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September 13, 2017 12:57
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LinearRegression with Gradient Descent Algorithm.
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| """ | |
| @author Victor I. Afolabi | |
| A.I. Engineer & Software developer | |
| javafolabi@gmail.com | |
| Created on 25 August, 2017 @ 8:15 PM. | |
| Copyright © 2017. victor. All rights reserved. | |
| """ | |
| import numpy as np | |
| class LinearRegression(object): | |
| def __init__(self, learning_rate=1e-4): | |
| """ | |
| Linear Regression model | |
| :param learning_rate: | |
| """ | |
| self.m = 0 | |
| self.b = 0 | |
| self.learning_rate = learning_rate | |
| def fit(self, data, num_iter=1000): | |
| for _ in range(num_iter): | |
| self.__gradient_descent(data) | |
| return self.m, self.b | |
| def predict(self, x): | |
| return (self.m * x) + self.b | |
| def error(self, data): | |
| total_error = 0 | |
| for _, d in enumerate(data): | |
| x = d[0] | |
| y = d[0] | |
| total_error += (y - self.predict(x)) ** 2 | |
| def __gradient_descent(self, data): | |
| n = len(data) | |
| m_gradient = 0 | |
| b_gradient = 0 | |
| for _, d in enumerate(data): | |
| x = d[0] | |
| y = d[1] | |
| m_gradient += (2 / n) * -x * (y - self.predict(x)) | |
| b_gradient += (2 / n) * -(y - self.predict(x)) | |
| self.m = self.m - (self.learning_rate * m_gradient) | |
| self.b = self.b - (self.learning_rate * b_gradient) | |
| if __name__ == '__main__': | |
| clf = LinearRegression(learning_rate=1e-5) | |
| data = np.genfromtxt('../datasets/data.csv', delimiter=',') | |
| clf.fit(data) | |
| print('m = {:.2f} b = {:.2f}'.format(clf.m, clf.b)) |
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