feat: update to our current standards and convert to type module

.github/workflows/typedoc.yml

@@ -6,24 +6,10 @@ on:
     types: [published]
 
 jobs:
-  deploy:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-      - uses: actions/setup-node@v3
-        with:
-          node-version: 16.x
-      - name: Install dependencies
-        run: npm install
-      - name: Build documentation
-        uses: zakodium/typedoc-action@v2
-        with:
-          entry: src/index.ts
-
-      - name: Deploy to GitHub pages
-        uses: JamesIves/github-pages-deploy-action@releases/v4
-        with:
-          token: ${{ secrets.BOT_TOKEN }}
-          branch: gh-pages
-          folder: docs
-          clean: true
+  typedoc:
+    # Documentation: https://github.com/zakodium/workflows#typedoc
+    uses: zakodium/workflows/.github/workflows/typedoc.yml@typedoc-v1
+    with:
+      entry: 'src/index.ts'
+    secrets:
+      github-token: ${{ secrets.BOT_TOKEN }}

.gitignore

@@ -1,10 +1,9 @@
+.claude
 .idea
 node_modules
 lib
 coverage
 
 .DS_Store
 
-lib-esm
-
 docs

.npmrc

@@ -1 +1,2 @@
 package-lock=false
+ignore-scripts=true

.prettierignore

@@ -0,0 +1,3 @@
+CHANGELOG.md
+coverage
+lib

README.md

@@ -1,20 +1,20 @@
 # ml-distance
 
 <h3 align="center">
-  
+
   <a href="https://www.zakodium.com">
    <img src="https://www.zakodium.com/brand/zakodium-logo-white.svg" width="50" alt="Zakodium logo" />
   </a>
-  
+
   <p>
     Maintained by <a href="https://www.zakodium.com">Zakodium</a>
   </p>
-  
+
 [![NPM version][npm-image]][npm-url]
 [![Node.js CI](https://github.com/mljs/distance/actions/workflows/nodejs.yml/badge.svg)](https://github.com/mljs/distance/actions/workflows/nodejs.yml)
 [![Test coverage][codecov-image]][codecov-url]
 [![npm download][download-image]][download-url]
-  
+
 </h3>
 
 Distance functions to compare vectors.
@@ -23,6 +23,22 @@ Distance functions to compare vectors.
 
 `$ npm i ml-distance`
 
+This package is ESM-only. CommonJS consumers need Node.js >= 20.19, >= 22.12, or
+any 24.x or later to `require()` it, or should migrate to `import`.
+
+## Usage
+
+```js
+import { distance, similarity } from 'ml-distance';
+
+const p = [0, 1, 2, 3, 4];
+const q = [4, 3, 2, 1, 0];
+
+distance.euclidean(p, q); // 6.324555320336759
+distance.manhattan(p, q); // 12
+similarity.cosine(p, q); // 0.3333333333333333
+```
+
 ## Methods
 
 ### Distances
@@ -65,13 +81,13 @@ $d(p,q)=\frac{\sum\limits_{i=1}^{n}{\left|p_i-q_i\right|}}{n}$
 
 Returns the [Soergel distance](http://www.orgchm.bas.bg/~vmonev/SimSearch.pdf) between vectors p and q
 
-$d(p,q)=\frac{\sum\limits_{i=1}^{n}{\left|p_i-q_i\right|}}{max(p_i,q_i)}$
+$d(p,q)=\frac{\sum\limits_{i=1}^{n}{\left|p_i-q_i\right|}}{\sum\limits_{i=1}^{n}{max(p_i,q_i)}}$
 
 - `kulczynski(p, q)`
 
 Returns the [Kulczynski distance](http://www.naun.org/main/NAUN/ijmmas/mmmas-49.pdf) between vectors p and q
 
-$d(p,q)=\frac{\sum\limits_{i=1}^{n}{\left|p_i-q_i\right|}}{min(p_i,q_i)}$
+$d(p,q)=\frac{\sum\limits_{i=1}^{n}{\left|p_i-q_i\right|}}{\sum\limits_{i=1}^{n}{min(p_i,q_i)}}$
 
 - `canberra(p, q)`
 
@@ -113,9 +129,9 @@ Note: distance between 2 identical vectors is 0.5 !
 
 - `ruzicka(p, q)`
 
-Returns the [Ruzicka similarity](http://www.naun.org/main/NAUN/ijmmas/mmmas-49.pdf) between vectors p and q
+Returns the [Ruzicka similarity](http://www.naun.org/main/NAUN/ijmmas/mmmas-49.pdf) between vectors p and q. The matching distance is `soergel`.
 
