perceptron.py

w = [0.0 for i in range(len(binary_train[0])-2)]
w.append(1.0)

weights = w.copy()
# Make a prediction with weights
def predict(row, weights):
	activation = 0
	for i in range(len(weights)-1):
		activation += weights[i] * row[i]
	return 1 if activation >= 0.0 else 2

def add_to_weights(weights, row):
    for i in range(len(weights)-1):
        weights[i] += row[i]
    return weights

def sub_from_weights(weights, row):
    for i in range(len(weights)-1):
        weights[i] -= row[i]
    return weights
        
while True :
    print(weights)
    new_weights = weights.copy()
    for row in binary_train:
        predicted = predict(row, weights)
#        print("Class : "+ str(row[-1]) +" Predicted: "+ str(predicted))
        if predicted != row[-1]:
            if predicted == 1:
                new_weights = sub_from_weights(new_weights, row)
            else:
                new_weights = add_to_weights(new_weights, row)
    if weights == new_weights:
        break
    weights = new_weights