Perceptron in Python v.1

perceptron.py

"""
MIT License

Copyright (c) 2018 Thomas Countz

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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"""

import numpy as np

class Perceptron(object):

    def __init__(self, no_of_inputs, threshold=100, learning_rate=0.01):
        self.threshold = threshold
        self.learning_rate = learning_rate
        self.weights = np.zeros(no_of_inputs + 1)
           
    def predict(self, inputs):
        summation = np.dot(inputs, self.weights[1:]) + self.weights[0]
        if summation > 0:
          activation = 1
        else:
          activation = 0            
        return activation

    def train(self, training_inputs, labels):
        for _ in range(self.threshold):
            for inputs, label in zip(training_inputs, labels):
                prediction = self.predict(inputs)
                self.weights[1:] += self.learning_rate * (label - prediction) * inputs
                self.weights[0] += self.learning_rate * (label - prediction)

How can this be modified for OR?

How can this be modified for OR?

To train for OR, you don't need to modify the code above. Instead, you update the training input such that the labels match the result of a logical OR and then train the model just as you would with AND.

import numpy as np
from perceptron import Perceptron

training_inputs = []
training_inputs.append(np.array([1, 1]))
training_inputs.append(np.array([1, 0]))
training_inputs.append(np.array([0, 1]))
training_inputs.append(np.array([0, 0]))

labels = np.array([1, 1, 1, 0])

perceptron = Perceptron(2)
perceptron.train(training_inputs, labels)

inputs = np.array([1, 1])
perceptron.predict(inputs) 
#=> 1

inputs = np.array([0, 1])
perceptron.predict(inputs) 
#=> 1

inputs = np.array([0, 0])
perceptron.predict(inputs) 
#=> 0

This demonstrates the flexibility of the perceptron/machine learning algorithms; they are adaptable general-purpose solvers!