Move h parameter of KDE from fit method to constructor (#692)

* Move h parameter of KDE from fit method to constructor

* Fix JSDoc

lib/model/kernel_density_estimator.js

@@ -5,10 +5,11 @@ export default class KernelDensityEstimator {
 	// https://ja.wikipedia.org/wiki/%E3%82%AB%E3%83%BC%E3%83%8D%E3%83%AB%E5%AF%86%E5%BA%A6%E6%8E%A8%E5%AE%9A
 	// http://ibis.t.u-tokyo.ac.jp/suzuki/lecture/2015/dataanalysis/L9.pdf
 	/**
+	 * @param {number} [h=0] Smoothing parameter for the kernel
 	 * @param {'gaussian' | 'rectangular' | 'triangular' | 'epanechnikov' | 'biweight' | 'triweight' | function (number): number} [kernel=gaussian] Kernel name
 	 */
-	constructor(kernel = 'gaussian') {
-		this._h = 0
+	constructor(h = 0, kernel = 'gaussian') {
+		this._h = h
 		if (typeof kernel === 'function') {
 			this._kernel = kernel
 		} else {
@@ -39,13 +40,11 @@ export default class KernelDensityEstimator {
 	 * Fit model.
 	 *
 	 * @param {Array<Array<number>>} x Training data
-	 * @param {number} h Smoothing parameter for the kernel
 	 */
-	fit(x, h = 0) {
+	fit(x) {
 		this._x = x
 
-		if (h > 0) {
-			this._h = h
+		if (this._h > 0) {
 			return
 		}
 

tests/lib/model/kernel_density_estimator.test.js

@@ -7,8 +7,27 @@ import KernelDensityEstimator from '../../../lib/model/kernel_density_estimator.
 import { correlation } from '../../../lib/evaluate/regression.js'
 
 describe('density estimation', () => {
+	test('default', () => {
+		const model = new KernelDensityEstimator()
+		const n = 500
+		const x = Matrix.concat(Matrix.randn(n, 2, 0, 0.1), Matrix.randn(n, 2, 5, 0.1)).toArray()
+
+		model.fit(x)
+		const y = model.predict(x)
+		expect(y).toHaveLength(x.length)
+
+		const p = []
+		for (let i = 0; i < x.length; i++) {
+			const p1 = Math.exp(-x[i].reduce((s, v) => s + v ** 2, 0) / (2 * 0.1)) / (2 * Math.PI * 0.1)
+			const p2 = Math.exp(-x[i].reduce((s, v) => s + (v - 5) ** 2, 0) / (2 * 0.1)) / (2 * Math.PI * 0.1)
+			p[i] = (p1 + p2) / 2
+		}
+		const corr = correlation(y, p)
+		expect(corr).toBeGreaterThan(0.9)
+	})
+
 	test.each([undefined, 'gaussian', 'triangular', 'epanechnikov', 'biweight', 'triweight'])('kernel %s', kernel => {
-		const model = new KernelDensityEstimator(kernel)
+		const model = new KernelDensityEstimator(0, kernel)
 		const n = 500
 		const x = Matrix.concat(Matrix.randn(n, 2, 0, 0.1), Matrix.randn(n, 2, 5, 0.1)).toArray()
 
@@ -27,7 +46,7 @@ describe('density estimation', () => {
 	})
 
 	test.each(['rectangular'])('kernel %s', kernel => {
-		const model = new KernelDensityEstimator(kernel)
+		const model = new KernelDensityEstimator(0, kernel)
 		const n = 500
 		const x = Matrix.concat(Matrix.randn(n, 2, 0, 0.1), Matrix.randn(n, 2, 5, 0.1)).toArray()
 
@@ -46,7 +65,7 @@ describe('density estimation', () => {
 	})
 
 	test('custom kernel', () => {
-		const model = new KernelDensityEstimator(v => 1 / (v + 1.0e-8))
+		const model = new KernelDensityEstimator(1.0, v => 1 / (v + 1.0e-8))
 		const n = 500
 		const x = Matrix.concat(Matrix.randn(n, 2, 0, 0.1), Matrix.randn(n, 2, 5, 0.1)).toArray()