image.png
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()
