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Welcome to MLPython’s documentation!

MLPython is a library for organizing machine learning research. There are two reasons why you might be interested in MLPython:

  1. to rapidly gain access to many datasets and learning algorithms or
  2. to organize your own machine learning research into a consistent, flexible and simple framework.

I developed MLPython to support my own research. I wanted a system that would allow me to quickly experiment with new research ideas. Having found it to deliver well on this account, I decided to share MLPython with others. I hope you enjoy it!

Here’s a quick peek at what MLPython let’s you do:

import numpy as np
import as dataset_store
from mlpython.learners.classification import NNet
from mlpython.learners.features import RBM'mnist')
trainset,validset,testset = dataset_store.get_classification_problem('mnist')

nnet = NNet(n_stages=10)

outputs,costs = nnet.test(testset)
print 'Classification error on test set is',np.mean(costs,axis=0)[0]

As is probably obvious, this code snippet downloads the MNIST dataset, trains a feed-forward neural network for 10 iterations on it and finally computes the average classification error on a test set.

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