Changelog for python39-MDP-3.5-2.70.noarch.rpm :

* Wed Aug 23 2017 toddrme2178AATTgmail.com- Rename to python-MDP to match openSUSE python package naming guidelines.- Implement single-spec version- Update to version 3.5
* fix use of str in biflow indexing
* remove obsolete scripts
* Add wrapper function for exec Old Python versions don\'t allow exec statement in nested functions- Update to version 3.4
* Make use of setuptools instead of distutils While we resisted as long as possible, it is now time to move to use setuptools officially. This makes MDP installable properly with pip. This means that our dependencies are now explicitly declared (numpy and future). Optional dependencies are also declared and can be installed using the \"pip -e MDP[optionaldep]\" command, where optionaldep can be scipy, or scikit-learn or ... Moreover, automatic testing using \"python setup.py test\" now works, which makes MDP finally conform this non-written standard and makes it easy to run tests for clueless people.
* add new modules in sklearn wrappers
* Remove self-contained py.test script... The use of the self-contained script for py.test has been deprecated for a while and has stop working. We now require py.test to be installed to run the tests.
* remove obsolete stuff related to py3 conversion with lib2to3
* Disable support for shogun > 1 it would be too much work to enable support for newer shogun versions...
* disable failing monkeypatching test for pp... .. the pp monkeypatching trick is not needed anymore anyways...
* futurize stage 2: code is now py2&3 compatible
* futurize stage1: code should still be python2.6 compatible
* compatibility fixes for numpy 1.11.., ...numpy now refuses to index arrays using floats and default dtype for zeros and ones has been changed from integers to floats.
* several new sklearn autogenerated nodes need additional arguments to be tested
* FIX: fixed broken inclusion of CSS data
* FIX: hacky fix for the unicode issue in bimdp autogen (fix for Bug [#11])
* NEW: added extension setup and teardown function decorator
* DOC: some small updates and fixes in docstrings and comments
* FIX: cStringIO does not support Unicode `autogen` was breaking in nodes with Unicode characters in their docstring. We traced this down to cStringIO, which unlike StringIO does not support unicode.
* FIX: wrap sklearn algs in modules ending with \"_\" For historical reasons we were not wrapping sklearn algs in modules with names ending with \"_\". This restriction prevented us from wrapping algs that we could have wrapped. We lift it now, since it does not seem to have any bad side effects anymore. A proper validation would be to really test that we can run the algs.
* NEW: update local copy of py.test to version 2.3.4
* FIX: made code and documentation consistent regarding the parameter k in invert_exp_funcs2
* DOC: indicated what expansion failed in the tests of GeneralExpansionNode
* DOC: added an example to GeneralExpansionNode and made the documentation more explicit
* FIX: make pip work without explicit URL. now we point directly to the tar archive in the download_url field of the setup.py file.
* DOC: moved coarse_limit description under FastICA and removed signature (present elsewhere)
* ENH: FastICA - test having some coarse_limit
* ENH: FastICA -- primary_limit -> coarse_limit + default to None Introduced to make things a bit clearer since there is already \"fine tuning\" stage where optimization switches to used_g upon reaching the limit. Calling the limit \"coarse\" instead of \"primary/secondary\" would help to preserve original messages/logic thus not confusing existing users, while still allowing to kick in gFine before reaching the limit
* ENH: 2 stage optimization for FastICA found in current AFNI
* DOC: bumped version number after release ;)
* OTH: updated CHANGES and COPYRIGHT
* NEW: add support for newest sklearn algorithms
