Linear regression in 1 var created by AdeetyaKulkarni - https://repl.it/@AdeetyaKulkarni/Linear-regression-in-1-var

main.py

import random

#Algorithm to predict the price of house of 50 acres using Linear regression model

def hypothesis(th0, th1, x):
  hx = th0 + th1*(x)
  return hx 

def costfunction(m, hx, h_price):
  t_cost = 0
  for i in range(0,m):
    curcost = (hx[i] - h_price[i])**2
    t_cost = t_cost + curcost
    

  return  (1/(2*m))*t_cost

def gradientdes(th0,th1,m,hx,h_area,h_price,alpha):
  one = 0 
  two = 0
  for i in range(m):
    one = one + (hx[i] - h_price[i])
  #print("one = ",one)
  for i in range(m):
    two = two + ((hx[i] - h_price[i])*h_area[i])  
  #print(two)
  temp0 = (th0 - (alpha*((1/m)*one)))
  temp1 = (th1 - (alpha*((1/m)*two)))
  th0 = temp0
  th1 = temp1
  #print(th0,"--", th1)
  return th0,th1

#MAIN
#alpha = 0.004255 #for small data
alpha = 0.0001 # For large data

h_area = [10,20,30,40,50,60,70,80,90,100] #Feature x
h_price= [10,20,30,40,50,60,70,80,90,100] #Predict y

m = len(h_area)
#print(m)
th0 = random.random()
#print(th0)
th1 = random.random()
#print(th1)

converge = 0
oricost = 100000000000

itr = 0;

for a in range(1000):
  hx = []
  itr+=1
 #Hypothesis mapping 
  for x in h_area :
    rethx = hypothesis(th0,th1,x)
    hx.append(rethx)
 
 #Calculating cost
  retcst = costfunction(m, hx, h_price)

  if(retcst == oricost):  
    break
  
  if(retcst > oricost):
    break

  if(retcst < oricost):
    oricost = retcst
    th0, th1 = gradientdes(th0,th1,m,hx,h_area,h_price,alpha)
    #Calculate hypo with new th0 and th1 (LOOP)
    
   
  #After convergence achieved predict val
  ret = hypothesis(th0,th1,11)
print("Predicted cost is = ", ret,"\n\n")