mvpa2.testing.datasets.normal_feature_dataset(perlabel=50, nlabels=2, nfeatures=4, nchunks=5, means=None, nonbogus_features=None, snr=3.0, normalize=True)

Generate a univariate dataset with normal noise and specified means.

Could be considered to be a generalization of pure_multivariate_signal where means=[ [0,1], [1,0] ].

Specify either means or nonbogus_features so means get assigned accordingly. If neither means nor nonbogus_features are provided, data will be pure noise and no per-label information.


perlabel : int, optional

Number of samples per each label

nlabels : int, optional

Number of labels in the dataset

nfeatures : int, optional

Total number of features (including bogus features which carry no label-related signal)

nchunks : int, optional

Number of chunks (perlabel should be multiple of nchunks)

means : None or ndarray of (nlabels, nfeatures) shape

Specified means for each of features (columns) for all labels (rows).

nonbogus_features : None or list of int

Indexes of non-bogus features (1 per label).

snr : float, optional

Signal-to-noise ration assuming that signal has std 1.0 so we just divide random normal noise by snr

normalize : bool, optional

Divide by max(abs()) value to bring data into [-1, 1] range.