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WCluster

Description:

  • 數據主成份分析與分群

Source:

數據主成份分析與分群

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)
    })

Methods

(async) PCA(data, optopt) → {Promise}

Description:

  • 數據主成分分析(Principal Component Analysis, PCA)

Source:
Example
Parameters:
Name Type Attributes Default Description
data Array

輸入數據陣列
opt Object <optional>
 {}

輸入設定物件，預設{}

Properties
Name Type Attributes Default Description
scale Boolean <optional>
 true

輸入是否對數據正規化布林值，預設true
nCompNIPALS Number <optional>
 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
Returns:

回傳Promise，resolve回傳指定維數數值的陣列，reject回傳錯誤訊息

Type
Promise

(async) cluster(data, optopt) → {Promise}

Description:

  • 針對數據陣列做分群

Source:
Example
Parameters:
Name Type Attributes Default Description
data Array

輸入數據陣列，可輸入純數據陣列(mat)或物件陣列(ltdt)
opt Object <optional>
 {}

輸入設定物件，預設{}

Properties
Name Type Attributes Default Description
kNumber Number <optional>
 2

輸入指定分群數整數，不能超過數據的長度，預設2
nCompNIPALS Number <optional>
 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
mode String <optional>
 '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'
Returns:

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

Type
Promise