| Multivariate Pattern Analysis in Python

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This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

# misc.data_generators

## Module: `misc.data_generators`

Miscelaneous data generators for unittests and demos

## Functions

`mvpa.misc.data_generators.``chirpLinear`(n_instances, n_features=4, n_nonbogus_features=2, data_noise=0.4, noise=0.1)

  Generates simple dataset for linear regressions

  Generates chirp signal, populates n_nonbogus_features out of n_features with it with different noise level and then provides signal itself with additional noise as labels

`mvpa.misc.data_generators.``dumbFeatureBinaryDataset`()

  Very simple binary (2 labels) dataset

`mvpa.misc.data_generators.``dumbFeatureDataset`()

  Create a very simple dataset with 2 features and 3 labels

`mvpa.misc.data_generators.``getMVPattern`(s2n)

  Simple multivariate dataset

`mvpa.misc.data_generators.``linear1d_gaussian_noise`(size=100, slope=0.5, intercept=1.0, x_min=-2.0, x_max=3.0, sigma=0.2)

  A straight line with some Gaussian noise.

`mvpa.misc.data_generators.``linear_awgn`(size=10, intercept=0.0, slope=0.4, noise_std=0.01, flat=False)

  Generate a dataset from a linear function with AWGN (Added White Gaussian Noise).

  It can be multidimensional if ‘slope’ is a vector. If flat is True (in 1 dimesion) generate equally spaces samples instead of random ones. This is useful for the test phase.

`mvpa.misc.data_generators.``multipleChunks`(func, n_chunks, *args, **kwargs)

  Replicate datasets multiple times raising different chunks

  Given some randomized (noisy) generator of a dataset with a single chunk call generator multiple times and place results into a distinct chunks

`mvpa.misc.data_generators.``noisy_2d_fx`(size_per_fx, dfx, sfx, center, noise_std=1)

`mvpa.misc.data_generators.``normalFeatureDataset`(perlabel=50, nlabels=2, nfeatures=4, nchunks=5, means=None, nonbogus_features=None, snr=3.0)

  Generate a univariate dataset with normal noise and specified means.

  Keywords :

  perlabel : int

    Number of samples per each label

  nlabels : int

    Number of labels in the dataset

  nfeatures : int

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

  nchunks : int

    Number of chunks (perlabel should be multiple of nchunks)

  means : None or list of float or ndarray

    Specified means for each of features among nfeatures.

  nonbogus_features : None or list of int

    Indexes of non-bogus features (1 per label)

  snr : float

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

  Probably it is a generalization of pureMultivariateSignal where means=[ [0,1], [1,0] ]

  Specify either means or nonbogus_features so means get assigned accordingly

`mvpa.misc.data_generators.``normalFeatureDataset__`(dataset=None, labels=None, nchunks=None, perlabel=50, activation_probability_steps=1, randomseed=None, randomvoxels=False)

  NOT FINISHED

`mvpa.misc.data_generators.``pureMultivariateSignal`(patterns, signal2noise=1.5, chunks=None)

  Create a 2d dataset with a clear multivariate signal, but no univariate information.

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`mvpa.misc.data_generators.``sinModulated`(n_instances, n_features, flat=False, noise=0.4)

  Generate a (quite) complex multidimensional non-linear dataset

  Used for regression testing. In the data label is a sin of a x^2 + uniform noise

`mvpa.misc.data_generators.``wr1996`(size=200)

  Generate ‘6d robot arm’ dataset (Williams and Rasmussen 1996)

  Was originally created in order to test the correctness of the implementation of kernel ARD. For full details see: http://www.gaussianprocess.org/gpml/code/matlab/doc/regression.html#ard

  x_1 picked randomly in [-1.932, -0.453] x_2 picked randomly in [0.534, 3.142] r_1 = 2.0 r_2 = 1.3 f(x_1,x_2) = r_1 cos (x_1) + r_2 cos(x_1 + x_2) + N(0,0.0025) etc.

  Expected relevances: ell_1 1.804377 ell_2 1.963956 ell_3 8.884361 ell_4 34.417657 ell_5 1081.610451 ell_6 375.445823 sigma_f 2.379139 sigma_n 0.050835

### Navigation

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