chore: modernize project

BREAKING CHANGE:

Node.js 4 and 6 are no longer supported.

.babelrc

@@ -0,0 +1,3 @@
+{
+  "plugins": ["@babel/plugin-transform-modules-commonjs"]
+}

.eslintrc.yml

@@ -1 +1,3 @@
-extends: 'eslint-config-cheminfo'
+extends: cheminfo
+parserOptions:
+  sourceType: module

.gitignore

@@ -4,3 +4,5 @@ logs
 *.log
 npm-debug.log*
 coverage
+distance-matrix.js
+distance-matrix.d.ts

.npmrc

@@ -0,0 +1 @@
+package-lock=false

.travis.yml

@@ -1,7 +1,7 @@
 language: node_js
 node_js:
-  - node
-  - lts/boron # v6
-  - lts/argon # v4
-script: "npm run test-travis "
-after_script: "bash <(curl -s https://codecov.io/bash)"
+  - 8
+  - 10
+  - 12
+after_success:
+  - bash <(curl -s https://codecov.io/bash)

README.md

@@ -1,16 +1,15 @@
 # distance-matrix
 
-  [![NPM version][npm-image]][npm-url]
-  [![build status][travis-image]][travis-url]
-  [![Test coverage][codecov-image]][codecov-url]
-  [![David deps][david-image]][david-url]
-  [![npm download][download-image]][download-url]
+[![NPM version][npm-image]][npm-url]
+[![build status][travis-image]][travis-url]
+[![Test coverage][codecov-image]][codecov-url]
+[![npm download][download-image]][download-url]
 
-Compute a distance/similarity matrix
+Compute a distance/similarity matrix.
 
 ## Installation
 
-`$ npm install ml-distance-matrix`
+`npm i ml-distance-matrix`
 
 ## [API Documentation](https://mljs.github.io/distance-matrix/)
 
@@ -21,14 +20,9 @@ const distanceMatrix = require('ml-distance-matrix');
 const euclidean = require('ml-distance-euclidean');
 
