Improved SparseVector constructor

src/utils/index.js

@@ -25,7 +25,7 @@ function sparsevecFromSql(value) {
   if (value === null) {
     return null;
   }
-  return SparseVector.fromSql(value);
+  return new SparseVector(value);
 }
 
 function sparsevecToSql(value) {

src/utils/sparse-vector.js

@@ -1,13 +1,14 @@
 const util = require('node:util');
 
 class SparseVector {
-  constructor(dimensions, indices, values) {
-    if (indices.length != values.length) {
-      throw new Error('indices and values must be the same length');
+  constructor(value, dimensions) {
+    if (typeof value === 'string') {
+      this.#fromSql(value);
+    } else if (dimensions !== undefined) {
+      this.#fromMap(value, dimensions);
+    } else {
+      this.#fromDense(value);
     }
-    this.dimensions = dimensions;
-    this.indices = indices;
-    this.values = values;
   }
 
   toString() {
@@ -32,43 +33,45 @@ class SparseVector {
     return arr;
   }
 
-  static fromSql(value) {
+  #fromSql(value) {
     const parts = value.split('/', 2);
+
+    this.dimensions = parseInt(parts[1]);
+    this.indices = [];
+    this.values = [];
+
     const elements = parts[0].slice(1, -1).split(',');
-    const dimensions = parseInt(parts[1]);
-    const indices = [];
-    const values = [];
     for (const element of elements) {
       const ep = element.split(':', 2);
-      indices.push(parseInt(ep[0]) - 1);
-      values.push(parseFloat(ep[1]));
+      this.indices.push(parseInt(ep[0]) - 1);
+      this.values.push(parseFloat(ep[1]));
     }
-    return new SparseVector(dimensions, indices, values);
   }
 
-  static fromDense(value) {
-    const dimensions = value.length;
-    const indices = [];
-    const values = [];
+  #fromDense(value) {
+    this.dimensions = value.length;
+    this.indices = [];
+    this.values = [];
+
     for (const [i, v] of value.entries()) {
       if (v != 0) {
-        indices.push(Number(i));
-        values.push(Number(v));
+        this.indices.push(Number(i));
+        this.values.push(Number(v));
       }
     }
-    return new SparseVector(dimensions, indices, values);
   }
 
-  static fromMap(map, dimensions) {
-    const indices = [];
-    const values = [];
+  #fromMap(map, dimensions) {
+    this.dimensions = Number(dimensions);
+    this.indices = [];
+    this.values = [];
+
     for (const [i, v] of map.entries()) {
       if (v != 0) {
-        indices.push(Number(i));
-        values.push(Number(v));
+        this.indices.push(Number(i));
+        this.values.push(Number(v));
       }
     }
-    return new SparseVector(Number(dimensions), indices, values);
   }
 }
 

tests/drizzle-orm/index.test.mjs

@@ -21,9 +21,9 @@ test('example', async () => {
   });
 
   const newItems = [
-    {embedding: [1, 1, 1], halfEmbedding: [1, 1, 1], binaryEmbedding: '000', sparseEmbedding: SparseVector.fromDense([1, 1, 1])},
-    {embedding: [2, 2, 2], halfEmbedding: [2, 2, 2], binaryEmbedding: '101', sparseEmbedding: SparseVector.fromDense([2, 2, 2])},
-    {embedding: [1, 1, 2], halfEmbedding: [1, 1, 2], binaryEmbedding: '111', sparseEmbedding: SparseVector.fromDense([1, 1, 2])},
+    {embedding: [1, 1, 1], halfEmbedding: [1, 1, 1], binaryEmbedding: '000', sparseEmbedding: new SparseVector([1, 1, 1])},
+    {embedding: [2, 2, 2], halfEmbedding: [2, 2, 2], binaryEmbedding: '101', sparseEmbedding: new SparseVector([2, 2, 2])},
+    {embedding: [1, 1, 2], halfEmbedding: [1, 1, 2], binaryEmbedding: '111', sparseEmbedding: new SparseVector([1, 1, 2])},
     {embedding: null}
   ];
   await db.insert(items).values(newItems);
@@ -48,7 +48,7 @@ test('example', async () => {
   // L2 distance - sparsevec
   allItems = await db.select()
     .from(items)
-    .orderBy(l2Distance(items.sparseEmbedding, SparseVector.fromDense([1, 1, 1])))
+    .orderBy(l2Distance(items.sparseEmbedding, new SparseVector([1, 1, 1])))
     .limit(5);
   expect(allItems.map(v => v.id)).toStrictEqual([1, 3, 2, 4]);
 

