Merge pull request #32 from gdalle/patch-1

Typos in docs
PdIPS · May 4, 2024 · 6f645fb · 6f645fb
2 parents 278685e + 596ba7c
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diff --git a/docs/src/distribution_sampling.md b/docs/src/distribution_sampling.md
@@ -2,7 +2,7 @@
 
 ConsensusBasedX.jl also provides [Consensus-Based Sampling](@ref).
 
-The package exports `sample`, which behaves exactly as `minimise` in [Function minimisation](@ref). It assumes you have defined a function `f(x::AbstractVector)` that takes a single vector argumemt `x` of length `D = length(x)`.
+The package exports `sample`, which behaves exactly as `minimise` in [Function minimisation](@ref). It assumes you have defined a function `f(x::AbstractVector)` that takes a single vector argument `x` of length `D = length(x)`.
 
 For instance, if `D = 2`, you can sample `exp(-αf)` by running:
 ```julia

diff --git a/docs/src/particle_initialisation.md b/docs/src/particle_initialisation.md
@@ -10,7 +10,7 @@ If no options are provided, ConsensusBasedX.jl initialises its particles by samp
 
 ## Initial guess
 
-If you have an initial guess for the global minimiser of the function `f`, you can pass the option `initial_guess` (or `initial_mean`). This can be a `Real`, if you want to use the same value for each coordinate of the initial guess, or an `AbstractVector` of size `size(initial_guess) = (D,)`. The particles will be initisalised by sampling a normal distribution with mean `initial_guess`/`initial_mean` and unit variance. [Full-code example](https://github.com/PdIPS/ConsensusBasedX.jl/blob/main/examples/basic_usage/initial_guess.jl).
+If you have an initial guess for the global minimiser of the function `f`, you can pass the option `initial_guess` (or `initial_mean`). This can be a `Real`, if you want to use the same value for each coordinate of the initial guess, or an `AbstractVector` of size `size(initial_guess) = (D,)`. The particles will be initialised by sampling a normal distribution with mean `initial_guess`/`initial_mean` and unit variance. [Full-code example](https://github.com/PdIPS/ConsensusBasedX.jl/blob/main/examples/basic_usage/initial_guess.jl).
 
 
 ### Specify a normal distribution

diff --git a/src/interface/maximise.jl b/src/interface/maximise.jl
@@ -4,7 +4,7 @@ maximise(f; keywords...)
 ```
 
 ```julia
-maximise(config::NamedTuple, f)
+maximise(f, config::NamedTuple)
 ```
 
 Maximise the function `f` using Consensus-Based Optimisation.

diff --git a/src/interface/minimise.jl b/src/interface/minimise.jl
@@ -9,7 +9,7 @@ minimise(f, config::NamedTuple)
 
 Minimise the function `f` using Consensus-Based Optimisation (see [Function minimisation](@ref)).
 
-You must specify the dimension `D` of the problem. Other paramters (e.g. the number of particles `N` or the number of ensembles `M` can also be specified; see [Summary of options](@ref).
+You must specify the dimension `D` of the problem. Other parameters (e.g. the number of particles `N` or the number of ensembles `M`) can also be specified; see [Summary of options](@ref).
 
 `minimize`, `optimise`, or `optimize` are aliases for `minimise`.
 

diff --git a/src/interface/sample.jl b/src/interface/sample.jl
@@ -9,7 +9,7 @@ sample(f, config::NamedTuple)
 
 Sample the distribution `exp(-αf)` using Consensus-Based Sampling (see [Distribution sampling](@ref)).
 
-You must specify the dimension `D` of the problem. Other paramters (e.g. the number of particles `N` or the number of ensembles `M` can also be specified; see [Summary of options](@ref).
+You must specify the dimension `D` of the problem. Other paramters (e.g. the number of particles `N` or the number of ensembles `M`) can also be specified; see [Summary of options](@ref).
 
 # Examples