razorthink-com/assisted-neuralnet.js

## assisted-neuralnet.js
var input = [ 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0 ];
var expected = [ 1, 0, 1, 0, 0, 0, 1, 1, 1 ];
var hiddenLayers = initHiddenLayers([ 18, 17, 15, 6, expected.length ]);

var neuralLayers = flatten([ [ input ], hiddenLayers ]);

var count = 0;
var start = Date.now();
var neuralNetwork = computeNeuralNet( neuralLayers, expected, [], 0 );

console.log( 'Runtime: ', Date.now() - start, 'ms' );
console.log( 'Layers Computed: ', count );

drawNeuralNetwork( neuralNetwork );

/*----------------------------------------------------*/

function computeNeuralNet( neuralLayers, expectedLayer, weightMatrixStack, stage ) {
	count ++;
    if ( stage + 1 == neuralLayers.length ) {
        return computeNeuralNet( neuralLayers, expectedLayer, weightMatrixStack, 0 );
    }

    var inputLayer = neuralLayers[ stage ];
    var nextLayerInit = neuralLayers[ stage + 1 ];

    var weightMatrix = createWeightMatrix( nextLayerInit.length, inputLayer.length );
    var nextLayer = weightMatrix.map( function ( weights ) {
        return Math.round( sigmoid( neuronWeight( weights, inputLayer ) ) );
    });

    neuralLayers[ stage + 1 ] = nextLayer;
    weightMatrixStack[ stage ] = weightMatrix;

    if ( arrayCompare( neuralLayers[ neuralLayers.length - 1 ], expectedLayer ) ) {
        return { layers: neuralLayers, weights: weightMatrixStack };
    }

    return computeNeuralNet( neuralLayers, expectedLayer, weightMatrixStack, stage + 1 );
}

function flatten( arr ) {
    return [].concat.apply( [], arr )
}

function arrayOfSize( size, n ) {
    n = n || 0;
    return ( new Array(size + 1) ).join('0').split('')
        .map(function (_) {
            return n
        });
}

function createMatrix( m, n, fn ) {
    return arrayOfSize( m ).map( function ( row, rowIndex ) {
        return arrayOfSize( n ).map( function ( col, colIndex ) {
            return fn() || 0;
        } );
    } );
}

function arrayCompare(arr1, arr2) {
    return arr1.every(function (n, index) {
        return ( n == arr2[index] );
    });
}

function randomNumber() {
    var n = Math.round(Math.random() * 100) / 10;
    return Math.round(Math.random() * 10) * ( Math.round(n) % 2 == 0 ) ? n : -n;
}

function initHiddenLayers(hiddenLayerCounts) {
    return arrayOfSize(hiddenLayerCounts.length)
        .map(function (__, index) {
            return arrayOfSize(hiddenLayerCounts[index], 0);
        });
}

function sigmoid(n) {
    return 1 / ( 1 + Math.pow(Math.E, -n) )
}

function neuronWeight(weights, layer) {
    return weights.reduce(function (sum, weight, index) {
        return sum + ( weight * layer[index] )
    }, 0);
}

function createWeightMatrix(m, n) {
    return createMatrix(m, n, randomNumber);
}

## index.html
<!DOCTYPE html>
<html lang="en">
<head>
	<meta charset="UTF-8">
	<title>JS Runner</title>
	<script src="d3.v3.min.js"></script>
	<style>
		body {
			font-family: sans-serif;
		}
	</style>
</head>
<body>
	<script src="neural-network-graph.js"></script>
	<script src="assisted-learning.js"></script>
</body>
</html>

## neural-network-graph.js
function drawNeuralNetwork( neuralNetwork ) {
	var h = window.innerHeight;
	var w = window.innerWidth;
	var svg = d3.select( 'body' )
		.append( 'svg' )
		.attr( 'height', h * 2 )
		.attr( 'width', w * 2 );

	var layers = neuralNetwork.layers;
	var weights = neuralNetwork.weights;

	layers.forEach( function ( layer, layerIndex ) {
		svg.selectAll( 'svg' )
			.data( layer )
			.enter()
			.append( 'circle' )
			.attr( 'class', 'node' )
			.attr( 'cx', function ( __, i ) { return ( i + 1 ) * 50 } )
			.attr( 'cy', function ( __, i ) { return ( layerIndex + 1 ) * 50 } )
			.attr( 'r', 15 )
			.attr( 'fill', function ( d ) { return ( d ? '#BDDC36' : '#C5C5C5' ) } )
			.on( 'mouseover', function ( d, itemIndex ) {
				if ( layerIndex == 0 ) return;
				d3.select(this).transition().duration(150).attr('r', 17);
				drawWeights( weights[ layerIndex - 1 ][ itemIndex ], layerIndex );
			} )
			.on( 'mouseout', function ( d ) {
				svg.selectAll( 'text' ).remove();
				d3.select(this).transition().duration(150).attr('r', 15);
			} )
	} );

	function drawWeights( weights, layerIndex ) {
		svg.selectAll( 'text' )
			.data( weights )
			.enter()
			.append( 'text' )
			.text( function ( d ) { return d } )
			.attr( 'x', function ( __, i ) { return ( ( i + 1 ) * 50 ) - 10 } )
			.attr( 'y', function ( __, i ) { return ( layerIndex * 50 ) + 5 } )
			.attr("font-family", "sans-serif")
			.attr("font-size", "13px")
			.attr("fill", "black")
	}
}
	var input = [ 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0 ];
	var expected = [ 1, 0, 1, 0, 0, 0, 1, 1, 1 ];
	var hiddenLayers = initHiddenLayers([ 18, 17, 15, 6, expected.length ]);

	var neuralLayers = flatten([ [ input ], hiddenLayers ]);

	var count = 0;
	var start = Date.now();
	var neuralNetwork = computeNeuralNet( neuralLayers, expected, [], 0 );

	console.log( 'Runtime: ', Date.now() - start, 'ms' );
	console.log( 'Layers Computed: ', count );

	drawNeuralNetwork( neuralNetwork );