tests/knex/index.test.mjs

@@ -19,9 +19,9 @@ test('example', async () => {
   });
 
   const newItems = [
-    {embedding: pgvector.toSql([1, 1, 1]), half_embedding: pgvector.toSql([1, 1, 1]), binary_embedding: '000', sparse_embedding: SparseVector.fromDense([1, 1, 1])},
-    {embedding: pgvector.toSql([2, 2, 2]), half_embedding: pgvector.toSql([2, 2, 2]), binary_embedding: '101', sparse_embedding: SparseVector.fromDense([2, 2, 2])},
-    {embedding: pgvector.toSql([1, 1, 2]), half_embedding: pgvector.toSql([1, 1, 2]), binary_embedding: '111', sparse_embedding: SparseVector.fromDense([1, 1, 2])},
+    {embedding: pgvector.toSql([1, 1, 1]), half_embedding: pgvector.toSql([1, 1, 1]), binary_embedding: '000', sparse_embedding: new SparseVector([1, 1, 1])},
+    {embedding: pgvector.toSql([2, 2, 2]), half_embedding: pgvector.toSql([2, 2, 2]), binary_embedding: '101', sparse_embedding: new SparseVector([2, 2, 2])},
+    {embedding: pgvector.toSql([1, 1, 2]), half_embedding: pgvector.toSql([1, 1, 2]), binary_embedding: '111', sparse_embedding: new SparseVector([1, 1, 2])},
     {embedding: null}
   ];
   await knex('knex_items').insert(newItems);
@@ -43,7 +43,7 @@ test('example', async () => {
 
   // L2 distance - sparsevec
   items = await knex('knex_items')
-    .orderBy(knex.l2Distance('sparse_embedding', SparseVector.fromDense([1, 1, 1])))
+    .orderBy(knex.l2Distance('sparse_embedding', new SparseVector([1, 1, 1])))
     .limit(5);
   expect(items.map(v => v.id)).toStrictEqual([1, 3, 2, 4]);
 

tests/kysely/index.test.mjs

@@ -30,9 +30,9 @@ test('example', async () => {
     .execute();
 
   const newItems = [
-    {embedding: pgvector.toSql([1, 1, 1]), half_embedding: pgvector.toSql([1, 1, 1]), binary_embedding: '000', sparse_embedding: SparseVector.fromDense([1, 1, 1])},
-    {embedding: pgvector.toSql([2, 2, 2]), half_embedding: pgvector.toSql([2, 2, 2]), binary_embedding: '101', sparse_embedding: SparseVector.fromDense([2, 2, 2])},
-    {embedding: pgvector.toSql([1, 1, 2]), half_embedding: pgvector.toSql([1, 1, 2]), binary_embedding: '111', sparse_embedding: SparseVector.fromDense([1, 1, 2])},
+    {embedding: pgvector.toSql([1, 1, 1]), half_embedding: pgvector.toSql([1, 1, 1]), binary_embedding: '000', sparse_embedding: new SparseVector([1, 1, 1])},
+    {embedding: pgvector.toSql([2, 2, 2]), half_embedding: pgvector.toSql([2, 2, 2]), binary_embedding: '101', sparse_embedding: new SparseVector([2, 2, 2])},
+    {embedding: pgvector.toSql([1, 1, 2]), half_embedding: pgvector.toSql([1, 1, 2]), binary_embedding: '111', sparse_embedding: new SparseVector([1, 1, 2])},
     {embedding: null}
   ];
   await db.insertInto('kysely_items')
@@ -61,7 +61,7 @@ test('example', async () => {
   // L2 distance - sparsevec
   items = await db.selectFrom('kysely_items')
     .selectAll()
-    .orderBy(l2Distance('sparse_embedding', SparseVector.fromDense([1, 1, 1])))
+    .orderBy(l2Distance('sparse_embedding', new SparseVector([1, 1, 1])))
     .limit(5)
     .execute();
   expect(items.map(v => v.id)).toStrictEqual([1, 3, 2, 4]);

tests/mikro-orm/index.test.mjs

@@ -27,9 +27,9 @@ test('example', async () => {
   const generator = orm.getSchemaGenerator();
   await generator.refreshDatabase();
 
