JS는 베이지안 분류기를 구현합니다.

function NB(data) {    this.fc = {}; //记录特征的数量 feature conut 例如 {a:{yes:5,no:2},b:{yes:1,no:6}}
    this.cc = {}; //记录分类的数量 category conut 例如 {yes:6,no:8} }
NB.prototype = {
    infc(w, cls) { //插入新特征值
        if (!this.fc[w]) this.fc[w] = {};        
        if (!this.fc[w][cls]) this.fc[w][cls] = 0;        
        this.fc[w][cls] += 1;
    },
    incc(cls) { //插入新分类
        if (!this.cc[cls]) this.cc[cls] = 0;        
        this.cc[cls] += 1;
    },
    allco() { //计算分类总数 all count
        var t = 0;        
        for (var k in this.cc) t += this.cc[k];        
        return t;
    },
    fprob(w, ct) { //特征标识概率   
        if (Object.keys(this.fc).indexOf(w) >= 0) {            
        if (Object.keys(this.fc[w]).indexOf(ct) < 0) {                
        this.fc[w][ct] = 0
            }            
            var c = parseFloat(this.fc[w][ct]);            
            return c / this.cc[ct];
        } else {            
        return 0.0;
        }
    },
    cprob(c) { //分类概率
        return parseFloat(this.cc[c] / this.allco());
    },
    train(data, cls) { //参数:学习的Array,标识类型(Yes|No)
        for (var w of data) this.infc(String(w), cls);        
        this.incc(cls);
    },
    test(data) {        var ccp = {}; //P(类别)
        var fccp = {}; //P(特征|类别)
        for (var k in this.cc) ccp[k] = this.cprob(k);        
        for (var i of data) {
            i = String(i);            
            if (!i) continue;            
            if (Object.keys(this.fc).indexOf(i)) {                
            for (var k in ccp) {                    
            if (!fccp[k]) fccp[k] = 1;
                    fccp[k] *= this.fprob(i, k); //P(特征1|类别1)*P(特征2|类别1)*P(特征3|类别1)...
                }
            }
        }        var tmpk = "";        
        for (var k in ccp) {
            ccp[k] = ccp[k] * fccp[k];            
            if (!tmpk) tmpk = k;            
            if (ccp[k] > ccp[tmpk]) tmpk = k;
        }        
        return tmpk;
    }
};

P( Category |Feature) = P ( Feature | Category ) * P( Category)/ P(Feature)