ex1-test.py

import numpy as np
from matplotlib import pyplot as plt
from numpy import zeros, ones, reshape, array, linspace, logspace, add, dot, transpose, shape, negative
# import matplotlib.pyplot as plt
from pylab import scatter, show, title, xlabel, ylabel, plot, contour

a = np.identity(5)

# print(a)

# data=np.loadtxt('ex1data1.txt',delimiter=',')

# print(data)

data=np.loadtxt('ex1data1.txt',delimiter=',')
plt.scatter(data[:,0],data[:,1])
plt.ylabel('Profit in $10,000s')
plt.xlabel('Population in 10,000s')
# plt.show()

# ======= format the data =======
x = data[:,0]
y = data[:,1]
m = len(y)

y = reshape(y,(m,1))
reshaping_x = ones(shape=(m, 2))
reshaping_x[:, 1] = x
x = reshaping_x

# Gradient Descent
theta = zeros(shape=(2, 1))
alpha = 0.01
iterations = 1500

def cost_function(theta, x, y):
    prediction = dot(x, theta)
    J = (1.0 / (2*m)) * dot(transpose(prediction - y) , (prediction - y))
    return J


print(cost_function(theta, x, y))


# z = ones(shape=(m, 2))
# x=[]
# X[:, 1] = z
# X[:, 1] = x
# X = ones(shape=(m, 2))
# X[:, 1] = x