ml-regression-simple-linear - npm Package Compare versions

Comparing version 2.0.3 to 2.0.4

lib/index.js

@@ -1,86 +0,145 @@
-'use strict';
-
-function _interopDefault (ex) { return (ex && (typeof ex === 'object') && 'default' in ex) ? ex['default'] : ex; }
-
-var BaseRegression = require('ml-regression-base');
-var BaseRegression__default = _interopDefault(BaseRegression);
-
-class SimpleLinearRegression extends BaseRegression__default {
-  constructor(x, y) {
-    super();
-    if (x === true) {
-      this.slope = y.slope;
-      this.intercept = y.intercept;
-      this.coefficients = [y.intercept, y.slope];
-    } else {
-      BaseRegression.checkArrayLength(x, y);
-      regress(this, x, y);
+"use strict";
+var __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) {
+    if (k2 === undefined) k2 = k;
+    var desc = Object.getOwnPropertyDescriptor(m, k);
+    if (!desc || ("get" in desc ? !m.__esModule : desc.writable || desc.configurable)) {
+      desc = { enumerable: true, get: function() { return m[k]; } };
     }
-  }
-
-  toJSON() {
-    return {
-      name: 'simpleLinearRegression',
-      slope: this.slope,
-      intercept: this.intercept,
-    };
-  }
-
-  _predict(x) {
-    return this.slope * x + this.intercept;
-  }
-
-  computeX(y) {
-    return (y - this.intercept) / this.slope;
-  }
-
-  toString(precision) {
-    let result = 'f(x) = ';
-    if (this.slope !== 0) {
-      const xFactor = BaseRegression.maybeToPrecision(this.slope, precision);
-      result += `${xFactor === '1' ? '' : `${xFactor} * `}x`;
-      if (this.intercept !== 0) {
-        const absIntercept = Math.abs(this.intercept);
-        const operator = absIntercept === this.intercept ? '+' : '-';
-        result += ` ${operator} ${BaseRegression.maybeToPrecision(absIntercept, precision)}`;
-      }
-    } else {
-      result += BaseRegression.maybeToPrecision(this.intercept, precision);
-    }
+    Object.defineProperty(o, k2, desc);
+}) : (function(o, m, k, k2) {
+    if (k2 === undefined) k2 = k;
+    o[k2] = m[k];
+}));
+var __setModuleDefault = (this && this.__setModuleDefault) || (Object.create ? (function(o, v) {
+    Object.defineProperty(o, "default", { enumerable: true, value: v });
+}) : function(o, v) {
+    o["default"] = v;
+});
+var __importStar = (this && this.__importStar) || function (mod) {
+    if (mod && mod.__esModule) return mod;
+    var result = {};
+    if (mod != null) for (var k in mod) if (k !== "default" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);
+    __setModuleDefault(result, mod);
     return result;
-  }
-
-  toLaTeX(precision) {
-    return this.toString(precision);
-  }
-
-  static load(json) {
-    if (json.name !== 'simpleLinearRegression') {
-      throw new TypeError('not a SLR model');
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+const ml_regression_base_1 = __importStar(require("ml-regression-base"));
+/**
+ * Class representing simple linear regression.
+ * The regression uses OLS to calculate intercept and slope.
+ */
+class SimpleLinearRegression extends ml_regression_base_1.default {
+    /**
+     * @param x - explanatory variable
+     * @param y - response variable
+     */
+    constructor(x, y) {
+        super();
+        // @ts-expect-error internal use of the constructor, from `this.load`
+        if (x === true) {
+            // @ts-expect-error internal use of the constructor, from `this.load`
+            const yObj = y;
+            this.slope = yObj.slope;
+            this.intercept = yObj.intercept;
+            this.coefficients = [yObj.intercept, yObj.slope];
+        }
+        else {
+            (0, ml_regression_base_1.checkArrayLength)(x, y);
