Refactor model initialization to accept configuration objects for affinity parameters

js/view/label_propagation.js

@@ -7,7 +7,7 @@ export default function (platform) {
 	let model = null
 	const fitModel = () => {
 		if (!model) {
-			model = new LabelPropagation(method.value, sigma.value, k.value)
+			model = new LabelPropagation({ name: method.value, sigma: sigma.value, k: k.value })
 			model.init(
 				platform.trainInput,
 				platform.trainOutput.map(v => v[0])

js/view/label_spreading.js

@@ -7,7 +7,7 @@ export default function (platform) {
 	let model = null
 	const fitModel = () => {
 		if (!model) {
-			model = new LabelSpreading(alpha.value, method.value, sigma.value, k.value)
+			model = new LabelSpreading(alpha.value, { name: method.value, sigma: sigma.value, k: k.value })
 			model.init(
 				platform.trainInput,
 				platform.trainOutput.map(v => v[0])

js/view/laplacian_eigenmaps.js

@@ -16,7 +16,7 @@ export default function (platform) {
 	const k = controller.input.number({ label: 'k =', name: 'k_nearest', min: 1, max: 100, value: 10 })
 	controller.input.button('Fit').on('click', () => {
 		const dim = platform.dimension
-		const model = new LaplacianEigenmaps(dim, method.value, k.value, sigma.value)
+		const model = new LaplacianEigenmaps(dim, { name: method.value, k: k.value, sigma: sigma.value })
 		const pred = model.predict(platform.trainInput)
 		platform.trainResult = pred
 	})

js/view/spectral.js

@@ -29,8 +29,8 @@ export default function (platform) {
 	knnSpan.element.style.display = 'none'
 
 	const slbConf = controller.stepLoopButtons().init(() => {
-		const param = { sigma: sigma.value, k: k.value }
-		model = new SpectralClustering(method.value, param)
+		const param = { name: method.value, sigma: sigma.value, k: k.value }
+		model = new SpectralClustering(param)
 		model.init(platform.trainInput)
 		clusters.value = model.size
 		runSpan.element.querySelectorAll('input').forEach(elm => (elm.disabled = null))

lib/model/label_propagation.js

@@ -9,14 +9,14 @@ export default class LabelPropagation {
 	// http://yamaguchiyuto.hatenablog.com/entry/2016/09/22/014202
 	// https://github.com/scikit-learn/scikit-learn/blob/15a949460/sklearn/semi_supervised/_label_propagation.py
 	/**
-	 * @param {'rbf' | 'knn'} [method] Method name
-	 * @param {number} [sigma] Sigma of normal distribution
-	 * @param {number} [k] Number of neighborhoods
+	 * @param {'rbf' | 'knn' | { name: 'rbf', sigma?: number, k?: number } | { name: 'knn', k?: number }} [method] Method name
 	 */
-	constructor(method = 'rbf', sigma = 0.1, k = Infinity) {
-		this._k = k
-		this._sigma = sigma
-		this._affinity = method
+	constructor(method = 'rbf') {
+		if (typeof method === 'string') {
+			this._affinity = { name: method }
+		} else {
+			this._affinity = method
+		}
 	}
 
 	_affinity_matrix(x) {
@@ -31,23 +31,25 @@ export default class LabelPropagation {
 		}
 
 		const con = Matrix.zeros(n, n)
-		if (this._k >= n) {
+		const k = this._affinity.k ?? Infinity
+		if (k >= n) {
 			con.fill(1)
-		} else if (this._k > 0) {
+		} else if (k > 0) {
 			for (let i = 0; i < n; i++) {
 				const di = distances.row(i).value.map((v, i) => [v, i])
 				di.sort((a, b) => a[0] - b[0])
-				for (let j = 1; j < Math.min(this._k + 1, di.length); j++) {
+				for (let j = 1; j < Math.min(k + 1, di.length); j++) {
 					con.set(i, di[j][1], 1)
 				}
 			}
 			con.add(con.t)
 			con.div(2)
 		}
 
