sklearn_logistic_regression.py

# -*- coding: utf-8 -*-
"""
Created on Sat Nov 25 15:29:51 2017

@author: Veeranjaneyulu Toka
"""

import numpy as np
from sklearn import datasets
from sklearn import linear_model
#import sklearn.datasets
import matplotlib.pyplot as plt

def plot_decision_boundary(pred_func):
    # Set min and max values and give it some padding
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    h = 0.01
    
    # Generate a grid of points with distance h between them
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    
    # Predict the function value for the whole gid
    Z = pred_func(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    
    # Plot the contour and training examples
    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)

def main():
    np.random.seed(0)
    
    X, y = datasets.make_moons(200, noise = 0.20)
    plt.scatter(X[:,0], X[:, 1], s=40, c=y, cmap=plt.cm.Spectral)
    
    clf = linear_model.LogisticRegressionCV()
    clf.fit(X, y)
    
    plot_decision_boundary(lambda x: clf.predict(x))
    plt.title("Logistic Regression")

if __name__ == "__main__":
    main()