87 lines
2.7 KiB
Go
87 lines
2.7 KiB
Go
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// Copyright (c) 2026 Petr Balvín <opensource@petrbalvin.org> (https://petrbalvin.org)
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// SPDX-License-Identifier: MIT
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package optim
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import (
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"math"
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"testing"
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"sourcedock.dev/petrbalvin/tensor/internal/core"
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)
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// Benchmarks for the fit surface of LevenbergMarquardtFit: the weighted
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// residual path and the covariance report, the two options the plain
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// entry point and its benchmarks never touch.
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// benchExponentialFit builds the two-parameter decay fit the older LM
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// benchmarks run, with the observations and their variances handed
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// back so the weighted and covariance variants measure the same model.
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func benchExponentialFit() (residual func(*core.Array) (*core.Array, error), jacobian func(*core.Array) (*core.Array, error), p0 *core.Array) {
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const nObs = 40
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t := make([]float64, nObs)
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y := make([]float64, nObs)
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for i := range nObs {
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t[i] = float64(i) / 4
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y[i] = 2.5*math.Exp(-0.7*t[i]) + 0.02*math.Sin(float64(i))
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}
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residual = func(p *core.Array) (*core.Array, error) {
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out := core.New(core.Float, nObs)
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vals := out.RawFloats()
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for i := range nObs {
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vals[i] = p.FloatAt(0)*math.Exp(-p.FloatAt(1)*t[i]) - y[i]
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}
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return out, nil
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}
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jacobian = func(p *core.Array) (*core.Array, error) {
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out := core.New(core.Float, nObs, 2)
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vals := out.RawFloats()
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for i := range nObs {
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e := math.Exp(-p.FloatAt(1) * t[i])
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vals[i*2] = e
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vals[i*2+1] = -p.FloatAt(0) * t[i] * e
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}
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return out, nil
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}
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p0, _ = core.FromFloats([]float64{1, 0.2}, 2)
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return residual, jacobian, p0
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}
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// BenchmarkLevenbergMarquardtFitWeighted measures the fit with one
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// variance per residual: the whitening of the residual and of the
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// finite-difference Jacobian rides every evaluation the run makes.
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func BenchmarkLevenbergMarquardtFitWeighted(b *testing.B) {
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residual, _, p0 := benchExponentialFit()
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const nObs = 40
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variance := make([]float64, nObs)
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for i := range nObs {
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variance[i] = 1 + float64(i)/8
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}
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sigma, err := core.FromFloats(variance, nObs)
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if err != nil {
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b.Fatal(err)
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}
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opts := LMOptions{MaxIterations: 30, Sigma: sigma}
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b.ReportAllocs()
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for b.Loop() {
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if _, err := LevenbergMarquardtFit(residual, p0, opts); err != nil {
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b.Fatal(err)
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}
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}
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}
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// BenchmarkLevenbergMarquardtFitCovariance measures the covariance
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// report: the fit converges and then rebuilds the Jacobian at the
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// answer, forms the normal equations from it and solves the identity
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// through them.
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func BenchmarkLevenbergMarquardtFitCovariance(b *testing.B) {
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residual, jacobian, p0 := benchExponentialFit()
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opts := LMOptions{MaxIterations: 30, Jacobian: jacobian, RequestCovariance: true}
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b.ReportAllocs()
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for b.Loop() {
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if _, err := LevenbergMarquardtFit(residual, p0, opts); err != nil {
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b.Fatal(err)
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}
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}
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}
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