Chore: use default value of initialCapacity from ml-hast-table

Author: jobo322

benchmark/benchmarkLinePlot.js

@@ -1,14 +1,14 @@
 import { run, bench, lineplot, do_not_optimize } from 'mitata';
 import { SparseMatrix } from '../src/index.js';
-import { randomSparseMatrix } from './utils/randomSparseMatrix.js';
-import { Matrix } from 'ml-matrix';
+import { xSequentialFillFromStep } from 'ml-spectra-processing';
 import { SparseMatrix as SparseMatrixOld } from './class/SparseMatrixOld.js';
 import { randomMatrix } from './utils/randomMatrix.js';
-const density = 0.01; // Fixed density for this comparison;
-const min = 32;
-const max = 128;
-// Prepare matrices once
+const density = 0.02; // Fixed density for this comparison;
 
+// Prepare matrices once
+const sizes = Array.from(
+  xSequentialFillFromStep({ from: 4, step: 4, size: 13 }),
+);
 lineplot(() => {
   bench('Sparse.mmul($size)', function* (ctx) {
     const size = ctx.get('size');
@@ -18,7 +18,7 @@ lineplot(() => {
     const B = new SparseMatrix(randomMatrix(size, size, density));
     // Benchmark the multiplication
     yield () => do_not_optimize(A.mmul(B));
-  }).range('size', min, max, 2); // 16, 32, 64, 128, 256
+  }).args('size', sizes); // 16, 32, 64, 128, 256
 
   bench('SparseOld.mmul($size)', function* (ctx) {
     const size = ctx.get('size');
@@ -29,20 +29,20 @@ lineplot(() => {
 
     // Benchmark the multiplication
     yield () => do_not_optimize(AOld.mmul(BOld));
-  }).range('size', min, max, 2);
+  }).args('size', sizes);
 
-  bench('Dense.mmul($size)', function* (ctx) {
-    const size = ctx.get('size');
+  // bench('Dense.mmul($size)', function* (ctx) {
+  //   const size = ctx.get('size');
 
-    // Prepare matrices once
-    const A = randomMatrix(size, size, density);
-    const B = randomMatrix(size, size, density);
-    const ADense = new Matrix(A);
-    const BDense = new Matrix(B);
+  //   // Prepare matrices once
+  //   const A = randomMatrix(size, size, density);
+  //   const B = randomMatrix(size, size, density);
+  //   const ADense = new Matrix(A);
+  //   const BDense = new Matrix(B);
 
-    // Benchmark the multiplication
-    yield () => do_not_optimize(ADense.mmul(BDense));
-  }).range('size', min, max, 2);
+  //   // Benchmark the multiplication
+  //   yield () => do_not_optimize(ADense.mmul(BDense));
+  // }).range('size', min, max, multiplier);
 });
 
 await run();

benchmark/benchmarkMitata.js

@@ -1,12 +1,10 @@
 import { run, bench, group, do_not_optimize } from 'mitata';
 import { Matrix } from 'ml-matrix';
 import { SparseMatrix as SparseMatrixOld } from './class/SparseMatrixOld2.js';
-import fs from 'fs';
 import { randomSparseMatrix } from './utils/randomSparseMatrix.js';
 
 const sizes = [64, 128, 256];
 const densities = [0.01, 0.015, 0.02, 0.025, 0.03];
-const results = [];
 
 for (const density of densities) {
   for (const size of sizes) {
@@ -19,11 +17,6 @@ for (const density of densities) {
     denseA = new Matrix(denseA);
     denseB = new Matrix(denseB);
 
-    // Warm up
-    A.mmul(B);
-
-    let mmulNewAvg, mmulAvg, denseAvg;
-
     group(`size:${size}-density:${density}`, () => {
       bench('mmulNew', () => {
         do_not_optimize(A.mmul(B));
@@ -35,11 +28,7 @@ for (const density of densities) {
         do_not_optimize(denseA.mmul(denseB));
       }); //.gc('inner');
     });
-
-    // mitata will handle timing and reporting
-    // You can extract results from mitata's output or use the save() function
   }
 }
 
 await run({ silent: false });
-// await save({ file: './benchmark/mitata_results.json', format: 'json' });

package.json

@@ -42,6 +42,7 @@
     "eslint-config-cheminfo-typescript": "^18.0.1",
     "mitata": "^1.0.34",
     "ml-matrix": "^6.12.1",
+    "ml-spectra-processing": "^14.12.0",
     "prettier": "^3.5.3",
     "rimraf": "^6.0.1",
     "typescript": "^5.8.3",

src/index.js

@@ -19,8 +19,7 @@ export class SparseMatrix {
     if (Array.isArray(rows)) {
       const matrix = rows;
       const nbRows = matrix.length;
-      const nbColmuns = matrix[0].length;
-      options = columns || { initialCapacity: nbRows * nbColmuns };
+      options = columns || {};
       columns = matrix[0].length;
       this._init(nbRows, columns, new HashTable(options), options.threshold);
       for (let i = 0; i < nbRows; i++) {