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import numpy as np | |
x = np.array([1, 2, 3, 4, 5]) | |
y = np.array([1, 4, 9, 16, 25]) | |
learningRate = 0.0001 | |
iterations = 200000 | |
def gradient(m, b): | |
m_gradient = 0 | |
b_gradient = 0 | |
#summation | |
for i in range(x.size): | |
h = (m * x[i] + b) | |
m_gradient += x[i] * (h - y[i]) | |
b_gradient += (h - y[i]) | |
m_gradient *= (2.0/x.size) | |
b_gradient *= (2.0/x.size) | |
m_new = m - (learningRate * m_gradient) | |
b_new = b - (learningRate * b_gradient) | |
return [m_new, b_new] | |
def gradient_iterations(m, b): | |
for i in range(iterations): | |
[m, b] = gradient(m, b) | |
return [m, b] | |
def computeError(m, b): | |
error = 0.0 | |
for i in range(x.size): | |
error += (y[i] - (m * x[i] + b)) ** 2 | |
return error | |
m = 0 | |
b = 0 | |
[m, b] = gradient_iterations(m, b) | |
error = computeError(m, b) | |
print "slope m:", m | |
print "intercept b:", b | |
print "error:", error |
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