Merge pull request #513 from vaerksted/master

fix typos

Author: ChrisRackauckas

lib/DataDrivenDMD/src/type.jl

@@ -15,7 +15,7 @@ same world-age evaluation. However, this can cause Julia to segfault
 on sufficiently large basis functions. By default eval_expression=false.
 """
 struct Koopman{T, B <: AbstractBasis, K, DISCRETE} <: AbstractKoopman
-    """The basis of observeables"""
+    """The basis of observables"""
     basis::B
     """The operator/generator of the dynamics"""
     K::K

lib/DataDrivenLux/src/algorithms/reinforce.jl

@@ -40,7 +40,7 @@ $(FIELDS)
     optim_options::Optim.Options = Optim.Options()
     "Observed model - if `nothing`is used, a normal distributed additive error with fixed variance is assumed."
     observed::Union{ObservedModel, Nothing} = nothing
-    "AD Backendend"
+    "AD Backend"
     ad_backend::AD.AbstractBackend = AD.ForwardDiffBackend()
     "Optimiser"
     optimiser::Optimisers.AbstractRule = ADAM()

lib/DataDrivenLux/src/algorithms/rewards.jl

@@ -19,7 +19,7 @@ end
 """
 $(TYPEDEF)
 
-Scales the losses in such a way that the minimum loss is the most influencial reward.
+Scales the losses in such a way that the minimum loss is the most influential reward.
 """
 struct AbsoluteReward{risk} <: AbstractRewardScale{risk} end
 

lib/DataDrivenSparse/src/algorithms/ADMM.jl

@@ -22,7 +22,7 @@ mutable struct ADMM{T, R <: Number} <: AbstractSparseRegressionAlgorithm
 
     function ADMM(threshold::T = 1e-1, ρ::R = 1.0) where {T, R}
         @assert all(threshold .> zero(eltype(threshold))) "Threshold must be positive definite"
-        @assert zero(R)<ρ "Augemented lagrangian parameter should be positive definite"
+        @assert zero(R)<ρ "Augmented Lagrangian parameter should be positive definite"
         return new{T, R}(threshold, ρ)
     end
 end

lib/DataDrivenSparse/src/commonsolve.jl

@@ -71,7 +71,7 @@ function __sparse_regression(ps::InternalDataDrivenProblem{<:ImplicitOptimizer},
     opt_iterations = []
 
     foreach(enumerate(eachcol(candidate_matrix))) do (i, idx)
-        # We enforce that one of the implicit variables is necessary for sucess
+        # We enforce that one of the implicit variables is necessary for success
         coeff, thresholds, iters = alg(X[idx, :], Y, options = options,
             necessary_idx = implicit_idx[idx, i])
         opt_coefficients[i:i, idx] .= coeff

src/DataDrivenDiffEq.jl

@@ -44,7 +44,7 @@ using RecipesBase
 @enum DDProbType begin
     Direct = 1 # Direct problem without further information
     Discrete = 2 # Time discrete problem
-    Continuous = 3 # Time continous problem
+    Continuous = 3 # Time continuous problem
 end
 
 # We want to export the ReturnCodes

src/commonsolve.jl

@@ -94,6 +94,6 @@ function CommonSolve.init(prob::AbstractDataDrivenProblem, basis::AbstractBasis,
 end
 
 function CommonSolve.solve!(::InternalDataDrivenProblem{ZeroDataDrivenAlgorithm})
-    @warn "No sufficient algorithm choosen! Return ErrorDataDrivenResult!"
+    @warn "No sufficient algorithm chosen! Return ErrorDataDrivenResult!"
     return ErrorDataDrivenResult()
 end

src/problem/type.jl

@@ -58,7 +58,7 @@ Three construction methods are available:
 
 + `DirectDataDrivenProblem` for direct mappings
 + `DiscreteDataDrivenProblem` for time discrete systems
-+ `ContinousDataDrivenProblem` for systems continuous in time
++ `ContinuousDataDrivenProblem` for systems continuous in time
 
 where all are aliases for constructing a problem.
 
@@ -123,7 +123,7 @@ function DataDrivenProblem(probType, X, t, DX, Y, U, p; name = gensym(:DDProblem
         if (isempty(DX) && !isempty(Y))
             probType = DDProbType(1) # Direct problem
         elseif !isempty(DX)
-            probType = DDProbType(3) # Continouos
+            probType = DDProbType(3) # Continuous
         end
     end
 
@@ -206,7 +206,7 @@ function DiscreteDataDrivenProblem(X::AbstractMatrix, t::AbstractVector, U::Func
     return DataDrivenProblem(X; t = t, U = U, probtype = DDProbType(2), kwargs...)
 end
 
-## Continouos Constructors
+## Continuous Constructors
 """
 A time continuous `DataDrivenProblem` useable for problems of the form `f(x,p,t,u) ↦ dx/dt`.