ml-regression - npm Package Compare versions

Comparing version 6.2.0 to 6.3.0

lib/index.js

@@ -25,5 +25,5 @@ 'use strict';
   /**
-   * @constructor
-   * @param x: Independent variable
-   * @param y: Dependent variable
+   * @class
+   * @param x - Independent variable
+   * @param y - Dependent variable
    * @param M
@@ -154,7 +154,5 @@ */
    * Constructor for the 2D polynomial fitting
-   *
    * @param inputs
    * @param outputs
    * @param options
-   * @constructor
    */
@@ -188,4 +186,3 @@ constructor(inputs, outputs, options = {}) {
    * The third argument is an object with the following options:
-   * * order: order of the polynomial to fit.
-   *
+   * order: order of the polynomial to fit.
    * @param {Matrix} X - A matrix with n rows and 2 columns.
@@ -223,5 +220,5 @@ * @param {Matrix} y - A vector of the prediction values.
 
-    let scaleX1 = 1.0 / x1.clone().abs().max();
-    let scaleX2 = 1.0 / x2.clone().abs().max();
-    let scaleY = 1.0 / y.clone().abs().max();
+    let scaleX1 = 1 / x1.clone().abs().max();
+    let scaleX2 = 1 / x2.clone().abs().max();
+    let scaleY = 1 / y.clone().abs().max();
 
@@ -319,6 +316,5 @@ x1.mulColumn(0, scaleX1);
  * Function that given a column vector return this: vector^power
- *
  * @param x - Column vector.
  * @param power - Pow number.
- * @return {Suite|Matrix}
+ * @returns {Suite|Matrix}
  */
@@ -325,0 +321,0 @@ function powColVector(x, power) {

package.json

 {
   "name": "ml-regression",
-  "version": "6.2.0",
+  "version": "6.3.0",
   "description": "Regression algorithms",
@@ -12,2 +12,3 @@ "main": "lib/index.js",
   "scripts": {
+    "build": "cheminfo-build --entry src/index.js --root Regression",
     "compile": "rollup -c",
@@ -20,4 +21,3 @@ "eslint": "eslint src",
     "test": "npm run test-coverage && npm run eslint && npm run prettier",
-    "test-only": "jest",
-    "test-coverage": "jest --coverage"
+    "test-only": "vitest run --coverage"
   },
@@ -42,20 +42,18 @@ "repository": {
   "homepage": "https://github.com/mljs/regression",
-  "jest": {
-    "testEnvironment": "node"
-  },
   "devDependencies": {
-    "@babel/plugin-transform-modules-commonjs": "^7.25.7",
-    "@types/jest": "^29.5.13",
-    "eslint": "^9.12.0",
-    "eslint-config-cheminfo": "^12.0.1",
-    "globals": "^15.11.0",
-    "jest": "^29.7.0",
-    "prettier": "^3.3.3",
-    "rollup": "^4.24.0"
+    "@babel/plugin-transform-modules-commonjs": "^7.27.1",
+    "@vitest/coverage-v8": "^3.1.3",
+    "cheminfo-build": "^1.2.1",
+    "eslint": "^9.26.0",
+    "eslint-config-cheminfo": "^14.1.1",
+    "globals": "^16.1.0",
+    "prettier": "^3.5.3",
+    "rollup": "^4.40.2",
+    "vitest": "^3.1.3"
   },
   "dependencies": {
     "ml-kernel": "^3.0.0",
-    "ml-matrix": "^6.12.0",
+    "ml-matrix": "^6.12.1",
     "ml-regression-base": "^4.0.0",
-    "ml-regression-exponential": "^3.0.1",
+    "ml-regression-exponential": "^3.0.2",
     "ml-regression-multivariate-linear": "^2.0.4",
@@ -62,0 +60,0 @@ "ml-regression-polynomial": "^3.0.1",

src/__tests__/2d-polynomial-fit.test.js

@@ -0,1 +1,3 @@
+import { describe, it, expect } from "vitest";
+
 import { PolinomialFitting2D as Polyfit } from "..";
@@ -52,3 +54,3 @@
     let X = new Array(len);
-    let val = 5.0;
+    let val = 5;
     let y = new Array(len);
@@ -55,0 +57,0 @@ for (let i = 0; i < len; ++i, val += 0.5) {

src/__tests__/kernel-ridge-regression.test.js

 import { Matrix } from "ml-matrix";
+import { describe, it, expect } from "vitest";
 
