Simple machine learning library
Project description
FukuML
Simple machine learning library
Installation
$ pip install FukuML
Algorithm
Perceptron Binary Classification Learning Algorithm
Perceptron Binary Classification Learning Algorithm with Linear Regression Accelerator
Perceptron Multi Classification Learning Algorithm
Perceptron Multi Classification Learning Algorithm with Linear Regression Accelerator
Pocket Perceptron Binary Classification Learning Algorithm
Pocket Perceptron Binary Classification Learning Algorithm with Linear Regression Accelerator
Pocket Perceptron Multi Classification Learning Algorithm
Pocket Perceptron Multi Classification Learning Algorithm with Linear Regression Accelerator
Linear Regression Learning Algorithm
Linear Regression Binary Classification Learning Algorithm
Linear Regression Multi Classification Learning Algorithm
Logistic Regression Learning Algorithm
Logistic Regression Learning Algorithm with Linear Regression Accelerator
Logistic Regression Binary Classification Learning Algorithm
Logistic Regression Binary Classification Learning Algorithm with Linear Regression Accelerator
Logistic Regression One vs All Multi Classification Learning Algorithm
Logistic Regression One vs All Multi Classification Learning Algorithm with Linear Regression Accelerator
Logistic Regression One vs One Multi Classification Learning Algorithm
Logistic Regression One vs One Multi Classification Learning Algorithm with Linear Regression Accelerator
Ridge Regression Learning Algorithm
Ridge Regression Binary Classification Learning Algorithm
Ridge Regression Multi Classification Learning Algorithm
Polynomial Feature Transform
Legendre Feature Transform
10 Fold Cross Validation
Usage
>>> import numpy as np
# we need numpy as a base libray
>>> import FukuML.PLA as pla
# import FukuML.PLA to do Perceptron Learning
>>> your_input_data_file = '/path/to/your/data/file'
# assign your input data file, please check the data format: https://github.com/fukuball/fuku-ml/blob/master/FukuML/dataset/pla_binary_train.dat
>>> pla_bc = pla.BinaryClassifier()
# new a PLA binary classifier
>>> pla_bc.load_train_data(your_input_data_file)
# load train data
>>> pla_bc.setParam()
# set parameter
>>> pla_bc.init_W()
# init the W
>>> W = pla_bc.train()
# train by Perceptron Learning Algorithm to find best W
>>> test_data = 'Each feature of data x separated with spaces. And the ground truth y put in the end of line separated by a space'
# assign test data, format like this '0.97681 0.10723 0.64385 ........ 0.29556 1'
>>> prediction = pla_bc.prediction(test_data)
# prediction by trained W
>>> print prediction['input_data_x']
# print test data x
>>> print prediction['input_data_y']
# print test data y
>>> print prediction['prediction']
# print the prediction, will find out prediction is the same as pla_bc.test_data_y
For detail, please check https://github.com/fukuball/fuku-ml/blob/master/doc/sample_code.rst
License
The MIT License (MIT)
Copyright (c) 2016 fukuball
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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