---data.csv-----
52.211796692214001,79.64197304980874
39.299566694317065,59.171489321869508
48.10504169176825,75.331242297063056
52.550014442733818,71.300879886850353
45.419730144973755,55.165677145959123
54.351634881228918,82.478846757497919
44.164049496773352,62.008923245725825
58.16847071685779,75.392870425994957
56.727208057096611,81.43619215887864
48.955888566093719,60.723602440673965
44.687196231480904,82.892503731453715
60.297326851333466,97.379896862166078
45.618643772955828,48.847153317355072
38.816817537445637,56.877213186268506
66.189816606752601,83.878564664602763
65.41605174513407,118.59121730252249
47.48120860786787,57.251819462268969
41.57564261748702,51.391744079832307
51.84518690563943,75.380651665312357
59.370822011089523,74.765564032151374
57.31000343834809,95.455052922574737
-------------------------------------
from numpy import *
# y = mx + b
# m is slope, b is y-intercept
def compute_error_for_line_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, learningRate):
b_gradient = 0
m_gradient = 0
nlen = len(points)
print "---step_gradient: b_current={0} m_current={1}----".format(b_current, m_current)
N = float(len(points))
for i in range(0, nlen):
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))
print "x={0},y={1},b_gradient={2},m_gradient={3}".format(x, y,b_gradient,m_gradient)
new_b = b_current - (learningRate * b_gradient)
new_m = m_current - (learningRate * m_gradient)
print "-------------new_b={0} = {1} - ({2} * {3})".format(new_b,b_current, learningRate, b_gradient)
print "-------------new_m={0} = {1} - ({2} * {3})".format(new_m,m_current, learningRate, 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=",")
learning_rate = 0.0005
initial_b = 0 # initial y-intercept guess
initial_m = 0 # initial slope guess
num_iterations = 200
print "len(points)={0}".format(len(points))
print "Starting gradient descent at b = {0}, m = {1}, error = {2}".format(initial_b, initial_m, compute_error_for_line_given_points(initial_b, initial_m, points))
print "Running..."
[b, m] = gradient_descent_runner(points, initial_b, initial_m, learning_rate, num_iterations)
print "After {0} iterations b = {1}, m = {2}, error = {3}".format(num_iterations, b, m, compute_error_for_line_given_points(b, m, points))
if __name__ == '__main__':
run()
