kde-simple

simple 1D kernel density estimation (KDE) with gaussian and triangular kernels

Installation

npm i kde-simple

Usage

var {kernelDensityEstimate, getRange, generateRandomFromKDE, calculateBandwidth, getPeakValue } = require('kde-simple');

var useTriangularKernel = true; //if false, uses gaussian kernel
var bandwidth = 1;

//weights affect the size of the samples relative to e/o, but
//the area under the KDE always sums to 1 regardless of weights
var samples = [
    {weight:2,x:0}, //default weight is 1.
    {weight:1,x:2},
    // {weight:2,x:-2}
];

//calculateBandwidth_scott(samples) = bandwidth via Scott's rule for 1D data [based on weights]
//calculateBandwidth_silverman(samples) = bandwidth via Silverman's rule for 1D data [based on x values]
//getPeakValue(samples, bandwidth, useTriangular, range) => description of peak of KDE
// format like { x: 0.266, val: 1.1388, sampleIndex: 796 }

//optional - get y min/max values [min, max] for this kde. 
// this can use this to normalize results by height instead of area
var range = getRange(samples, bandwidth, useTriangularKernel);

var pts =[];
for(var x=-3; x<5; x+=0.02){
    pts.push(
        {
            x: x,
            //get values normalized by area [total area = 1]
            y: kernelDensityEstimate(samples, x, bandwidth, useTriangularKernel) //returns values normalized by total area = 1
            //alt: returns values normalized by height[max height = 1]
            //y: kernelDensityEstimate(samples, x, bandwidth, useTriangularKernel, range) 
        }
    );
}

//save the result as a scatter chart...

//var scs = require('scatter-chart-simple');
//var title = `${samples.length} ${useTriangularKernel ? 'triangle' : 'gaussian'}s bw ${bandwidth}`;
//var resultFile = `./${title}.png`.replace(/ /g,'_');
//var width = 320;
//var height = 240;
//var bgColor = 'white';
//var fgColor = 'rgb(255, 99, 132)'
//scs.plotSingleDataChart(resultFile, pts, title, width, height, bgColor, fgColor)


//you can also extract a random number using the KDE as a probability distribution 
// -- uses rejection sampling 

var samplesForKDE = [
    {weight: 1, x:1}
]

var results = []

for(var i=0;i<100000;i++){
    var bandwidth = 0.1;
    results.push(generateRandomFromKDE(samplesForKDE,bandwidth));
}

console.log(require('histogram-simple')(results).toString(80));

// 0.8000: ##
// 0.8400: #####
// 0.8800: ########
// 0.9200: ###########
// 0.9600: ############
// 1.0000: ############
// 1.0400: ###########
// 1.0800: ########
// 1.1200: #####
// 1.1600: ##

//get 1st and 2nd derivatives using central differences over distance h 
//outputs { firstDerivative: value1, secondDerivative: value2 }
//  .getKDEDerivatives(data, point, bandwidth, useTriangular, range, h=0.0001)