graph.md

Graph utils

Provides visual capabilities for various algorithms, which automatically open your browser and draw visual graphics

drawkNN(dataSet: Array<Array<number>>,labels: Array,inx: Array,options: object)

Plot the scatter diagram of KNN algorithm dataSet: matrix of datas for training.

labels: vector of training datas' classes.

inx: vector of data to test.

options

• options.width: <string> the width of the graph. default to "600px".
• options.height: <string> the height of the graph. default to "400px".
• options.size: <number> the size of every point. default to 20.

const charts = require('mlhelper').utils.charts;
//...
let inx = [7.0,0.27,0.36,20.7,0.045,45.0,170.0,1.001,3.0,0.45,8.8],
    normalInx = knn.autoNormalVector(inx);

console.log(knn.classify(inx,100)); // 6
charts.drawkNN(kNN.autoNormal(dataSet),labels,normalInx,{
    width: "500px",
    height: "400px",
    size: 15
});

drawDT(tree: object,options: object)

Decision tree visualization.

tree

the decision tree object.

options

• options.width: <number> the width of the graph.
• options.height: <number> the height of the graph. size: the size of every point.

const charts = require('mlhelper').utils.charts;
// ...
charts.drawDT(dt.getTree(),{
    width:600,
    height:400
});

drawLogistic(dataSet: Array<Array<number>>,labels: Array<any>,weights: Array<number>,options: object)

Visualization of logistic regression algorithm

dataSet

The matrix like dataset for training.

labels:

the classes for training dataset.

weights:

Random gradient ascent method for optimal regression coefficients of each feature

options:

• options.width: <string> the width of the graph. default to "600px".
• options.height: <string> the height of the graph. default to "400px".
• options.size: <number> the size of every point. deault to 20.

const charts = require('mlhelper').utils.charts;
// ...
let weights = logi.getWeights()
console.log(weights);

charts.drawLogistic(dataSet,labels,weights)