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dumbFeatureDataset()
Create a very simple dataset with 2 features and 3 labels |
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dumbFeatureBinaryDataset()
Very simple binary (2 labels) dataset |
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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. |
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pureMultivariateSignal(patterns,
signal2noise=1.5,
chunks=None)
Create a 2d dataset with a clear multivariate signal, but no
univariate information. |
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normalFeatureDataset__(dataset=None,
labels=None,
nchunks=None,
perlabel=50,
activation_probability_steps=1,
randomseed=None,
randomvoxels=False)
NOT FINISHED |
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getMVPattern(s2n)
Simple multivariate dataset |
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wr1996(size=200)
Generate '6d robot arm' dataset (Williams and Rasmussen 1996) |
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sinModulated(n_instances,
n_features,
flat=False,
noise=0.4)
Generate a (quite) complex multidimensional non-linear dataset |
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chirpLinear(n_instances,
n_features=4,
n_nonbogus_features=2,
data_noise=0.4,
noise=0.1)
Generates simple dataset for linear regressions |
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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). |
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noisy_2d_fx(size_per_fx,
dfx,
sfx,
center,
noise_std=1) |
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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. |
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