feat: initial release

Assisted-by: GLM 5.3 Flash

grad/helpers_test.go

@@ -0,0 +1,47 @@
+// Copyright (c) 2026 Petr Balvín <opensource@petrbalvin.org> (https://petrbalvin.org)
+// SPDX-License-Identifier: MIT
+
+package grad
+
+import (
+	"math"
+
+	"sourcedock.dev/petrbalvin/tensor/internal/core"
+)
+
+// numericGrad estimates the gradient of a scalar function at a by
+// central differences, the reference the analytic backward is checked
+// against.
+func numericGrad(f func(a *core.Array) float64, a *core.Array) []float64 {
+	n := a.Len()
+	out := make([]float64, n)
+	for i := range n {
+		hi := 1e-6
+		up := cloneFlat(a)
+		down := cloneFlat(a)
+		up[i] += hi
+		down[i] -= hi
+		au, _ := core.FromFloats(up, a.Shape()...)
+		ad, _ := core.FromFloats(down, a.Shape()...)
+		out[i] = (f(au) - f(ad)) / (2 * hi)
+	}
+	return out
+}
+
+func cloneFlat(a *core.Array) []float64 {
+	out := make([]float64, a.Len())
+	for i := range a.Len() {
+		out[i] = a.FloatAt(i)
+	}
+	return out
+}
+
+func maxAbsDiff(a, b []float64) float64 {
+	m := 0.0
+	for i := range a {
+		if d := math.Abs(a[i] - b[i]); d > m {
+			m = d
+		}
+	}
+	return m
+}