: %i)" % len(samples.shape))
# handling of 1D-samples
# i.e. 1D is treated as multiple samples with a single feature
if len(samples.shape) == 1:
samples = np.atleast_2d(samples).T
# that's all -- accepted
self.samples = samples
# Everything in a dataset (except for samples) is organized in
# collections
# Number of samples is .shape[0] for sparse matrix support
self.sa = SampleAttributesCollection(length=len(self))
if not sa is None:
self.sa.update(sa)
self.fa = FeatureAttributesCollection(length=self.nfeatures)
if not fa is None:
self.fa.update(fa)
self.a = DatasetAttributesCollection()
if not a is None:
self.a.update(a)