-		if (this._affinity === 'rbf') {
-			return Matrix.map(distances, (v, i) => (con.at(i) > 0 ? Math.exp(-(v ** 2) / this._sigma ** 2) : 0))
-		} else if (this._affinity === 'knn') {
+		if (this._affinity.name === 'rbf') {
+			const sigma = this._affinity.sigma ?? 0.1
+			return Matrix.map(distances, (v, i) => (con.at(i) > 0 ? Math.exp(-(v ** 2) / sigma ** 2) : 0))
+		} else if (this._affinity.name === 'knn') {
 			return Matrix.map(con, v => (v > 0 ? 1 : 0))
 		}
 	}

lib/model/label_spreading.js

@@ -8,14 +8,14 @@ export default class LabelSpreading {
 	// https://github.com/scikit-learn/scikit-learn/blob/15a949460/sklearn/semi_supervised/_label_propagation.py
 	/**
 	 * @param {number} [alpha] Clamping factor
-	 * @param {'rbf' | 'knn'} [method] Method name
-	 * @param {number} [sigma] Sigma of normal distribution
-	 * @param {number} [k] Number of neighborhoods
+	 * @param {'rbf' | 'knn' | { name: 'rbf', sigma?: number, k?: number } | { name: 'knn', k?: number }} [method] Method name
 	 */
-	constructor(alpha = 0.2, method = 'rbf', sigma = 0.1, k = Infinity) {
-		this._k = k
-		this._sigma = sigma
-		this._affinity = method
+	constructor(alpha = 0.2, method = 'rbf') {
+		if (typeof method === 'string') {
+			this._affinity = { name: method }
+		} else {
+			this._affinity = method
+		}
 
 		this._alpha = alpha
 	}
@@ -32,23 +32,25 @@ export default class LabelSpreading {
 		}
 
 		const con = Matrix.zeros(n, n)
-		if (this._k >= n) {
+		const k = this._affinity.k ?? Infinity
+		if (k >= n) {
 			con.fill(1)
-		} else if (this._k > 0) {
+		} else if (k > 0) {
 			for (let i = 0; i < n; i++) {
 				const di = distances.row(i).value.map((v, i) => [v, i])
 				di.sort((a, b) => a[0] - b[0])
-				for (let j = 1; j < Math.min(this._k + 1, di.length); j++) {
+				for (let j = 1; j < Math.min(k + 1, di.length); j++) {
 					con.set(i, di[j][1], 1)
 				}
 			}
 			con.add(con.t)
 			con.div(2)
 		}
 
-		if (this._affinity === 'rbf') {
-			return Matrix.map(distances, (v, i) => (con.at(i) > 0 ? Math.exp(-(v ** 2) / this._sigma ** 2) : 0))
-		} else if (this._affinity === 'knn') {
+		if (this._affinity.name === 'rbf') {
+			const sigma = this._affinity.sigma ?? 0.1
+			return Matrix.map(distances, (v, i) => (con.at(i) > 0 ? Math.exp(-(v ** 2) / sigma ** 2) : 0))
+		} else if (this._affinity.name === 'knn') {
 			return Matrix.map(con, v => (v > 0 ? 1 : 0))
 		}
 	}

lib/model/laplacian_eigenmaps.js

@@ -9,16 +9,16 @@ export default class LaplacianEigenmaps {
 	// https://scikit-learn.org/stable/modules/generated/sklearn.manifold.SpectralEmbedding.html
 	/**
 	 * @param {number} rd Reduced dimension
-	 * @param {'rbf' | 'knn'} [affinity] Affinity type name
-	 * @param {number} [k] Number of neighborhoods
-	 * @param {number} [sigma] Sigma of normal distribution
+	 * @param {'rbf' | 'knn' | { name: 'rbf', sigma?: number, k?: number } | { name: 'knn', k?: number }} [affinity] Affinity type name
 	 * @param {'unnormalized' | 'normalized'} [laplacian] Normalized laplacian matrix or not
 	 */
-	constructor(rd, affinity = 'rbf', k = 10, sigma = 1, laplacian = 'unnormalized') {
+	constructor(rd, affinity = 'rbf', laplacian = 'unnormalized') {
 		this._rd = rd
-		this._affinity = affinity
-		this._k = k
-		this._sigma = sigma
+		if (typeof affinity === 'string') {
+			this._affinity = { name: affinity }
+		} else {
+			this._affinity = affinity
+		}
 		this._laplacian = laplacian
 	}
 
