import WPCAMat from './WPCAMat.mjs'
import WClusterCore from './WClusterCore.mjs'
/**
* 數據主成分分析(Principal Component Analysis, PCA)
*
* @param {Array} data 輸入數據陣列
* @param {Object} [opt={}] 輸入設定物件，預設{}
* @param {Boolean} [opt.scale=true] 輸入是否對數據正規化布林值，預設true
* @param {Number} [opt.nCompNIPALS=2] 輸入指定降維的維度整數，不能超過數據的維度(各列有效元素數量)，預設2
* @return {Promise} 回傳Promise，resolve回傳指定維數數值的陣列，reject回傳錯誤訊息
* @example
*/
async function PCA(data, opt = {}) {
return WPCAMat(data, opt)
}
/**
* 針對數據陣列做分群
*
* @param {Array} data 輸入數據陣列，可輸入純數據陣列(mat)或物件陣列(ltdt)
* @param {Object} [opt={}] 輸入設定物件，預設{}
* @param {Number} [opt.kNumber=2] 輸入指定分群數整數，不能超過數據的長度，預設2
* @param {Number} [opt.nCompNIPALS=2] 輸入指定降維的維度整數，不能超過數據的維度(各列有效元素數量)，預設2
* @param {String} [opt.mode='k-medoids'] 輸入分群方法字串，可為'k-means'、'k-medoids'，k-means受初始隨機群中心影響較大，預設'k-medoids'
* @return {Promise} 回傳Promise，resolve回傳分群後結果物件，keys代表分群有使用到的欄位，ginds代表分群後的指標陣列，gltdt代表分群後的物件陣列，gmat代表分群後且經過PCA處理的數據陣列，其內因是降維後的數據，可例如取最左2欄的數據代表x,y來繪製分群後二維分佈圖，reject回傳錯誤訊息
* @example
*/
async function cluster(data, opt = {}) {
try {
return WClusterCore(data, opt)
}
catch (err) {
return Promise.reject(err)
}
}
/**
* 數據主成份分析與分群
*
* @return {Object} 回傳物件，其內有PCA與cluster函式，PCA為主成份分析，cluster為分群分析
* @example
*
* async function testPCA() {
*
* let mat = [
* [40, 50, 60],
* [50, 70, 60],
* [80, 70, 90],
* [50, 60, 80]
* ]
* console.log('mat', mat)
* // => mat [ [ 40, 50, 60 ], [ 50, 70, 60 ], [ 80, 70, 90 ], [ 50, 60, 80 ] ]
*
* let resMat = await WCluster.PCA(mat, { nCompNIPALS: 2 })
* console.log(resMat)
* // => [
* // [ -1.704002697669786, 0.43087564048799354 ],
* // [ -0.2509699164590232, -1.1519035967558147 ],
* // [ 1.9913098008410954, 0.23244503334983269 ],
* // [ -0.03633718671228592, 0.48858292291798827 ]
* // ]
*
* let mat2 = [
* [1040, 50, 60],
* [1050, 70, 60],
* [1080, 70, 90],
* [1050, 60, 80]
* ]
* console.log('mat2', mat2)
* // => mat [ [ 1040, 50, 60 ], [ 1050, 70, 60 ], [ 1080, 70, 90 ], [ 1050, 60, 80 ] ]
*
* let resMat2 = await WCluster.PCA(mat2, { nCompNIPALS: 2 })
* console.log(resMat2)
* // => [
* // [ -1.704002697669786, 0.43087564048799354 ],
* // [ -0.2509699164590232, -1.1519035967558147 ],
* // [ 1.9913098008410954, 0.23244503334983269 ],
* // [ -0.03633718671228592, 0.48858292291798827 ]
* // ]
*
* let mat3 = [
* [11040, 50, 60],
* [13050, 70, 60],
* [15080, 70, 90],
* [17050, 60, 80]
* ]
* console.log('mat3', mat3)
* // => mat [ [ 11040, 50, 60 ], [ 13050, 70, 60 ], [ 15080, 70, 90 ], [ 17050, 60, 80 ] ]
*
* let resMat3 = await WCluster.PCA(mat3, { nCompNIPALS: 2 })
* console.log(resMat3)
* // => [
* // [ -1.8599655569892897, 0.4908764271508211 ],
* // [ -0.3950941652793108, -1.0977355294143445 ],
* // [ 1.3430199944041517, -0.17213692267477554 ],
* // [ 0.912039727864449, 0.778996024938299 ]
* // ]
*
* }
* testPCA()
* .catch((err) => {
* console.log(err)
* })
*
* async function testCluster() {
* let mode = 'k-medoids'
*
* let mat = [
* [40, 50, 60],
* [50, 70, 60],
* [80, 70, 90],
* [50, 60, 80]
* ]
* console.log('mat', mat)
* // => mat [ [ 40, 50, 60 ], [ 50, 70, 60 ], [ 80, 70, 90 ], [ 50, 60, 80 ] ]
*
* let resMat = await WCluster.cluster(mat, { mode, kNumber: 2, nCompNIPALS: 2 })
* console.log(JSON.stringify(resMat, null, 2))
* // => {
* // "keys": null,
* // "ginds": [
* // [ 0, 1, 3 ],
* // [ 2 ]
* // ],
* // "gmat": [
* // [
