Algorithm for Logistic Regression

Building an algorithm for logistic regression in python

Usage

Import Module

import logistic_regression

Fit variables x and outcome y

lr = logistic_regression.fit(x, y[, ...])

Description

class fit(x, y, **karg)

parameters

x: {array-like, sparse matrix}, shape (n_samples, n_features)

y: array-like, shape (n_samples, 1)

tol: float, default: 1e-8

        Tolerance for stopping criteria.

lamb: float, default: 1.0

        regularization strength; larger values specify stronger regularization.

fit_intercept: bool, default: True

    if a constant should be added to the decision function.

iter_lim : int, default: 100

        Maximum number of iterations taken for the solvers to converge.

attributes

weight: array, shape (n_features, 1)

    Coefficient of the features in the decision function. The features contain the constant item if it added.

Class Diagram

logistic_regression

    ├── init 
    │     └── fit(algorithm.newton)
    ├── algorithm
    │     └── newton(core.LogisticRegression)
    └── core
          └── LogisticRegression

Algorithm

see also algorithm document

Requirements

• Python 2.7
• numpy
• pandas
• matplotlib