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/*
* 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>
// 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
//
// If you have fewer than 4 parameters, set the unused P to zero, don't touch their dfdP.
//
// 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 void*, const float P[4]),
const void* f_ctx,
void (*grad_f)(float x, const void*, const float P[4], float dfdP[4]),
const void* g_ctx,
float P[4],
float x0, float x1, int N);
// A target function for skcms_Curve, passed as ctx.
float skcms_eval_curve(float x, const void* ctx);
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