From 55b0f03f0bbf700c01f8aa4aba8064332dff95e9 Mon Sep 17 00:00:00 2001
From: Olivier Grisel <olivier.grisel@ensta.org>
Date: Wed, 3 Mar 2010 14:32:41 +0000
Subject: [PATCH] cosmit
git-svn-id: https://scikit-learn.svn.sourceforge.net/svnroot/scikit-learn/trunk@453 22fbfee3-77ab-4535-9bad-27d1bd3bc7d8
---
.../learn/datasets/samples_generator/linear.py | 17 ++++++++---------
1 file changed, 8 insertions(+), 9 deletions(-)
diff --git a/scikits/learn/datasets/samples_generator/linear.py b/scikits/learn/datasets/samples_generator/linear.py
index cf649ea5a0..966bcd84b7 100755
--- a/scikits/learn/datasets/samples_generator/linear.py
+++ b/scikits/learn/datasets/samples_generator/linear.py
@@ -1,7 +1,7 @@
import numpy as np
import numpy.random as nr
-def sparse_uncorrelated(nb_samples=100,nb_features=10):
+def sparse_uncorrelated(nb_samples=100, nb_features=10):
"""
Function creating simulated data with sparse uncorrelated design.
(cf.Celeux et al. 2009, Bayesian regularization in regression)
@@ -15,14 +15,13 @@ def sparse_uncorrelated(nb_samples=100,nb_features=10):
number of samples (defaut is 100).
nb_features : int
number of features (defaut is 10).
-
+
Returns
-------
- X : numpy array of shape (nb_samples,nb_features)
- simulated samples.
- Y : numpy array of shape (nb_samples)
+ X : numpy array of shape (nb_samples, nb_features) for input samples
+ Y : numpy array of shape (nb_samples) for labels
"""
- X = nr.normal(loc=0,scale=1,size=(nb_samples,nb_features))
- Y = nr.normal(loc=X[:,2]+2*X[:,3]-2*X[:,6]-1.5*X[:,7],
- scale=np.ones(nb_samples))
- return X,Y
\ No newline at end of file
+ X = nr.normal(loc=0, scale=1, size=(nb_samples, nb_features))
+ Y = nr.normal(loc=X[:, 2] + 2 * X[:, 3] - 2 * X[:,6] - 1.5 * X[:, 7],
+ scale = np.ones(nb_samples))
+ return X, Y
--
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