import numpy as np
import matplotlib.pyplot as plt

D=[]

# reading data from file
file=open("iris.data", "r")
for line in file:
    R=line.split(",")
    if len(R)==5:
        D.append(R)



D= [x for x in D if x[4] != 'Iris-setosa\n']

for i in range(len(D)):
    print(D[i])

X=[x[0:4]+[-1] for x in D]
Y=[x[4] for x in D]

# converting labels to +1,-1
d=0
for i in range(len(Y)):
    if Y[i]=='Iris-versicolor\n':
        Y[i]=1
        d+=1
    else:
        Y[i]=-1

# selection of 10 first records
t=10
X1=X[0:t]
for i in range(len(X)):
    if i>d-1 and i<d+t:
        X1.append(X[i])

Y1=Y[0:t]
for i in range(len(Y)):
    if i>d-1 and i<d+t:
        Y1.append(Y[i])

#converting string data into float
for i,value in enumerate(X1):
    X1[i][0]=float(X1[i][0])
    X1[i][1]=float(X1[i][1])
    X1[i][2]=float(X1[i][2])
    X1[i][3]=float(X1[i][3])


#converting list to numpy.array
X1=np.vstack(X1)
Y1=np.vstack(Y1)


def perceptron(X, Y):
     w = np.ones(len(X[0]))
     epochs = 100
     for t in range(epochs):
         for i, x in enumerate(X):
             if (np.dot(X[i], w)*Y[i,0]) <= 0:
                 w = w + X[i]*Y[i,0]
     return w

w = perceptron(X1, Y1)
print(w)


# calculating error
error=0
for i in range(len(X)):
    if (np.dot(np.array(X[i],dtype=np.float32), w)*Y[i]) <= 0:
        error+=1

print("error=",error)