Multivariate Pattern Analysis in Python

# mvpa2.misc.data_generators.normal_feature_dataset

`mvpa2.misc.data_generators.``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.

  Parameters:

  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.