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misc.data_generators
Module: misc.data_generators
Miscelaneous data generators for unittests and demos
Functions
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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
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mvpa.misc.data_generators.dumbFeatureBinaryDataset()
- Very simple binary (2 labels) dataset
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mvpa.misc.data_generators.dumbFeatureDataset()
- Create a very simple dataset with 2 features and 3 labels
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mvpa.misc.data_generators.getMVPattern(s2n)
- Simple multivariate dataset
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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 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.
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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
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mvpa.misc.data_generators.noisy_2d_fx(size_per_fx, dfx, sfx, center, noise_std=1)
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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
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mvpa.misc.data_generators.normalFeatureDataset__(dataset=None, labels=None, nchunks=None, perlabel=50, activation_probability_steps=1, randomseed=None, randomvoxels=False)
- NOT FINISHED
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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
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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