ml-regression-power - npm Package Compare versions

Comparing version 1.0.0 to 1.0.1

History.md

@@ -0,1 +1,10 @@
+## [1.0.1](https://github.com/mljs/regression-power/compare/v1.0.0...v1.0.1) (2019-01-11)
+
+
+### Bug Fixes
+
+* latex formatting with big / small numbers ([8a6f9be](https://github.com/mljs/regression-power/commit/8a6f9be))
+
+
+
 <a name="1.0.0"></a>
@@ -2,0 +11,0 @@ # 1.0.0 (2017-04-28)

lib/index.js

@@ -10,59 +10,72 @@ 'use strict';
 class PowerRegression extends BaseRegression__default {
-    constructor(x, y) {
-        super();
-        if (x === true) { // reloading model
-            this.A = y.A;
-            this.B = y.B;
-        } else {
-            BaseRegression.checkArrayLength(x, y);
-            regress(this, x, y);
-        }
+  constructor(x, y) {
+    super();
+    if (x === true) {
+      // reloading model
+      this.A = y.A;
+      this.B = y.B;
+    } else {
+      BaseRegression.checkArrayLength(x, y);
+      regress(this, x, y);
     }
+  }
 
-    _predict(newInputs) {
-        return this.A * Math.pow(newInputs, this.B);
-    }
+  _predict(newInputs) {
+    return this.A * Math.pow(newInputs, this.B);
+  }
 
-    toJSON() {
-        return {
-            name: 'powerRegression',
-            A: this.A,
-            B: this.B
-        };
-    }
+  toJSON() {
+    return {
+      name: 'powerRegression',
+      A: this.A,
+      B: this.B
+    };
+  }
 
-    toString(precision) {
-        return 'f(x) = ' + BaseRegression.maybeToPrecision(this.A, precision) + ' * x^' + BaseRegression.maybeToPrecision(this.B, precision);
-    }
+  toString(precision) {
+    return `f(x) = ${BaseRegression.maybeToPrecision(
+      this.A,
+      precision
+    )} * x^${BaseRegression.maybeToPrecision(this.B, precision)}`;
+  }
 
-    toLaTeX(precision) {
-        if (this.B >= 0) {
-            return 'f(x) = ' + BaseRegression.maybeToPrecision(this.A, precision) + 'x^{' + BaseRegression.maybeToPrecision(this.B, precision) + '}';
-        } else {
-            return 'f(x) = \\frac{' + BaseRegression.maybeToPrecision(this.A, precision) + '}{x^{' + BaseRegression.maybeToPrecision(-this.B, precision) + '}}';
-        }
+  toLaTeX(precision) {
+    let latex = '';
+    if (this.B >= 0) {
+      latex = `f(x) = ${BaseRegression.maybeToPrecision(
+        this.A,
+        precision
+      )}x^{${BaseRegression.maybeToPrecision(this.B, precision)}}`;
+    } else {
+      latex = `f(x) = \\frac{${BaseRegression.maybeToPrecision(
+        this.A,
+        precision
+      )}}{x^{${BaseRegression.maybeToPrecision(-this.B, precision)}}}`;
     }
+    latex = latex.replace(/e([+-]?[0-9]+)/g, 'e^{$1}');
+    return latex;
+  }
 
-    static load(json) {
-        if (json.name !== 'powerRegression') {
-            throw new TypeError('not a power regression model');
-        }
-        return new PowerRegression(true, json);
+  static load(json) {
+    if (json.name !== 'powerRegression') {
+      throw new TypeError('not a power regression model');
     }
+    return new PowerRegression(true, json);
+  }
 }
 
 function regress(pr, x, y) {
-    const n = x.length;
-    const xl = new Array(n);
-    const yl = new Array(n);
-    for (let i = 0; i < n; i++) {
-        xl[i] = Math.log(x[i]);
-        yl[i] = Math.log(y[i]);
-    }
+  const n = x.length;
+  const xl = new Array(n);
+  const yl = new Array(n);
+  for (let i = 0; i < n; i++) {
+    xl[i] = Math.log(x[i]);
+    yl[i] = Math.log(y[i]);
+  }
 
-    const linear = new SimpleLinearRegression(xl, yl);
-    pr.A = Math.exp(linear.intercept);
-    pr.B = linear.slope;
+  const linear = new SimpleLinearRegression(xl, yl);
+  pr.A = Math.exp(linear.intercept);
+  pr.B = linear.slope;
 }
 
 module.exports = PowerRegression;

package.json

 {
   "name": "ml-regression-power",
-  "version": "1.0.0",
+  "version": "1.0.1",
   "description": "",
@@ -33,10 +33,13 @@ "main": "lib/index.js",
   "devDependencies": {
-    "babel-jest": "^19.0.0",
+    "babel-jest": "^23.6.0",
     "babel-plugin-transform-es2015-modules-commonjs": "^6.24.1",
-    "eslint": "^3.19.0",
-    "eslint-config-cheminfo": "^1.7.0",
+    "eslint": "^5.12.0",
+    "eslint-config-cheminfo": "^1.20.0",
+    "eslint-plugin-import": "^2.14.0",
+    "eslint-plugin-jest": "^22.1.3",
     "eslint-plugin-no-only-tests": "^1.1.0",
-    "jest": "^19.0.2",
+    "jest": "^23.6.0",
     "npm-run-all": "^4.0.2",
-    "rollup": "^0.41.6"
+    "rollup": "^0.41.6",
+    "undefined": "^0.1.0"
   },
@@ -43,0 +46,0 @@ "dependencies": {

src/__tests__/test.js

 import PowerRegression from '..';
 
 describe('Power regression', () => {
-    it('basic test', () =>{
-        const x = [17.6, 26, 31.9, 38.9, 45.8, 51.2, 58.1, 64.7, 66.7, 80.8, 82.9];
-        const y = [159.9, 206.9, 236.8, 269.9, 300.6, 323.6, 351.7, 377.6, 384.1, 437.2, 444.7];
-        const result = new PowerRegression(x, y);
+  it('basic test', () => {
+    const x = [17.6, 26, 31.9, 38.9, 45.8, 51.2, 58.1, 64.7, 66.7, 80.8, 82.9];
+    const y = [
+      159.9,
+      206.9,
+      236.8,
+      269.9,
+      300.6,
+      323.6,
+      351.7,
+      377.6,
+      384.1,
+      437.2,
+      444.7
+    ];
+    const result = new PowerRegression(x, y);
 
-        const expected = {
-            A: 24.12989312,
-            B: 0.65949782
-        };
-        expect(result.A).toBeCloseTo(expected.A, 10e-4);
-        expect(result.B).toBeCloseTo(expected.B, 10e-4);
+    const expected = {
+      A: 24.12989312,
+      B: 0.65949782
+    };
+    expect(result.A).toBeCloseTo(expected.A, 10e-4);
+    expect(result.B).toBeCloseTo(expected.B, 10e-4);
 
-        const x2 = [20, 30];
-        const y2 = result.predict(x2);
+    const x2 = [20, 30];
+    const y2 = result.predict(x2);
 
-        expect(y2[0]).toBeCloseTo(expected.A * Math.pow(x2[0], expected.B), 10e-4);
-        expect(y2[1]).toBeCloseTo(expected.A * Math.pow(x2[1], expected.B), 10e-4);
+    expect(y2[0]).toBeCloseTo(expected.A * Math.pow(x2[0], expected.B), 10e-4);
+    expect(y2[1]).toBeCloseTo(expected.A * Math.pow(x2[1], expected.B), 10e-4);
 
-        const score = result.score(x, y);
-        expect(score.r2).toBeCloseTo(0.999, 1e-2);
-        expect(score.chi2).toBeCloseTo(0.03, 1e-2);
-        expect(score.rmsd).toBeCloseTo(0.03, 1e-2);
-        expect(result.toString(4)).toEqual('f(x) = 24.13 * x^0.6595');
-        expect(result.toLaTeX(4)).toEqual('f(x) = 24.13x^{0.6595}');
+    const score = result.score(x, y);
+    expect(score.r2).toBeCloseTo(0.999, 1e-2);
+    expect(score.chi2).toBeCloseTo(0.03, 1e-2);
+    expect(score.rmsd).toBeCloseTo(0.03, 1e-2);
+    expect(result.toString(4)).toStrictEqual('f(x) = 24.13 * x^0.6595');
+    expect(result.toLaTeX(4)).toStrictEqual('f(x) = 24.13x^{0.6595}');
+  });
+
+  it('toJSON / load', () => {
+    const regression = PowerRegression.load({
+      name: 'powerRegression',
+      A: 1,
+      B: -1
     });
 
-    it('toJSON / load', () =>{
-        const regression = PowerRegression.load({
-            name: 'powerRegression',
-            A: 1,
-            B: -1,
-        });
+    expect(regression.predict(4)).toStrictEqual(0.25);
 
-        expect(regression.predict(4)).toEqual(0.25);
+    const model = regression.toJSON();
+    expect(model).toStrictEqual({
+      name: 'powerRegression',
+      A: 1,
+      B: -1
+    });
+  });
 
-        const model = regression.toJSON();
-        expect(model).toEqual({
-            name: 'powerRegression',
-            A: 1,
-            B: -1
-        });
+  it('test latex formatting of big / small numbers', () => {
+    const regression = PowerRegression.load({
+      name: 'powerRegression',
+      A: 1000000000,
+      B: -0.000000001
     });
+
+    expect(regression.toLaTeX(4)).toStrictEqual(
+      'f(x) = \\frac{1.000e^{+9}}{x^{1.000e^{-9}}}'
+    );
+  });
 });

src/index.js

@@ -1,60 +0,76 @@
-import BaseRegression, {checkArrayLength, maybeToPrecision} from 'ml-regression-base';
+import BaseRegression, {
+  checkArrayLength,
+  maybeToPrecision
+} from 'ml-regression-base';
 import SimpleLinearRegression from 'ml-regression-simple-linear';
 
 export default class PowerRegression extends BaseRegression {
-    constructor(x, y) {
-        super();
-        if (x === true) { // reloading model
-            this.A = y.A;
-            this.B = y.B;
-        } else {
-            checkArrayLength(x, y);
-            regress(this, x, y);
-        }
+  constructor(x, y) {
+    super();
+    if (x === true) {
+      // reloading model
+      this.A = y.A;
+      this.B = y.B;
+    } else {
+      checkArrayLength(x, y);
+      regress(this, x, y);
     }
+  }
 
-    _predict(newInputs) {
-        return this.A * Math.pow(newInputs, this.B);
-    }
+  _predict(newInputs) {
+    return this.A * Math.pow(newInputs, this.B);
+  }
 
-    toJSON() {
-        return {
-            name: 'powerRegression',
-            A: this.A,
-            B: this.B
-        };
-    }
+  toJSON() {
+    return {
+      name: 'powerRegression',
+      A: this.A,
+      B: this.B
+    };
+  }
 
-    toString(precision) {
-        return 'f(x) = ' + maybeToPrecision(this.A, precision) + ' * x^' + maybeToPrecision(this.B, precision);
-    }
+  toString(precision) {
+    return `f(x) = ${maybeToPrecision(
+      this.A,
+      precision
+    )} * x^${maybeToPrecision(this.B, precision)}`;
+  }
 
-    toLaTeX(precision) {
-        if (this.B >= 0) {
-            return 'f(x) = ' + maybeToPrecision(this.A, precision) + 'x^{' + maybeToPrecision(this.B, precision) + '}';
-        } else {
-            return 'f(x) = \\frac{' + maybeToPrecision(this.A, precision) + '}{x^{' + maybeToPrecision(-this.B, precision) + '}}';
-        }
+  toLaTeX(precision) {
+    let latex = '';
+    if (this.B >= 0) {
+      latex = `f(x) = ${maybeToPrecision(
+        this.A,
+        precision
+      )}x^{${maybeToPrecision(this.B, precision)}}`;
+    } else {
+      latex = `f(x) = \\frac{${maybeToPrecision(
+        this.A,
+        precision
+      )}}{x^{${maybeToPrecision(-this.B, precision)}}}`;
     }
+    latex = latex.replace(/e([+-]?[0-9]+)/g, 'e^{$1}');
+    return latex;
+  }
 
-    static load(json) {
-        if (json.name !== 'powerRegression') {
-            throw new TypeError('not a power regression model');
-        }
-        return new PowerRegression(true, json);
+  static load(json) {
+    if (json.name !== 'powerRegression') {
+      throw new TypeError('not a power regression model');
     }
+    return new PowerRegression(true, json);
+  }
 }
 
 function regress(pr, x, y) {
-    const n = x.length;
-    const xl = new Array(n);
-    const yl = new Array(n);
-    for (let i = 0; i < n; i++) {
-        xl[i] = Math.log(x[i]);
-        yl[i] = Math.log(y[i]);
-    }
+  const n = x.length;
+  const xl = new Array(n);
+  const yl = new Array(n);
+  for (let i = 0; i < n; i++) {
+    xl[i] = Math.log(x[i]);
+    yl[i] = Math.log(y[i]);
+  }
 
-    const linear = new SimpleLinearRegression(xl, yl);
-    pr.A = Math.exp(linear.intercept);
-    pr.B = linear.slope;
+  const linear = new SimpleLinearRegression(xl, yl);
+  pr.A = Math.exp(linear.intercept);
+  pr.B = linear.slope;
 }

New alerts

New author

Supply chain risk

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.

Found 1 instance in 1 package

Improved metrics

Total package byte prevSize

9016

increased by6.43%

Lines of code

197

increased by34.93%

Worsened metrics

Dev dependency count

11

increased by37.5%

Number of medium supply chain risk alerts

1

increased byInfinity%

No dependency changes