Out of date and unmaintained: use scikit-learn

This is the code that I use for my research projects.

Where can I get it? Github as usual. Alternatively the python packages index also contains official releases,the latest of which can be obtained by: easy_install milk or: pip install milk if you use these tools.

Examples Here is how to test how well you can classify some features,labels data, measured by cross-validation: import numpy as np import milk features = np.random.rand(100,10) # 2d array of features: 100 examples of 10 features each labels = np.zeros(100) features[50:] += .5 labels[50:] = 1 confusion_matrix, names = milk.nfoldcrossvalidation(features, labels) print 'Accuracy:', confusion_matrix.trace()/float(confusion_matrix.sum()) If want to use a classifier, you instanciate a learner object and call its train() method: import numpy as np import milk features = np.random.rand(100,10) labels = np.zeros(100) features[50:] += .5 labels[50:] = 1 learner = milk.defaultclassifier() model = learner.train(features, labels) # Now you can use the model on new examples: example = np.random.rand(10) print model.apply(example) example2 = np.random.rand(10) example2 += .5 print model.apply(example2)

More Documentation API Documentation: http://packages.python.org/milk/ Mailing list: http://groups.google.com/group/milk-users