def GDescent(X, Y, theta, alpha, num_iters):
m = float(Y.size)
J_history = np.zeros((num_iters, 1))
for iter in xrange(0, num_iters):
th0 = theta[0, 0] - alpha * (1 / m) * np.dot((np.dot(X, theta) - Y).T, X[:, 0])
th1 = theta[1, 0] - alpha * (1 / m) * np.dot((np.dot(X, theta) - Y).T, X[:, 1])
# th2 = - alpha * (1/m) * np.dot( (np.dot(X, theta) - Y).T, X[:, 1])
# print th2, alpha * (1/m)
theta[0, 0] = th0
theta[1, 0] = th1
J_history[iter] = cC.compCost(X, Y, theta)
return theta, J_history
def GDescent(X, Y, theta, alpha, num_iters):
m = float(Y.size)
J_history = np.zeros((num_iters, 1))
for iter in xrange(0, num_iters):
th0 = theta[0, 0] - alpha * (1/m) * np.dot((np.dot(X, theta) - Y).T , X[:, 0])
th1 = theta[1, 0] - alpha * (1/m) * np.dot((np.dot(X, theta) - Y).T , X[:, 1])
#th2 = - alpha * (1/m) * np.dot( (np.dot(X, theta) - Y).T, X[:, 1])
#print th2, alpha * (1/m)
theta[0, 0] = th0
theta[1, 0] = th1
J_history[iter] = cC.compCost(X, Y, theta)
return theta, J_history
import loaddata as ld
import computeCost as cC
import gradientdescent as GD
import math
X, Y, m = ld.load('ex1data.txt')#load X's and Y's m - len of dataset
theta = np.zeros((2, 1))#init fitting params
#should be 32.07
print 'Cost:', cC.compCost(X, Y, theta)
#Some gradient descent settings
iterations = 1500;
alpha = 0.01;
theta,J_history = GD.GDescent(X, Y, theta, alpha, iterations)
print 'theta: ', theta
pvalue = 3.5
#predict = 1/(1+ math.exp( np.dot( np.array([1, pvalue]).reshape(1, 2), theta)))
predict = np.dot( np.array([1, pvalue]).reshape(1, 2), theta )
print 'predict for ',pvalue, predict
import numpy as np
import loaddata as ld
import computeCost as cC
import gradientdescent as GD
import math
X, Y, m = ld.load('ex1data.txt') #load X's and Y's m - len of dataset
theta = np.zeros((2, 1)) #init fitting params
#should be 32.07
print 'Cost:', cC.compCost(X, Y, theta)
#Some gradient descent settings
iterations = 1500
alpha = 0.01
theta, J_history = GD.GDescent(X, Y, theta, alpha, iterations)
print 'theta: ', theta
pvalue = 3.5
#predict = 1/(1+ math.exp( np.dot( np.array([1, pvalue]).reshape(1, 2), theta)))
predict = np.dot(np.array([1, pvalue]).reshape(1, 2), theta)
print 'predict for ', pvalue, predict