* // [ -1.704002697669786, 0.43087564048799354 ],
* // [ -0.2509699164590232, -1.1519035967558147 ],
* // [ -0.03633718671228592, 0.48858292291798827 ]
* // ],
* // [
* // [ 1.9913098008410954, 0.23244503334983269 ]
* // ]
* // ],
* // "gltdt": [
* // [
* // [ 40, 50, 60 ],
* // [ 50, 70, 60 ],
* // [ 50, 60, 80 ]
* // ],
* // [
* // [ 80, 70, 90 ]
* // ]
* // ]
* // }
*
* let mat2 = [
* [1040, 50, 60],
* [1050, 70, 60],
* [1080, 70, 90],
* [1050, 60, 80]
* ]
* console.log('mat2', mat2)
* // => mat [ [ 1040, 50, 60 ], [ 1050, 70, 60 ], [ 1080, 70, 90 ], [ 1050, 60, 80 ] ]
*
* let resMat2 = await WCluster.cluster(mat2, { mode, kNumber: 2, nCompNIPALS: 2 })
* console.log(JSON.stringify(resMat2, null, 2))
* // => {
* // "keys": null,
* // "ginds": [
* // [ 0, 1, 3 ],
* // [ 2 ]
* // ],
* // "gmat": [
* // [
* // [ -1.704002697669786, 0.43087564048799354 ],
* // [ -0.2509699164590232, -1.1519035967558147 ],
* // [ -0.03633718671228592, 0.48858292291798827 ]
* // ],
* // [
* // [ 1.9913098008410954, 0.23244503334983269 ]
* // ]
* // ],
* // "gltdt": [
* // [
* // [ 1040, 50, 60 ],
* // [ 1050, 70, 60 ],
* // [ 1050, 60, 80 ]
* // ],
* // [
* // [ 1080, 70, 90 ]
* // ]
* // ]
* // }
*
* let mat3 = [
* [11040, 50, 60],
* [13050, 70, 60],
* [15080, 70, 90],
* [17050, 60, 80]
* ]
* console.log('mat3', mat3)
* // => mat [ [ 11040, 50, 60 ], [ 13050, 70, 60 ], [ 15080, 70, 90 ], [ 17050, 60, 80 ] ]
*
* let resMat3 = await WCluster.cluster(mat3, { mode, kNumber: 2, nCompNIPALS: 2 })
* console.log(JSON.stringify(resMat3, null, 2))
* // => {
* // "keys": null,
* // "ginds": [
* // [ 1, 2, 3 ],
* // [ 0 ]
* // ],
* // "gmat": [
* // [
* // [ -0.3950941652793108, -1.0977355294143445 ],
* // [ 1.3430199944041517, -0.17213692267477554 ],
* // [ 0.912039727864449, 0.778996024938299 ]
* // ],
* // [
* // [ -1.8599655569892897, 0.4908764271508211 ]
* // ]
* // ],
* // "gltdt": [
* // [
* // [ 13050, 70, 60 ],
* // [ 15080, 70, 90 ],
* // [ 17050, 60, 80 ]
* // ],
* // [
* // [ 11040, 50, 60 ]
* // ]
* // ]
* // }
*
* let ltdt = [
* { name: 'Cameron', a: 40, b: 50, c: 60 },
* { name: 'Buckley', a: 50, b: 70, c: 60 },
* { name: 'Paul', a: 80, b: 70, c: 90 },
* { name: 'Fawcett', a: 50, b: 60, c: 80 },
* ]
* console.log('ltdt', ltdt)
* // => ltdt [
* // { name: 'Cameron', a: 40, b: 50, c: 60 },
* // { name: 'Buckley', a: 50, b: 70, c: 60 },
* // { name: 'Paul', a: 80, b: 70, c: 90 },
* // { name: 'Fawcett', a: 50, b: 60, c: 80 }
* // ]
*
* let resLtdt = await WCluster.cluster(ltdt, { mode, kNumber: 2, nCompNIPALS: 2 })
* console.log(JSON.stringify(resLtdt, null, 2))
* // => {
* // "keys": [ "a", "b", "c" ],
* // "ginds": [
* // [ 0, 1, 3 ],
* // [ 2 ]
* // ],
* // "gmat": [
* // [
* // [ -1.704002697669786, 0.43087564048799354 ],
* // [ -0.2509699164590232, -1.1519035967558147 ],
* // [ -0.03633718671228592, 0.48858292291798827 ]
* // ],
* // [
* // [ 1.9913098008410954, 0.23244503334983269 ]
* // ]
* // ],
* // "gltdt": [
* // [
* // { "name": "Cameron", "a": 40, "b": 50, "c": 60 },
* // { "name": "Buckley", "a": 50, "b": 70, "c": 60 },
* // { "name": "Fawcett", "a": 50, "b": 60, "c": 80 }
* // ],
* // [
* // { "name": "Paul", "a": 80, "b": 70, "c": 90 }
* // ]
* // ]
* // }
*
* }
* testCluster()
* .catch((err) => {
* console.log(err)
* })
*
*/
let WCluster = {
PCA,
cluster,
}
export default WCluster