Merge pull request #3 from ikegami-yukino/sklearn-interface

v 0.2

CHANGES.rst

@@ -2,6 +2,14 @@
 CHANGES
 =======
 
+0.2 (2016-11-26)
+-------------------
+
+- scikit-learn like fit/predict interfaces are available
+- Setting C and bias parameters is available in initialization
+- Support Python 3.5
+- Unsupport Python 2.6 and 3.3
+
 0.1.2 (2015-01-11)
 -------------------
 

README.rst

@@ -5,7 +5,7 @@ oll-python
 
 This is a Python binding of the OLL library for machine learning.
 
-Currently, OLL 0.03 supports following algorithms:
+Currently, OLL 0.03 supports following binary classification algorithms:
 
 - Perceptron
 - Averaged Perceptron
@@ -30,12 +30,33 @@ Usage
 .. code:: python
 
  import oll
- o = oll.oll("CW")
+ # 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]
+
 
 Note
 ----

oll/__init__.py

@@ -1,5 +1,5 @@
 from .oll import oll
 
-VERSION = (0, 1, 2)
-__version__ = "0.1.2"
+VERSION = (0, 2)
+__version__ = "0.2"
 __all__ = ["oll"]