feat!: throw error if insufficient number of points

src/__tests__/2d-polinomial-fit.js

@@ -9,20 +9,18 @@ describe('2D polinomial fit', () => {
   }
 
   const pf = new Polyfit(X, y, {
-    order: 2
+    order: 2,
   });
 
   it('Training coefficients', () => {
     const estimatedCoefficients = [
-      1.5587e1,
-      3.8873e-1,
-      5.2582e-3,
-      4.8498e-1,
-      2.1127e-3,
-      -7.3709e-3
+      1.5587e1, 3.8873e-1, 5.2582e-3, 4.8498e-1, 2.1127e-3, -7.3709e-3,
     ];
     for (let i = 0; i < estimatedCoefficients.length; ++i) {
-      expect(pf.coefficients.get(i, 0)).toBeCloseTo(estimatedCoefficients[i], 1e-2);
+      expect(pf.coefficients.get(i, 0)).toBeCloseTo(
+        estimatedCoefficients[i],
+        1e-2,
+      );
     }
   });
 
@@ -44,16 +42,8 @@ describe('2D polinomial fit', () => {
 
   it('Other function test', () => {
     var testValues = [
-      15.041667,
-      9.375,
-      5.041667,
-      2.041667,
-      0.375,
-      0.041667,
-      1.041667,
-      3.375,
-      7.041667,
-      12.041667
+      15.041667, 9.375, 5.041667, 2.041667, 0.375, 0.041667, 1.041667, 3.375,
+      7.041667, 12.041667,
     ];
 
     var len = 21;
@@ -67,7 +57,7 @@ describe('2D polinomial fit', () => {
     }
 
     var polyFit = new Polyfit(X, y, {
-      order: 2
+      order: 2,
     });
 
     var test = 10;
@@ -85,4 +75,19 @@ describe('2D polinomial fit', () => {
       expect(predict[i]).toBeCloseTo(testValues[i], 1e-2);
     }
   });
+  it('must throw error', () => {
+    const X = new Array(5);
+    const y = new Array(5);
+    for (let i = 0; i < 5; ++i) {
+      X[i] = [i, i + 10];
+      y[i] = i + 20;
+    }
+
+    expect(() => {
+      const polyfit = new Polyfit(X, y, {
+        order: 4,
+      });
+      return polyfit;
+    }).toThrow('Insufficient number of points to create regression model.');
+  });
 });

src/regression/poly-fit-regression2d.js

@@ -2,7 +2,7 @@ import { Matrix, SVD } from 'ml-matrix';
 import BaseRegression from 'ml-regression-base';
 
 const defaultOptions = {
-  order: 2
+  order: 2,
 };
 // Implements the Kernel ridge regression algorithm.
 // http://www.ics.uci.edu/~welling/classnotes/papers_class/Kernel-Ridge.pdf
@@ -57,7 +57,7 @@ export default class PolynomialFitRegression2D extends BaseRegression {
 
     if (X.columns !== 2) {
       throw new RangeError(
-        `You give X with ${X.columns} columns and it must be 2`
+        `You give X with ${X.columns} columns and it must be 2`,
       );
     }
     if (X.rows !== y.rows) {
@@ -66,29 +66,19 @@ export default class PolynomialFitRegression2D extends BaseRegression {
 
     var examples = X.rows;
     var coefficients = ((this.order + 2) * (this.order + 1)) / 2;
+    if (examples < coefficients) {
+      throw new Error(
+        'Insufficient number of points to create regression model.',
+      );
+    }
     this.coefficients = new Array(coefficients);
 
     var x1 = X.getColumnVector(0);
     var x2 = X.getColumnVector(1);
 
-    var scaleX1 =
-      1.0 /
-      x1
-        .clone()
-        .abs()
-        .max();
-    var scaleX2 =
-      1.0 /
-      x2
-        .clone()
-        .abs()
-        .max();
-    var scaleY =
-      1.0 /
-      y
-        .clone()
-        .abs()
-        .max();
+    var scaleX1 = 1.0 / x1.clone().abs().max();
+    var scaleX2 = 1.0 / x2.clone().abs().max();
+    var scaleY = 1.0 / y.clone().abs().max();
 
     x1.mulColumn(0, scaleX1);
     x2.mulColumn(0, scaleX2);
@@ -109,7 +99,7 @@ export default class PolynomialFitRegression2D extends BaseRegression {
     var svd = new SVD(A.transpose(), {
       computeLeftSingularVectors: true,
       computeRightSingularVectors: true,
-      autoTranspose: false
+      autoTranspose: false,
     });
 
     var qqs = Matrix.rowVector(svd.diagonal);
@@ -128,9 +118,7 @@ export default class PolynomialFitRegression2D extends BaseRegression {
     var U = svd.rightSingularVectors;
     var V = svd.leftSingularVectors;
 
-    this.coefficients = V.mmul(qqs.transpose())
-      .mmul(U.transpose())
-      .mmul(y);
+    this.coefficients = V.mmul(qqs.transpose()).mmul(U.transpose()).mmul(y);
 
     col = 0;
 
@@ -143,7 +131,7 @@ export default class PolynomialFitRegression2D extends BaseRegression {
           (this.coefficients.get(col, 0) *
             Math.pow(scaleX1, i) *
             Math.pow(scaleX2, j)) /
-            scaleY
+            scaleY,
         );
         col++;
       }
@@ -172,7 +160,7 @@ export default class PolynomialFitRegression2D extends BaseRegression {
     return {
       name: 'polyfit2D',
       order: this.order,
-      coefficients: this.coefficients
+      coefficients: this.coefficients,
     };
   }