* Sun Dec 08 2013 p.drouandAATTgmail.com- Update to version 3.3 + fix error in automatic testing for MultinomialNB. + make sklearn nodes automatic testing more robust The previous solution was actually ignoring the special definitions in NODES. + disable pp support if server can not start Being able to import pp is not enough to be sure that pp works. For example in a low memory situation, the following can happen: >>> import pp >>> server = pp.Server() [...] OSError: [Errno 12] Cannot allocate memory This fix just disables pp support if the server can not be started. + Fix wrapping of sklearn 0.11 classifiers + make test_SFA2Node even more robust + make FastICANode test more robust + make test_SFA2Node more robust + fix pp_tests when run multiple times. pp tests were failing when run twice in a row. hugly work-around, but it seems to work... + fixed broken test_reload. test_reload was failing when called twice in a row. + fix random seed tests. The tests were failing when called twice in a row: >>> import mdp >>> mdp.test() >>> mdp.test() the first call was working, the second one was giving failures. + added tests for learning of bias parameters + replace third remaing test for pp_monkeypatch_dirname Hopefully this will fix test suite failures. + Decrease the noise level in the DiscreteHopfieldClassifier. + honor MDP_DISABLE_SHOGUN env variable + fix left-over directories from testing pp. I do not know why, but this simple change fixes the leftover directories problem when testig with python-pp and pp monkey-patching. It should have worked even as it was before, but apparently some race condition happens. + fix determinant of random rotation matrix determinant sign was wrong if dimensions of rotation matrix were odd. Actual Fix. + remove duplicated and overly verbose code + remove FastICA stabilization from tests + remove unused parameter stabilization in FastICA. + added new sklearn algorithms wrapping We now wrap 93 sklearn algorithms! + fix another imcompatibility with sklearn 0.10 Although EllipticEnvelop is derived from sklearn.base.ClassifierMixin, it is not a classifier. It is actually a \"predictor\". Added a check in the sklearn wrappers. + fix sklearn wrappers for version 0.10 New sklearn version introduces classifiers and predictors mixin classes that do not have a \'fit\' method. Typical error: AttributeError: type object \'OutlierDetectionMixin\' has no attribute \'fit\' Just added a check that the method is really present before wrapping. + fix failing test for no eigenvalues left problem Check that PCANode now raises the right exception. + add useful exception in case of no eigenvalues left. Check for the condition explained in b0810d72ce11925e1db6204c3a20bdfc77741a82 and raise a nicely formatted exception: Traceback: [...] File \".../mdp/nodes/pca_nodes.py\", line 223, in _stop_training \' var_abs=%e!\'%self.var_abs) NodeException: No eigenvalues larger than var_abs=1.000000e-15! + OTH: added failing test for no eigenvalues left problem. When PCANode is set to use SVD and automatic dimensionality reduction it may happen that after removing directions corresponding to eigenvalues smaller than var_abs (1e-12 default), nothing is left. This happens for example if the data is a matrix of (almost) zeros. The error looks like this: Traceback (most recent call last): [...] File \".../mdp/nodes/pca_nodes.py\", line 220, in _stop_training d = d[ d / d.max() > self.var_rel ] ValueError: zero-size array to ufunc.reduce without identity + old joblib breaks imports from sklearn.decomposition >>> import sklearn.decomposition Traceback (most recent call last): File \"\", line 1, in File \"/usr/lib/pymodules/python2.6/sklearn/decomposition/__init__.py\", line 8, in from .sparse_pca import SparsePCA, MiniBatchSparsePCA File \"/usr/lib/pymodules/python2.6/sklearn/decomposition/sparse_pca.py\", line 10, in from .dict_learning import dict_learning, dict_learning_online File \"/usr/lib/pymodules/python2.6/sklearn/decomposition/dict_learning.py\", line 17, in from ..externals.joblib import Parallel, delayed, cpu_count ImportError: cannot import name cpu_count >>> joblib.__version__ \'0.4.3\' + py3k compatibility for reload reload() is moved to imp.reload(). It also seems that Python 3 behaves slightly differently wrt. reloads. For some reason, mdp.configuration is not imported properly on reload. Let\'s just not remove the name from the namespace, as this is the easiest fix. + added a failing test for reload MDP does not work with reload. See https://github.com/mdp-toolkit/mdp-toolkit/issues/1 for details. + Removed the leftover _global_message_emitter attribute + fix wrong sorting of eigenvectors in degenerate case for SVD. + added failing test for wrong sorting of SVDs + updated sklearn wrappers to silence warnings for version >= 0.9- Remove fix-sklearn-{0.9/0.10-1/0.10-2/0.11}.patch; fixed on upstream release- Remove useless CFLAGS definition- Remove depreciated python(abi) >= %{py_ver} requirement- Fix Url; pypi redirect on most recent version page
* Thu Sep 06 2012 toddrme2178AATTgmail.com- Backport python-scikit-learn compatibility fixes
* Wed Sep 05 2012 toddrme2178AATTgmail.com- Initial version