grzegorz2047/Perceptron.html

## Perceptron.html
<html>
<head>
</head>
<body>
<script>
    function usePerceptron(){
        perceptron(0.01);
        perceptron(0.10);
        perceptron(1.00);
    }

    function neuron(weights,args){
        let sum = 0.0;
        for(let i = 0; i < args.length; i++){
        let single = weights[i] * args[i];
        sum += single;

        }
        return decide(sum);
    }

    function createZeroedVector(length){
        let vec=[];
        for(let i = 0; i < length; i++){
            vec.push(1);
        }
        return vec;
    }

    function decide(u){
        if(u >= 0) return 1;
        else return 0;
    }

    function perceptron(cIntense){ //u1-u5 liniowo niezależne
    pics=[];
    u1=[
        0.0,0.0,0.0,0.0,0.0,
        0.0,1.0,1.0,0.0,0.0,
        0.0,0.0,1.0,0.0,0.0,
        0.0,0.0,1.0,0.0,0.0,
        0.0,0.0,1.0,0.0,0.0,1.0
    ];
    u2=[
        0.0,0.0,0.0,0.0,0.0,
        1.0,1.0,0.0,0.0,0.0,
        0.0,1.0,0.0,0.0,0.0,
        0.0,1.0,0.0,0.0,0.0,
        0.0,1.0,0.0,0.0,0.0,1.0
    ];
    u3=[
        0.0,0.0,1.0,1.0,0.0,
        0.0,0.0,0.0,1.0,0.0,
        0.0,0.0,0.0,1.0,0.0,
        0.0,0.0,0.0,0.0,0.0,
        0.0,0.0,0.0,0.0,0.0,1.0
    ];

    u4=[
        0.0,0.0,0.0,0.0,0.0,
        0.0,1.0,1.0,1.0,0.0,
        0.0,1.0,0.0,1.0,0.0,
        0.0,1.0,1.0,1.0,0.0,
        0.0,0.0,0.0,0.0,0.0,1.0
    ];

    u5=[
        0.0,0.0,0.0,0.0,0.0,
        0.0,0.0,0.0,0.0,0.0,
        1.0,1.0,1.0,0.0,0.0,
        1.0,0.0,1.0,0.0,0.0,
        1.0,1.0,1.0,0.0,0.0,1.0
    ];


    pics.push(u1);
    pics.push(u2);
    pics.push(u3);
    pics.push(u4);
    pics.push(u5);

    let time = 1;
    let good = 0;
    let weights = createZeroedVector(26);
    while(good < 5){
        let teacherResult = learnDecider(time);
        let currentPic = pics[time % 5];
        let neuronResult = neuron(weights,currentPic);
        let imageSize = 26;

        for(let pixelPos = 0; pixelPos < imageSize; pixelPos++){
            weights[pixelPos] = weights[pixelPos] + cIntense * (teacherResult - neuronResult) * currentPic[pixelPos];
        }
        if(learnDecider(time) == neuronResult){
            good+=1
        }else{
            good=0;
        }
            time=time+1;
        }
    console.log("time = ",time);
    console.log("weights = "+weights);
    console.log("Wynik to ",neuron(weights,pics[4]));
    }

    function learnDecider(time){//sygnal nauczyciela
        if(time % 5 <= 2){
            return 1.0;
        }
        else {
            return 0.0;
        }
    }

    function addToVector(vector,number){
        for(let i = 0; i < vector.length; i++){
            vector[i] = vector[i] + number;
        }
        return vector;
    }

    function multiplyToVector(vector,number){
        for(let i = 0; i < vector.length; i++){
            vector[i] = vector[i] * number;
        }
        return vector;
    }

    usePerceptron();
</script>
</body>
</head>

</html>
	<html>
	<head>
	</head>
	<body>
	<script>
	function usePerceptron(){
	perceptron(0.01);
	perceptron(0.10);
	perceptron(1.00);
	}

	function neuron(weights,args){
	let sum = 0.0;
	for(let i = 0; i < args.length; i++){
	let single = weights[i] * args[i];
	sum += single;

	}
	return decide(sum);
	}

	function createZeroedVector(length){
	let vec=[];
	for(let i = 0; i < length; i++){
	vec.push(1);
	}
	return vec;
	}

	function decide(u){
	if(u >= 0) return 1;
	else return 0;
	}

	function perceptron(cIntense){ //u1-u5 liniowo niezależne
	pics=[];
	u1=[
	0.0,0.0,0.0,0.0,0.0,
	0.0,1.0,1.0,0.0,0.0,
	0.0,0.0,1.0,0.0,0.0,
	0.0,0.0,1.0,0.0,0.0,
	0.0,0.0,1.0,0.0,0.0,1.0
	];
	u2=[
	0.0,0.0,0.0,0.0,0.0,
	1.0,1.0,0.0,0.0,0.0,
	0.0,1.0,0.0,0.0,0.0,
	0.0,1.0,0.0,0.0,0.0,
	0.0,1.0,0.0,0.0,0.0,1.0
	];
	u3=[
	0.0,0.0,1.0,1.0,0.0,
	0.0,0.0,0.0,1.0,0.0,
	0.0,0.0,0.0,1.0,0.0,
	0.0,0.0,0.0,0.0,0.0,
	0.0,0.0,0.0,0.0,0.0,1.0
	];

	u4=[
	0.0,0.0,0.0,0.0,0.0,
	0.0,1.0,1.0,1.0,0.0,
	0.0,1.0,0.0,1.0,0.0,
	0.0,1.0,1.0,1.0,0.0,
	0.0,0.0,0.0,0.0,0.0,1.0
	];

	u5=[
	0.0,0.0,0.0,0.0,0.0,
	0.0,0.0,0.0,0.0,0.0,
	1.0,1.0,1.0,0.0,0.0,
	1.0,0.0,1.0,0.0,0.0,
	1.0,1.0,1.0,0.0,0.0,1.0
	];


	pics.push(u1);
	pics.push(u2);
	pics.push(u3);
	pics.push(u4);
	pics.push(u5);

	let time = 1;
	let good = 0;
	let weights = createZeroedVector(26);
	while(good < 5){
	let teacherResult = learnDecider(time);
	let currentPic = pics[time % 5];
	let neuronResult = neuron(weights,currentPic);
	let imageSize = 26;

	for(let pixelPos = 0; pixelPos < imageSize; pixelPos++){
	weights[pixelPos] = weights[pixelPos] + cIntense * (teacherResult - neuronResult) * currentPic[pixelPos];
	}
	if(learnDecider(time) == neuronResult){
	good+=1
	}else{
	good=0;
	}
	time=time+1;
	}
	console.log("time = ",time);
	console.log("weights = "+weights);
	console.log("Wynik to ",neuron(weights,pics[4]));
	}

	function learnDecider(time){//sygnal nauczyciela
	if(time % 5 <= 2){
	return 1.0;
	}
	else {
	return 0.0;
	}
	}

	function addToVector(vector,number){
	for(let i = 0; i < vector.length; i++){
	vector[i] = vector[i] + number;
	}
	return vector;
	}

	function multiplyToVector(vector,number){
	for(let i = 0; i < vector.length; i++){
	vector[i] = vector[i] * number;
	}
	return vector;
	}

	usePerceptron();
	</script>
	</body>
	</head>

	</html>