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| <html> | |
| <head> | |
| <link href="style.css" rel="stylesheet" /> | |
| </head> | |
| <body> | |
| <h3>Graph: Dragons</h3> | |
| <div class="legend legendX"><div>Scariness ️→</div></div> | |
| <div class="legend legendY"><div>Actual Power →</div></div> | |
| <!-- http://bit.ly/neuralvanilla --> | |
| <canvas id="wowacanvas" width="1000" height="1000"></canvas> | |
| <script> | |
| const someDragons = generateDragons(500) | |
| function generateDragons(numberOfDragonsToGenerate) { | |
| let dragons = [] | |
| for (let i = 0; i < numberOfDragonsToGenerate; i++) { | |
| // A dragon is just an object literal with two properties, | |
| // scariness and power, each of which is assigned | |
| // a random integer between 1 and 1000. | |
| const dragon = { | |
| scariness: randomNumber(0, 1000), | |
| power: randomNumber(0, 1000) | |
| } | |
| dragons.push(dragon) | |
| } | |
| return dragons | |
| } | |
| const initialWeights = { | |
| scariness: randomNumber(-1, 1), | |
| power: randomNumber(-1, 1) | |
| } | |
| let trainedWeights = initialWeights | |
| /* | |
| const fluffykins = { | |
| scariness: 10, | |
| power: 1000 | |
| } | |
| const smaug = { | |
| scariness: 900, | |
| power: 700 | |
| } | |
| const minmoo = { | |
| scariness: 25, | |
| power: 30 | |
| } | |
| trainedWeights = train(trainedWeights, fluffykins, 1) | |
| trainedWeights = train(trainedWeights, smaug, -1) | |
| trainedWeights = train(trainedWeights, minmoo, 1)*/ | |
| const trainingSetSize = 1250000 | |
| const noiseProbability = 0.01 | |
| for(const dragon of generateDragons(trainingSetSize)) { | |
| const correctClassification = powerClassification(dragon) | |
| let trainOnClassification = correctClassification | |
| if (Math.random() < noiseProbability) { | |
| if (correctClassification === -1) { | |
| trainOnClassification = 1 | |
| } else { | |
| trainOnClassification = -1 | |
| } | |
| } | |
| trainedWeights = train( | |
| trainedWeights, dragon, trainOnClassification) | |
| } | |
| function train(weights, dragon, actualPowerClassification) { | |
| const guessResult = guessPowerClassification(weights, dragon) | |
| const error = actualPowerClassification - guessResult | |
| const learningRate = 0.5 | |
| return { | |
| scariness: weights.scariness + error * dragon.scariness * learningRate, | |
| power: weights.power + error * dragon.power * learningRate | |
| } | |
| } | |
| const canvas = document.getElementById("wowacanvas") | |
| const ctx = canvas.getContext("2d") | |
| ctx.beginPath() | |
| ctx.moveTo(0, 0) | |
| ctx.lineTo(1000, 1000) | |
| ctx.stroke() | |
| for (const dragon of someDragons) { | |
| const x = dragon.scariness | |
| const y = dragon.power | |
| const rectangleHeight = 5 | |
| const rectangleWidth = 5 | |
| if (guessPowerClassification(trainedWeights, dragon) === 1) { | |
| ctx.fillStyle = "black" | |
| } else if(guessPowerClassification(trainedWeights, dragon) === -1) { | |
| ctx.fillStyle = "hotpink" | |
| } | |
| ctx.fillRect(x, y, rectangleWidth, rectangleHeight) | |
| } | |
| function powerClassification(dragon) { | |
| return dragon.power > dragon.scariness ? 1 : -1 | |
| } | |
| function guessPowerClassification(weights, dragon) { | |
| const someMathGarble = | |
| dragon.power * weights.power + | |
| dragon.scariness * weights.scariness | |
| return someMathGarble >= 0 ? 1 : -1 | |
| } | |
| // randomNumber - generate number in a certain range. | |
| function randomNumber (lower, higher) { | |
| return Math.random() * (higher - lower) + lower | |
| } | |
| </script> | |
| </body> | |
| </html> |
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| body { | |
| font-family: Helvetica; | |
| padding: 1rem; | |
| } | |
| .legend { | |
| background-color: black; | |
| color: white; | |
| font-weight: bold; | |
| } | |
| .legendX { | |
| padding-left: 30px; | |
| width: 1000px; | |
| height: 20px; | |
| } | |
| .legendY { | |
| writing-mode: vertical-rl; | |
| text-orientation: mixed; | |
| float: left; | |
| height: 1000px; | |
| padding-top: 10px; | |
| } | |
| .description { | |
| width: 1000px; | |
| clear: both; | |
| } | |
| canvas { | |
| float: left; | |
| margin-bottom: 2rem; | |
| } |
// Sample is a object just like:
// const smaug = {
// scariness: 900,
// power: 700
// }
function agnosticTrain(weight, sample, expected) {
const guessResult = guessPowerClassification(weight, sample)
const error = expected - guessResult
const learningRate = 0.05
const attributes = Object.keys(sample)
const trainedSample = {}
attributes.forEach(attr => {
trainedSample[attr] = error * object[attr] * learningRate
})
return trainedSample
}
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Cool 😎