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classifier.js
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classifier.js
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const brain = require('brain.js');
const fs = require('fs');
const pos = require('pos');
let net;
let cachedClassifier = null;
class classifier {
constructor() {
this.trainingData = [];
}
add(txt, label) {
txt = processText(txt);
this.trainingData.push({ input: {[[txt]]: 1}, output: {[[label]]: 1}});
}
train(options = { iterations: 1000, erroThresh: 0.000 }) {
net = new brain.NeuralNetwork({ hiddenLayers: [3] });
net.train(this.trainingData, options);
cachedClassifier = net.toFunction();
}
classify(txt) {
txt = processText(txt);
if(cachedClassifier != null) {
let category = cachedClassifier({[[txt]]: 1});
let highest = {}
highest.val = category[Object.keys(category)[0]];
highest.name = Object.keys(category)[0];
for (let key in category) {
if(category[key] > highest.val) {
highest.val = category[key];
highest.name = key;
}
}
return highest.name;
} else {
console.error("Classifier not trained to preform this operation.");
return "#idk@classifier";
}
}
save(path) {
return new Promise((resolve, reject) => {
const json = net.toJSON();
fs.writeFile(path, JSON.stringify(json), (err) => {
if(err) reject(err);
else resolve();
});
})
}
restore(path) {
return new Promise((resolve, reject) => {
fs.readFile(path,'utf-8', (err, data) => {
if (err) reject(err);
else {
net = new brain.NeuralNetwork({ hiddenLayers: [3] });
net.fromJSON(JSON.parse(data));
cachedClassifier = net.toFunction();
resolve();
}
});
})
}
}
const processText = (txt) => {
let processedtext = [];
const tagger = new pos.Tagger();
const tokens = txt.replace(/[^\w\s]|_/g, "").replace(/ {2,}/g, ' ').trim().toLowerCase().split(' ');
tokens.forEach(token => {
let tag = tagger.tag([token])[0][1];
if(tag != 'IN' & tag != 'PRP$') {
processedtext.push(token);
}
});
return processedtext.join(' ');
}
module.exports = classifier;