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pdfast
Advanced tools
Readme
Kernel Density Estimation, generating probability density function (pdf) using triangular kernel, optimized to run in O(N + K).
Where:
Create pdf with given array and options.
Options:
min
: min value for the pdf's x range. If resulting pdf won't fit, the pdf's left part will be squeezed, as described here. Defaults to smallest value in the array minus some threshold.max
: max value for the pdf's x range. If resulting pdf won't fit, the pdf's right will be squeezed. Defaults to largest value in the array plus some threshold.size
: number of points to represent the pdf. Defaults to 50.width
: determine how many points to the left and right does an element affect, similar to bandwidth in kernel density estimation. Defaults to 2.var arr = [1, 2, 3, 3, 4, 5, 5, 5, 6, 8, 9, 9];
var options = {
min: 0,
max: 10,
size: 12,
width: 2
};
var pdf = pdfast.create(arr, options);
pdf
's value:
[ { x: 0, y: 0.020833333333333332 },
{ x: 0.9090909090909091, y: 0.0625 },
{ x: 1.8181818181818181, y: 0.10416666666666667 },
{ x: 2.727272727272727, y: 0.125 },
{ x: 3.6363636363636362, y: 0.14583333333333334 },
{ x: 4.545454545454545, y: 0.16666666666666666 },
{ x: 5.454545454545454, y: 0.10416666666666667 },
{ x: 6.363636363636363, y: 0.041666666666666664 },
{ x: 7.2727272727272725, y: 0.08333333333333333 },
{ x: 8.181818181818182, y: 0.10416666666666667 },
{ x: 9.09090909090909, y: 0.041666666666666664 },
{ x: 10, y: 0 } ]
expect(
pdfast.getExpectedValueFromPdf([
{x: 1, y: 0.2},
{x: 2, y: 0.3},
{x: 3, y: 0.3},
{x: 4, y: 0.2},
{x: 5, y: 0.0}
])
).closeTo(2.5, 1e-8);
var pdf = [
{x: 1, y: 0.2},
{x: 2, y: 0.4},
{x: 3, y: 0.3},
{x: 4, y: 0.075},
{x: 5, y: 0.025}
];
expect(pdfast.getXWithLeftTailArea(pdf, 0)).equal(1);
expect(pdfast.getXWithLeftTailArea(pdf, 0.12)).equal(1);
expect(pdfast.getXWithLeftTailArea(pdf, 0.19)).equal(1);
expect(pdfast.getXWithLeftTailArea(pdf, 0.21)).equal(2);
expect(pdfast.getXWithLeftTailArea(pdf, 0.95)).equal(4);
expect(pdfast.getXWithLeftTailArea(pdf, 1)).equal(5);
expect(
pdfast.getPerplexity([
{x: 1, y: 0.2},
{x: 2, y: 0.4},
{x: 3, y: 0.3},
{x: 4, y: 0.075},
{x: 5, y: 0.025}
])
).closeTo(3.8041316039860336, EPS);
Takes the same options as create
. Returns an object with key min
and max
.
If you left min
or max
or both to be non number, it will be filled with number which will fit the data distribution.
Similar with getUnifiedMinMax
, but takes list of arrays. The generated min
and/or max
will fit all the arrays' distribution.
Useful when trying to generate pdf for multiple labelled data and want to display them in the same chart. With same min
and max
, one can combine the pdf correctly.
MIT
FAQs
Fast kernel density estimation library
The npm package pdfast receives a total of 91,051 weekly downloads. As such, pdfast popularity was classified as popular.
We found that pdfast demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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