// 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
}