typos CI

.github/dependabot.yml

@@ -5,3 +5,6 @@ updates:
     directory: "/" # Location of package manifests
     schedule:
       interval: "weekly"
+    ignore:
+      - dependency-name: "crate-ci/typos"
+        update-types: ["version-update:semver-patch"]

.github/workflows/SpellCheck.yml

@@ -0,0 +1,13 @@
+name: Spell Check
+
+on: [pull_request]
+
+jobs:
+  typos-check:
+    name: Spell Check with Typos
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Actions Repository
+        uses: actions/checkout@v3
+      - name: Check spelling
+        uses: crate-ci/[email protected] 

.typos.toml

@@ -0,0 +1,2 @@
+[default.extend-words]
+ND = "ND"

docs/src/optimizations.md

@@ -28,5 +28,5 @@ surrogate_optimize(obj::Function,sop1::SOP,lb::Number,ub::Number,surrSOP::Abstra
 To add another optimization method, you just need to define a new
 SurrogateOptimizationAlgorithm and write its corresponding algorithm, overloading the following:
 ```
-surrogate_optimize(obj::Function,::NewOptimizatonType,lb,ub,surr::AbstractSurrogate,sample_type::SamplingAlgorithm;maxiters=100,num_new_samples=100)
+surrogate_optimize(obj::Function,::NewOptimizationType,lb,ub,surr::AbstractSurrogate,sample_type::SamplingAlgorithm;maxiters=100,num_new_samples=100)
 ```

docs/src/randomforest.md

@@ -32,7 +32,7 @@ plot!(f, label="True function", xlims=(lower_bound, upper_bound), legend=:top)
 
 With our sampled points we can build the Random forests surrogate using the `RandomForestSurrogate` function.
 
-`randomforest_surrogate` behaves like an ordinary function which we can simply plot. Addtionally you can specify the number of trees created
+`randomforest_surrogate` behaves like an ordinary function which we can simply plot. Additionally you can specify the number of trees created
 using the parameter num_round
 
 ```@example RandomForestSurrogate_tutorial

docs/src/surrogate.md

@@ -48,7 +48,7 @@ It's great that you want to add another surrogate to the library!
 You will need to:
 
 1. Define a new mutable struct and a constructor function
-2. Define add\_point!(your\_surrogate::AbstactSurrogate,x\_new,y\_new)
+2. Define add\_point!(your\_surrogate::AbstractSurrogate,x\_new,y\_new)
 3. Define your\_surrogate(value) for the approximation
 
 **Example**

src/GEK.jl

@@ -93,7 +93,7 @@ end
 
 function GEK(x, y, lb::Number, ub::Number; p = 1.0, theta = 1.0)
     if length(x) != length(unique(x))
-        println("There exists a repetion in the samples, cannot build Kriging.")
+        println("There exists a repetition in the samples, cannot build Kriging.")
         return
     end
     mu, b, sigma, inverse_of_R = _calc_gek_coeffs(x, y, p, theta)

src/GEKPLS.jl

@@ -201,8 +201,8 @@ function _ge_compute_pls(X, y, n_comp, grads, delta_x, xlimits, extra_points)
         bb_vals = bb_vals .* grads[i, :]'
         _y = y[i, :] .+ sum(bb_vals, dims = 2)
 
-        #_pls.fit(_X, _y) # relic from sklearn versiom; retained for future reference.
-        #coeff_pls[:, :, i] = _pls.x_rotations_ #relic from sklearn versiom; retained for future reference.
+        #_pls.fit(_X, _y) # relic from sklearn version; retained for future reference.
+        #coeff_pls[:, :, i] = _pls.x_rotations_ #relic from sklearn version; retained for future reference.
 
         coeff_pls[:, :, i] = _modified_pls(_X, _y, n_comp) #_modified_pls returns the equivalent of SKLearn's _pls.x_rotations_
         if extra_points != 0
@@ -304,7 +304,7 @@ end
 ######end of bb design######
 
 """
-We substract the mean from each variable. Then, we divide the values of each
+We subtract the mean from each variable. Then, we divide the values of each
 variable by its standard deviation.
 
 Parameters

src/Kriging.jl

@@ -104,7 +104,7 @@ Constructor for type Kriging.
 function Kriging(x, y, lb::Number, ub::Number; p = 2.0,
                  theta = 0.5 / max(1e-6 * abs(ub - lb), std(x))^p)
     if length(x) != length(unique(x))
-        println("There exists a repetion in the samples, cannot build Kriging.")
+        println("There exists a repetition in the samples, cannot build Kriging.")
         return
     end
 

src/Optimization.jl

@@ -1701,7 +1701,7 @@ function surrogate_optimize(obj::Function, sopd::SOP, lb, ub, surrSOPD::Abstract
             new_points_y[i] = y_best
         end
 
-        #new_points[i] is splitted in new_points_x and new_points_y now contains:
+        #new_points[i] is split in new_points_x and new_points_y now contains:
         #[x_1,y_1; x_2,y_2,...,x_{num_new_samples},y_{num_new_samples}]
 
         #2.4 Adaptive learning and tabu archive