In [168]:

    

import numpy as np
csv = np.genfromtxt('train_data.csv', delimiter = ",")
size_data = len(csv[1:,:])
var0 = np.ones(size_data).reshape(1, size_data)
var = np.concatenate((var0, np.transpose(csv[1:, 0:4])))
target = csv[1:, 4]


    

In [169]:

    

theta = np.zeros(5)


    

In [170]:

    

def dif_cost(n):
    dJ = 0
    alpha = 0.5
    h = np.dot(theta, var)
    dJ = np.sum(np.dot((h - target), var[n, :]))
    dJ = alpha * dJ / size_data  
    return dJ


    

In [171]:

    

new_th = np.zeros(5)    
n_iter = 1000
for i in range(0, n_iter):
    for j in range(0, 5):
        new_th[j] = theta[j] - dif_cost(j)   
    theta = new_th


    

In [172]:

    

print(theta)


    

    




[ 1.66824986  0.71837337  3.12743084  0.42506237  1.2152172 ]

In [173]:

    

csv2 = np.genfromtxt('test_input.csv', delimiter = ",")
size_inp = len(csv2[1:,:])
inp_var0 = np.ones(size_inp).reshape(1, size_inp)
inp_var = np.concatenate((inp_var0, np.transpose(csv2[1:, 0:4])))


    

In [174]:

    

out_h = np.dot(theta, inp_var)


    

In [175]:

    

np.savetxt("test_output2.csv", out_h, delimiter=",")


    

In [ ]: