Python Solution for Perceptron Learning Algorithm (Artificial Neural Networks Course) Page 9 Problem

Python Solution for Perceptron Learning Algorithm (Artificial Neural Networks Course) Page 9 Problem

def vecsum(a,b):
    return [e1 + e2 for e1,e2 in zip(a,b)]

def scalarproduct(c,a):
    return [c*e for e in a]

def dotproduct(a,b):
    return sum([e1*e2 for e1,e2 in zip(a,b)])

def perceptron(w,x,b):
    out = dotproduct(w,x) + b
    if out == 0:
        return 0
    elif out > 0:
        return 1
    elif out < 0:
        return -1

def vecequal(a,b):
    return all([e1 == e2 for e1,e2 in zip(a,b)])

b = 0
w = [0,0]
x = [(-1, 1),(0, -1),(10, 1)]
y = [1,-1,1]
outputs = [perceptron(w,e,b) for e in x]
size = len(y)
turn = 0

while not vecequal(y,outputs):
    print(turn,w,b)
    print(y,outputs)

    outputs[turn] = perceptron(w,x[turn],b)
    if  outputs[turn] != y[turn]:
        w = vecsum(w,scalarproduct(y[turn],x[turn]))
        b = b + y[turn]

    turn = (turn + 1) % size
    raw_input("Press Enter to continue...")

print("CONVERGENCE REACHED!")
print(w,b)
print(y,outputs)

Note by Talles Brito
2 months ago

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