Created
November 19, 2015 13:54
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#!/usr/bin/python | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from scipy.signal import square | |
#Mean Square error function | |
def costf(X, y, theta): | |
m = y.shape[0] | |
#print m | |
return (1.0/m)*np.sum(np.power(np.dot(X,theta) - y, 2)) | |
#Gradient of error function | |
def gradientf(X, y, theta): | |
m = y.shape[0] | |
err = np.dot(X, theta) - y | |
return (2.0/m)*np.dot(np.transpose(X), err) | |
t = np.arange(0,10,0.01) | |
y = 2*square(t) + 0*np.random.random(t.shape) | |
X = np.array([[1, np.sin(x), np.sin(3*x), np.sin(5*x), np.sin(7*x), np.sin(9*x)] for x in t]) | |
th = np.zeros(6) | |
errors = [] | |
thetas = [] | |
#Optimizing using gradient descent algorithm | |
numiters = 1000 | |
alpha = 0.02 #Learning rate | |
errors.append(costf(X,y,th)) | |
for i in xrange(numiters): | |
#Gradient descent | |
grad = gradientf(X,y,th) | |
th = th - alpha*grad | |
errors.append(costf(X,y,th)) | |
thetas.append(th) | |
if(i%20 == 0): | |
print "Iteration: "+str(i) | |
print "Costf: "+ str(costf(X,y,th)) | |
print "Gradient: " + str(gradientf(X, t, th)) | |
print "Theta: "+ str(th) | |
y_ = np.dot(X, th) | |
#Closed form solution | |
th_opt = np.dot(np.linalg.pinv(X), y) | |
y_opt = np.dot(X, th_opt) | |
#Plotting results | |
plt.plot(t, y) | |
plt.ylim(-3,3) | |
plt.xlabel('x') | |
plt.ylabel('y') | |
plt.hold(True) | |
plt.plot(t, y_) | |
plt.plot(t, y_opt) | |
plt.figure() | |
plt.plot(errors) | |
plt.title("Error over time") | |
plt.ylabel("Error") | |
plt.xlabel("Number of iterations") | |
plt.show() |
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