@@ -41,11 +41,12 @@ export default class LaplacianEigenmaps {
 		}
 
 		const con = Matrix.zeros(n, n)
-		if (this._k > 0) {
+		const k = this._affinity.k ?? 10
+		if (k > 0) {
 			for (let i = 0; i < n; i++) {
 				const di = distances.row(i).value.map((v, i) => [v, i])
 				di.sort((a, b) => a[0] - b[0])
-				for (let j = 1; j < Math.min(this._k + 1, di.length); j++) {
+				for (let j = 1; j < Math.min(k + 1, di.length); j++) {
 					con.set(i, di[j][1], 1)
 				}
 			}
@@ -54,9 +55,10 @@ export default class LaplacianEigenmaps {
 		}
 
 		let W
-		if (this._affinity === 'rbf') {
-			W = Matrix.map(distances, (v, i) => (con.at(i) > 0 ? Math.exp(-(v ** 2) / this._sigma ** 2) : 0))
-		} else if (this._affinity === 'knn') {
+		if (this._affinity.name === 'rbf') {
+			const sigma = this._affinity.sigma ?? 1
+			W = Matrix.map(distances, (v, i) => (con.at(i) > 0 ? Math.exp(-(v ** 2) / sigma ** 2) : 0))
+		} else if (this._affinity.name === 'knn') {
 			W = Matrix.map(con, v => (v > 0 ? 1 : 0))
 		}
 		let d = W.sum(1).value

lib/model/spectral.js

@@ -7,18 +7,13 @@ import LaplacianEigenmaps from './laplacian_eigenmaps.js'
 export default class SpectralClustering {
 	// https://mr-r-i-c-e.hatenadiary.org/entry/20121214/1355499195
 	/**
-	 * @param {'rbf' | 'knn'} [affinity] Affinity type name
-	 * @param {object} [param] Config
-	 * @param {number} [param.sigma] Sigma of normal distribution
-	 * @param {number} [param.k] Number of neighborhoods
+	 * @param {'rbf' | 'knn' | { name: 'rbf', sigma?: number, k?: number } | { name: 'knn', k?: number }} [affinity] Affinity type name
 	 */
-	constructor(affinity = 'rbf', param = {}) {
+	constructor(affinity = 'rbf') {
 		this._size = 0
 		this._epoch = 0
 		this._clustering = new KMeanspp()
 		this._affinity = affinity
-		this._sigma = param.sigma || 1.0
-		this._k = param.k || 10
 	}
 
 	/**
@@ -44,7 +39,7 @@ export default class SpectralClustering {
 	init(datas) {
 		const n = datas.length
 		this._n = n
-		const le = new LaplacianEigenmaps(datas[0].length, this._affinity, this._k, this._sigma, 'normalized')
+		const le = new LaplacianEigenmaps(datas[0].length, this._affinity, 'normalized')
 		this.ready = false
 		le.predict(datas)
 		this._ev = le._ev

tests/lib/model/label_propagation.test.js

@@ -3,25 +3,22 @@ import LabelPropagation from '../../../lib/model/label_propagation.js'
 
 import { accuracy } from '../../../lib/evaluate/classification.js'
 
-test.each([{}, { method: 'rbf', sigma: 0.2 }, { method: 'knn', k: 10 }])(
-	'semi-classifier %p',
-	({ method, sigma, k }) => {
-		const model = new LabelPropagation(method, sigma, k)
-		const x = Matrix.concat(Matrix.randn(50, 2, 0, 0.2), Matrix.randn(50, 2, 5, 0.2)).toArray()
-		const t = []
-		const t_org = []
-		for (let i = 0; i < x.length; i++) {
-			t_org[i] = t[i] = String.fromCharCode('a'.charCodeAt(0) + Math.floor(i / 50))
-			if (Math.random() < 0.5) {
-				t[i] = null
-			}
+test.each([undefined, 'rbf', { name: 'rbf', sigma: 0.2 }, { name: 'knn', k: 10 }])('semi-classifier %p', method => {
+	const model = new LabelPropagation(method)
+	const x = Matrix.concat(Matrix.randn(50, 2, 0, 0.2), Matrix.randn(50, 2, 5, 0.2)).toArray()
+	const t = []
+	const t_org = []
+	for (let i = 0; i < x.length; i++) {
+		t_org[i] = t[i] = String.fromCharCode('a'.charCodeAt(0) + Math.floor(i / 50))
+		if (Math.random() < 0.5) {
+			t[i] = null
 		}
-		model.init(x, t)
-		for (let i = 0; i < 20; i++) {
-			model.fit()
-		}
-		const y = model.predict(x)
-		const acc = accuracy(y, t_org)
-		expect(acc).toBeGreaterThan(0.95)
 	}
-)
+	model.init(x, t)
+	for (let i = 0; i < 20; i++) {
+		model.fit()
+	}
+	const y = model.predict(x)
+	const acc = accuracy(y, t_org)
+	expect(acc).toBeGreaterThan(0.95)
+})

tests/lib/model/label_spreading.test.js

@@ -3,25 +3,26 @@ import LabelSpreading from '../../../lib/model/label_spreading.js'
 
 import { accuracy } from '../../../lib/evaluate/classification.js'
 
