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richard512/web-naive-bayes.js

Created July 26, 2018 07:57
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JavaScript Naive Bayes Classifier -- Web Browser Compatible
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web-naive-bayes.js
needsClassifier = (typeof Classifier === 'undefined')
if (needsClassifier) {
Classifier = class {
constructor() {
this.dict = {};
this.categories = {};
this.wordList = [];
this.categoryList = []
}
static validate(token) {
return /\w+/.test(token)
}
increment(token, category) {
this.categories[category].tokenCount += 1;
let word = this.dict[token];
if (word === undefined) {
this.dict[token] = {
word: token,
[category]: {
count: 1
}
};
this
.wordList
.push(token)
} else if (word[category] === undefined) {
word[category] = {
count: 1
}
} else {
word[category].count += 1
}
}
train(data, category) {
if (this.categories[category] === undefined) {
this.categories[category] = {
docCount: 1,
tokenCount: 0
};
this
.categoryList
.push(category)
} else {
this.categories[category].docCount += 1
}
let tokens = data.split(/\W+/);
tokens.forEach(token => {
token = token.toLowerCase();
if (Classifier.validate(token)) {
this.increment(token, category)
}
})
}
trainlist(datalist, category) {
let i = 0;
if (this.categories[category] === undefined) {
this.categories[category] = {
docCount: 1,
tokenCount: 0
};
this
.categoryList
.push(category)
} else {
this.categories[category].docCount += 1
}
for (i=0; i<datalist.length; i++) {
let data = datalist[i]
let tokens = data.split(/\W+/);
tokens.forEach(token => {
token = token.toLowerCase();
if (Classifier.validate(token)) {
this.increment(token, category)
}
})
}
}
probabilities() {
this
.wordList
.forEach(key => {
let word = this.dict[key];
this
.categoryList
.forEach(category => {
if (word[category] === undefined) {
word[category] = {
count: 0
}
}
let wordCat = word[category];
let cat = this.categories[category];
let freq = wordCat.count / cat.docCount;
wordCat.freq = freq
})
});
this
.wordList
.forEach(key => {
let word = this.dict[key];
this
.categoryList
.forEach(category => {
let sum = this
.categoryList
.reduce((p, cat) => {
let freq = word[cat].freq;
if (freq) {
return p + freq
}
return p
}, 0);
let wordCat = word[category];
let prob = wordCat.freq / sum;
wordCat.prob = Math.max(0.01, Math.min(0.99, prob))
})
})
}
guess(data) {
let tokens = data.split(/\W+/);
let words = [];
tokens.forEach(token => {
token = token.toLowerCase();
if (Classifier.validate(token)) {
if (this.dict[token] !== undefined) {
let word = this.dict[token];
words.push(word)
}
} else {}
});
let sum = 0;
let products = this
.categoryList
.reduce((product, category) => {
product[category] = words.reduce((prob, word) => {
return prob * word[category].prob
}, 1);
sum += product[category];
return product
}, {});
let results = {};
this
.categoryList
.forEach(category => {
results[category] = {
probability: products[category] / sum
};
});
return results
}
}
}
if (typeof classifier == 'undefined') {
classifier = new Classifier();
}
classifier.trainlist(["happy"], "happy");
classifier.train("I am sad and I am very sad.", "sad");
classifier.train("I have mixed feelings.", "mixed");
classifier.probabilities();
results = classifier.guess("Yesterday, I was very very happy, so happy.");
console.log(results);
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