[SubiculumCode]

I wish that the tutorial went just one more step. It presents a one dimensional perceptron. But most perceptrons are multi-input. Adapting the article's 1D perceptron to three-input, for example:

    import random

    learning_rate = 0.1
    EPOCHS = 50
    NUM_INPUTS = 3

    weights = [random.uniform(-1, 1) for _ in range(NUM_INPUTS)]
    bias = random.uniform(-1, 1)

    data = []
    for _ in range(100):
        inputs = [random.uniform(-1, 1) for _ in range(NUM_INPUTS)]
        result = sum(inputs) > 0
        data.append((inputs, result))

    for epoch in range(EPOCHS):
        for inputs, result in data:
            weighted_sum = bias
            for i in range(NUM_INPUTS):
                weighted_sum += inputs[i] * weights[i]
            
            prediction = weighted_sum > 0
            
            if prediction != result:
                error = int(result) - int(prediction)
                for i in range(NUM_INPUTS):
                    weights[i] += learning_rate * error * inputs[i]
                bias += learning_rate * error

    print(f"Final weights: {[round(w, 3) for w in weights]}")
    print(f"Final bias: {round(bias, 3)}")

[DevarshRanpara]

My next article would be on multi dimensional problem statements.