gradient descent

gradient_descent.py

theta0 = 0
theta1 = 0

alpha = .2

dataset = [
	{'x': 1, 'y': 0.5},
	{'x': 2, 'y': 1},
	{'x': 4, 'y': 2},
	{'x': 0, 'y': 0},
]

def sum_theta0():
	val = 0
	for i in range(0, len(dataset)-1):
		val += (theta0 + theta1*dataset[i]['x']) - dataset[i]['y']

	return val

def sum_theta1():
	val = 0
	for i in range(0, len(dataset)-1):
		val += ((theta0 + theta1*dataset[i]['x']) - dataset[i]['y']) * dataset[i]['x']

	return val

for i in range(1, 100):
	theta0 = theta0 - alpha * 1/(len(dataset)) * sum_theta0()
	theta1 = theta1 - alpha * 1/(len(dataset)) * sum_theta1()

	print('theta0: %s, theta1: %s' % (theta0, theta1))