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mvpa.misc.data_generators

Miscelaneous data generators for unittests and demos

The comprehensive API documentation for this module, including all technical details, is available in the Epydoc-generated API reference for mvpa.misc.data_generators (for developers).

Functions

mvpa.misc.data_generators.chirpLinear(n_instances, n_features=4, n_nonbogus_features=2, data_noise=0.40000000000000002, noise=0.10000000000000001)

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

See also

Full API documentation of chirpLinear() in module mvpa.misc.data_generators.

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

See also

Full API documentation of dumbFeatureDataset() in module mvpa.misc.data_generators.

mvpa.misc.data_generators.getMVPattern(s2n)
Simple multivariate dataset

See also

Full API documentation of getMVPattern() in module mvpa.misc.data_generators.

mvpa.misc.data_generators.linear_awgn(size=10, intercept=0.0, slope=0.40000000000000002, noise_std=0.01, flat=False)
Generate a dataset from a linear function with Added White Gaussian Noise (AWGN). 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.

See also

Full API documentation of linear_awgn() in module mvpa.misc.data_generators.

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

See also

Full API documentation of multipleChunks() in module mvpa.misc.data_generators.

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

See also

Full API documentation of noisy_2d_fx() in module mvpa.misc.data_generators.

mvpa.misc.data_generators.normalFeatureDataset(perlabel=50, nlabels=2, nfeatures=4, nchunks=5, means=None, nonbogus_features=None, snr=1.0)

Generate a dataset where each label is some normally distributed beastie around specified mean (0 if None).

snr is assuming that signal has std 1.0 so we just divide 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=0.40000000000000002)

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

See also

Full API documentation of sinModulated() in module mvpa.misc.data_generators.

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

See also

Full API documentation of wr1996() in module mvpa.misc.data_generators.