28 lines
910 B
Python
Executable File
28 lines
910 B
Python
Executable File
import numpy as np
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import numpy.random as nr
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def sparse_uncorrelated(nb_samples=100, nb_features=10):
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"""
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Function creating simulated data with sparse uncorrelated design.
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(cf.Celeux et al. 2009, Bayesian regularization in regression)
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X = NR.normal(0,1)
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Y = NR.normal(X[:,0]+2*X[:,1]-2*X[:,2]-1.5*X[:,3])
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The number of features is at least 10.
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Parameters
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----------
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nb_samples : int
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number of samples (defaut is 100).
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nb_features : int
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number of features (defaut is 5).
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Returns
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-------
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X : numpy array of shape (nb_samples, nb_features) for input samples
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Y : numpy array of shape (nb_samples) for labels
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"""
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X = nr.normal(loc=0, scale=1, size=(nb_samples, nb_features))
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Y = nr.normal(loc=X[:, 0] + 2 * X[:, 1] - 2 * X[:,2] - 1.5 * X[:, 3],
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scale = np.ones(nb_samples))
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return X, Y
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