From 604e8b7754960d549de682d2d7c3590c18a88252 Mon Sep 17 00:00:00 2001
From: Olivier Grisel <olivier.grisel@ensta.org>
Date: Sat, 11 Dec 2010 15:12:27 +0100
Subject: [PATCH] sed -i "s/\<n_componentsonents\>/n_components/g"
---
scikits/learn/tests/test_pca.py | 12 ++++++------
1 file changed, 6 insertions(+), 6 deletions(-)
diff --git a/scikits/learn/tests/test_pca.py b/scikits/learn/tests/test_pca.py
index 2e63012e47..713e66746e 100644
--- a/scikits/learn/tests/test_pca.py
+++ b/scikits/learn/tests/test_pca.py
@@ -33,7 +33,7 @@ def test_whitening():
np.random.seed(0)
n_samples = 100
n_features = 80
- n_componentsonents = 30
+ n_components = 30
rank = 50
# some low rank data with correlated features
@@ -50,18 +50,18 @@ def test_whitening():
assert_almost_equal(X.std(axis=0).std(), 43.9, 1)
# whiten the data while projecting to the lower dim subspace
- pca = PCA(n_components=n_componentsonents, whiten=True).fit(X)
+ pca = PCA(n_components=n_components, whiten=True).fit(X)
X_whitened = pca.transform(X)
- assert_equal(X_whitened.shape, (n_samples, n_componentsonents))
+ assert_equal(X_whitened.shape, (n_samples, n_components))
# all output component have unit variances
- assert_almost_equal(X_whitened.std(axis=0), np.ones(n_componentsonents))
+ assert_almost_equal(X_whitened.std(axis=0), np.ones(n_components))
# is possible to project on the low dim space without scaling by the
# singular values
- pca = PCA(n_components=n_componentsonents, whiten=False).fit(X)
+ pca = PCA(n_components=n_components, whiten=False).fit(X)
X_unwhitened = pca.transform(X)
- assert_equal(X_unwhitened.shape, (n_samples, n_componentsonents))
+ assert_equal(X_unwhitened.shape, (n_samples, n_components))
# in that case the output components still have varying variances
assert_almost_equal(X_unwhitened.std(axis=0).std(), 74.1, 1)
--
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