 // Computes euclidan distance for each pair of possible vectors
-const matrix = distanceMatrix([
-    [8, -1, 2],
-    [-4, 0, 3],
-    [-3, -1, 6]
-], euclidean);
+const matrix = distanceMatrix([[8, -1, 2], [-4, 0, 3], [-3, -1, 6]], euclidean);
 ```
 
-
 ## License
 
 [MIT](./LICENSE)
@@ -39,7 +33,5 @@ const matrix = distanceMatrix([
 [travis-url]: https://travis-ci.org/mljs/distance-matrix
 [codecov-image]: https://img.shields.io/codecov/c/github/mljs/distance-matrix.svg?style=flat-square
 [codecov-url]: https://codecov.io/gh/mljs/distance-matrix
-[david-image]: https://img.shields.io/david/mljs/distance-matrix.svg?style=flat-square
-[david-url]: https://david-dm.org/mljs/distance-matrix
 [download-image]: https://img.shields.io/npm/dm/ml-distance-matrix.svg?style=flat-square
 [download-url]: https://npmjs.org/package/ml-distance-matrix

package.json

@@ -2,17 +2,23 @@
   "name": "ml-distance-matrix",
   "version": "1.0.0",
   "description": "Compute a distance/similarity matrix",
-  "main": "./src/index.js",
+  "main": "distance-matrix.js",
+  "module": "src/index.js",
+  "types": "distance-matrix.d.ts",
+  "sideEffects": false,
   "files": [
+    "distance-matrix.js",
+    "distance-matrix.d.ts",
     "src"
   ],
   "scripts": {
-    "eslint": "eslint src test",
-    "eslint-fix": "npm run eslint -- --fix",
-    "test": "npm run test-mocha && npm run eslint",
-    "test-cov": "istanbul cover node_modules/.bin/_mocha -- --require should --reporter dot --recursive",
-    "test-travis": "istanbul cover node_modules/.bin/_mocha --report lcovonly -- --require should --reporter dot --recursive",
-    "test-mocha": "mocha --require should --reporter mocha-better-spec-reporter"
+    "compile": "rollup -c",
+    "eslint": "eslint src",
+    "eslint-fix": "eslint src --fix",
+    "prepublishOnly": "npm run compile",
+    "test": "npm run test-coverage && npm run eslint",
+    "test-only": "jest",
+    "test-coverage": "jest --coverage"
   },
   "repository": {
     "type": "git",
@@ -23,7 +29,9 @@
     "learning",
     "data",
     "mining",
-    "datamining"
+    "datamining",
+    "distance",
+    "matrix"
   ],
   "author": "Daniel Kostro <kostro.d@gmail.com> (https://github.com/Daniel Kostro)",
   "license": "MIT",
@@ -32,12 +40,12 @@
   },
   "homepage": "https://github.com/mljs/distance-matrix#readme",
   "devDependencies": {
-    "eslint": "^3.12.2",
-    "eslint-config-cheminfo": "^1.6.0",
-    "eslint-plugin-no-only-tests": "^1.1.0",
-    "istanbul": "^0.4.5",
-    "mocha": "^3.2.0",
-    "mocha-better-spec-reporter": "^3.1.0",
-    "should": "^11.1.2"
+    "@babel/plugin-transform-modules-commonjs": "^7.5.0",
+    "eslint": "^6.0.1",
+    "eslint-config-cheminfo": "^1.20.1",
+    "eslint-plugin-import": "^2.18.0",
+    "eslint-plugin-jest": "^22.7.2",
+    "jest": "^24.8.0",
+    "rollup": "^1.16.7"
   }
 }

rollup.config.js

@@ -0,0 +1,8 @@
+export default {
+  input: 'src/index.js',
+  output: {
+    file: 'distance-matrix.js',
+    format: 'cjs',
+  },
+  external: [],
+};

src/.npmignore

@@ -0,0 +1,2 @@
+__tests__
+.npmignore

src/__tests__/test.js

@@ -0,0 +1,19 @@
+import distanceMatrix from '..';
+
+describe('distance-matrix test', () => {
+  it('identity distance (0 if the same, 1 if not', () => {
+    // [1, 0, 1, 1]
+    // =>
+    // [0, 1, 0, 0]
+    // [1, 0, 1, 1]
+    // [0, 1, 0, 0]
+    // [0, 1, 0, 0]
+    const result = distanceMatrix([1, 0, 1, 1], (a, b) => Number(a !== b));
+    expect(result).toStrictEqual([
+      [0, 1, 0, 0],
+      [1, 0, 1, 1],
+      [0, 1, 0, 0],
+      [0, 1, 0, 0],
+    ]);
+  });
+});

src/index.js

@@ -1,32 +1,32 @@
-'use strict';
-
-
 /**
  * Computes a distance/similarity matrix given an array of data and a distance/similarity function.
  * @param {Array} data An array of data
  * @param {function} distanceFn  A function that accepts two arguments and computes a distance/similarity between them
  * @return {Array<Array>} The distance/similarity matrix. The matrix is square and has a size equal to the length of
  * the data array
  */
-function distanceMatrix(data, distanceFn) {
-    const length = data.length;
-    let result = Array.from({length}).map(() => Array.from({length}));
-
-    // Compute upper distance matrix
-    for (let i = 0; i < length; i++) {
-        for (let j = 0; j <= i; j++) {
-            result[i][j] = distanceFn(data[i], data[j]);
-        }
-    }
+export default function distanceMatrix(data, distanceFn) {
+  const result = getMatrix(data.length);
 
-    // Copy to lower distance matrix
-    for (let i = 0; i < length; i++) {
-        for (let j = i + 1; j < length; j++) {
-            result[i][j] = result[j][i];
-        }
+  // Compute upper distance matrix
+  for (let i = 0; i < data.length; i++) {
+    for (let j = 0; j <= i; j++) {
+      result[i][j] = distanceFn(data[i], data[j]);
+      result[j][i] = result[i][j];
     }
+  }
 
-    return result;
+  return result;
 }
 
-module.exports = distanceMatrix;
+function getMatrix(size) {
+  const matrix = [];
+  for (let i = 0; i < size; i++) {
+    const row = [];
+    matrix.push(row);
+    for (let j = 0; j < size; j++) {
+      row.push(0);
+    }
+  }
+  return matrix;
+}