const AdaBoost = require('mlhelper/lib/algorithm').AdaBoost;
//or const AdaBoost = require('mlhelper').algorithm.AdaBoost;

const dataSet = [
    [1.0,2.1],
    [2.0,1.1],
    [1.3,1.0],
    [1.0,1.0],
    [2.0,1.0]
]
const labels = [1.0,1.0,-1.0,-1.0,1.0];
let ada = new AdaBoost(dataSet,labels,40);
let result = ada.classify([[1.0,2.1],
    [2.0,1.1],
    [1.3,1.0],
    [1.0,1.0],
    [2.0,1.0]]);
console.log(result); // [ 1, 1, -1, -1, -1 ]

const Matrix = require('mlhelper/lib/utils').Matrix;

let m1 = new Matrix([
    [1,2,3],
    [3,4,5]
]);

let m2 = new Matrix([
    [2,2,6],
    [3,1,5]
]);

console.log(m2.sub(m1)) // Matrix { arr: [ [ 1, 0, 3 ], [ 0, -3, 0 ] ] }
console.log(m1.mult(m2)) // Matrix { arr: [ [ 2, 4, 18 ], [ 9, 4, 25 ] ] }

const Vector = require('mlhelper/lib/utils').Vector;

let v = new Vector([5,10,7,1]);
console.log(v.argSort()) // [ 3, 0, 2, 1 ]

const parser = require('mlhelper/lib/utils').fileParser;

let dt = parser.read_csv(path.join(__dirname,'./train.csv'),{
    index_col: 0,
    delimiter: ',',
    header: 0,
    dataType: 'number'
});
let labels = dt.getClasses();
let dataSet =dt.drop('quality').values;

// preprocessing features
const preprocessing = require('mlhelper/lib/utils').features.preprocessing;

// make the features obey the standard normal distribution(Standardization)
let testStandardScaler = preprocessing.standardScaler(dataSet);

let testNormalize = preprocessing.normalize(dataSet);

let testBinarizer = preprocessing.binarizer(dataSet);

// ...

charts.drawDT(dt.getTree(),{
    width:600,
    height:400
});

charts.drawLogistic(dataSet,labels,weights);