// Inside src/NeuralNetwork.js
/*
ⓝⓔⓤⓡⓞⓝⓢ.ⓜⓔ
🆂🆄🅸🅶🅽  
This class provides the basics for creating and managing a neural network.
 The initialize, feedforward, and train methods are placeholders where you would add the actual logic for initializing network weights,
feeding input data through the network, and training the network with a dataset, respectively.
The inputLayer and outputLayer properties can be used to reference the first and last layers in your network, 
which can be important for certain types of network architectures and when constructing the network topology.
*/

/**
 * Class representing a neural network.
 */
class NeuralNetwork {
    /**
     * Create a neural network.
     */
    constructor() {
      this.layers = [];
      this.inputLayer = null;
      this.outputLayer = null;
    }
  
    /**
     * Add a layer to the neural network.
     * @param {Layer} layer - The layer to add.
     */
    addLayer(layer) {
      this.layers.push(layer);
    }
  
    /**
     * Remove a layer from the neural network.
     * @param {Layer} layer - The layer to remove.
     */
    removeLayer(layer) {
      const index = this.layers.indexOf(layer);
      if (index > -1) {
        this.layers.splice(index, 1);
      }
    }
  
    /**
     * Set a layer as the input layer of the neural network.
     * @param {Layer} layer - The layer to set as input layer.
     */
    setInputLayer(layer) {
      this.inputLayer = layer;
    }
  
    /**
     * Set a layer as the output layer of the neural network.
     * @param {Layer} layer - The layer to set as output layer.
     */
    setOutputLayer(layer) {
      this.outputLayer = layer;
    }
  
    /**
     * Initialize the neural network with random weights.
     */
    initialize() {
      // Initialization code, typically randomizing weights
    }
  
    /**
     * Feedforward the input through the network.
     * @param {Array} input - The input values.
     * @return {Array} The output of the network.
     */
    feedforward(input) {
      // Feedforward logic, computing the output from input data
    }
  
    /**
     * Train the neural network with given data.
     * @param {Array} data - The training data.
     */
    train(data) {
      // Training logic
    }
  
    // Additional methods as needed for backpropagation, evaluation, etc.
  }
  
  export default NeuralNetwork;