Add AbstractCostFunction in ParamEstim

docs/src/ParamEstim.md

@@ -8,6 +8,7 @@ The `ParamEstim` submodule provides support for optimization-based parameter est
 
 ```@docs
 ScaledSolution
+AbstractCostFunction
 RSSCostFunction
 candidate
 ```

src/ParamEstim.jl

@@ -56,7 +56,42 @@
 end
 
 """
-    RSSCostFunction{fit_D0}(func, prob::InverseProblem[; D0tol, oi_hint])
+    abstract type AbstractCostFunction{fit_D0} end
+
+Abstract cost function for parameter estimation.
+
+# Type parameters
+- `fit_D0::Bool`: whether to fit an additional constant factor `D0` that affects the diffusivity. Values 
+of `D0` can be found with relative efficiency without additional solver calls; so if any such constant
+factors affecting the diffusivity are unknown, it is recommended not to fit those factors directly but set
+`fit_D0` to `true` instead. Values of `D0` are found internally by local optimization. If `true`, the
+`candidate` function will return a `ScaledSolution` that includes the found value of `D0`.
+"""
+abstract type AbstractCostFunction{fit_D0} end
+
+function (cf::AbstractCostFunction)(params::AbstractVector,
+        ::NullParameters = NullParameters())
+    cf(candidate(cf, params))
+end
+
+_solve(cf::AbstractCostFunction, params::AbstractVector) = _solve(cf, cf._func(params))
+_solve(::AbstractCostFunction, prob::AbstractProblem) = solve(prob, verbose = false)
+_solve(::AbstractCostFunction, sol::Solution) = sol
+
+"""
+    candidate(cf::AbstractCostFunction, ::AbstractVector)
+    candidate(cf::AbstractCostFunction, ::Fronts.AbstractProblem)
+    candidate(cf::AbstractCostFunction, ::Fronts.Solution)
+
+Return the candidate solution for a given cost function and parameter values, problem, or solution.
+"""
+candidate(cf::AbstractCostFunction, params::AbstractVector) = candidate(cf,
+    _solve(cf, params))
+candidate(cf::AbstractCostFunction, prob::AbstractProblem) = candidate(cf, _solve(cf, prob))
+candidate(::AbstractCostFunction{false}, sol::Solution) = sol
+
+"""
+    RSSCostFunction{fit_D0}(func, prob::InverseProblem[; D0tol, oi_hint]) <: AbstractCostFunction
 
 Residual sum of squares cost function for parameter estimation.
 
@@ -97,7 +132,8 @@
 
 If you need to know more than just the cost, call the `candidate` function instead.
 """
-struct RSSCostFunction{fit_D0, _Tfunc, _Tprob, _TD0tol, _Toi_hint, _Tsorptivity}
+struct RSSCostFunction{fit_D0, _Tfunc, _Tprob, _TD0tol, _Toi_hint, _Tsorptivity} <:
+       AbstractCostFunction{fit_D0}
     _func::_Tfunc
     _prob::_Tprob
     _D0tol::_TD0tol
@@ -135,25 +171,6 @@
     end
 end
 
-function (cf::RSSCostFunction)(params::AbstractVector, ::NullParameters = NullParameters())
-    cf(candidate(cf, params))
-end
-
-_solve(cf::RSSCostFunction, params::AbstractVector) = _solve(cf, cf._func(params))
-_solve(::RSSCostFunction, prob::AbstractProblem) = solve(prob, verbose = false)
-_solve(::RSSCostFunction, sol::Solution) = sol
-
-"""
-    candidate(cf::RSSCostFunction, ::AbstractVector)
-    candidate(cf::RSSCostFunction, ::Fronts.AbstractProblem)
-    candidate(cf::RSSCostFunction, ::Fronts.Solution)
-
-Return the candidate solution for a given cost function and parameter values, problem, or solution.
-"""
-candidate(cf::RSSCostFunction, params::AbstractVector) = candidate(cf, _solve(cf, params))
-candidate(cf::RSSCostFunction, prob::AbstractProblem) = candidate(cf, _solve(cf, prob))
-candidate(::RSSCostFunction{false}, sol::Solution) = sol
-
 function candidate(cf::RSSCostFunction{true}, sol::Solution)
     if !successful_retcode(sol)
         return ScaledSolution(sol, NaN)
@@ -184,6 +201,6 @@
     return ScaledSolution(sol, scaling.param[1])
 end
 
-export RSSCostFunction, candidate
+export AbstractCostFunction, RSSCostFunction, candidate
 
 end