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

Comparing version 1.0.2 to 2.0.0

History.md

@@ -0,1 +1,15 @@
+# [2.0.0](https://github.com/mljs/regression-simple-linear/compare/v1.0.2...v2.0.0) (2019-04-30)
+
+
+### chore
+
+* update dependencies ([aea8acc](https://github.com/mljs/regression-simple-linear/commit/aea8acc))
+
+
+### BREAKING CHANGES
+
+* Support for Node.js 6 was removed.
+
+
+
 <a name="1.0.2"></a>
@@ -2,0 +16,0 @@ ## [1.0.2](https://github.com/mljs/regression-simple-linear/compare/v1.0.1...v1.0.2) (2017-04-28)

lib/index.js

@@ -9,79 +9,79 @@ 'use strict';
 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);
-        }
+  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);
     }
+  }
 
-    toJSON() {
-        return {
-            name: 'simpleLinearRegression',
-            slope: this.slope,
-            intercept: this.intercept
-        };
-    }
+  toJSON() {
+    return {
+      name: 'simpleLinearRegression',
+      slope: this.slope,
+      intercept: this.intercept
+    };
+  }
 
-    _predict(x) {
-        return this.slope * x + this.intercept;
-    }
+  _predict(x) {
+    return this.slope * x + this.intercept;
+  }
 
-    computeX(y) {
-        return (y - this.intercept) / this.slope;
-    }
+  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) {
-                const absIntercept = Math.abs(this.intercept);
-                const operator = absIntercept === this.intercept ? '+' : '-';
-                result += ` ${operator} ${BaseRegression.maybeToPrecision(absIntercept, precision)}`;
-            }
-        } else {
-            result += BaseRegression.maybeToPrecision(this.intercept, precision);
-        }
-        return result;
+  toString(precision) {
+    let result = 'f(x) = ';
+    if (this.slope !== 0) {
+      const xFactor = BaseRegression.maybeToPrecision(this.slope, precision);
+      result += `${xFactor === '1' ? '' : `${xFactor} * `}x`;
+      if (this.intercept) {
+        const absIntercept = Math.abs(this.intercept);
+        const operator = absIntercept === this.intercept ? '+' : '-';
+        result += ` ${operator} ${BaseRegression.maybeToPrecision(absIntercept, precision)}`;
+      }
+    } else {
+      result += BaseRegression.maybeToPrecision(this.intercept, precision);
     }
+    return result;
+  }
 
-    toLaTeX(precision) {
-        return this.toString(precision);
-    }
+  toLaTeX(precision) {
+    return this.toString(precision);
+  }
 
-    static load(json) {
-        if (json.name !== 'simpleLinearRegression') {
-            throw new TypeError('not a SLR model');
-        }
-        return new SimpleLinearRegression(true, json);
+  static load(json) {
+    if (json.name !== 'simpleLinearRegression') {
+      throw new TypeError('not a SLR model');
     }
+    return new SimpleLinearRegression(true, json);
+  }
 }
 
 function regress(slr, x, y) {
-    const n = x.length;
-    let xSum = 0;
-    let ySum = 0;
+  const n = x.length;
+  let xSum = 0;
+  let ySum = 0;
 
-    let xSquared = 0;
-    let xY = 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];
-    }
+  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];
+  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];
 }
 
 module.exports = SimpleLinearRegression;

package.json

 {
   "name": "ml-regression-simple-linear",
-  "version": "1.0.2",
+  "version": "2.0.0",
   "description": "Simple Linear Regression",
   "main": "lib/index.js",
   "module": "src/index.js",
+  "types": "regression-simple-linear.d.ts",
+  "sideEffects": false,
   "files": [
+    "regression-simple-linear.d.ts",
     "lib",
@@ -12,7 +15,9 @@ "src"
   "scripts": {
+    "compile": "rollup -c",
     "eslint": "eslint src",
     "eslint-fix": "npm run eslint -- --fix",
-    "prepublish": "rollup -c",
-    "test": "run-s testonly eslint",
-    "testonly": "jest"
+    "prepublishOnly": "npm run compile",
+    "test": "npm run test-coverage && npm run eslint",
+    "test-only": "jest",
+    "test-coverage": "jest --coverage"
   },
@@ -34,14 +39,13 @@ "repository": {
   "devDependencies": {
-    "babel-jest": "^19.0.0",
-    "babel-plugin-transform-es2015-modules-commonjs": "^6.24.1",
-    "eslint": "^3.19.0",
-    "eslint-config-cheminfo": "^1.7.0",
-    "eslint-plugin-no-only-tests": "^1.1.0",
-    "jest": "^19.0.2",
-    "npm-run-all": "^4.0.2",
-    "rollup": "^0.41.6"
+    "@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"
   },
   "dependencies": {
-    "ml-regression-base": "^1.0.0"
+    "ml-regression-base": "^2.0.1"
   }
 }

src/__tests__/test.js

 import SLR from '..';
 
 describe('Simple Linear Regression', () => {
-    it('SLR1', () => {
-        const inputs = [80, 60, 10, 20, 30];
-        const outputs = [20, 40, 30, 50, 60];
+  it('SLR1', () => {
+    const inputs = [80, 60, 10, 20, 30];
+    const outputs = [20, 40, 30, 50, 60];
 
-        const regression = new SLR(inputs, outputs);
+    const regression = new SLR(inputs, outputs);
 
-        expect(regression.slope).toEqual(regression.coefficients[1]);
-        expect(regression.intercept).toEqual(regression.coefficients[0]);
+    expect(regression.slope).toStrictEqual(regression.coefficients[1]);
+    expect(regression.intercept).toStrictEqual(regression.coefficients[0]);
 
-        expect(regression.slope).toBeCloseTo(-0.264706, 1e-5);
-        expect(regression.intercept).toBeCloseTo(50.588235, 1e-5);
+    expect(regression.slope).toBeCloseTo(-0.264706, 1e-5);
+    expect(regression.intercept).toBeCloseTo(50.588235, 1e-5);
 
-        const y = regression.predict(85);
-        expect(regression.computeX(y)).toEqual(85);
-        expect(y).toBeCloseTo(28.088235294117649, 1e-10);
+    const y = regression.predict(85);
+    expect(regression.computeX(y)).toStrictEqual(85);
+    expect(y).toBeCloseTo(28.088235294117649, 1e-10);
 
-        expect(regression.toString(3)).toEqual('f(x) = - 0.265 * x + 50.6');
-    });
+    expect(regression.toString(3)).toStrictEqual('f(x) = - 0.265 * x + 50.6');
+  });
 
-    it('SLR2', () => {
-        // example from https://en.wikipedia.org/wiki/Simple_linear_regression#Numerical_example
-        const inputs = [1.47, 1.50, 1.52, 1.55, 1.57, 1.60, 1.63, 1.65, 1.68, 1.70, 1.73, 1.75, 1.78, 1.80, 1.83];
-        const outputs = [52.21, 53.12, 54.48, 55.84, 57.20, 58.57, 59.93, 61.29, 63.11, 64.47, 66.28, 68.10, 69.92, 72.19, 74.46];
+  it('SLR2', () => {
+    // example from https://en.wikipedia.org/wiki/Simple_linear_regression#Numerical_example
+    const inputs = [
+      1.47,
+      1.5,
+      1.52,
+      1.55,
+      1.57,
+      1.6,
+      1.63,
+      1.65,
+      1.68,
+      1.7,
+      1.73,
+      1.75,
+      1.78,
+      1.8,
+      1.83
+    ];
+    const outputs = [
+      52.21,
+      53.12,
+      54.48,
+      55.84,
+      57.2,
+      58.57,
+      59.93,
+      61.29,
+      63.11,
+      64.47,
+      66.28,
+      68.1,
+      69.92,
+      72.19,
+      74.46
+    ];
 
-        const regression = new SLR(inputs, outputs);
+    const regression = new SLR(inputs, outputs);
 
-        expect(regression.slope).toBeCloseTo(61.272, 1e-3);
-        expect(regression.intercept).toBeCloseTo(-39.062, 1e-3);
+    expect(regression.slope).toBeCloseTo(61.272, 1e-3);
+    expect(regression.intercept).toBeCloseTo(-39.062, 1e-3);
 
-        const score = regression.score(inputs, outputs);
-        expect(score.r).toBeCloseTo(0.9945, 1e-4);
-        expect(score.r2).toEqual(score.r * score.r);
-        expect(score.chi2).toBeLessThan(1);
-        expect(score.rmsd).toBeLessThan(1);
-    });
+    const score = regression.score(inputs, outputs);
+    expect(score.r).toBeCloseTo(0.9945, 1e-4);
+    expect(score.r2).toStrictEqual(score.r * score.r);
+    expect(score.chi2).toBeLessThan(1);
+    expect(score.rmsd).toBeLessThan(1);
+  });
 
-    it('SLR3', () =>{
-        const inputs = [0, 1, 2, 3, 4, 5];
-        const outputs = [10, 8, 6, 4, 2, 0];
+  it('SLR3', () => {
+    const inputs = [0, 1, 2, 3, 4, 5];
+    const outputs = [10, 8, 6, 4, 2, 0];
 
-        const regression = new SLR(inputs, outputs);
+    const regression = new SLR(inputs, outputs);
 
-        expect(regression.slope).toEqual(-2);
-        expect(regression.intercept).toEqual(10);
-        expect(regression.predict(6)).toEqual(-2);
-        expect(regression.predict(-1)).toEqual(12);
-        expect(regression.predict(2.5)).toEqual(5);
-        expect(regression.computeX(5)).toEqual(2.5);
-        expect(regression.computeX(9)).toEqual(0.5);
-        expect(regression.computeX(-12)).toEqual(11);
+    expect(regression.slope).toStrictEqual(-2);
+    expect(regression.intercept).toStrictEqual(10);
+    expect(regression.predict(6)).toStrictEqual(-2);
+    expect(regression.predict(-1)).toStrictEqual(12);
+    expect(regression.predict(2.5)).toStrictEqual(5);
+    expect(regression.computeX(5)).toStrictEqual(2.5);
+    expect(regression.computeX(9)).toStrictEqual(0.5);
+    expect(regression.computeX(-12)).toStrictEqual(11);
 
-        const score = regression.score(inputs, outputs);
-        expect(score.r).toBeGreaterThan(0);
-        expect(score.r2).toEqual(1);
-        expect(score.chi2).toEqual(0);
-        expect(score.rmsd).toEqual(0);
+    const score = regression.score(inputs, outputs);
+    expect(score.r).toBeGreaterThan(0);
+    expect(score.r2).toStrictEqual(1);
+    expect(score.chi2).toStrictEqual(0);
+    expect(score.rmsd).toStrictEqual(0);
 
-        expect(regression.toString(3)).toEqual('f(x) = - 2.00 * x + 10.0');
-    });
+    expect(regression.toString(3)).toStrictEqual('f(x) = - 2.00 * x + 10.0');
+  });
 
-    it('SLR constant', () => {
-        const inputs = [0, 1, 2, 3];
-        const outputs = [2, 2, 2, 2];
+  it('SLR constant', () => {
+    const inputs = [0, 1, 2, 3];
+    const outputs = [2, 2, 2, 2];
 
-        const regression = new SLR(inputs, outputs);
+    const regression = new SLR(inputs, outputs);
 
-        expect(regression.toLaTeX()).toEqual('f(x) = 2');
-        expect(regression.toString()).toEqual('f(x) = 2');
-        expect(regression.toString(1)).toEqual('f(x) = 2');
-        expect(regression.toString(5)).toEqual('f(x) = 2.0000');
-    });
+    expect(regression.toLaTeX()).toStrictEqual('f(x) = 2');
+    expect(regression.toString()).toStrictEqual('f(x) = 2');
+    expect(regression.toString(1)).toStrictEqual('f(x) = 2');
+    expect(regression.toString(5)).toStrictEqual('f(x) = 2.0000');
+  });
 
-    it('negative intercept and slope', () => {
-        const inputs = [-1, 0, 1];
-        const outputs = [-2, -1, 0];
+  it('negative intercept and slope', () => {
+    const inputs = [-1, 0, 1];
+    const outputs = [-2, -1, 0];
 
-        const regression = new SLR(inputs, outputs);
+    const regression = new SLR(inputs, outputs);
 
-        expect(regression.toString()).toEqual('f(x) = x - 1');
-    });
+    expect(regression.toString()).toStrictEqual('f(x) = x - 1');
+  });
 
-    it('different size on input and output', () => {
-        const inputs = [0, 1, 2];
-        const outputs = [0, 1];
-        expect(() => {
-            new SLR(inputs, outputs);
-        }).toThrow(/x and y arrays must have the same length/);
+  it('different size on input and output', () => {
+    const inputs = [0, 1, 2];
+    const outputs = [0, 1];
+    expect(() => new SLR(inputs, outputs)).toThrow(
+      /x and y arrays must have the same length/
+    );
+  });
+
+  it('Load and export model', () => {
+    const regression = SLR.load({
+      name: 'simpleLinearRegression',
+      slope: 1,
+      intercept: 1
     });
+    expect(regression.slope).toStrictEqual(1);
+    expect(regression.intercept).toStrictEqual(1);
+    expect(regression.coefficients).toStrictEqual([1, 1]);
 
-    it('Load and export model', () => {
-        const regression = SLR.load({
-            name: 'simpleLinearRegression',
-            slope: 1,
-            intercept: 1
-        });
-        expect(regression.slope).toEqual(1);
-        expect(regression.intercept).toEqual(1);
-        expect(regression.coefficients).toEqual([1, 1]);
-
-        const model = regression.toJSON();
-        expect(model.name).toEqual('simpleLinearRegression');
-        expect(model.slope).toEqual(1);
-        expect(model.intercept).toEqual(1);
-    });
+    const model = regression.toJSON();
+    expect(model.name).toStrictEqual('simpleLinearRegression');
+    expect(model.slope).toStrictEqual(1);
+    expect(model.intercept).toStrictEqual(1);
+  });
 });

src/index.js

@@ -1,79 +0,82 @@
-import BaseRegression, {checkArrayLength, maybeToPrecision} from 'ml-regression-base';
+import BaseRegression, {
+  checkArrayLength,
+  maybeToPrecision
+} from 'ml-regression-base';
 
 export default class SimpleLinearRegression extends BaseRegression {
-    constructor(x, y) {
-        super();
-        if (x === true) {
-            this.slope = y.slope;
-            this.intercept = y.intercept;
-            this.coefficients = [y.intercept, y.slope];
-        } else {
-            checkArrayLength(x, y);
-            regress(this, x, y);
-        }
+  constructor(x, y) {
+    super();
+    if (x === true) {
+      this.slope = y.slope;
+      this.intercept = y.intercept;
+      this.coefficients = [y.intercept, y.slope];
+    } else {
+      checkArrayLength(x, y);
+      regress(this, x, y);
     }
+  }
 
-    toJSON() {
-        return {
-            name: 'simpleLinearRegression',
-            slope: this.slope,
-            intercept: this.intercept
-        };
-    }
+  toJSON() {
+    return {
+      name: 'simpleLinearRegression',
+      slope: this.slope,
+      intercept: this.intercept
+    };
+  }
 
-    _predict(x) {
-        return this.slope * x + this.intercept;
-    }
+  _predict(x) {
+    return this.slope * x + this.intercept;
+  }
 
-    computeX(y) {
-        return (y - this.intercept) / this.slope;
-    }
+  computeX(y) {
+    return (y - this.intercept) / this.slope;
+  }
 
-    toString(precision) {
-        let result = 'f(x) = ';
-        if (this.slope !== 0) {
-            const xFactor = maybeToPrecision(this.slope, precision);
-            result += (xFactor === '1' ? '' : xFactor + ' * ') + 'x';
-            if (this.intercept) {
-                const absIntercept = Math.abs(this.intercept);
-                const operator = absIntercept === this.intercept ? '+' : '-';
-                result += ` ${operator} ${maybeToPrecision(absIntercept, precision)}`;
-            }
-        } else {
-            result += maybeToPrecision(this.intercept, precision);
-        }
-        return result;
+  toString(precision) {
+    let result = 'f(x) = ';
+    if (this.slope !== 0) {
+      const xFactor = maybeToPrecision(this.slope, precision);
+      result += `${xFactor === '1' ? '' : `${xFactor} * `}x`;
+      if (this.intercept) {
+        const absIntercept = Math.abs(this.intercept);
+        const operator = absIntercept === this.intercept ? '+' : '-';
+        result += ` ${operator} ${maybeToPrecision(absIntercept, precision)}`;
+      }
+    } else {
+      result += maybeToPrecision(this.intercept, precision);
     }
+    return result;
+  }
 
-    toLaTeX(precision) {
-        return this.toString(precision);
-    }
+  toLaTeX(precision) {
+    return this.toString(precision);
+  }
 
-    static load(json) {
-        if (json.name !== 'simpleLinearRegression') {
-            throw new TypeError('not a SLR model');
-        }
-        return new SimpleLinearRegression(true, json);
+  static load(json) {
+    if (json.name !== 'simpleLinearRegression') {
+      throw new TypeError('not a SLR model');
     }
+    return new SimpleLinearRegression(true, json);
+  }
 }
 
 function regress(slr, x, y) {
-    const n = x.length;
-    let xSum = 0;
-    let ySum = 0;
+  const n = x.length;
+  let xSum = 0;
+  let ySum = 0;
 
-    let xSquared = 0;
-    let xY = 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];
-    }
+  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];
+  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];
 }

No alert changes

Improved metrics

Total package byte prevSize

12929

increased by3.26%

Dev dependency count

7

decreased by-12.5%

Number of package files

8

increased by14.29%

Lines of code

271

increased by20.98%

Dependency changes

• + Addedis-any-array@2.0.1(transitive)

• + Addedml-regression-base@2.1.6(transitive)

• - Removedml-regression-base@1.2.1(transitive)

• Updatedml-regression-base@^2.0.1