Tensor Operations

1/**2 * Example 02: Tensor Operations3 *4 * Arithmetic, math functions and reductions on tensors, with broadcasting.5 * Every operation is available as a function (add(a, b)) and as a tensor6 * method (a.add(b)). The two forms give the same result. Methods chain, which7 * keeps longer expressions readable.8 */9 10import { tensor } from "deepbox/ndarray";11 12console.log("=== Tensor Operations ===\n");13 14// Basic arithmetic15const a = tensor([1, 2, 3, 4]);16const b = tensor([5, 6, 7, 8]);17 18console.log("a =", a.toString());19console.log("b =", b.toString());20 21console.log("\nArithmetic Operations:");22console.log("a + b =", a.add(b).toString());23console.log("a * b =", a.mul(b).toString());24console.log("a - b =", a.sub(b).toString());25console.log("a / b =", a.div(b).toString());26 27// A JavaScript number is broadcast against every element. It never changes28// the tensor's dtype.29console.log("a * 10 + 1 =", a.mul(10).add(1).toString());30 31// Broadcasting between shapes: a [2, 1] column against a [3] row gives [2, 3].32const column = tensor([[10], [20]]);33const row = tensor([1, 2, 3]);34console.log("\nBroadcasting [2, 1] + [3]:");35console.log(column.add(row).toString());36 37// Mathematical functions38const x = tensor([1, 4, 9, 16]);39console.log("\nMathematical Functions:");40console.log("x =", x.toString());41console.log("sqrt(x) =", x.sqrt().toString());42console.log("exp([0, 1, 2]) =", tensor([0, 1, 2]).exp().toString());43console.log("log(x) =", x.log().toString());44 45// Trigonometric functions46const angles = tensor([0, Math.PI / 4, Math.PI / 2, Math.PI]);47console.log("\nTrigonometric Functions:");48console.log("angles =", angles.toString());49console.log("sin(angles) =", angles.sin().toString());50console.log("cos(angles) =", angles.cos().toString());51 52// Reduction operations53const matrix = tensor([54  [1, 2, 3],55  [4, 5, 6],56]);57 58console.log("\nReduction Operations:");59console.log("matrix =");60console.log(matrix.toString());61console.log("sum(matrix) =", matrix.sum().toString());62console.log("mean(matrix) =", matrix.mean().toString());63console.log("max(matrix) =", matrix.max().toString());64console.log("min(matrix) =", matrix.min().toString());65 66// Axis-wise reductions67console.log("\nAxis-wise Reductions:");68console.log("sum(matrix, axis=0) =", matrix.sum(0).toString());69console.log("sum(matrix, axis=1) =", matrix.sum(1).toString());70console.log("mean(matrix, axis=0) =", matrix.mean(0).toString());71 72// Reductions return tensors. Use item() to get a plain number.73console.log(`\nmean(matrix) as a number: ${matrix.mean().item()}`);74 75// Index results are int32 tensors.76console.log("argmax(matrix, axis=1) =", matrix.argmax(1).toString());