Various cleanups

.prettierignore

@@ -1,4 +1,3 @@
-benchmark
 coverage
 lib
 CHANGELOG.md

README.md

@@ -1,33 +1,35 @@
-# levenberg-marquardt
+# ml-levenberg-marquardt
 
-[![NPM version][npm-image]][npm-url]
-[![build status][ci-image]][ci-url]
-[![Test coverage][codecov-image]][codecov-url]
-[![npm download][download-image]][download-url]
+[![NPM version](https://img.shields.io/npm/v/ml-levenberg-marquardt.svg)](https://www.npmjs.com/package/ml-levenberg-marquardt)
+[![npm download](https://img.shields.io/npm/dm/ml-levenberg-marquardt.svg)](https://www.npmjs.com/package/ml-levenberg-marquardt)
+[![test coverage](https://img.shields.io/codecov/c/github/mljs/levenberg-marquardt.svg)](https://codecov.io/gh/mljs/levenberg-marquardt)
+[![license](https://img.shields.io/npm/l/ml-levenberg-marquardt.svg)](https://github.com/mljs/levenberg-marquardt/blob/main/LICENSE)
 
 Curve fitting method in javascript.
 
 ## [API Documentation](https://mljs.github.io/levenberg-marquardt/)
 
 This algorithm is based on the article [Brown, Kenneth M., and J. E. Dennis. "Derivative free analogues of the Levenberg-Marquardt and Gauss algorithms for nonlinear least squares approximation." Numerische Mathematik 18.4 (1971): 289-297.](https://doi.org/10.1007/BF01404679) and [http://people.duke.edu/~hpgavin/ce281/lm.pdf](http://people.duke.edu/~hpgavin/ce281/lm.pdf)
 
-In order to get a general idea of the problem you could also check the [Wikipedia article](https://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm).
+To get a general idea of the problem, you could also check the [Wikipedia article](https://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm).
 
 ## Installation
 
-`$ npm i ml-levenberg-marquardt`
+```console
+npm i ml-levenberg-marquardt
+```
 
 ## Options
 
-Next there is some options could change the behavior of the code.
+Next, there are some options could change the behaviour of the code.
 
 ### centralDifference
 
 The jacobian matrix is approximated by finite difference; forward differences or central differences (one additional function evaluation). The option centralDifference select one of them, by default the jacobian is calculated by forward difference.
 
 ### gradientDifference
 
-The jacobian matrix is approximated as mention above, the gradientDifference option is the step size (dp) to calculate de difference between the function with the current parameter state and the perturbation added. It could be a number (same step size for all parameters) or an array with different values for each parameter, if the gradientDifference is zero the derive will be zero, and the parameter will hold fixed
+The jacobian matrix is approximated as mentioned above, the gradientDifference option is the step size (dp) to calculate the difference between the function with the current parameter state and the perturbation added. It could be a number (same step size for all parameters) or an array with different values for each parameter, if the gradientDifference is zero, the derive will be zero, and the parameter will hold fixed
 
 ## Examples
 
@@ -107,17 +109,6 @@ const options = {
 let fittedParams = LM(data, sinFunction, options);
 ```
 
-Or test it in [Runkit](https://runkit.com/npm/ml-levenberg-marquardt)
-
 ## License
 
 [MIT](./LICENSE)
-
-[npm-image]: https://img.shields.io/npm/v/ml-levenberg-marquardt.svg
-[npm-url]: https://npmjs.org/package/ml-levenberg-marquardt
-[codecov-image]: https://img.shields.io/codecov/c/github/mljs/levenberg-marquardt.svg
-[codecov-url]: https://codecov.io/gh/mljs/levenberg-marquardt
-[ci-image]: https://github.com/mljs/levenberg-marquardt/workflows/Node.js%20CI/badge.svg?branch=main
-[ci-url]: https://github.com/mljs/levenberg-marquardt/actions?query=workflow%3A%22Node.js+CI%22
-[download-image]: https://img.shields.io/npm/dm/ml-levenberg-marquardt.svg
-[download-url]: https://npmjs.org/package/ml-levenberg-marquardt

eslint.config.mjs → eslint.config.js

@@ -1,7 +1,4 @@
 import { defineConfig, globalIgnores } from 'eslint/config';
 import ts from 'eslint-config-cheminfo-typescript/base';
 
-export default defineConfig(
-  globalIgnores(['coverage', 'lib', 'benchmark']),
-  ts,
-);
+export default defineConfig(globalIgnores(['coverage', 'lib']), ts);

package.json

@@ -42,24 +42,19 @@
   },
   "homepage": "https://github.com/mljs/levenberg-marquardt#readme",
   "devDependencies": {
+    "@types/node": "^24.0.1",
     "@vitest/coverage-v8": "^3.2.3",
-    "benchmark": "^2.1.4",
-    "cz-conventional-changelog": "^3.3.0",
+    "@zakodium/tsconfig": "^1.0.1",
     "eslint": "^9.28.0",
     "eslint-config-cheminfo-typescript": "^18.0.1",
     "jest-matcher-deep-close-to": "^3.0.2",
-    "prettier": "^2.8.7",
-    "rimraf": "^5.0.0",
-    "typescript": "^5.0.4",
+    "prettier": "^3.5.3",
+    "rimraf": "^6.0.1",
+    "typescript": "^5.8.3",
     "vitest": "^3.2.3"
   },
   "dependencies": {
-    "is-any-array": "^2.0.0",
-    "ml-matrix": "^6.10.4"
-  },
-  "config": {
-    "commitizen": {
-      "path": "./node_modules/cz-conventional-changelog"
-    }
+    "is-any-array": "^2.0.1",
+    "ml-matrix": "^6.12.1"
   }
 }

src/__tests__/curve.test.ts

@@ -2,10 +2,10 @@ import * as os from 'node:os';
 
 import { describe, expect, it, test } from 'vitest';
 
-import { levenbergMarquardt } from '../index.js';
+import { levenbergMarquardt } from '../index.ts';
 
 function sinFunction([a, b]: number[]) {
-  return (t) => a * Math.sin(b * t);
+  return (t: number) => a * Math.sin(b * t);
 }
 
 test('linear regression', () => {
@@ -105,8 +105,8 @@ describe('curve', () => {
       },
       {
         name: 'Sum of lorentzians, central differences',
-        getFunctionFromParameters: function sumOfLorentzians(p) {
-          return (t) => {
+        getFunctionFromParameters: function sumOfLorentzians(p: number[]) {
+          return (t: number) => {
             const nL = p.length;
             let factor: number, p2: number;
             let result = 0;

src/__tests__/error_calculation.test.ts

@@ -1,11 +1,11 @@
 import { describe, expect, it } from 'vitest';
 
-import errorCalculation from '../error_calculation.js';
+import errorCalculation from '../error_calculation.ts';
 
 describe('parameterError', () => {
   describe('Linear functions', () => {
     function linearFunction([slope, intercept]: number[]) {
-      return (x) => slope * x + intercept;
+      return (x: number) => slope * x + intercept;
     }
 
     /** @type [number, number] */
@@ -38,7 +38,7 @@ describe('parameterError', () => {
 
   describe('Linear functions with typed array', () => {
     function linearFunction([slope, intercept]: number[]) {
-      return (x) => slope * x + intercept;
+      return (x: number) => slope * x + intercept;
     }
 
     /** @type [number, number] */

src/__tests__/exceptions.test.ts

@@ -1,6 +1,6 @@
 import { describe, expect, it } from 'vitest';
 
-import { levenbergMarquardt } from '../index.js';
+import { levenbergMarquardt } from '../index.ts';
 
 function sinFunction([a, b]: number[]) {
   return (t: number) => a * Math.sin(b * t);
@@ -98,7 +98,7 @@ describe('Handling of invalid arguments', () => {
 
 describe('Handling of ill-behaved functions', () => {
   function fourParamEq([a, b, c, d]: number[]) {
-    return (t) => a + (b - a) / (1 + c ** d * t ** -d);
+    return (t: number) => a + (b - a) / (1 + c ** d * t ** -d);
   }
 
   const data = {

src/check_options.ts

@@ -1,6 +1,6 @@
 import { isAnyArray } from 'is-any-array';
 
-import type { Data2D, LevenbergMarquardtOptions } from './types.js';
+import type { Data2D, LevenbergMarquardtOptions } from './types.ts';
 
 export interface CheckedOptions {
   checkTimeout: () => boolean;

src/error_calculation.ts

@@ -1,4 +1,4 @@
-import type { Data2D, ParameterizedFunction } from './types.js';
+import type { Data2D, ParameterizedFunction } from './types.ts';
 
 /**
  * the sum of the weighted squares of the errors (or weighted residuals) between the data.y

src/gradient_function.ts

@@ -1,6 +1,6 @@
 import { Matrix } from 'ml-matrix';
 
-import type { Data2D, ParameterizedFunction } from './types.js';
+import type { Data2D, ParameterizedFunction } from './types.ts';
 
 /**
  * Difference of the matrix function over the parameters

src/index.ts

@@ -1,2 +1,2 @@
-export { levenbergMarquardt } from './levenberg_marquardt.js';
-export type * from './types.js';
+export { levenbergMarquardt } from './levenberg_marquardt.ts';
+export type * from './types.ts';

src/levenberg_marquardt.ts

@@ -1,12 +1,12 @@
-import checkOptions from './check_options.js';
-import errorCalculation from './error_calculation.js';
-import step from './step.js';
+import checkOptions from './check_options.ts';
+import errorCalculation from './error_calculation.ts';
+import step from './step.ts';
 import type {
   Data2D,
   LevenbergMarquardtOptions,
   LevenbergMarquardtReturn,
   ParameterizedFunction,
-} from './types.js';
+} from './types.ts';
 
 /**
  * Curve fitting algorithm

src/step.ts

@@ -1,7 +1,7 @@
 import { inverse, Matrix } from 'ml-matrix';
 
-import gradientFunction from './gradient_function.js';
-import type { Data2D, ParameterizedFunction } from './types.js';
+import gradientFunction from './gradient_function.ts';
+import type { Data2D, ParameterizedFunction } from './types.ts';
 
 /**
  * Matrix function over the samples

tsconfig.json

@@ -1,21 +1,9 @@
 {
+  "extends": "@zakodium/tsconfig",
   "compilerOptions": {
-    "lib": ["ES2022", "WebWorker"],
-    "target": "ES2022",
+    "types": ["node"],
     "outDir": "lib",
-    "module": "NodeNext",
-    "strict": true,
-    "skipLibCheck": false,
-    "resolveJsonModule": false,
-    "forceConsistentCasingInFileNames": true,
-    "allowJs": true,
-    "isolatedModules": true,
-    "verbatimModuleSyntax": true,
-    "sourceMap": true,
-    "declaration": true,
-    "declarationMap": true,
-    "checkJs": true,
-    "noImplicitAny": false
+    "noUncheckedIndexedAccess": false
   },
   "include": ["src", "vite*.ts"]
 }