als-statistics/lib/table/instruments/linear-regression/matrix-utils.js

A powerful and lightweight JavaScript library for descriptive statistics, regression, clustering, outlier detection, and noise analysis using a flexible table/column architecture.

const MatrixUtils = {
   transpose(A) {
      return A[0].map((_, i) => A.map(row => row[i]));
   },

   multiply(A, B) {
      const result = Array.from({ length: A.length }, () => Array(B[0].length).fill(0));
      for (let i = 0; i < A.length; i++) {
         for (let j = 0; j < B[0].length; j++) {
            for (let k = 0; k < B.length; k++) {
               result[i][j] += A[i][k] * B[k][j];
            }
         }
      }
      return result;
   },

   multiplyVec(A, b) {
      return A.map(row => row.reduce((acc, aij, j) => acc + aij * b[j], 0));
   },

   inverse(A) {
      const n = A.length;
      const I = Array.from({ length: n }, (_, i) => Array.from({ length: n }, (_, j) => (i === j ? 1 : 0)));
      const AI = A.map((row, i) => [...row, ...I[i]]);

      for (let i = 0; i < n; i++) {
         let maxRow = i;
         for (let k = i + 1; k < n; k++) {
            if (Math.abs(AI[k][i]) > Math.abs(AI[maxRow][i])) maxRow = k;
         }
         [AI[i], AI[maxRow]] = [AI[maxRow], AI[i]];
         const diag = AI[i][i];
         for (let j = 0; j < 2 * n; j++) AI[i][j] /= diag;
         for (let k = 0; k < n; k++) {
            if (k !== i) {
               const factor = AI[k][i];
               for (let j = 0; j < 2 * n; j++) AI[k][j] -= factor * AI[i][j];
            }
         }
      }
      return AI.map(row => row.slice(n));
   }
};

module.exports = MatrixUtils