@@ -3,0 +4,0 @@ import { KernelRidgeRegression } from "..";

src/__tests__/non-linear-regression.test.js

@@ -0,1 +1,3 @@
+import { describe, it, expect } from "vitest";
+
 import { NLR } from "..";
@@ -6,3 +8,3 @@
     it("Potential regression", () => {
-      let x = [0.2, 0.4, 0.6, 0.8, 1.0];
+      let x = [0.2, 0.4, 0.6, 0.8, 1];
       let y = [0.196, 0.785, 1.7665, 3.1405, 4.9075];
@@ -32,3 +34,3 @@ let result = new NLR.PotentialRegression(x, y, 2, {
       expect(regression.M).toBe(-1);
-      expect(regression.toLaTeX()).toBe("f(x) = \\frac{1}{x^{1}}");
+      expect(regression.toLaTeX()).toBe(String.raw`f(x) = \frac{1}{x^{1}}`);
 
@@ -35,0 +37,0 @@ let model = regression.toJSON();

src/__tests__/re-export.test.js

@@ -0,1 +1,3 @@
+import { describe, it, expect } from "vitest";
+
 import * as regression from "..";
@@ -2,0 +4,0 @@

src/regression/poly-fit-regression2d.js

@@ -12,7 +12,5 @@ import { Matrix, SVD } from "ml-matrix";
    * Constructor for the 2D polynomial fitting
-   *
    * @param inputs
    * @param outputs
    * @param options
-   * @constructor
    */
@@ -46,4 +44,3 @@ constructor(inputs, outputs, options = {}) {
    * The third argument is an object with the following options:
-   * * order: order of the polynomial to fit.
-   *
+   * order: order of the polynomial to fit.
    * @param {Matrix} X - A matrix with n rows and 2 columns.
@@ -81,5 +78,5 @@ * @param {Matrix} y - A vector of the prediction values.
 
-    let scaleX1 = 1.0 / x1.clone().abs().max();
-    let scaleX2 = 1.0 / x2.clone().abs().max();
-    let scaleY = 1.0 / y.clone().abs().max();
+    let scaleX1 = 1 / x1.clone().abs().max();
+    let scaleX2 = 1 / x2.clone().abs().max();
+    let scaleY = 1 / y.clone().abs().max();
 
@@ -177,6 +174,5 @@ x1.mulColumn(0, scaleX1);
  * Function that given a column vector return this: vector^power
- *
  * @param x - Column vector.
  * @param power - Pow number.
- * @return {Suite|Matrix}
+ * @returns {Suite|Matrix}
  */
@@ -183,0 +179,0 @@ function powColVector(x, power) {

src/regression/potential-regression.js

@@ -15,5 +15,5 @@ import { BaseRegression, maybeToPrecision } from "ml-regression-base";
   /**
-   * @constructor
-   * @param x: Independent variable
-   * @param y: Dependent variable
+   * @class
+   * @param x - Independent variable
+   * @param y - Dependent variable
    * @param M
@@ -20,0 +20,0 @@ */

No alert changes

Improved metrics

Total package byte prevSize

30141

0.5%

Worsened metrics

Dev dependency count

9

12.5%

Lines of code

820

-0.49%

Dependency changes

• Updated

  ml-matrix@^6.12.1

• Updated

  ml-regression-exponential@^3.0.2