| /* |
| * Copyright 2018 Google Inc. |
| * |
| * Use of this source code is governed by a BSD-style license that can be |
| * found in the LICENSE file. |
| */ |
| |
| #pragma once |
| |
| #include <stdbool.h> |
| #include "LinearAlgebra.h" |
| |
| // One Gauss-Newton step, tuning up to 4 parameters P to minimize [ t(x,ctx) - f(x,P) ]^2. |
| // |
| // t: target function of x to approximate |
| // t_ctx: any context needed for t, passed blindly into calls to t() |
| // f: function of x,P we're tuning to match t() |
| // grad_f: gradient of f() at x |
| // P: in-out, both your initial guess for parameters of f(), and our updated values |
| // x0,x1,N: N x-values to test in [x0,x1] (both inclusive) with even spacing |
| // fixup: an optional hook to tweak the Jf^T Jf matrix (given P) before we invert it |
| // |
| // If you have fewer than 4 parameters, set the unused P to zero, don't touch their dfdP, |
| // and use fixup() to set 1 along their diagonal entries. |
| // |
| // Returns true and updates P on success, or returns false on failure. |
| bool skcms_gauss_newton_step(float (* t)(float x, const void*), const void* t_ctx, |
| float (* f)(float x, const float P[4]), |
| void (*grad_f)(float x, float dfdP[4]), |
| float P[4], |
| float x0, float x1, int N, |
| void (*fixup)(skcms_Matrix4x4*, const float P[4])); |
