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Gaspard Jankowiak 2025-02-12 11:21:19 +01:00
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README.md
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@ -5,15 +5,23 @@ Simple package to check derivatives
# Usage # Usage
``` ```
check(f, Jf, x[, constant_components]; f_kwargs...) check(f, Jf, x[, constant_components]; taylortestplot=false, taylortestdirection=nothing, f_kwargs...)
``` ```
Returns true if `Jf` approximates the derivative/gradient/Jacobian of `f` at point `x` (along a random direction). Returns true if `Jf` approximates the derivative/gradient/Jacobian of `f` at point `x` (along a random direction unless specified using `taylortestdirection`).
`f_kwargs` are keywords arguments to be passed to `f` and `Jf`. `f_kwargs` are keywords arguments to be passed to `f` and `Jf`.
`constant_components` is an optional `Vector{Int}` corresponding to components of the direction which should be set to zero, `constant_components` is an optional `Vector{Int}` corresponding to components of the direction which should be set to zero,
effectively ignoring the dependency of `f` on these components. effectively ignoring the dependency of `f` on these components.
If `taylortestplot` is `true`, a log-log plot of the error against the perturbation size will be shown.
## Examples
```
check!(f!, Jf!, x, size_f_x, size_Jf_x, [, constant_components]; taylortestplot=false, taylortestdirection=nothing, f_kwargs...)
```
Like `check` but handling non-allocating functions. Output size for both `f!` and the Jacobian `Jf!` must be provided (as `Tuple`s) via `size_f_x` and `size_Jf_x`.
## Examples (see `test` directory for more)
```julia ```julia
import TaylorTest import TaylorTest

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src/TaylorTest.jl
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@ -7,12 +7,17 @@ import TensorOperations: @tensor
import UnicodePlots import UnicodePlots
""" """
`check(f, Jf, x[, constant_components]; f_kwargs...)` ```
check(f, Jf, x[, constant_components]; taylortestplot=false, taylortestdirection=nothing, f_kwargs...)
```
Returns true if `Jf` approximates the derivative/gradient/Jacobian of `f` at point `x` (along a random direction). Returns true if `Jf` approximates the derivative/gradient/Jacobian of `f` at point `x` (along a random direction unless specified using `taylortestdirection`).
`f_kwargs` are keywords arguments to be passed to `f` and `Jf`. `f_kwargs` are keywords arguments to be passed to `f` and `Jf`.
`constant_components` is an optional `Vector{Int}` corresponding to components of the direction which should be set to zero, `constant_components` is an optional `Vector{Int}` corresponding to components of the direction which should be set to zero,
effectively ignoring the dependency of `f` on these components. effectively ignoring the dependency of `f` on these components.
If `taylortestplot` is `true`, a log-log plot of the error against the perturbation size will be shown.
See also: `check!`
# Examples # Examples
```julia-repl ```julia-repl
@ -70,7 +75,16 @@ function check(f, Jf, x, constant_components::Vector{Int}=Int[]; taylortestplot:
return isapprox(order, 1; atol=0.5) return isapprox(order, 1; atol=0.5)
end end
function check!(f!, Jf!, x, size_f_x, size_Jf_x, constant_components::Vector{Int}=Int[]; """
```
check!(f!, Jf!, x, size_f_x, size_Jf_x, [, constant_components]; taylortestplot=false, taylortestdirection=nothing, f_kwargs...)
```
Like `check` but handling non-allocating functions. Output size for both `f!` and the Jacobian `Jf!` must be provided (as `Tuple`s) via `size_f_x` and `size_Jf_x`.
See also: `check`
"""
function check!(f!, Jf!, x, size_f_x::Tuple, size_Jf_x::Tuple, constant_components::Vector{Int}=Int[];
taylortestdirection=nothing, taylortestplot::Bool=false, f_kwargs...) taylortestdirection=nothing, taylortestplot::Bool=false, f_kwargs...)
f = x -> begin f = x -> begin