深度学习笔记 - 103 - Gradient Descent in Linear Regression

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from numpy import *


def compute_error_for_given_points(b, m, points):
    totalError = 0
    for i in range(0, len(points)):
        x = points[i, 0]
        y = points[i, 1]
        totalError += (y - (m * x + b)) ** 2

    return totalError / float(len(points))


def step_gradient(b_current, m_current, points, learning_rate):
    # Gradient descent
    b_gradient = 0
    m_gradient = 0
    N = float(len(points))
    for i in range(0, len(points)):
        x = points[i, 0]
        y = points[i, 1]
        b_gradient += -(2 / N) * (y - ((m_current * x) + b_current))
        m_gradient += -(2 / N) * x * (y - ((m_current * x) + b_current))
    new_b = b_current - (learning_rate * b_gradient)
    new_m = m_current - (learning_rate * m_gradient)
    return [new_b, new_m]


def gradient_descent_runner(points, starting_b, starting_m, learning_rate, num_iterations):
    b = starting_b
    m = starting_m

    for i in range(num_iterations):
        b, m = step_gradient(b, m, array(points), learning_rate)
    return [b, m]


def run():
    points = genfromtxt('data.csv', delimiter=',')
    # Hyper parameters
    learning_rate = 0.0001

    # y = mx + b (slope formula)
    initial_b = 0
    initial_m = 0
    num_iterations = 1000
    [b, m] = gradient_descent_runner(points, initial_b, initial_m, learning_rate, num_iterations)

    print(b)
    print(m)


if __name__ == 'main':
    run()