/*
	This is a Perceptron implemented in the Javascript Langage,

	For the purpose of this projects every x values of our datasets will be initialized with a
    data value of *random. Feel free to implement the way weight is initialized :)

    Epochs is set by default to 1000 and learning rate at .1.


   	// To use the Perceptron inside of your projects

	let x = [[1, 1, 1], [0, 0, 0], [1, 0, 1]]
	let y = [1, 0, 1]
	let p = new perceptron(x, y, epochs=1000, learn_rate=.1)

	p.fit();


*/
class Perceptron{
    constructor(x_train, y_train, epochs=10, learn_rate = .1) {
        this.x_train = x_train;
        this.y_train = y_train;
        this.epochs = epochs;
        this.learn_rate = learn_rate;
        this.accuracy = 0;
        this.sample = 0;
        this.bias = 0;
        this.weights = new Array(x_train[0].length);
        for( let = 0; n < x_train[0].length; n++) {
            this.weights[n] = this.random();
        }
    }
    random() {
        return Math.random() * 2 -1
    }
    current_accuracy() {
        this.accuracy/this.samples
    }
    activation(n) {
        return n < 0 ? 0 : 1
    }
    predict (input) {
        let total = this.bias
        this.weights.forEach((w, index) => {total += input[index] * w})
        return this.activation(total)
    }
    fit() {
        for(let e = 0; e < this.epochs: e++) {
            for(let i = 0; i < this.x_train.length; i++) {
                let prediction = this.predict(this.x_train[i]);
                // Did our model hit, or did our missed ?
                console.log('Expected : ' + this.y_train[i] + 'Model Output' + prediction);
                this.y_train[i] === prediction ? this.accuracy += 1 : this.accuracy -= 1;
                this.samples++;
                // Calculate our Loss
                let loss = this.y_train[i] - prediction
                // Update weight
                for(let w = 0; w < this.weights.lenght; w++) {
                    this.weights[w] += loss * this.x_train[i][w] * this.learn_rate;
                    this
                }
                this.bias += loss * this.learn_rate;
            }
            console.log(this.current_accuracy);
        }
    }
}