-$d(p,q)=\frac{\sum\limits_{i=1}^{n}{max(p_i,q_i)}}{\sum\limits_{i=1}^{n}{min(p_i,q_i)}}$
+$s(p,q)=\frac{\sum\limits_{i=1}^{n}{min(p_i,q_i)}}{\sum\limits_{i=1}^{n}{max(p_i,q_i)}}$
 
 - `tanimoto(p, q, [bitVector])`
 
@@ -133,18 +149,6 @@ Returns the [Harmonic mean similarity](http://www.naun.org/main/NAUN/ijmmas/mmma
 
 $d(p,q)=2\sum\limits_{i=1}^{n}\frac{p_i\cdot{q_i}}{p_i+q_i}$
 
-- `cosine(p, q)`
-
-Returns the [Cosine similarity](http://www.naun.org/main/NAUN/ijmmas/mmmas-49.pdf) between vectors p and q
-
-$d(p,q)=\frac{\sum\limits_{i=1}^{n}{p_i\cdot{q_i}}}{\sqrt{\sum\limits_{i=1}^{n}{p_i^2}}\sqrt{\sum\limits_{i=1}^{n}{q_i^2}}}$
-
-- `kumarHassebrook(p, q)`
-
-Returns the [Kumar-Hassebrook similarity](http://www.naun.org/main/NAUN/ijmmas/mmmas-49.pdf) between vectors p and q
-
-$d(p,q)=\frac{\sum\limits_{i=1}^{n}{p_i\cdot{q_i}}}{\sum\limits_{i=1}^{n}{p_i^2}+\sum\limits_{i=1}^{n}{q_i^2}-\sum\limits_{i=1}^{n}{p_i\cdot{q_i}}}$
-
 - `jaccard(p, q)`
 
 Returns the [Jaccard distance](http://www.naun.org/main/NAUN/ijmmas/mmmas-49.pdf) between vectors p and q
@@ -155,7 +159,7 @@ $d(p,q)=1-\frac{\sum\limits_{i=1}^{n}{p_i\cdot{q_i}}}{\sum\limits_{i=1}^{n}{p_i^
 
 Returns the [Dice distance](http://www.naun.org/main/NAUN/ijmmas/mmmas-49.pdf) between vectors p and q
 
-$d(p,q)=1-\frac{\sum\limits_{i=1}^{n}{(p_i-q_i)^2}}{\sum\limits_{i=1}^{n}{p_i^2}+\sum\limits_{i=1}^{n}{q_i^2}}$
+$d(p,q)=\frac{\sum\limits_{i=1}^{n}{(p_i-q_i)^2}}{\sum\limits_{i=1}^{n}{p_i^2}+\sum\limits_{i=1}^{n}{q_i^2}}$
 
 - `fidelity(p, q)`
 
@@ -311,9 +315,21 @@ Returns the [Kulczynski similarity](http://www.naun.org/main/NAUN/ijmmas/mmmas-4
 
 Returns the [Squared-chord similarity](http://www.naun.org/main/NAUN/ijmmas/mmmas-49.pdf) between vectors p and q
 
-- `jaccard(p, q)`
+- `cosine(p, q)`
+
+Returns the [Cosine similarity](http://www.naun.org/main/NAUN/ijmmas/mmmas-49.pdf) between vectors p and q
+
+$s(p,q)=\frac{\sum\limits_{i=1}^{n}{p_i\cdot{q_i}}}{\sqrt{\sum\limits_{i=1}^{n}{p_i^2}}\sqrt{\sum\limits_{i=1}^{n}{q_i^2}}}$
+
+- `kumarHassebrook(p, q)`
+
+Returns the [Kumar-Hassebrook similarity](http://www.naun.org/main/NAUN/ijmmas/mmmas-49.pdf) between vectors p and q
+
+$s(p,q)=\frac{\sum\limits_{i=1}^{n}{p_i\cdot{q_i}}}{\sum\limits_{i=1}^{n}{p_i^2}+\sum\limits_{i=1}^{n}{q_i^2}-\sum\limits_{i=1}^{n}{p_i\cdot{q_i}}}$
+
+- `pearson(p, q)`
 
-Returns the [Jaccard similarity](http://www.naun.org/main/NAUN/ijmmas/mmmas-49.pdf) between vectors p and q
+Returns the [Pearson correlation](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) between vectors p and q, i.e. the cosine similarity of the mean-centred vectors
 
 - `dice(p, q)`
 
@@ -329,8 +345,8 @@ Refer to [ml-tree-similarity](https://github.com/mljs/tree-similarity)
 
 ## Contributing
 
-A new metric should normally be in its own file in the src/dist directory. There should be a corresponding test file in test/dist.  
-The metric should be then added in the exports of src/index.js with a relatively small but understandable name (use camelCase).  
+A new metric should normally be in its own file in the `src/distances` (or `src/similarities`) directory. There should be a corresponding test file in the neighbouring `__tests__` directory.  
+The metric should be then added in the exports of `src/distances.ts` (or `src/similarities.ts`) with a relatively small but understandable name (use camelCase).  
 It should also be added to this README with either a link to the formula or an inline description.
 
