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ANN.py
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ANN.py
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# Libraries
import csv
import random as rd
import math
#_______________________________________________________________
# CSV File Reading
with open('iris dataset.csv', newline='') as f:
reader = csv.reader(f)
data = list(reader)
#_______________________________________________________________
# General Variables
match=0
mismatch=0
Acurracy=0
print("Enter Number of Percetrons in Hidden Layer:")
N=int(input())
#________________________________________________________________
# Initializing Random Weights for Hidden Layer
rows=5
cols=N
HLWs = [[0 for i in range(cols)] for j in range(rows)]
for i in range(rows):
for j in range(cols):
HLWs[i][j] = rd.randint(-10,10)
print("Random Generated Weights for Hidden Layer are:")
for row in HLWs:
print(row)
#_________________________________________________________________
# Initializing Random Weights for Output Layer
rows1=1
cols1=N+1
OLWs = [[0 for i in range(cols1)] for j in range(rows1)]
for i in range(rows1):
for j in range(cols1):
OLWs[i][j] = rd.randint(-10,10)
print("Random Generated Weights for Output Layer are:")
for row in OLWs:
print(row)
#__________________________________________________________________
# Sigmoid Function
def sigmoid(z1):
zs=1/(1+(pow(2.718,-(z1))))
return zs
#__________________________________________________________________
# Hidden Layer Perceptron Function
def HLPerceptron(I,W,i,j):
z=W[0][j]
z= z + float(I[i][0])*W[1][j] + float(I[i][1])*W[2][j] + float(I[i][2])*W[3][j] + float(I[i][3])*W[4][j]
s=sigmoid(z)
return s
#_____________________________________________________________________
# Output Layer Perceptron Function
def OLPercetron(L1,OL):
z1=OL[0][0]
for i in range(1,N+1):
z1=z1+(L1[i]*OL[0][i])
s1=sigmoid(z1)
return s1
#_____________________________________________________________________
# Main
for i in range (1,151):
L=list()
L.append(0)
for j in range (N):
x=HLPerceptron(data,HLWs,i,j)
L.append(x)
yhat=OLPercetron(L,OLWs)
#print(yhat)
specie=0
if yhat>=0.63 and yhat<1:
specie=3
elif yhat>=0.33 and yhat<0.63:
specie=2
elif yhat>=0 and yhat<0.33:
specie=1
#print("Specie is:",specie)
if specie==float(data[i][4]):
match=match+1
else:
mismatch=mismatch+1
Accuracy=(match/150)*100
print("Match Count is: ",match)
print("Mismatch Count is: ",mismatch)
print("Accuracy is:",Accuracy,"%")