+            const result = regress(x, y);
+            this.slope = result.slope;
+            this.intercept = result.intercept;
+            this.coefficients = [result.intercept, result.slope];
+        }
     }
-    return new SimpleLinearRegression(true, json);
-  }
+    /**
+     * Get the parameters and model name in JSON format
+     * @returns
+     */
+    toJSON() {
+        return {
+            name: 'simpleLinearRegression',
+            slope: this.slope,
+            intercept: this.intercept,
+        };
+    }
+    _predict(x) {
+        return this.slope * x + this.intercept;
+    }
+    /**
+     * Finds x for the given y value.
+     * @param y - response variable value
+     * @returns - x value
+     */
+    computeX(y) {
+        return (y - this.intercept) / this.slope;
+    }
+    /**
+     * Strings the linear function in the form 'f(x) = ax + b'
+     * @param precision - number of significant figures.
+     * @returns
+     */
+    toString(precision) {
+        let result = 'f(x) = ';
+        if (this.slope !== 0) {
+            const xFactor = (0, ml_regression_base_1.maybeToPrecision)(this.slope, precision);
+            result += `${xFactor === '1' ? '' : `${xFactor} * `}x`;
+            if (this.intercept !== 0) {
+                const absIntercept = Math.abs(this.intercept);
+                const operator = absIntercept === this.intercept ? '+' : '-';
+                result += ` ${operator} ${(0, ml_regression_base_1.maybeToPrecision)(absIntercept, precision)}`;
+            }
+        }
+        else {
+            result += (0, ml_regression_base_1.maybeToPrecision)(this.intercept, precision);
+        }
+        return result;
+    }
+    /**
+     * Strings the linear function in the form 'f(x) = ax + b'
+     * @param precision - number of significant figures.
+     * @returns
+     */
+    toLaTeX(precision) {
+        return this.toString(precision);
+    }
+    /**
+     * Class instance from a JSON Object.
+     * @param json
+     * @returns
+     */
+    static load(json) {
+        if (json.name !== 'simpleLinearRegression') {
+            throw new TypeError('not a SLR model');
+        }
+        // @ts-expect-error internal use of the constructor
+        return new SimpleLinearRegression(true, json);
+    }
 }
-
-function regress(slr, x, y) {
-  const n = x.length;
-  let xSum = 0;
-  let ySum = 0;
-
-  let xSquared = 0;
-  let xY = 0;
-
-  for (let i = 0; i < n; i++) {
-    xSum += x[i];
-    ySum += y[i];
-    xSquared += x[i] * x[i];
-    xY += x[i] * y[i];
-  }
-
-  const numerator = n * xY - xSum * ySum;
-  slr.slope = numerator / (n * xSquared - xSum * xSum);
-  slr.intercept = (1 / n) * ySum - slr.slope * (1 / n) * xSum;
-  slr.coefficients = [slr.intercept, slr.slope];
+exports.default = SimpleLinearRegression;
+/**
+ * Internal  function.
+ * It determines the parameters (slope, intercept) of the line that best fit the `x,y` vector-data (simple linear regression).
+ * @param x - explanatory variable
+ * @param y - response variable
+ * @returns - slope and intercept of the best fit line
+ */
+function regress(x, y) {
+    const n = x.length;
+    let xSum = 0;
+    let ySum = 0;
+    let xSquared = 0;
+    let xY = 0;
+    for (let i = 0; i < n; i++) {
+        xSum += x[i];
+        ySum += y[i];
+        xSquared += x[i] * x[i];
+        xY += x[i] * y[i];
+    }
+    const numerator = n * xY - xSum * ySum;
+    const slope = numerator / (n * xSquared - xSum * xSum);
+    return {
+        slope,
+        intercept: (1 / n) * ySum - slope * (1 / n) * xSum,
+    };
 }
-
-module.exports = SimpleLinearRegression;
+//# sourceMappingURL=index.js.map