 ## Authors

eslint.config.js

@@ -0,0 +1,4 @@
+import { defineConfig, globalIgnores } from 'eslint/config';
+import ts from 'eslint-config-cheminfo-typescript';
+
+export default defineConfig(globalIgnores(['coverage', 'lib']), ts);

package.json

@@ -2,28 +2,26 @@
   "name": "ml-distance",
   "version": "4.0.1",
   "description": "Distance and similarity functions to compare vectors",
-  "main": "lib/index.js",
-  "module": "./lib-esm/index.js",
+  "type": "module",
+  "exports": {
+    ".": "./lib/index.js"
+  },
   "files": [
-    "src",
     "lib",
-    "lib-esm"
+    "src"
   ],
-  "types": "./lib/index.d.ts",
   "scripts": {
     "check-types": "tsc --noEmit",
-    "clean": "rimraf lib lib-esm",
-    "eslint": "eslint src",
-    "eslint-fix": "npm run eslint -- --fix",
+    "clean": "rimraf coverage lib",
+    "eslint": "eslint .",
+    "eslint-fix": "eslint . --fix",
     "prepack": "npm run tsc",
-    "prettier": "prettier --check src",
-    "prettier-write": "prettier --write src",
-    "test": "npm run test-coverage && npm run eslint && npm run prettier && npm run check-types",
-    "test-only": "vitest run",
-    "test-coverage": "vitest run --coverage",
-    "tsc": "npm run clean && npm run tsc-cjs && npm run tsc-esm",
-    "tsc-cjs": "tsc --project tsconfig.cjs.json",
-    "tsc-esm": "tsc --project tsconfig.esm.json"
+    "prettier": "prettier --check .",
+    "prettier-write": "prettier --write .",
+    "test": "npm run test-only && npm run check-types && npm run eslint && npm run prettier",
+    "test-only": "vitest run --coverage",
+    "tsc": "npm run clean && npm run tsc-build",
+    "tsc-build": "tsc --project tsconfig.build.json"
   },
   "repository": {
     "type": "git",
@@ -49,23 +47,21 @@
     "url": "https://github.com/mljs/distance/issues"
   },
   "homepage": "https://github.com/mljs/distance",
-  "devDependencies": {
-    "@babel/plugin-transform-modules-commonjs": "^7.25.7",
-    "@babel/preset-typescript": "^7.25.7",
-    "@types/node": "^22.7.6",
-    "@vitest/coverage-v8": "^2.1.3",
-    "cheminfo-types": "^1.8.0",
-    "eslint": "^9.12.0",
-    "eslint-config-cheminfo-typescript": "^16.0.0",
-    "esm": "^3.2.25",
-    "prettier": "^3.3.3",
-    "rimraf": "^6.0.1",
-    "typescript": "^5.6.3",
-    "vitest": "^2.1.3"
-  },
   "dependencies": {
-    "ml-array-mean": "^1.1.6",
-    "ml-distance-euclidean": "^2.0.0",
+    "cheminfo-types": "^1.15.0",
+    "ml-array-mean": "^2.0.0",
+    "ml-distance-euclidean": "^3.0.1",
     "ml-tree-similarity": "^1.0.0"
+  },
+  "devDependencies": {
+    "@types/node": "^26.1.1",
+    "@vitest/coverage-v8": "^4.1.10",
+    "@zakodium/tsconfig": "^1.0.5",
+    "eslint": "^9.39.5",
+    "eslint-config-cheminfo-typescript": "^22.1.0",
+    "prettier": "^3.9.6",
+    "rimraf": "^6.1.3",
+    "typescript": "^6.0.3",
+    "vitest": "^4.1.10"
   }
 }

scripts/similarity.js

@@ -1,4 +1,4 @@
-import { distance } from '../src';
+import { distance } from '../src/index.ts';
 
 let results = [];
 
@@ -16,9 +16,7 @@ let v11 = [-0.6, 0.7];
 
 for (let algorithm in distance) {
   if (algorithm.match(/fidelity|harmonicMean|innerProduct|ruzicka/)) continue;
-  if (algorithm.match(/minkowski/)) {
-    if (algorithm.match(/motyka/)) continue;
-  } // does not give a 0 with identical vector
+  if (algorithm.match(/minkowski/) && algorithm.match(/motyka/)) continue; // does not give a 0 with identical vector
   let result = { algorithm };
   let callback = distance[algorithm];
   result.self = callback(v1, v1);

src/.npmignore

@@ -1,2 +1,3 @@
 __tests__
+.DS_Store
 .npmignore