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Multivariate Pattern Analysis in Python |
Miscelaneous data generators for unittests and demos
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
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
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
Create a 2d dataset with a clear multivariate signal, but no univariate information.
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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
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