2D Logistic Regression

logistic_regression_2d.py

import numpy as np
import matplotlib.pyplot as plt


if __name__ == '__main__':
    fin = file('logreg.txt', 'r')
    npdata = np.genfromtxt(fin, delimiter=',')
    posData = npdata[npdata[:,2] == 1,:]
    negData = npdata[npdata[:,2] == 0,:]

    plt.figure(1)
    plt.plot(posData[:,0], posData[:,1], 'r^')
    plt.plot(negData[:,0], negData[:,1], 'bs')

    m = len(npdata)
    X = np.concatenate((np.ones((m, 1)), npdata[:,0:2].reshape(m, 2)), axis=1)
    Y = npdata[:,2].reshape(m, 1)
    O = np.ones((3, 1))
    alpha = 0.1
    iteration = 10000

    history = np.ndarray((iteration, 3))

    for i in range(iteration):
        hOx = 1 / (1 + np.exp(-np.dot(O.T, X.T)))
        # left = Y * np.log(hOx.T)
        # right = (1 - Y) * np.log(1 - hOx.T)
        # JO = np.sum((left + right) * X, axis=0) / -m

        O = O - np.sum(alpha * (hOx.T - Y) * X, axis=0).reshape(3, 1)
        history[i] = O[:,0]

    coordX = np.linspace(1, iteration, num=iteration)
    plt.figure(2)
    plt.plot(coordX, history[:,0], 'r-')
    plt.plot(coordX, history[:,1], 'g-')
    plt.plot(coordX, history[:,2], 'b-')

    resultX = np.linspace(-10, 10, num=21)
    plt.figure(1)
    plt.plot(resultX, (-O[0][0] - O[1][0] * resultX) / O[2][0], 'g-')

    plt.show()