Adaptable to various length of feature of your dataset

perceptron-sample.py

import math

class ActivationFunction():
    def sigmoid(self, x):
        return 1 / (1 + math.exp(-x))


    def binary_hard_limit(self, x):
        if x <= 0:
            return 0
        elif x > 0:
            return 1


class Perceptron():
    def __init__(self, bias=0, learning_rate=0):
        self.weight = {}
        self.bias = bias
        self.learning_rate = learning_rate
        self.activation = ActivationFunction()

    def generate_weight(self, feature_len):
        for k in range(0, feature_len):
            idx = 'w'+str(k+1)
            self.weight.update({idx:0})

    def hebb_rule(self, feature):
        y_in = 0
        for k in range(0, len(feature)):
            y_in = y_in + (feature[k] * self.weight['w'+str(k+1)])
        y_in = y_in + self.bias

        return y_in

    def fit(self, dataset=[], epoch=1):
        dataset_len = len(dataset)
        self.generate_weight(len(dataset[0][0]))

        for i in range(0, EPOCH):
            print ("EPOCH --- ", i+1)
            print ("--------------")

            for j in range(0, dataset_len):
                feature = dataset[j][0]
                target = dataset[j][1]

                y_in = self.hebb_rule(feature)
                y = self.activation.binary_hard_limit(y_in)

                if y != target:
                    learning_error = target - y

                    for k in range(0, len(feature)):
                        idx = 'w'+str(k+1)
                        new_weight = self.weight[idx] + (self.learning_rate * learning_error * feature[k])
                        self.weight[idx] = new_weight

                    new_bias = self.bias + (self.learning_rate * learning_error)
                    self.bias = new_bias

                print ("weight: ", self.weight, " | BIAS : ", self.bias)
            print ("--------------")

    def predict(self, feature):
        y_in = self.hebb_rule(feature)
        y = self.activation.binary_hard_limit(y_in)
        return y



BIAS = 0
LEARNING_RATE = 1
EPOCH = 10
weight = {'w1': 0, 'w2':0}

# dataset = [
#     ([0, 0], 0),
#     ([0, 1], 0),
#     ([1, 0], 0),
#     ([1, 1], 1)
# ]

dataset = [
    ([0, 0, 0], 0),
    ([0, 1, 0], 0),
    ([1, 0, 0], 0),
    ([1, 1, 0], 1),
    ([0, 0, 1], 0),
    ([0, 1, 1], 1),
    ([1, 0, 1], 1),
    ([1, 1, 1], 1)
]

model = Perceptron(bias=BIAS, learning_rate=LEARNING_RATE)
model.fit(dataset=dataset, epoch=EPOCH)

# print (model.predict([1, 1]))
# print (model.predict([0, 0]))
# print (model.predict([0, 1]))
# print (model.predict([1, 0]))

print (model.predict([1, 1, 1]))
print (model.predict([0, 0, 1]))
print (model.predict([0, 1, 0]))
print (model.predict([1, 0, 0]))