node.py

# Neural network node class
import numpy as np
from functions import sigmoid


class Node:
    def __init__(self, weights, bias):
        self.weights = weights
        self.bias = bias
        # self.z = 0
        self.value = 0
        self.activations = None

    def getValue(self):
        return self.value

    def getWeights(self):
        return self.weights

    def feedForward(self, activations):
        self.value = 0
        self.activations = activations
        for i, act in enumerate(activations):
            self.value += act * self.weights[i]
        # self.z = self.value + self.bias
        self.value = sigmoid(self.value + self.bias)
        return self.value

    def get_d_weights(self, error):
        return np.multiply(
            error, np.multiply((self.value * (1 - self.value)), self.activations)
        )

    def get_d_bias(self, error):
        return np.multiply(error, (self.value * (1 - self.value)))

    def get_d_error(self, error):
        return np.multiply(error, (self.value * (1 - self.value)) * self.weights)

    def __repr__(self):
        return str(self.value)