DO NOT USE ! This is an unsupported fork of https://github.com/bitanath/pca

- Original version has an issue with generating lots of logs, which makes it expensive to run in some cloud environments. https://github.com/bitanath/pca/issues/14
- This fork is fixing just that, and will be removed once original is fixed.

DO NOT USE ! This is an unsupported fork of https://github.com/bitanath/pca

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Principal Components Analysis in Javascript!

A JS library to compute Principal Components from a given matrix of data. Use in either node.js or the browser. Look below for the API and some ideas 💡.

CDN: https://cdn.jsdelivr.net/npm/pca-js@1.0.0/pca.min.js

NPM: npm install --save pca-js

Usage:

Node 🛠: var PCA = require('pca-js')
Browser 🌎: PCA (global)

How to use the API

All methods are exposed through PCA global variable

Say you have data for marks of a class 4 students in 3 examinations on the same subject:

``
Student 1: 40,50,60
Student 2: 50,70,60
Student 3: 80,70,90
Student 4: 50,60,80
`

You want to examine whether it is possible to come up with a single descriptive set of scores which explains performance across the class. Alternatively, whether it would make sense to replace 3 exams with just one (and reduce stress on students).


First get the set of eigenvectors and eigenvalues (principal components and adjusted loadings)
`js
var data = [[40,50,60],[50,70,60],[80,70,90],[50,60,80]];
var vectors = PCA.getEigenVectors(data);
//Outputs
// [{
// "eigenvalue": 520.0992658908312,
// "vector": [0.744899700771276, 0.2849796479974595, 0.6032503924724023]
// }, {
// "eigenvalue": 78.10455398035167,
// "vector": [0.2313199078283626, 0.7377809866160473, -0.6341689964277106]
// }, {
// "eigenvalue": 18.462846795484058,
// "vector": [0.6257919271076777, -0.6119361208615616, -0.4836513702572988]
// }]
`

Now you'd need to find a set of eigenvectors that would explain a decent amount of variance across your exams (thus telling you if 1 test or 2 tests would suffice instead of three)

`js
var first = PCA.computePercentageExplained(vectors,vectors[0])
// 0.8434042149581044
var topTwo = PCA.computePercentageExplained(vectors,vectors[0],vectors[1])
// 0.9700602484397556
`

So if you wanted to have 97% certainty, that someone wouldn't just flunk out accidentally, you'd take 2 exams. But let's say you just wanted to take 1, explaining 84% of variance is good enough. And instead of taking the examination again, you just wanted a normalized score

`js
var adData = PCA.computeAdjustedData(data,vectors[0])
// {
// "adjustedData": [
// [-22.27637101744241, -9.127781049780463, 31.316721747529886, 0.08743031969298887]
// ],
// "formattedAdjustedData": [
// [-22.28, -9.13, 31.32, 0.09]
// ],
// "avgData": [
// [-55, -62.5, -72.5],
// [-55, -62.5, -72.5],
// [-55, -62.5, -72.5],
// [-55, -62.5, -72.5]
// ],
// "selectedVectors": [
// [0.744899700771276, 0.2849796479974595, 0.6032503924724023]
// ]
// }
`

The adjustedData is centered (mean = 0), but you could always set the mean to something like 50, to get scores of [-22.27637101744241, -9.127781049780463, 31.316721747529886, 0.08743031969298887].map(score=>Math.round(score+50)) equal to [28, 41, 81, 50] , and that's how well your students would have done, in the order of students.

$3

#### Compression (lossy):
`js
var compressed = adData.formattedAdjustedData;
//[
// [-22.28, -9.13, 31.32, 0.09]
// ]
var uncompressed = PCA.computeOriginalData(compressed,adData.selectedVectors,adData.avgData);
//uncompressed.formattedOriginalData (lossy since 2 eigenvectors are removed)
// [
// [38.4, 56.15, 59.06],
// [48.2, 59.9, 66.99],
// [78.33, 71.43, 91.39],
// [55.07, 62.53, 72.55]
// ]
`

Compare this to the original data to understand just how lossy the compression was
`
//Original Data
[
[40, 50, 60],
[50, 70, 60],
[80, 70, 90],
[50, 60, 80]
]
//Uncompressed Data
[
[38.4, 56.15, 59.06],
[48.2, 59.9, 66.99],
[78.33, 71.43, 91.39],
[55.07, 62.53, 72.55]
]
`


List of Methods


#### computeDeviationMatrix(data)
Find centered matrix from original data

#### computeDeviationScores(centeredMatrix)
Find deviation from mean for values in matrix

#### computeSVD(deviationScores)
Singular Value Decomposition of matrix

#### computePercentageExplained(allvectors, ...selected)
Find percentage explained variance by selected vectors as opposed to the whole

#### computeOriginalData(compressedData,selectedVectors,avgData)
Get original data from the adjusted data after selecting a few eigenvectors

#### computeVarianceCovariance(devSumOfSquares,isSample)
Get variance covariance matrix from the data, adjust n by one if the data is from a sample

#### computeAdjustedData(initialData, ...selectedVectors)
Get adjusted data using principal components as selected

#### getEigenVectors(initialData)
Get the principal components of data using the steps outlined above.

#### analyseTopResult(initialData)
Same as computeAdjustedData(initialData,vectors[0]). Selecting only the top eigenvector which explains the most variance.

#### transpose(A)
Utility function to transpose a matrix A to A(T)

#### multiply(A,B)
Utility function to multiply AXB

#### clone(A)
Utility function to clone a matrix A

#### scale(A,n)
Utility function to scale all elements in A by a factor of n


LICENSE: MIT`