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cross-validate.js
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152 lines (137 loc) · 3.33 KB
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/**
*
* @param {NeuralNetwork|constructor} Classifier
* @param {object} opts
* @param {object} trainOpts
* @param {object} trainSet
* @param {object} testSet
* @returns {void|*}
*/
export function testPartition(Classifier, opts, trainOpts, trainSet, testSet) {
let classifier = new Classifier(opts);
let beginTrain = Date.now();
let trainingStats = classifier.train(trainSet, trainOpts);
let beginTest = Date.now();
let testStats = classifier.test(testSet);
let endTest = Date.now();
let stats = Object.assign({}, testStats, {
trainTime : beginTest - beginTrain,
testTime : endTest - beginTest,
iterations: trainingStats.iterations,
trainError: trainingStats.error,
learningRate: trainOpts.learningRate,
hidden: classifier.hiddenSizes,
network: classifier.toJSON()
});
return stats;
}
/**
* Randomize array element order in-place.
* Using Durstenfeld shuffle algorithm.
* source: http://stackoverflow.com/a/12646864/1324039
*/
export function shuffleArray(array) {
for (let i = array.length - 1; i > 0; i--) {
let j = Math.floor(Math.random() * (i + 1));
let temp = array[i];
array[i] = array[j];
array[j] = temp;
}
return array;
}
/**
*
* @param {NeuralNetwork|constructor} Classifier
* @param {object} data
* @param {object} opts
* @param {object} trainOpts
* @param {number} k
* @returns {
* {
* avgs: {
* error: number,
* trainTime: number,
* testTime: number,
* iterations: number,
* trainError: number
* },
* stats: {
* truePos: number,
* trueNeg: number,
* falsePos: number,
* falseNeg: number,
* total: number
* },
* sets: Array,
* misclasses: Array
* }
* }
*/
export default function crossValidate(Classifier, data, opts, trainOpts, k) {
k = k || 4;
let size = data.length / k;
if (data.constructor === Array) {
shuffleArray(data);
} else {
let newData = {};
shuffleArray(Object.keys(data)).forEach((key) => {
newData[key] = data[key];
});
data = newData;
}
let avgs = {
error : 0,
trainTime : 0,
testTime : 0,
iterations: 0,
trainError: 0
};
let stats = {
truePos: 0,
trueNeg: 0,
falsePos: 0,
falseNeg: 0,
total: 0
};
let misclasses = [];
let results = [];
let stat;
let sum;
for (let i = 0; i < k; i++) {
let dclone = data.slice(0);
let testSet = dclone.splice(i * size, size);
let trainSet = dclone;
let result = testPartition(Classifier, opts, trainOpts, trainSet, testSet);
for (stat in avgs) {
if (stat in avgs) {
sum = avgs[stat];
avgs[stat] = sum + result[stat];
}
}
for (stat in stats) {
if (stat in stats) {
sum = stats[stat];
stats[stat] = sum + result[stat];
}
}
misclasses.concat(results.misclasses);
results.push(result);
}
for (stat in avgs) {
if (stat in avgs) {
sum = avgs[stat];
avgs[stat] = sum / k;
}
}
stats.precision = stats.truePos / (stats.truePos + stats.falsePos);
stats.recall = stats.truePos / (stats.truePos + stats.falseNeg);
stats.accuracy = (stats.trueNeg + stats.truePos) / stats.total;
stats.testSize = size;
stats.trainSize = data.length - size;
return {
avgs: avgs,
stats: stats,
sets: results,
misclasses: misclasses
};
}