MyML

MyML will implement simple machine-learning algorithms for classification and clustering, as well as model evaluation tools such as performance measures, cross-validation and tests. The goal of this repo is to keep track of my findings and personal skills with machine learning in python. In order to allow scalability, each algorithm implements a Classifier, Clustering or DimensionalityReduction object depending on the type of task it performs. Theory can be found in the Wiki.

Currently (21/05/2017) the library includes the following methods:

• Perceptron or Single-layer perceptron: is an algorithm for supervised learning of binary classifiers.

Usage example

from myML.classification.perceptron import Perceptron

x_train =  [
            [2.7810836, 2.550537003],
            [1.465489372, 2.362125076],
            [3.396561688, 4.400293529],
            [1.38807019, 1.850220317],
            [3.06407232, 3.005305973],
            [7.627531214, 2.759262235],
            [5.332441248, 2.088626775],
            [6.922596716, 1.77106367],
            [8.675418651, -0.242068655],
            [7.673756466,   3.508563011]
        ]
y_train =  [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
l_rate = 0.1
iter = 5
perceptron = Perceptron()
perceptron.fit(x_train, y_train, l_rate, iter)
perceptron.predict([2.7810836,2.550537003])

Install

If you do not have pip installed in your machine, run the following command:

• easy_install pip

For installing MyML run (you might have to use sudo for super user privileges):

• make install

For running tests:

• make test