from ast import arg
import numpy as np # type: ignore

# Fix the seed for reproducibility
np.random.seed(100)

def sigmoid(z):
    return 1 / (1 + np.exp(-z))

class Neuron:
    def __init__(self, *args):
        # 1. Initialize weights and bias with random values
        self.weights = np.random.uniform(-1, 1, size=args[0])
        self.bias = np.random.uniform(-1, 1)

    def activate(self, inputs):
        # 2. Compute the weighted sum using dot product and add bias
        input_sum_with_bias = np.dot(inputs, self.weights) + self.bias
        # 3. Apply the sigmoid activation function
        output = sigmoid(input_sum_with_bias)
        return output

# Create a neuron with 6 inputs
neuron = Neuron(6)
# Generate inputs for the neuron
neuron_inputs = np.array([-0.5, 0.4, -0.8, 0.2, 0.1, -0.3])
# Pass the inputs to the created neuron
neuron_output = neuron.activate(neuron_inputs)

print(f'Output of the neuron is {neuron_output:.3f}')

'''
How it works:
# The neuron takes any number of inputs, computes the weighted sum, applies the sigmoid function, and returns the output which is a value between 0 and 1.

'''