In [1]:

    

%reload_ext autoreload
%autoreload 2
%matplotlib inline

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

import sys
sys.path.append('..')

from helper import linear_regression as lr  # my own module
from helper import general as general


    

In [2]:

    

data = pd.read_csv('ex1data1.txt', names=['population', 'profit'])

data.head()


    

    
Out[2]:






  

    

      

      
population
      
profit
    
  
  

    

      
0
      
6.1101
      
17.5920
    
    

      
1
      
5.5277
      
9.1302
    
    

      
2
      
8.5186
      
13.6620
    
    

      
3
      
7.0032
      
11.8540
    
    

      
4
      
5.8598
      
6.8233
    
  

In [3]:

    

X = general.get_X(data)
print(X.shape, type(X))

y = general.get_y(data)
print(y.shape, type(y))


    

    




(97, 2) <class 'numpy.ndarray'>
(97,) <class 'numpy.ndarray'>

In [4]:

    

theta = np.zeros(X.shape[1])


    

In [5]:

    

lr.cost(theta, X, y)


    

    
Out[5]:





32.072733877455676

In [6]:

    

epoch = 500
final_theta, cost_data = lr.batch_gradient_decent(theta, X, y, epoch)


    

In [7]:

    

# compute final cost
lr.cost(final_theta, X, y)


    

    
Out[7]:





4.7138095311168664

In [8]:

    

ax = sns.tsplot(cost_data, time=np.arange(epoch+1))
ax.set_xlabel('epoch')
ax.set_ylabel('cost')


    

    
Out[8]:





<matplotlib.text.Text at 0x1182d8f98>






    

In [9]:

    

b = final_theta[0] # intercept
m = final_theta[1] # slope

plt.scatter(data.population, data.profit, label="Training data")
plt.plot(data.population, data.population*m + b, label="Prediction")
plt.legend(loc=2)


    

    
Out[9]:





<matplotlib.legend.Legend at 0x1186c6780>






    

In [ ]: