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

Comparing version 2.0.1 to 2.0.2

History.md

@@ -0,1 +1,10 @@
+## [2.0.2](https://github.com/mljs/regression-simple-linear/compare/v2.0.1...v2.0.2) (2019-06-19)
+
+
+### Bug Fixes
+
+* correct TS definitions ([27ad52f](https://github.com/mljs/regression-simple-linear/commit/27ad52f))
+
+
+
 ## [2.0.1](https://github.com/mljs/regression-simple-linear/compare/v2.0.0...v2.0.1) (2019-05-02)
@@ -2,0 +11,0 @@

lib/index.js

@@ -42,3 +42,3 @@ 'use strict';
       result += `${xFactor === '1' ? '' : `${xFactor} * `}x`;
-      if (this.intercept) {
+      if (this.intercept !== 0) {
         const absIntercept = Math.abs(this.intercept);
@@ -45,0 +45,0 @@ const operator = absIntercept === this.intercept ? '+' : '-';

package.json

 {
   "name": "ml-regression-simple-linear",
-  "version": "2.0.1",
+  "version": "2.0.2",
   "description": "Simple Linear Regression",
@@ -5,0 +5,0 @@ "main": "lib/index.js",

regression-simple-linear.d.ts

@@ -8,3 +8,7 @@ import BaseRegression from 'ml-regression-base';
 
-  export default class SimpleLinearRegression extends BaseRegression {
+  class SimpleLinearRegression extends BaseRegression {
+    slope: number;
+    intercept: number;
+    coefficients: [number, number];
+
     constructor(x: number[], y: number[]);
@@ -17,2 +21,4 @@
   }
+
+  export = SimpleLinearRegression;
 }

src/__tests__/test.js

@@ -10,4 +10,4 @@ import SLR from '..';
 
-    expect(regression.slope).toStrictEqual(regression.coefficients[1]);
-    expect(regression.intercept).toStrictEqual(regression.coefficients[0]);
+    expect(regression.slope).toBe(regression.coefficients[1]);
+    expect(regression.intercept).toBe(regression.coefficients[0]);
 
@@ -18,6 +18,6 @@ expect(regression.slope).toBeCloseTo(-0.264706, 1e-5);
     const y = regression.predict(85);
-    expect(regression.computeX(y)).toStrictEqual(85);
+    expect(regression.computeX(y)).toBe(85);
     expect(y).toBeCloseTo(28.088235294117649, 1e-10);
 
-    expect(regression.toString(3)).toStrictEqual('f(x) = - 0.265 * x + 50.6');
+    expect(regression.toString(3)).toBe('f(x) = - 0.265 * x + 50.6');
   });
@@ -69,3 +69,3 @@
     expect(score.r).toBeCloseTo(0.9945, 1e-4);
-    expect(score.r2).toStrictEqual(score.r * score.r);
+    expect(score.r2).toBe(score.r * score.r);
     expect(score.chi2).toBeLessThan(1);
@@ -81,18 +81,18 @@ expect(score.rmsd).toBeLessThan(1);
 
-    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);
+    expect(regression.slope).toBe(-2);
+    expect(regression.intercept).toBe(10);
+    expect(regression.predict(6)).toBe(-2);
+    expect(regression.predict(-1)).toBe(12);
+    expect(regression.predict(2.5)).toBe(5);
+    expect(regression.computeX(5)).toBe(2.5);
+    expect(regression.computeX(9)).toBe(0.5);
+    expect(regression.computeX(-12)).toBe(11);
 
     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(score.r2).toBe(1);
+    expect(score.chi2).toBe(0);
+    expect(score.rmsd).toBe(0);
 
-    expect(regression.toString(3)).toStrictEqual('f(x) = - 2.00 * x + 10.0');
+    expect(regression.toString(3)).toBe('f(x) = - 2.00 * x + 10.0');
   });
@@ -105,7 +105,6 @@
     const regression = new SLR(inputs, outputs);
-
-    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');
+    expect(regression.toLaTeX()).toBe('f(x) = 2');
+    expect(regression.toString()).toBe('f(x) = 2');
+    expect(regression.toString(1)).toBe('f(x) = 2');
+    expect(regression.toString(5)).toBe('f(x) = 2.0000');
   });
@@ -119,5 +118,12 @@
 
-    expect(regression.toString()).toStrictEqual('f(x) = x - 1');
+    expect(regression.toString()).toBe('f(x) = x - 1');
   });
 
+  it('intercept = 0', () => {
+    const regression = new SLR([0, 1, 2], [0, 1, 2]);
+    expect(regression.slope).toBe(1);
+    expect(regression.intercept).toBe(0);
+    expect(regression.toString()).toBe('f(x) = x');
+  });
+
   it('different size on input and output', () => {
@@ -137,11 +143,15 @@ const inputs = [0, 1, 2];
     });
-    expect(regression.slope).toStrictEqual(1);
-    expect(regression.intercept).toStrictEqual(1);
+    expect(regression.slope).toBe(1);
+    expect(regression.intercept).toBe(1);
     expect(regression.coefficients).toStrictEqual([1, 1]);
 
     const model = regression.toJSON();
-    expect(model.name).toStrictEqual('simpleLinearRegression');
-    expect(model.slope).toStrictEqual(1);
-    expect(model.intercept).toStrictEqual(1);
+    expect(model.name).toBe('simpleLinearRegression');
+    expect(model.slope).toBe(1);
+    expect(model.intercept).toBe(1);
   });
+
+  it('wrong model', () => {
+    expect(() => SLR.load({})).toThrow(/^not a SLR model$/);
+  });
 });

src/index.js

@@ -40,3 +40,3 @@ import BaseRegression, {
       result += `${xFactor === '1' ? '' : `${xFactor} * `}x`;
-      if (this.intercept) {
+      if (this.intercept !== 0) {
         const absIntercept = Math.abs(this.intercept);
@@ -43,0 +43,0 @@ const operator = absIntercept === this.intercept ? '+' : '-';

No alert changes

Improved metrics

Total package byte prevSize

13569

increased by3.09%

Lines of code

284

increased by4.8%

No dependency changes