package.json

 {
   "name": "ml-regression-simple-linear",
-  "version": "2.0.3",
+  "version": "2.0.4",
   "description": "Simple Linear Regression",
-  "main": "lib/index.js",
-  "module": "src/index.js",
-  "types": "regression-simple-linear.d.ts",
+  "main": "./lib/index.js",
+  "module": "./lib-esm/index.js",
+  "types": "./lib/index.d.ts",
   "sideEffects": false,
   "files": [
-    "regression-simple-linear.d.ts",
     "lib",
+    "lib-esm",
     "src"
   ],
   "scripts": {
-    "compile": "rollup -c",
+    "check-types": "tsc --noEmit",
+    "clean": "rimraf lib lib-esm",
     "eslint": "eslint src",
     "eslint-fix": "npm run eslint -- --fix",
-    "prepublishOnly": "npm run compile",
-    "test": "npm run test-coverage && npm run eslint",
-    "test-only": "jest",
-    "test-coverage": "jest --coverage"
+    "prepack": "npm run tsc",
+    "prettier": "prettier --check src",
+    "prettier-write": "prettier --write src",
+    "test": "npm run test-only && npm run eslint && npm run prettier && npm run check-types",
+    "test-only": "vitest --coverage --run",
+    "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"
   },
@@ -27,3 +32,7 @@ "repository": {
   },
-  "keywords": [],
+  "keywords": [
+    "linear regression",
+    "least squares",
+    "linear fit"
+  ],
   "author": "Michaël Zasso",
@@ -35,17 +44,15 @@ "license": "MIT",
   "homepage": "https://github.com/mljs/regression-simple-linear#readme",
-  "jest": {
-    "testEnvironment": "node"
-  },
   "devDependencies": {
-    "@babel/plugin-transform-modules-commonjs": "^7.4.4",
-    "eslint": "^5.16.0",
-    "eslint-config-cheminfo": "^1.20.1",
-    "eslint-plugin-import": "^2.17.2",
-    "eslint-plugin-jest": "^22.5.1",
-    "jest": "^24.7.1",
-    "rollup": "^1.10.1"
+    "@vitest/coverage-v8": "^0.34.1",
+    "eslint": "^8.47.0",
+    "eslint-config-cheminfo-typescript": "^12.0.4",
+    "prettier": "^3.0.1",
+    "rimraf": "^5.0.1",
+    "typescript": "^5.1.6",
+    "vitest": "^0.34.1"
   },
   "dependencies": {
-    "ml-regression-base": "^2.0.1"
+    "cheminfo-types": "^1.7.2",
+    "ml-regression-base": "^3.0.0"
   }
 }

README.md

 # regression-simple-linear
 
-  [![NPM version][npm-image]][npm-url]
-  [![build status][travis-image]][travis-url]
-  [![npm download][download-image]][download-url]
+[![NPM version][npm-image]][npm-url]
+[![build status][ci-image]][ci-url]
+[![npm download][download-image]][download-url]
+[![codecov][codecov-image]][codecov-url]
 
@@ -23,5 +24,5 @@ Simple Linear Regression.
 
-regression.slope // 2
-regression.intercept // -1
-regression.coefficients // [-1, 2]
+regression.slope; // 2
+regression.intercept; // -1
+regression.coefficients; // [-1, 2]
 
@@ -39,3 +40,3 @@ regression.predict(3); // 5
 const loaded = SimpleLinearRegression.load(json);
-loaded.predict(5) // 9
+loaded.predict(5); // 9
 ```
@@ -45,9 +46,11 @@
 
-  [MIT](./LICENSE)
+[MIT](./LICENSE)
 
 [npm-image]: https://img.shields.io/npm/v/ml-regression-simple-linear.svg?style=flat-square
 [npm-url]: https://npmjs.org/package/ml-regression-simple-linear
-[travis-image]: https://img.shields.io/travis/mljs/regression-simple-linear/master.svg?style=flat-square
-[travis-url]: https://travis-ci.org/mljs/regression-simple-linear
+[ci-image]: https://github.com/mljs/regression-simple-linear/workflows/Node.js%20CI/badge.svg?branch=master
+[ci-url]: https://github.com/mljs/regression-simple-linear/actions?query=workflow%3A%22Node.js+CI%22
 [download-image]: https://img.shields.io/npm/dm/ml-regression-simple-linear.svg?style=flat-square
 [download-url]: https://npmjs.org/package/ml-regression-simple-linear
+[codecov-image]: https://img.shields.io/codecov/c/github/mljs/regression-simple-linear.svg
+[codecov-url]: https://codecov.io/gh/mljs/regression-simple-linear

New alerts

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New author

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A new npm collaborator published a version of the package for the first time. New collaborators are usually benign additions to a project, but do indicate a change to the security surface area of a package.

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Improved metrics

Total package byte prevSize

30937

increased by116.95%

Number of package files

13

increased by62.5%

Lines of code

591

increased by108.1%

Number of lines in readme file

54

increased by5.88%

Worsened metrics

Dependency count

2

increased by100%

Number of medium supply chain risk alerts

2

increased byInfinity%

Dependency changes

• + Addedcheminfo-types@^1.7.2

• + Addedcheminfo-types@1.8.1(transitive)

• + Addedml-regression-base@3.0.0(transitive)

• - Removedml-regression-base@2.1.6(transitive)

• Updatedml-regression-base@^3.0.0