About: Python module to ease pattern classification analyses of large datasets. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, [...]

Changes:

0.4.4 (Mon, Feb 2 2010) (Total: 144 commits)

Primarily a bugfix release, probably the last in 0.4 series since development for 0.5 release is leaping forward.

• New functionality (19 NF commits):

o GNB implements Gaussian Naïve Bayes Classifier.

o read_fsl_design() to read FSL FEAT design.fsf files (Contributed by Russell A. Poldrack).

o SequenceStats to provide basic statistics on labels sequence (counter-balancing, autocorrelation).

o New exceptions DegenerateInputError and FailedToTrainError to be thrown by classifiers primarily during training/testing.

o Debug target STATMC to report on progress of Monte-Carlo sampling (during permutation testing).

• Refactored (15 RF commits):

o To get users prepared to 0.5 release, internally and in some examples/documentation, access to states and parameters is done via corresponding collections, not from the top level object (e.g. clf.states.predictions instead of soon-to-be-deprecated clf.predictions). That should lead also to improved performance.

o Adopted copy.py from python2.6 (support Ellipsis as well). ed (38 BF commits):

o GLM output does not depend on the enabled states any more.

o Variety of docstrings fixed and/or improved.

o Do not derive NaN scaling for SVM’s C whenever data is degenerate (lead to never finishing SVM training).

o sg : + KRR is optional now – avoids crashing if KRR is not available.

• tolerance to absent set_precompute_matrix in svmlight in recent shogun versions.

• support for recent (present in 0.9.1) API change in exposing debug levels.

o Python 2.4 compatibility issues: kNN and IFS

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Authors: Michael Hanke, Per B. Sederberg, Yaroslav Halchenko, Emanuele Olivetti License: Mit Programming Language: Python, C

Operating System: Agnostic Data Formats: None Tags: Shogun, Python, Eeg, Classification, Regression, Support Vector Machines, K Nearest Neighbor Classification, Pca, Rfe, Neuroscience, Fmri, Framework, Gpr, Lars, Smlr, Meg