-  em.create(Item, {embedding: [1, 1, 1], half_embedding: [1, 1, 1], binary_embedding: '000', sparse_embedding: SparseVector.fromDense([1, 1, 1])});
-  em.create(Item, {embedding: [2, 2, 2], half_embedding: [2, 2, 2], binary_embedding: '101', sparse_embedding: SparseVector.fromDense([2, 2, 2])});
-  em.create(Item, {embedding: [1, 1, 2], half_embedding: [1, 1, 2], binary_embedding: '111', sparse_embedding: SparseVector.fromDense([1, 1, 2])});
+  em.create(Item, {embedding: [1, 1, 1], half_embedding: [1, 1, 1], binary_embedding: '000', sparse_embedding: new SparseVector([1, 1, 1])});
+  em.create(Item, {embedding: [2, 2, 2], half_embedding: [2, 2, 2], binary_embedding: '101', sparse_embedding: new SparseVector([2, 2, 2])});
+  em.create(Item, {embedding: [1, 1, 2], half_embedding: [1, 1, 2], binary_embedding: '111', sparse_embedding: new SparseVector([1, 1, 2])});
   em.create(Item, {embedding: null});
 
   // L2 distance
@@ -51,7 +51,7 @@ test('example', async () => {
 
   // L2 distance - sparsevec
   items = await em.createQueryBuilder(Item)
-    .orderBy({[l2Distance('sparse_embedding', SparseVector.fromDense([1, 1, 1]))]: 'ASC'})
+    .orderBy({[l2Distance('sparse_embedding', new SparseVector([1, 1, 1]))]: 'ASC'})
     .limit(5)
     .getResult();
   expect(items.map(v => v.id)).toStrictEqual([1, 3, 2, 4]);

tests/objection/index.test.mjs

@@ -29,9 +29,9 @@ test('example', async () => {
   });
 
   const newItems = [
-    {embedding: pgvector.toSql([1, 1, 1]), half_embedding: pgvector.toSql([1, 1, 1]), binary_embedding: '000', sparse_embedding: SparseVector.fromDense([1, 1, 1])},
-    {embedding: pgvector.toSql([2, 2, 2]), half_embedding: pgvector.toSql([2, 2, 2]), binary_embedding: '101', sparse_embedding: SparseVector.fromDense([2, 2, 2])},
-    {embedding: pgvector.toSql([1, 1, 2]), half_embedding: pgvector.toSql([1, 1, 2]), binary_embedding: '111', sparse_embedding: SparseVector.fromDense([1, 1, 2])},
+    {embedding: pgvector.toSql([1, 1, 1]), half_embedding: pgvector.toSql([1, 1, 1]), binary_embedding: '000', sparse_embedding: new SparseVector([1, 1, 1])},
+    {embedding: pgvector.toSql([2, 2, 2]), half_embedding: pgvector.toSql([2, 2, 2]), binary_embedding: '101', sparse_embedding: new SparseVector([2, 2, 2])},
+    {embedding: pgvector.toSql([1, 1, 2]), half_embedding: pgvector.toSql([1, 1, 2]), binary_embedding: '111', sparse_embedding: new SparseVector([1, 1, 2])},
     {embedding: null}
   ];
   await Item.query().insert(newItems);
@@ -53,7 +53,7 @@ test('example', async () => {
 
   // L2 distance - sparsevec
   items = await Item.query()
-    .orderBy(l2Distance('sparse_embedding', SparseVector.fromDense([1, 1, 1])))
+    .orderBy(l2Distance('sparse_embedding', new SparseVector([1, 1, 1])))
     .limit(5);
   expect(items.map(v => v.id)).toStrictEqual([1, 3, 2, 4]);
 

tests/pg-promise/index.test.mjs

@@ -16,9 +16,9 @@ test('example', async () => {
   await db.none('CREATE TABLE pg_promise_items (id serial PRIMARY KEY, embedding vector(3), half_embedding halfvec(3), binary_embedding bit(3), sparse_embedding sparsevec(3))');
 
