Delete unnecessary empty line from JSDoc (#842)

* Delete unnecessary empty line from JSDoc

* Update Codacy Badge

Author: ishii-norimi

README.md

@@ -2,7 +2,7 @@
 
 [![npm version](https://badge.fury.io/js/@ai-on-browser%2Fdata-analysis-models.svg)](https://badge.fury.io/js/@ai-on-browser%2Fdata-analysis-models)
 [![Coverage Status](https://coveralls.io/repos/github/ai-on-browser/ai-on-browser.github.io/badge.svg?branch=main)](https://coveralls.io/github/ai-on-browser/ai-on-browser.github.io?branch=main)
-[![Codacy Badge](https://app.codacy.com/project/badge/Grade/ea85dab39fff442685faeaff53afa1a0)](https://www.codacy.com/gh/ai-on-browser/ai-on-browser.github.io/dashboard?utm_source=github.com&utm_medium=referral&utm_content=ai-on-browser/ai-on-browser.github.io&utm_campaign=Badge_Grade)
+[![Codacy Badge](https://app.codacy.com/project/badge/Grade/ea85dab39fff442685faeaff53afa1a0)](https://app.codacy.com/gh/ai-on-browser/ai-on-browser.github.io/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
 Machine learning and data analysis package implemented in JavaScript and its online demo.

create_import_list.js

@@ -87,7 +87,6 @@ import Complex from './util/complex.js'
 	code += `
 /**
  * Default export object.
- *
  * @module default
  * @property {Tensor} Tensor Tensor class
  * @property {Matrix} Matrix Matrix class

js/data/loader/audio.js

@@ -1,4 +1,9 @@
 export default class AudioLoader {
+	/**
+	 * Load audio data
+	 * @param {Blob} data Audio data
+	 * @returns {Promise<AudioBuffer>} Loaded AudioBuffer
+	 */
 	static load(data) {
 		return new Promise(resolve => {
 			const reader = new FileReader()

js/data/loader/csv.js

@@ -1,6 +1,10 @@
 import 'https://cdnjs.cloudflare.com/ajax/libs/pako/2.1.0/pako.min.js'
 
 export default class CSV {
+	/**
+	 * @param {Array<Array<*>>} data data
+	 * @param {*} config Config
+	 */
 	constructor(data, { header = 0 } = {}) {
 		this._data = data
 		this._header = header
@@ -37,6 +41,9 @@ export default class CSV {
 		})
 	}
 
+	/**
+	 * @type {{name: string; type: string; out?: boolean}}
+	 */
 	get info() {
 		const names = this.columns
 		const types = this.type
@@ -49,10 +56,10 @@ export default class CSV {
 	}
 
 	/**
-	 *
-	 * @param {string} str
-	 * @param {*} [config]
-	 * @returns {CSV}
+	 * Parse CSV string
+	 * @param {string} str CSV string
+	 * @param {*} [config] Config value
+	 * @returns {CSV} Parsed CSV data
 	 */
 	static parse(str, config = {}) {
 		const delimiter = config.delimiter || ','
@@ -97,10 +104,10 @@ export default class CSV {
 	}
 
 	/**
-	 *
-	 * @param {string | File} value
-	 * @param {*} [config]
-	 * @returns {Promise<CSV>}
+	 * Load CSV data
+	 * @param {string | File} value URL or File
+	 * @param {*} [config] Config
+	 * @returns {Promise<CSV>} Parsed CSV data
 	 */
 	static async load(value, config = {}) {
 		if (typeof value === 'string') {

js/data/loader/document.js

@@ -4,6 +4,11 @@ import 'https://cdnjs.cloudflare.com/ajax/libs/encoding-japanese/2.0.0/encoding.
 pdfjsLib.GlobalWorkerOptions.workerSrc = 'https://cdn.jsdelivr.net/npm/[email protected]/build/pdf.worker.min.js'
 
 export default class DocumentLoader {
+	/**
+	 * Load text data
+	 * @param {Blob} data Plain text or PDF data
+	 * @returns {Promise<string>} Loaded string
+	 */
 	static load(data) {
 		return new Promise(resolve => {
 			const reader = new FileReader()
@@ -40,10 +45,21 @@ export default class DocumentLoader {
 		})
 	}
 
