From e9a550631c28a83e219727a3b806954c057f5e2c Mon Sep 17 00:00:00 2001
From: Michel Vincent <vm.michel@gmail.com>
Date: Wed, 3 Mar 2010 17:57:09 +0000
Subject: [PATCH] Some docs and examples - move bayes.bayes to bayes.regression
git-svn-id: https://scikit-learn.svn.sourceforge.net/svnroot/scikit-learn/trunk@487 22fbfee3-77ab-4535-9bad-27d1bd3bc7d8
---
scikits/learn/bayes/{bayes.py => regression.py} | 0
scikits/learn/bayes/tests/test_bayes.py | 2 +-
scikits/learn/datasets/samples_generator/linear.py | 6 +++---
3 files changed, 4 insertions(+), 4 deletions(-)
rename scikits/learn/bayes/{bayes.py => regression.py} (100%)
diff --git a/scikits/learn/bayes/bayes.py b/scikits/learn/bayes/regression.py
similarity index 100%
rename from scikits/learn/bayes/bayes.py
rename to scikits/learn/bayes/regression.py
diff --git a/scikits/learn/bayes/tests/test_bayes.py b/scikits/learn/bayes/tests/test_bayes.py
index c65152a8c2..74bde12c42 100644
--- a/scikits/learn/bayes/tests/test_bayes.py
+++ b/scikits/learn/bayes/tests/test_bayes.py
@@ -1,5 +1,5 @@
import numpy as np
-from scikits.learn.bayes.bayes import *
+from scikits.learn.bayes.regression import *
from numpy.testing import assert_array_almost_equal
from scikits.learn.datasets.samples_generator import linear,nonlinear
diff --git a/scikits/learn/datasets/samples_generator/linear.py b/scikits/learn/datasets/samples_generator/linear.py
index 966bcd84b7..8492442812 100755
--- a/scikits/learn/datasets/samples_generator/linear.py
+++ b/scikits/learn/datasets/samples_generator/linear.py
@@ -6,7 +6,7 @@ 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)
X = NR.normal(0,1)
- Y = NR.normal(X[:,2]+2*X[:,3]-2*X[:,6]-1.5*X[:,7])
+ Y = NR.normal(X[:,0]+2*X[:,1]-2*X[:,2]-1.5*X[:,3])
The number of features is at least 10.
Parameters
@@ -14,7 +14,7 @@ def sparse_uncorrelated(nb_samples=100, nb_features=10):
nb_samples : int
number of samples (defaut is 100).
nb_features : int
- number of features (defaut is 10).
+ number of features (defaut is 5).
Returns
-------
@@ -22,6 +22,6 @@ def sparse_uncorrelated(nb_samples=100, nb_features=10):
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],
+ Y = nr.normal(loc=X[:, 0] + 2 * X[:, 1] - 2 * X[:,2] - 1.5 * X[:, 3],
scale = np.ones(nb_samples))
return X, Y
--
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