   const params = [
-    pgvector.toSql([1, 1, 1]), pgvector.toSql([1, 1, 1]), '000', SparseVector.fromDense([1, 1, 1]),
-    pgvector.toSql([2, 2, 2]), pgvector.toSql([2, 2, 2]), '101', SparseVector.fromDense([2, 2, 2]),
-    pgvector.toSql([1, 1, 2]), pgvector.toSql([1, 1, 2]), '111', SparseVector.fromDense([1, 1, 2]),
+    pgvector.toSql([1, 1, 1]), pgvector.toSql([1, 1, 1]), '000', new SparseVector([1, 1, 1]),
+    pgvector.toSql([2, 2, 2]), pgvector.toSql([2, 2, 2]), '101', new SparseVector([2, 2, 2]),
+    pgvector.toSql([1, 1, 2]), pgvector.toSql([1, 1, 2]), '111', new SparseVector([1, 1, 2]),
     null, null, null, null
   ];
   await db.none('INSERT INTO pg_promise_items (embedding, half_embedding, binary_embedding, sparse_embedding) VALUES ($1, $2, $3, $4), ($5, $6, $7, $8), ($9, $10, $11, $12), ($13, $14, $15, $16)', params);

tests/pg/index.test.mjs

@@ -13,9 +13,9 @@ test('example', async () => {
   await client.query('CREATE TABLE pg_items (id serial PRIMARY KEY, embedding vector(3), half_embedding halfvec(3), binary_embedding bit(3), sparse_embedding sparsevec(3))');
 
   const params = [
-    pgvector.toSql([1, 1, 1]), pgvector.toSql([1, 1, 1]), '000', SparseVector.fromDense([1, 1, 1]),
-    pgvector.toSql([2, 2, 2]), pgvector.toSql([2, 2, 2]), '101', SparseVector.fromDense([2, 2, 2]),
-    pgvector.toSql([1, 1, 2]), pgvector.toSql([1, 1, 2]), '111', SparseVector.fromDense([1, 1, 2]),
+    pgvector.toSql([1, 1, 1]), pgvector.toSql([1, 1, 1]), '000', new SparseVector([1, 1, 1]),
+    pgvector.toSql([2, 2, 2]), pgvector.toSql([2, 2, 2]), '101', new SparseVector([2, 2, 2]),
+    pgvector.toSql([1, 1, 2]), pgvector.toSql([1, 1, 2]), '111', new SparseVector([1, 1, 2]),
     null, null, null, null
   ];
   await client.query('INSERT INTO pg_items (embedding, half_embedding, binary_embedding, sparse_embedding) VALUES ($1, $2, $3, $4), ($5, $6, $7, $8), ($9, $10, $11, $12), ($13, $14, $15, $16)', params);

tests/postgres/index.test.mjs

@@ -10,9 +10,9 @@ test('example', async () => {
   await sql`CREATE TABLE postgres_items (id serial PRIMARY KEY, embedding vector(3), half_embedding halfvec(3), binary_embedding bit(3), sparse_embedding sparsevec(3))`;
 
   const newItems = [
-    {embedding: pgvector.toSql([1, 1, 1]), half_embedding: pgvector.toSql([1, 1, 1]), binary_embedding: '000', sparse_embedding: SparseVector.fromDense([1, 1, 1])},
-    {embedding: pgvector.toSql([2, 2, 2]), half_embedding: pgvector.toSql([2, 2, 2]), binary_embedding: '101', sparse_embedding: SparseVector.fromDense([2, 2, 2])},
-    {embedding: pgvector.toSql([1, 1, 2]), half_embedding: pgvector.toSql([1, 1, 2]), binary_embedding: '111', sparse_embedding: SparseVector.fromDense([1, 1, 2])}
+    {embedding: pgvector.toSql([1, 1, 1]), half_embedding: pgvector.toSql([1, 1, 1]), binary_embedding: '000', sparse_embedding: new SparseVector([1, 1, 1])},
+    {embedding: pgvector.toSql([2, 2, 2]), half_embedding: pgvector.toSql([2, 2, 2]), binary_embedding: '101', sparse_embedding: new SparseVector([2, 2, 2])},
+    {embedding: pgvector.toSql([1, 1, 2]), half_embedding: pgvector.toSql([1, 1, 2]), binary_embedding: '111', sparse_embedding: new SparseVector([1, 1, 2])}
   ];
   await sql`INSERT INTO postgres_items ${ sql(newItems, 'embedding', 'half_embedding', 'binary_embedding', 'sparse_embedding') }`;
 
