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@AdeetyaKulkarni
Created December 20, 2018 22:32
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Linear regression in 1 var created by AdeetyaKulkarni - https://repl.it/@AdeetyaKulkarni/Linear-regression-in-1-var
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")
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