Version bump

(Portage version: 2.2.01.21688-prefix/cvs/Linux x86_64, signed Manifest commit with key 0x13CB1360)

2 files changed, 0 insertions, 85 deletions

diff --git a/sci-libs/scikits_learn/files/0.10-atlas.patch b/sci-libs/scikits_learn/files/0.10-atlas.patch
deleted file mode 100644
index 4d1743dc7a34..000000000000
--- a/sci-libs/scikits_learn/files/0.10-atlas.patch
+++ /dev/null
@@ -1,56 +0,0 @@
-This patch remove the hardcoding on cblas which was assuming only ATLAS
-provides a cblas implementation.
-bicatali - apr 2012
-
-diff -Nur scikit-learn-0.10.orig/sklearn/cluster/setup.py scikit-learn-0.10/sklearn/cluster/setup.py
---- scikit-learn-0.10.orig/sklearn/cluster/setup.py	2012-01-11 13:48:21.000000000 +0000
-+++ scikit-learn-0.10/sklearn/cluster/setup.py	2012-04-23 19:59:54.000000000 +0100
-@@ -10,8 +10,7 @@
-     from numpy.distutils.system_info import get_info
- 
-     blas_info = get_info('blas_opt', 0)
--    if (not blas_info) or (
--        ('NO_ATLAS_INFO', 1) in blas_info.get('define_macros', [])):
-+    if (not blas_info):
-         cblas_libs = ['cblas']
-         blas_info.pop('libraries', None)
-     else:
-diff -Nur scikit-learn-0.10.orig/sklearn/linear_model/setup.py scikit-learn-0.10/sklearn/linear_model/setup.py
---- scikit-learn-0.10.orig/sklearn/linear_model/setup.py	2012-01-11 13:48:21.000000000 +0000
-+++ scikit-learn-0.10/sklearn/linear_model/setup.py	2012-04-23 20:00:44.000000000 +0100
-@@ -9,8 +9,7 @@
- 
-     # cd fast needs CBLAS
-     blas_info = get_info('blas_opt', 0)
--    if (not blas_info) or (
--        ('NO_ATLAS_INFO', 1) in blas_info.get('define_macros', [])):
-+    if (not blas_info):
-         cblas_libs = ['cblas']
-         blas_info.pop('libraries', None)
-     else:
-diff -Nur scikit-learn-0.10.orig/sklearn/setup.py scikit-learn-0.10/sklearn/setup.py
---- scikit-learn-0.10.orig/sklearn/setup.py	2012-01-11 13:48:21.000000000 +0000
-+++ scikit-learn-0.10/sklearn/setup.py	2012-04-23 20:39:34.000000000 +0100
-@@ -44,8 +44,7 @@
- 
-     # some libs needs cblas, fortran-compiled BLAS will not be sufficient
-     blas_info = get_info('blas_opt', 0)
--    if (not blas_info) or (
--        ('NO_ATLAS_INFO', 1) in blas_info.get('define_macros', [])):
-+    if (not blas_info):
-         config.add_library('cblas',
-                            sources=[join('src', 'cblas', '*.c')])
-         warnings.warn(BlasNotFoundError.__doc__)
-diff -Nur scikit-learn-0.10.orig/sklearn/utils/setup.py scikit-learn-0.10/sklearn/utils/setup.py
---- scikit-learn-0.10.orig/sklearn/utils/setup.py	2012-01-11 13:48:21.000000000 +0000
-+++ scikit-learn-0.10/sklearn/utils/setup.py	2012-04-23 20:02:13.000000000 +0100
-@@ -12,8 +12,7 @@
- 
-     # cd fast needs CBLAS
-     blas_info = get_info('blas_opt', 0)
--    if (not blas_info) or (
--        ('NO_ATLAS_INFO', 1) in blas_info.get('define_macros', [])):
-+    if (not blas_info):
-         cblas_libs = ['cblas']
-         blas_info.pop('libraries', None)
-     else:
diff --git a/sci-libs/scikits_learn/files/0.12.1-linalg.patch b/sci-libs/scikits_learn/files/0.12.1-linalg.patch
deleted file mode 100644
index ca113ffe6416..000000000000
--- a/sci-libs/scikits_learn/files/0.12.1-linalg.patch
+++ /dev/null
@@ -1,29 +0,0 @@
---- a/sklearn/linear_model/least_angle.py
-+++ b/sklearn/linear_model/least_angle.py
-@@ -236,8 +236,8 @@ def lars_path(X, y, Xy=None, Gram=None,
-                 L_.flat[::n_active + 1] += (2 ** i) * eps
-                 least_squares, info = solve_cholesky(L_,
-                                     sign_active[:n_active], lower=True)
--                AA = 1. / np.sqrt(np.sum(least_squares
--                                         * sign_active[:n_active]))
-+                tmp = max(np.sum(least_squares * sign_active[:n_active]), eps)
-+                AA = 1. / np.sqrt(tmp)
-                 i += 1
-         least_squares *= AA
- 
---- a/sklearn/linear_model/tests/test_least_angle.py
-+++ b/sklearn/linear_model/tests/test_least_angle.py
-@@ -101,6 +101,13 @@ def test_collinearity():
-     assert_true(not np.isnan(coef_path_).any())
-     residual = np.dot(X, coef_path_[:, -1]) - y
-     assert_less((residual ** 2).sum(), 1.)  # just make sure it's bounded
-+    
-+    n_samples = 10
-+    X = np.random.rand(n_samples, 5)
-+    y = np.zeros(n_samples)
-+    _, _, coef_path_ = linear_model.lars_path(X, y, Gram='auto', copy_X=False, 
-+         copy_Gram=False, alpha_min=0., method='lasso', verbose=0, max_iter=500)
-+    assert_array_almost_equal(coef_path_, np.zeros_like(coef_path_))
- 
- 
- def test_no_path():