@@ -22,7 +22,7 @@ test('example', async () => {
   expect(pgvector.fromSql(items[0].embedding)).toStrictEqual([1, 1, 1]);
   expect(pgvector.fromSql(items[0].half_embedding)).toStrictEqual([1, 1, 1]);
   expect(items[0].binary_embedding).toStrictEqual('000');
-  expect(SparseVector.fromSql(items[0].sparse_embedding).toArray()).toStrictEqual([1, 1, 1]);
+  expect((new SparseVector(items[0].sparse_embedding)).toArray()).toStrictEqual([1, 1, 1]);
 
   await sql`CREATE INDEX ON postgres_items USING hnsw (embedding vector_l2_ops)`;
 

tests/prisma/index.test.mjs

@@ -64,18 +64,18 @@ test('sparsevec', async () => {
 
   // TODO use create when possible (field is not available in the generated client)
   // https://www.prisma.io/docs/concepts/components/prisma-schema/features-without-psl-equivalent#unsupported-field-types
-  const embedding1 = SparseVector.fromDense([1, 1, 1]).toSql();
-  const embedding2 = SparseVector.fromDense([2, 2, 2]).toSql();
-  const embedding3 = SparseVector.fromDense([1, 1, 2]).toSql();
+  const embedding1 = (new SparseVector([1, 1, 1])).toSql();
+  const embedding2 = (new SparseVector([2, 2, 2])).toSql();
+  const embedding3 = (new SparseVector([1, 1, 2])).toSql();
   await prisma.$executeRaw`INSERT INTO prisma_items (sparse_embedding) VALUES (${embedding1}::sparsevec), (${embedding2}::sparsevec), (${embedding3}::sparsevec)`;
 
   // TODO use raw orderBy when available
   // https://github.com/prisma/prisma/issues/5848
-  const embedding = SparseVector.fromDense([1, 1, 1]).toSql();
+  const embedding = (new SparseVector([1, 1, 1])).toSql();
   const items = await prisma.$queryRaw`SELECT id, sparse_embedding::text FROM prisma_items ORDER BY sparse_embedding <-> ${embedding}::sparsevec LIMIT 5`;
-  expect(SparseVector.fromSql(items[0].sparse_embedding).toArray()).toStrictEqual([1, 1, 1]);
-  expect(SparseVector.fromSql(items[1].sparse_embedding).toArray()).toStrictEqual([1, 1, 2]);
-  expect(SparseVector.fromSql(items[2].sparse_embedding).toArray()).toStrictEqual([2, 2, 2]);
+  expect((new SparseVector(items[0].sparse_embedding)).toArray()).toStrictEqual([1, 1, 1]);
+  expect((new SparseVector(items[1].sparse_embedding)).toArray()).toStrictEqual([1, 1, 2]);
+  expect((new SparseVector(items[2].sparse_embedding)).toArray()).toStrictEqual([2, 2, 2]);
 });
 
 beforeEach(async () => {

tests/slonik/index.test.mjs

@@ -18,9 +18,9 @@ test('example', async () => {
   const binaryEmbedding1 = '000';
   const binaryEmbedding2 = '101';
   const binaryEmbedding3 = '111';
-  const sparseEmbedding1 = SparseVector.fromDense([1, 1, 1]).toSql();
-  const sparseEmbedding2 = SparseVector.fromDense([2, 2, 2]).toSql();
-  const sparseEmbedding3 = SparseVector.fromDense([1, 1, 2]).toSql();
+  const sparseEmbedding1 = (new SparseVector([1, 1, 1])).toSql();
+  const sparseEmbedding2 = (new SparseVector([2, 2, 2])).toSql();
+  const sparseEmbedding3 = (new SparseVector([1, 1, 2])).toSql();
   await pool.query(sql.unsafe`INSERT INTO slonik_items (embedding, half_embedding, binary_embedding, sparse_embedding) VALUES (${embedding1}, ${halfEmbedding1}, ${binaryEmbedding1}, ${sparseEmbedding1}), (${embedding2}, ${halfEmbedding2}, ${binaryEmbedding2}, ${sparseEmbedding2}), (${embedding3}, ${halfEmbedding3}, ${binaryEmbedding3}, ${sparseEmbedding3})`);
 
   const embedding = pgvector.toSql([1, 1, 1]);
@@ -29,7 +29,7 @@ test('example', async () => {
   expect(pgvector.fromSql(items.rows[0].embedding)).toStrictEqual([1, 1, 1]);
   expect(pgvector.fromSql(items.rows[0].half_embedding)).toStrictEqual([1, 1, 1]);
   expect(items.rows[0].binary_embedding).toStrictEqual('000');
-  expect(SparseVector.fromSql(items.rows[0].sparse_embedding).toArray()).toStrictEqual([1, 1, 1]);
+  expect((new SparseVector(items.rows[0].sparse_embedding)).toArray()).toStrictEqual([1, 1, 1]);
 
