This is a Python binding of the OLL library for machine learning.
Currently, OLL 0.03 supports following binary classification algorithms:
- Perceptron
- Averaged Perceptron
- Passive Agressive (PA, PA-I, PA-II, Kernelized)
- ALMA (modified slightly from original)
- Confidence Weighted Linear-Classification.
For details of oll, see: http://code.google.com/p/oll
$ pip install oll
OLL library is bundled, so you don't need to install it separately.
import oll
# You can choose algorithms in
# "P" -> Perceptron,
# "AP" -> Averaged Perceptron,
# "PA" -> Passive Agressive,
# "PA1" -> Passive Agressive-I,
# "PA2" -> Passive Agressive-II,
# "PAK" -> Kernelized Passive Agressive,
# "CW" -> Confidence Weighted Linear-Classification,
# "AL" -> ALMA
o = oll.oll("CW", C=1.0, bias=0.0)
o.add({0: 1.0, 1: 2.0, 2: -1.0}, 1) # train
o.classify({0:1.0, 1:1.0}) # predict
o.save('oll.model')
o.load('oll.model')
# scikit-learn like fit/predict interface
import numpy as np
array = np.array([[1, 2, -1], [0, 0, 1]])
o.fit(array, [1, -1])
o.predict(np.array([[1, 2, -1], [0, 0, 1]]))
# => [1, -1]
from scipy.sparse import csr_matrix
matrix = csr_matrix([[1, 2, -1], [0, 0, 1]])
o.fit(matrix, [1, -1])
o.predict(matrix)
# => [1, -1]- This module requires C++ compiler to build.
- oll.cpp & oll.hpp : Copyright (c) 2011, Daisuke Okanohara
- oll_swig_wrap.cxx is generated based on 'oll_swig.i' in oll-ruby (https://github.com/syou6162/oll-ruby)
New BSD License.