代碼示例來(lái)自 Udacity 課程中 Siraj 的一個(gè)教學(xué)視頻，版權(quán)歸屬于原作者及 Udacity 所有虫啥，源代碼及數(shù)據(jù)源可見(jiàn) Siraj 的 Github，盡管代碼可以直接下載壮啊，我還是選擇跟隨視頻手動(dòng)完成的方式漾脂，也對(duì)于引用方式和變量命名做了一點(diǎn)修改，放在這里方便隨時(shí)查看辆亏。
這個(gè)代碼示例主要是為了演示梯度下降的實(shí)現(xiàn)過(guò)程风秤，用它來(lái)求解線(xiàn)性回歸稍微有些大材小用，但不妨礙說(shuō)明問(wèn)題扮叨。
import numpy as np
def compute_error_for_points(b, m, points):
total_error = 0
for i in range(len(points)):
x = points[i, 0]
y = points[i, 1]
total_error += (y - (m * x + b)) ** 2
return total_error / float(len(points))
def step_gradient(b_current, m_current, points, learning_rate):
# core gradient descent computation
b_gradient = 0
m_gradient = 0
N = float(len(points))
for i in range(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, np.array(points), learning_rate)
return [b, m]
def run():
points = np.genfromtxt('data.csv', delimiter=',')
# hyperparameters
learning_rate = 0.0001
# y = mx + b
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()
