Merge branch 'master' into bb/bump_QMC

.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@v4
+      - name: Check spelling
+        uses: crate-ci/[email protected] 

.typos.toml

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

Project.toml

@@ -22,6 +22,7 @@ GLM = "1.3"
 IterativeSolvers = "0.9"
 PolyChaos = "0.2"
 QuasiMonteCarlo = "0.3"
+Statistics = "1"
 Zygote = "0.4, 0.5, 0.6"
 julia = "1.9"
 

docs/src/Salustowicz.md

@@ -5,7 +5,7 @@ The true underlying function HyGP had to approximate is the 1D Salustowicz funct
 
 The Salustowicz benchmark function is as follows:
 
-``f(x) = e^{(-x)} x^3 cos(x) sin(x) (cos(x) sin^2(x) - 1)``
+``f(x) = e^{-x} x^3 \cos(x) \sin(x) (\cos(x) \sin^2(x) - 1)``
 
 Let's import these two packages  `Surrogates` and `Plots`:
 

docs/src/ackley.md

@@ -1,7 +1,7 @@
 # Ackley Function
 
 The Ackley function is defined as:
-``f(x) = -a*exp(-b\sqrt{\frac{1}{d}\sum_{i=1}^d x_i^2}) - exp(\frac{1}{d} \sum_{i=1}^d cos(cx_i)) + a + exp(1)``
+``f(x) = -a*\exp(-b\sqrt{\frac{1}{d}\sum_{i=1}^d x_i^2}) - \exp(\frac{1}{d} \sum_{i=1}^d \cos(cx_i)) + a + \exp(1)``
 Usually the recommended values are: ``a =  20``, ``b = 0.2`` and ``c =  2\pi``
 
 Let's see the 1D case.
@@ -16,15 +16,15 @@ Now, let's define the `Ackley` function:
 
 ```@example ackley
 function ackley(x)
-    a, b, c = 20.0, -0.2, 2.0*π
+    a, b, c = 20.0, 0.2, 2.0*π
     len_recip = inv(length(x))
     sum_sqrs = zero(eltype(x))
     sum_cos = sum_sqrs
     for i in x
         sum_cos += cos(c*i)
         sum_sqrs += i^2
     end
-    return (-a * exp(b * sqrt(len_recip*sum_sqrs)) -
+    return (-a * exp(-b * sqrt(len_recip*sum_sqrs)) -
             exp(len_recip*sum_cos) + a + 2.71)
 end
 ```

docs/src/gramacylee.md

@@ -4,7 +4,7 @@ Gramacy & Lee Function is a continuous function. It is not convex. The function
 ``x \in [-0.5, 2.5]``.
 
 The Gramacy & Lee is as follows:
-``f(x) = \frac{sin(10\pi x)}{2x} + (x-1)^4``.
+``f(x) = \frac{\sin(10\pi x)}{2x} + (x-1)^4``.
 
 Let's import these two packages `Surrogates` and `Plots`:
 

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**

docs/src/tensor_prod.md

@@ -1,6 +1,6 @@
 # Tensor product function
 The tensor product function is defined as:
-``f(x) = \prod_{i=1}^d cos(a\pi x_i)``
+``f(x) = \prod_{i=1}^d \cos(a\pi x_i)``
 
 Let's import Surrogates and Plots:
 ```@example tensor

docs/src/water_flow.md

@@ -1,9 +1,9 @@
 # Water flow function
 
 The water flow function is defined as:
-``f(r_w,r,T_u,H_u,T_l,H_l,L,K_w) = \frac{2*\pi*T_u(H_u - H_l)}{log(\frac{r}{r_w})*[1 + \frac{2LT_u}{log(\frac{r}{r_w})*r_w^2*K_w}+ \frac{T_u}{T_l} ]}``
+``f(r_w,r,T_u,H_u,T_l,H_l,L,K_w) = \frac{2*\pi*T_u(H_u - H_l)}{\log(\frac{r}{r_w})*[1 + \frac{2LT_u}{\log(\frac{r}{r_w})*r_w^2*K_w}+ \frac{T_u}{T_l} ]}``
 
-It has 8 dimension.
+It has 8 dimensions.
 
 ```@example water
 using Surrogates

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

@@ -1691,7 +1691,7 @@ 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