const {Bayes} = require('nodeml');
let bayes = new Bayes();

bayes.train({'fun': 3, 'couple': 1}, 'comedy');
bayes.train({'couple': 1, 'fast': 1, 'fun': 3}, 'comedy');
bayes.train({'fast': 3, 'furious': 2, 'shoot': 2}, 'action');
bayes.train({'furious': 2, 'shoot': 4, 'fun': 1}, 'action');
bayes.train({'fly': 2, 'fast': 3, 'shoot': 2, 'love': 1}, 'action');

let result = bayes.test({'fun': 3, 'fast': 3, 'shoot': 2});
console.log(result); // this print {answer: , score: }

const {sample} = require('nodeml');

// bbc: Function() => { dataset: [ {} , ... ], labels: [ ... ] }
// bbc news dataset, sparse matrix
const bbc = sample.bbc();

// yeast: Function() => { dataset: [ [] , ... ], labels: [ ... ] }
// yeast dataset, array data
const yeast = sample.yeast();

// iris: Function() => { dataset: [ [] , ... ], labels: [ ... ] }
// iris dataset, array data
const iris = sample.iris();

// movie: Function() => [{ movie_id: '1', user_id: '97', rating: '5', like: '17' }, ...]
// movie dataset, array data
const movie = sample.movie();

const {Bayes} = require('nodeml');
let bayes = new Bayes(); // this is bayes classfier

bayes.train([0.2, 0.5, 0.7, 0.4], 1);       
bayes.train({ 'my': 20, 'home': 30 }, 1);   

// training bulk
bayes.train([[2, 5,], [2, 1,]], [1, 2]);    
bayes.train([{}, {}], [1, 2]);              

let result = bayes.test([2, 5, 1, 4]);
let result = bayes.test({'fun': 3, 'fast': 3, 'shoot': 2});

let model = bayes.getModel();
let str = JSON.stringify(model);

bayes.setModel(JSON.parse(str));

const {kNN} = require('nodeml');
let knn = new kNN();

knn.train([0.2, 0.5, 0.7, 0.4], 1);       
knn.train({ 'my': 20, 'home': 30 }, 1);   

// training bulk
knn.train([[2, 5,], [2, 1,]], [1, 2]);    
knn.train([{ 'my': 20, 'home': 30 }, { 'my': 5, 'home': 10 }], [1, 2]);              

let result = knn.test([2, 5, 1, 4]);
let result = knn.test({'fun': 3, 'fast': 3, 'shoot': 2}, 5);

let model = knn.getModel();
let str = JSON.stringify(model);

knn.setModel(JSON.parse(str));

const {CNN} = require('nodeml');
let cnn = new CNN();

cnn.configure({learning_rate: 0.1, momentum: 0.001, batch_size: 5, l2_decay: 0.0001});

var layer = [];
layer.push({type: 'input', out_sx: 1, out_sy: 1, out_depth: 8});
layer.push({type: 'svm', num_classes: 10});

cnn.makeLayer(layer);

// set pre-trained
cnn.setModel(JSON.parse(str));

cnn.train([0.2, 0.5, 0.7, 0.4], 1);       
cnn.train({ 'my': 20, 'home': 30 }, 1);   

// training bulk
cnn.train([[2, 5,], [2, 1,]], [1, 2]);    
cnn.train([{}, {}], [1, 2]);   

let result = cnn.test([2, 5, 1, 4]);
let result = cnn.test({'fun': 3, 'fast': 3, 'shoot': 2});

let model = cnn.getModel();
let str = JSON.stringify(model);

const {kMeans} = require('nodeml');
let kmeans = new kMeans();

kmeans.train([[2, 5,], [2, 1,]], {
    k: 10, dm: 0.00001, iter: 100,  
    proc: (iter, j, d)=> { console.log(iter, j, d); }
});

let result = kmeans.test([[2, 5,], [2, 1,]]);

let model = kmeans.getModel();
let str = JSON.stringify(model);

kmeans.setModel(JSON.parse(str));

const {CF, evaluation} = require('../index');

let train = [[1, 1, 2], [1, 2, 2], [1, 4, 5], [2, 3, 2],
    [2, 5, 1], [3, 1, 2], [3, 2, 3], [3, 3, 3]];
let test = [[3, 4, 1]];

const cf = new CF();
cf.train(train);
let gt = cf.gt(test);
let result = cf.recommendGT(gt, 1);

let ndcg = evaluation.ndcg(gt, result);

console.log(gt);
console.log(result);
console.log(ndcg);

let {evaluate} = require('nodeml');

let original = [1, 2, 1, 1, 3]; // original label
let result = [1, 1, 2, 1, 3]; // train result label

// exec evaluate, this contains accuracy, micro/macro precision/recall/f-measure
let accuracy = evaluate.accuracy(original, result);

let {CF, evaluate} = require('nodeml');
const cf = new CF();
let gt = cf.gt(test, 'user_id', 'movie_id', 'rating');

let result = cf.recommandToUsers(users, 40);

let ndcg = evaluation.ndcg(gt, result);