Perceptron in Python v.1

perceptron.py

"""
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Copyright (c) 2018 Thomas Countz

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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)

line 46: "inputs" is a tuple which can not be multiplied by a number. My suggestion is to convert it into an array.
for example : self.weights[1:] += self.learning_rate * (label - prediction) * np.array(inputs)

@hoanW, As written,training_inputs should be a list (or other iterable) of numpy arrays when passed into the train() function.

Here's an example usage and writeup:

https://gist.github.com/Thomascountz/baeecd8de6eb73f179d1342fd4519748
https://www.thomascountz.com/2018/04/05/19-line-line-by-line-python-perceptron

Sorry for the confusion and thank you for your feedback! Please let me know if this doesn't solve the problem.

thank you for clearing that up.

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!