-test.each([{}, { alpha: 0.5, method: 'rbf', sigma: 0.2 }, { alpha: 0.8, method: 'knn', k: 10 }])(
-	'semi-classifier %s %p',
-	({ alpha, method, sigma, k }) => {
-		const model = new LabelSpreading(alpha, method, sigma, k)
-		const x = Matrix.concat(Matrix.randn(50, 2, 0, 0.2), Matrix.randn(50, 2, 5, 0.2)).toArray()
-		const t = []
-		const t_org = []
-		for (let i = 0; i < x.length; i++) {
-			t_org[i] = t[i] = String.fromCharCode('a'.charCodeAt(0) + Math.floor(i / 50))
-			if (Math.random() < 0.5) {
-				t[i] = null
-			}
+test.each([
+	[undefined, undefined],
+	[0.5, { name: 'rbf', sigma: 0.2 }],
+	[0.8, { name: 'knn', k: 10 }],
+])('semi-classifier %p %p', (alpha, method) => {
+	const model = new LabelSpreading(alpha, method)
+	const x = Matrix.concat(Matrix.randn(50, 2, 0, 0.2), Matrix.randn(50, 2, 5, 0.2)).toArray()
+	const t = []
+	const t_org = []
+	for (let i = 0; i < x.length; i++) {
+		t_org[i] = t[i] = String.fromCharCode('a'.charCodeAt(0) + Math.floor(i / 50))
+		if (Math.random() < 0.5) {
+			t[i] = null
 		}
-		model.init(x, t)
-		for (let i = 0; i < 20; i++) {
-			model.fit()
-		}
-		const y = model.predict(x)
-		const acc = accuracy(y, t_org)
-		expect(acc).toBeGreaterThan(0.95)
 	}
-)
+	model.init(x, t)
+	for (let i = 0; i < 20; i++) {
+		model.fit()
+	}
+	const y = model.predict(x)
+	const acc = accuracy(y, t_org)
+	expect(acc).toBeGreaterThan(0.95)
+})

tests/lib/model/laplacian_eigenmaps.test.js

@@ -6,10 +6,10 @@ import LaplacianEigenmaps from '../../../lib/model/laplacian_eigenmaps.js'
 
 import { coRankingMatrix } from '../../../lib/evaluate/dimensionality_reduction.js'
 
-describe.each([undefined, 'knn'])('dimensionality reduction affinity:%p', affinity => {
+describe.each([undefined, 'knn', { name: 'rbf' }])('dimensionality reduction affinity:%p', affinity => {
 	test.each([undefined, 'normalized'])('laplacian: %p', laplacian => {
 		const x = Matrix.concat(Matrix.randn(30, 5, 0, 0.2), Matrix.randn(30, 5, 5, 0.2)).toArray()
-		const model = new LaplacianEigenmaps(2, affinity, undefined, undefined, laplacian)
+		const model = new LaplacianEigenmaps(2, affinity, laplacian)
 
 		const y = model.predict(x)
 		expect(y[0]).toHaveLength(2)

tests/lib/model/spectral.test.js

@@ -3,8 +3,8 @@ import SpectralClustering from '../../../lib/model/spectral.js'
 
 import { randIndex } from '../../../lib/evaluate/clustering.js'
 
-test('clustering', () => {
-	const model = new SpectralClustering()
+test.each([undefined, 'rbf', { name: 'rbf', sigma: 0.5 }, { name: 'knn', k: 4 }])('clustering %p', affinity => {
+	const model = new SpectralClustering(affinity)
 	const n = 5
 	const x = Matrix.concat(
 		Matrix.concat(Matrix.randn(n, 2, 0, 0.1), Matrix.randn(n, 2, 5, 0.1)),