   await pool.query(sql.unsafe`CREATE INDEX ON slonik_items USING hnsw (embedding vector_l2_ops)`);
 

tests/typeorm/index.test.mjs

@@ -44,9 +44,9 @@ test('example', async () => {
   await AppDataSource.query('CREATE TABLE typeorm_items (id bigserial PRIMARY KEY, embedding vector(3), half_embedding halfvec(3), binary_embedding bit(3), sparse_embedding sparsevec(3))');
 
   const itemRepository = AppDataSource.getRepository(Item);
-  await itemRepository.save({embedding: pgvector.toSql([1, 1, 1]), half_embedding: pgvector.toSql([1, 1, 1]), binary_embedding: '000', sparse_embedding: SparseVector.fromDense([1, 1, 1])});
-  await itemRepository.save({embedding: pgvector.toSql([2, 2, 2]), half_embedding: pgvector.toSql([2, 2, 2]), binary_embedding: '101', sparse_embedding: SparseVector.fromDense([2, 2, 2])});
-  await itemRepository.save({embedding: pgvector.toSql([1, 1, 2]), half_embedding: pgvector.toSql([1, 1, 2]), binary_embedding: '111', sparse_embedding: SparseVector.fromDense([1, 1, 2])});
+  await itemRepository.save({embedding: pgvector.toSql([1, 1, 1]), half_embedding: pgvector.toSql([1, 1, 1]), binary_embedding: '000', sparse_embedding: new SparseVector([1, 1, 1])});
+  await itemRepository.save({embedding: pgvector.toSql([2, 2, 2]), half_embedding: pgvector.toSql([2, 2, 2]), binary_embedding: '101', sparse_embedding: new SparseVector([2, 2, 2])});
+  await itemRepository.save({embedding: pgvector.toSql([1, 1, 2]), half_embedding: pgvector.toSql([1, 1, 2]), binary_embedding: '111', sparse_embedding: new SparseVector([1, 1, 2])});
 
   const items = await itemRepository
     .createQueryBuilder('item')
@@ -58,7 +58,7 @@ test('example', async () => {
   expect(pgvector.fromSql(items[0].embedding)).toStrictEqual([1, 1, 1]);
   expect(pgvector.fromSql(items[0].half_embedding)).toStrictEqual([1, 1, 1]);
   expect(items[0].binary_embedding).toStrictEqual('000');
-  expect(SparseVector.fromSql(items[0].sparse_embedding).toArray()).toStrictEqual([1, 1, 1]);
+  expect((new SparseVector(items[0].sparse_embedding).toArray())).toStrictEqual([1, 1, 1]);
 
   await AppDataSource.destroy();
 });

tests/utils/sparse-vector.test.mjs

@@ -1,15 +1,15 @@
 import { SparseVector } from 'pgvector/utils';
 
 test('fromSql', () => {
-  const vec = SparseVector.fromSql('{1:1,3:2,5:3}/6');
+  const vec = new SparseVector('{1:1,3:2,5:3}/6');
   expect(vec.toArray()).toStrictEqual([1, 0, 2, 0, 3, 0]);
   expect(vec.dimensions).toStrictEqual(6);
   expect(vec.indices).toStrictEqual([0, 2, 4]);
   expect(vec.values).toStrictEqual([1, 2, 3]);
 });
 
 test('fromDense', () => {
-  const vec = SparseVector.fromDense([1, 0, 2, 0, 3, 0]);
+  const vec = new SparseVector([1, 0, 2, 0, 3, 0]);
   expect(vec.toSql()).toStrictEqual('{1:1,3:2,5:3}/6');
   expect(vec.dimensions).toStrictEqual(6);
   expect(vec.indices).toStrictEqual([0, 2, 4]);
@@ -22,13 +22,13 @@ test('fromMap', () => {
   map.set(4, 3);
   map.set(0, 1);
   map.set(3, 0);
-  const vec = SparseVector.fromMap(map, 6);
+  const vec = new SparseVector(map, 6);
   expect(vec.dimensions).toStrictEqual(6);
   expect(vec.indices).toStrictEqual([2, 4, 0]);
   expect(vec.values).toStrictEqual([2, 3, 1]);
 });
 
 test('toSql', () => {
-  const vec = SparseVector.fromDense([1.23456789]);
+  const vec = new SparseVector([1.23456789]);
   expect(vec.toSql()).toStrictEqual('{1:1.23456789}/1');
 });