+	/**
+	 * Split text
+	 * @param {string} text text
+	 * @returns {string[]} Splitted text
+	 */
 	static segment(text) {
 		return text.split(/[ -@\[-`{-~\s]+/)
 	}
 
+	/**
+	 * Ordinalize texts
+	 * @param {string[]} texts Texts
+	 * @param {*} config Config
+	 * @returns {[string[], number[]]} Containing text and ordinalized texts
+	 */
 	static ordinal(texts, { ignoreCase = true } = {}) {
 		const words = []
 		const ord = []

js/data/loader/image.js

@@ -61,6 +61,11 @@ export default class ImageLoader {
 		}
 	}
 
+	/**
+	 * Load image data
+	 * @param {Blob | HTMLImageElement | HTMLVideoElement} data Audio data
+	 * @returns {Promise<number[][][]>} Loaded AudioBuffer
+	 */
 	static async load(data) {
 		if (data instanceof Blob) {
 			return new Promise(resolve => {
@@ -110,6 +115,13 @@ export default class ImageLoader {
 		}
 	}
 
+	/**
+	 * Reduce image data
+	 * @param {number[][][]} im Image data
+	 * @param {number} step Step size
+	 * @param {'mean' | 'max'} algorithm Reduce algorithm
+	 * @returns {number[][][]} Reduced image data
+	 */
 	static reduce(im, step, algorithm = 'mean') {
 		const x = []
 		const d = im[0][0].length
@@ -220,6 +232,14 @@ export default class ImageLoader {
 		}
 	}
 
+	/**
+	 * Convert color space of data
+	 * @param {number[][][]} data Input data
+	 * @param {string} space Convert space name
+	 * @param {boolean} normalize Normalize data between 0 and 1 or not
+	 * @param {number} binary_threshold Threshold for binary space
+	 * @returns {number[][][]} Converted data
+	 */
 	static applySpace(data, space, normalize = false, binary_threshold = 180) {
 		const cp = []
 		for (let i = 0; i < data.length; i++) {
@@ -231,6 +251,13 @@ export default class ImageLoader {
 		return cp
 	}
 
+	/**
+	 * Create canvas element
+	 * @param {number[][][]} data Image data
+	 * @param {number} width Width
+	 * @param {number | null} height Height
+	 * @returns {HTMLCanvasElement} Canvas
+	 */
 	static createCanvas(data, width = 80, height = null) {
 		const orgwidth = data[0].length
 		const orgheight = data.length

js/data/loader/json.js

@@ -1,4 +1,8 @@
 export default class JSONLoader {
+	/**
+	 * @param {*[]} json data
+	 * @param {*} [config] Config
+	 */
 	constructor(json, { columnInfos } = {}) {
 		this._json = json
 		this._columnInfos = columnInfos
@@ -38,22 +42,32 @@ export default class JSONLoader {
 		this._info[this._info.length - 1].out = true
 	}
 
+	/**
+	 * @type {*[]}
+	 */
 	get json() {
 		return this._json
 	}
 
+	/**
+	 * @type {Array<Array<*>>}
+	 */
 	get data() {
 		return this._data
 	}
 
+	/**
+	 * @type {{name: string; type: string; out?: boolean}}
+	 */
 	get info() {
 		return this._info
 	}
 
 	/**
-	 * @param {string | File} urlOrFile
-	 * @param {*} [config]
-	 * @returns {Promise<JSONLoader>}
+	 * Load JSON data
+	 * @param {string | File} urlOrFile URL string or File
+	 * @param {*} [config] Config
+	 * @returns {Promise<JSONLoader>} JSON data
 	 */
 	static async load(urlOrFile, config) {
 		if (urlOrFile instanceof File) {

lib/evaluate/classification.js

@@ -1,6 +1,5 @@
 /**
  * Returns accuracy.
- *
  * @param {*[]} pred Predicted classes
  * @param {*[]} t True classes
  * @returns {number} Accuracy
@@ -18,7 +17,6 @@ export function accuracy(pred, t) {
 
