docs: update docs to use HaltonSample

docs/src/InverseDistance.md

@@ -15,15 +15,15 @@ default()
 
 ### Sampling
 
-We choose to sample f in 25 points between 0 and 10 using the `sample` function. The sampling points are chosen using a Low Discrepancy, this can be done by passing `LowDiscrepancySample()` to the `sample` function.
+We choose to sample f in 25 points between 0 and 10 using the `sample` function. The sampling points are chosen using a Low Discrepancy, this can be done by passing `HaltonSample()` to the `sample` function.
 
 ```@example Inverse_Distance1D
 f(x) = sin(x) + sin(x)^2 + sin(x)^3
 
 n_samples = 25
 lower_bound = 0.0
 upper_bound = 10.0
-x = sample(n_samples, lower_bound, upper_bound, LowDiscrepancySample(;base=2))
+x = sample(n_samples, lower_bound, upper_bound, HaltonSample())
 y = f.(x)
 
 scatter(x, y, label="Sampled points", xlims=(lower_bound, upper_bound), legend=:top)

docs/src/moe.md

@@ -92,7 +92,7 @@ end
 lb = [-1.0, -1.0]
 ub = [1.0, 1.0]
 n = 150
-x = sample(n, lb, ub, SobolSample())
+x = sample(n, lb, ub, RandomSample())
 y = discont_NDIM.(x)
 x_test = sample(10, lb, ub, GoldenSample())
 
@@ -110,7 +110,6 @@ rbf = RadialBasis(x, y, lb, ub)
 rbf_pred_vals = rbf.(x_test)
 rbf_rmse = rmse(true_vals, rbf_pred_vals)
 println(rbf_rmse > moe_rmse)
-
 ```
 
 ### Usage Notes - Example With Other Surrogates

docs/src/parallel.md

@@ -17,24 +17,24 @@ To ensure that points of interest returned by `potential_optimal_points` are suf
 
 The following strategies are available for virtual point selection for all optimization algorithms:
 
-- "Minimum Constant Liar (CLmin)":
+- "Minimum Constant Liar (MinimumConstantLiar)":
   - The virtual point is assigned using the lowest known value of the merit function across all evaluated points.
-- "Mean Constant Liar (CLmean)":
+- "Mean Constant Liar (MeanConstantLiar)":
   - The virtual point is assigned using the mean of the merit function across all evaluated points.
-- "Maximum Constant Liar (CLmax)":
+- "Maximum Constant Liar (MaximumConstantLiar)":
   - The virtual point is assigned using the great known value of the merit function across all evaluated points.
 
 For Kriging surrogates, specifically, the above and follow strategies are available:  
 
-- "Kriging Believer (KB)":
+- "Kriging Believer (KrigingBeliever):
   - The virtual point is assigned using the mean of the Kriging surrogate at the virtual point.
-- "Kriging Believer Upper Bound (KBUB)":
+- "Kriging Believer Upper Bound (KrigingBelieverUpperBound)":
   - The virtual point is assigned using 3$\sigma$ above the mean of the Kriging surrogate at the virtual point.
-- "Kriging Believer Lower Bound (KBLB)":
+- "Kriging Believer Lower Bound (KrigingBelieverLowerBound)":
   - The virtual point is assigned using 3$\sigma$ below the mean of the Kriging surrogate at the virtual point.
 
 
-In general, CLmin and KBLB tend to favor exploitation while CLmax and KBUB tend to favor exploration. CLmean and KB tend to be a compromise between the two.
+In general, MinimumConstantLiar and KrigingBelieverLowerBound tend to favor exploitation while MaximumConstantLiar and KrigingBelieverUpperBound tend to favor exploration. MeanConstantLiar and KrigingBeliever tend to be a compromise between the two.
 
 ## Examples
 
@@ -50,7 +50,7 @@ y = f.(x)
 my_k = Kriging(x, y, lb, ub)
 
 for _ in 1:10
-    new_x, eis = potential_optimal_points(EI(), lb, ub, my_k, SobolSample(), 3, CLmean!)
+    new_x, eis = potential_optimal_points(EI(), MeanConstantLiar(), lb, ub, my_k, SobolSample(), 3)
     add_point!(my_k, new_x, f.(new_x))
 end
 ```

docs/src/polychaos.md

@@ -9,7 +9,7 @@ we are trying to fit. Under the hood, PolyChaos.jl has been used.
 It is possible to specify a type of polynomial for each dimension of the problem.
 ### Sampling
 
-We choose to sample f in 25 points between 0 and 10 using the `sample` function. The sampling points are chosen using a Low Discrepancy, this can be done by passing `LowDiscrepancySample()` to the `sample` function.
+We choose to sample f in 25 points between 0 and 10 using the `sample` function. The sampling points are chosen using a Low Discrepancy, this can be done by passing `HaltonSample()` to the `sample` function.
 
 ```@example polychaos
 using Surrogates
@@ -20,7 +20,7 @@ default()
 n = 20
 lower_bound = 1.0
 upper_bound = 6.0
-x = sample(n,lower_bound,upper_bound,LowDiscrepancySample(2))
+x = sample(n,lower_bound,upper_bound,HaltonSample())
 f = x -> log(x)*x + sin(x)
 y = f.(x)
 scatter(x, y, label="Sampled points", xlims=(lower_bound, upper_bound), legend=:top)

docs/src/samples.md

@@ -32,8 +32,7 @@ sample(n,lb,ub,::LatinHypercubeSample)
 
 * Low Discrepancy sample
 ```
-LowDiscrepancySample{T}
-sample(n,lb,ub,S::LowDiscrepancySample)
+sample(n,lb,ub,S::HaltonSample)
 ```
 
 * Sample on section

docs/src/secondorderpoly.md

@@ -18,7 +18,7 @@ f = x -> 3*sin(x) + 10/x
 lb = 3.0
 ub = 6.0
 n = 10
-x = sample(n,lb,ub,LowDiscrepancySample(2))
+x = sample(n,lb,ub,HaltonSample())
 y = f.(x)
 scatter(x, y, label="Sampled points", xlims=(lb, ub))
 plot!(f, label="True function", xlims=(lb, ub))