	/----------------------------------------------------/

	function computeNeuralNet( neuralLayers, expectedLayer, weightMatrixStack, stage ) {
	count ++;
	if ( stage + 1 == neuralLayers.length ) {
	return computeNeuralNet( neuralLayers, expectedLayer, weightMatrixStack, 0 );
	}

	var inputLayer = neuralLayers[ stage ];
	var nextLayerInit = neuralLayers[ stage + 1 ];

	var weightMatrix = createWeightMatrix( nextLayerInit.length, inputLayer.length );
	var nextLayer = weightMatrix.map( function ( weights ) {
	return Math.round( sigmoid( neuronWeight( weights, inputLayer ) ) );
	});

	neuralLayers[ stage + 1 ] = nextLayer;
	weightMatrixStack[ stage ] = weightMatrix;

	if ( arrayCompare( neuralLayers[ neuralLayers.length - 1 ], expectedLayer ) ) {
	return { layers: neuralLayers, weights: weightMatrixStack };
	}

	return computeNeuralNet( neuralLayers, expectedLayer, weightMatrixStack, stage + 1 );
	}

	function flatten( arr ) {
	return [].concat.apply( [], arr )
	}

	function arrayOfSize( size, n ) {
	n = n \|\| 0;
	return ( new Array(size + 1) ).join('0').split('')
	.map(function (_) {
	return n
	});
	}

	function createMatrix( m, n, fn ) {
	return arrayOfSize( m ).map( function ( row, rowIndex ) {
	return arrayOfSize( n ).map( function ( col, colIndex ) {
	return fn() \|\| 0;
	} );
	} );
	}

	function arrayCompare(arr1, arr2) {
	return arr1.every(function (n, index) {
	return ( n == arr2[index] );
	});
	}

	function randomNumber() {
	var n = Math.round(Math.random() * 100) / 10;
	return Math.round(Math.random() * 10) * ( Math.round(n) % 2 == 0 ) ? n : -n;
	}

	function initHiddenLayers(hiddenLayerCounts) {
	return arrayOfSize(hiddenLayerCounts.length)
	.map(function (__, index) {
	return arrayOfSize(hiddenLayerCounts[index], 0);
	});
	}

	function sigmoid(n) {
	return 1 / ( 1 + Math.pow(Math.E, -n) )
	}

	function neuronWeight(weights, layer) {
	return weights.reduce(function (sum, weight, index) {
	return sum + ( weight * layer[index] )
	}, 0);
	}

	function createWeightMatrix(m, n) {
	return createMatrix(m, n, randomNumber);
	}
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<title>JS Runner</title>
	<script src="d3.v3.min.js"></script>
	<style>
	body {
	font-family: sans-serif;
	}
	</style>
	</head>
	<body>
	<script src="neural-network-graph.js"></script>
	<script src="assisted-learning.js"></script>
	</body>
	</html>
	function drawNeuralNetwork( neuralNetwork ) {
	var h = window.innerHeight;
	var w = window.innerWidth;
	var svg = d3.select( 'body' )
	.append( 'svg' )
	.attr( 'height', h * 2 )
	.attr( 'width', w * 2 );

	var layers = neuralNetwork.layers;
	var weights = neuralNetwork.weights;

	layers.forEach( function ( layer, layerIndex ) {
	svg.selectAll( 'svg' )
	.data( layer )
	.enter()
	.append( 'circle' )
	.attr( 'class', 'node' )
	.attr( 'cx', function ( __, i ) { return ( i + 1 ) * 50 } )
	.attr( 'cy', function ( __, i ) { return ( layerIndex + 1 ) * 50 } )
	.attr( 'r', 15 )
	.attr( 'fill', function ( d ) { return ( d ? '#BDDC36' : '#C5C5C5' ) } )
	.on( 'mouseover', function ( d, itemIndex ) {
	if ( layerIndex == 0 ) return;
	d3.select(this).transition().duration(150).attr('r', 17);
	drawWeights( weights[ layerIndex - 1 ][ itemIndex ], layerIndex );
	} )
	.on( 'mouseout', function ( d ) {
	svg.selectAll( 'text' ).remove();
	d3.select(this).transition().duration(150).attr('r', 15);
	} )
	} );

	function drawWeights( weights, layerIndex ) {
	svg.selectAll( 'text' )
	.data( weights )
	.enter()
	.append( 'text' )
	.text( function ( d ) { return d } )
	.attr( 'x', function ( __, i ) { return ( ( i + 1 ) * 50 ) - 10 } )
	.attr( 'y', function ( __, i ) { return ( layerIndex * 50 ) + 5 } )
	.attr("font-family", "sans-serif")
	.attr("font-size", "13px")
	.attr("fill", "black")
	}
	}