feat: update benchmark parameters and add comprehensive tests for SparseMatrix operations

Author: jobo322

benchmark/benchmark.js

@@ -54,10 +54,12 @@ function printWinner(label1, avg1, label2, avg2) {
 
 function runBenchmarks() {
   const m = 128;
-  const n = 64;
+  const n = 128;
   const p = 128;
-  const densityA = 0.03;
-  const densityB = 0.03;
+  const densityA = 0.01;
+  const densityB = 0.01;
+
+  const nbIterations = 3;
   const A = randomSparseMatrix(m, n, densityA);
   const B = randomSparseMatrix(n, p, densityB);
 
@@ -66,10 +68,11 @@ function runBenchmarks() {
 
   const AOld = new SparseMatrixOld(denseA);
   const BOld = new SparseMatrixOld(denseB);
-  A.cardinality;
+
   denseA = new Matrix(denseA);
   denseB = new Matrix(denseB);
 
+  denseA.mmul(denseB);
   // 1. add vs addNew
   // const addAvg = benchmark(() => {
   //   const a = AOld.clone();
@@ -90,23 +93,23 @@ function runBenchmarks() {
       A.mmul(B);
     },
     'mmulNew',
-    3,
+    nbIterations,
   );
 
   const mmulAvg = benchmark(
     () => {
       AOld.mmul(BOld);
     },
     'mmul',
-    3,
+    nbIterations,
   );
 
   const denseAvg = benchmark(
     () => {
       denseA.mmul(denseB);
     },
     'denseMatrix',
-    3,
+    nbIterations,
   );
 
   printWinner('mmul', mmulAvg, 'mmulNew', mmulNewAvg);

package.json

@@ -12,7 +12,7 @@
     "compile": "rollup -c",
     "eslint": "eslint src",
     "eslint-fix": "npm run eslint -- --fix",
-    "prepack": "npm run compile",    
+    "prepack": "npm run compile",
     "prettier": "prettier --check src",
     "prettier-write": "prettier --write src",
     "test": "npm run test-only && npm run eslint && npm run prettier",
@@ -37,6 +37,7 @@
     "eslint": "^8.10.0",
     "eslint-config-cheminfo": "^7.2.2",
     "jest": "^27.5.1",
+    "ml-matrix": "^6.12.1",
     "prettier": "^2.5.1",
     "rollup": "^2.69.0"
   }

src/__tests__/test.js renamed to src/__tests__/matrix.test.js

@@ -40,6 +40,29 @@ describe('Sparse Matrix', () => {
       [0, 2],
       [0, 0],
     ]);
+
+    // Compare with dense multiplication
+    const denseM1 = m1.to2DArray();
+    const denseM2 = m2.to2DArray();
+    const expectedDense = denseMatrixMultiply(denseM1, denseM2);
+    expect(m3.to2DArray()).toStrictEqual(expectedDense);
+  });
+
+  it('mmul', () => {
+    const size = 32;
+    const density = 0.1;
+    const m1 = randomSparseMatrix(size, size, density);
+    const m2 = randomSparseMatrix(size, size, density);
+    let m3 = m1.mmul(m2);
+
+    const denseM1 = m1.to2DArray();
+    const denseM2 = m2.to2DArray();
+
+    const newSparse = new SparseMatrix(denseM1);
+    expect(newSparse.to2DArray()).toStrictEqual(denseM1);
+    const expectedDense = denseMatrixMultiply(denseM1, denseM2);
+
+    expect(m3.to2DArray()).toStrictEqual(expectedDense);
   });
 
   it('kronecker', () => {
@@ -142,3 +165,32 @@ describe('Banded matrices', () => {
     expect(matrix4.isBanded(1)).toBe(true);
   });
 });
+
+function denseMatrixMultiply(A, B) {
+  const rowsA = A.length;
+  const colsA = A[0].length;
+  const colsB = B[0].length;
+  const result = Array.from({ length: rowsA }, () => Array(colsB).fill(0));
+  for (let i = 0; i < rowsA; i++) {
+    for (let j = 0; j < colsB; j++) {
+      for (let k = 0; k < colsA; k++) {
+        result[i][j] += A[i][k] * B[k][j];
+      }
+    }
+  }
+  return result;
+}
+
+function randomSparseMatrix(rows, cols, density = 0.01) {
+  const matrix = [];
+  for (let i = 0; i < rows; i++) {
+    const row = new Float64Array(cols);
+    for (let j = 0; j < cols; j++) {
+      if (Math.random() < density) {
+        row[j] = Math.random() * 10;
+      }
+    }
+    matrix.push(row);
+  }
+  return new SparseMatrix(matrix);
+}