 /**
  * Returns precision with macro average.
- *
  * @param {*[]} pred Predicted classes
  * @param {*[]} t True classes
  * @returns {number} Precision
@@ -44,7 +42,6 @@ export function precision(pred, t) {
 
 /**
  * Returns recall with macro average.
- *
  * @param {*[]} pred Predicted classes
  * @param {*[]} t True classes
  * @returns {number} Recall
@@ -70,7 +67,6 @@ export function recall(pred, t) {
 
 /**
  * Returns F-score with macro average.
- *
  * @param {*[]} pred Predicted classes
  * @param {*[]} t True classes
  * @param {number} [beta] Positive real factor. Recall is considered `beta` times as important as precision.
@@ -100,7 +96,6 @@ export function fScore(pred, t, beta = 1) {
 
 /**
  * Returns Cohen's kappa coefficient.
- *
  * @param {*[]} pred Predicted classes
  * @param {*[]} t True classes
  * @returns {number} Cohen's kappa coefficient

lib/evaluate/clustering.js

@@ -1,6 +1,5 @@
 /**
  * Returns Davies-Bouldin index.
- *
  * @param {Array<Array<number>>} data Original data
  * @param {*[]} pred Predicted categories
  * @param {number} p P
@@ -64,7 +63,6 @@ export function davisBouldinIndex(data, pred, p = 2, q = 1) {
 
 /**
  * Returns Silhouette coefficient.
- *
  * @param {Array<Array<number>>} data Original data
  * @param {*[]} pred Predicted categories
  * @returns {number[]} Silhouette coefficient
@@ -121,7 +119,6 @@ export function silhouetteCoefficient(data, pred) {
 
 /**
  * Returns Dunn index.
- *
  * @param {Array<Array<number>>} data Original data
  * @param {*[]} pred Predicted categories
  * @param {'max' | 'mean' | 'centroid'} intra_d Intra-cluster distance type
@@ -227,7 +224,6 @@ export function dunnIndex(data, pred, intra_d = 'max', inter_d = 'centroid') {
 
 /**
  * Returns Purity.
- *
  * @param {*[]} pred Predicted categories
  * @param {*[]} t True categories
  * @returns {number} Purity
@@ -258,7 +254,6 @@ export function purity(pred, t) {
 
 /**
  * Returns Rand index.
- *
  * @param {*[]} pred Predicted categories
  * @param {*[]} t True categories
  * @returns {number} Rank index
@@ -280,7 +275,6 @@ export function randIndex(pred, t) {
 
 /**
  * Returns Dice index.
- *
  * @param {*[]} pred Predicted categories
  * @param {*[]} t True categories
  * @param {number} [beta] Positive real factor. Recall is considered `beta` times as important as precision.
@@ -307,7 +301,6 @@ export function diceIndex(pred, t, beta = 1) {
 
 /**
  * Returns Jaccard index.
- *
  * @param {*[]} pred Predicted categories
  * @param {*[]} t True categories
  * @returns {number} Jaccard index
@@ -333,7 +326,6 @@ export function jaccardIndex(pred, t) {
 
 /**
  * Returns Fowlkes-Mallows index.
- *
  * @param {*[]} pred Predicted categories
  * @param {*[]} t True categories
  * @returns {number} Fowlkes-Mallows index

lib/evaluate/dimensionality_reduction.js

@@ -1,6 +1,5 @@
 /**
  * Returns Co-Ranking Matrix.
- *
  * @param {Array<Array<number>>} x Reduced values
  * @param {Array<Array<number>>} z Original values
  * @param {number} ks Rank significance