Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

docs: fix improved branin benchmark and run this example while building #453

Merged
merged 1 commit into from
Dec 14, 2023
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion docs/pages.jl
Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
pages = ["index.md"
"Tutorials" => [
"Tutorials" => [
"Basics" => "tutorials.md",
"Radials" => "radials.md",
"Kriging" => "kriging.md",
Expand Down
22 changes: 16 additions & 6 deletions docs/src/ImprovedBraninFunction.md
Original file line number Diff line number Diff line change
Expand Up @@ -2,11 +2,12 @@

The Branin Function is commonly used as a test function for metamodelling in computer experiments, especially in the context of optimization.


# Modifications for Improved Branin Function:

To enhance the Branin function, changes were made to introduce irregularities, variability, and a dynamic aspect to its landscape. Here's an example:

```function improved_branin(x, time_step)
```@example improved_branin
function improved_branin(x, time_step)
x1 = x[1]
x2 = x[2]
b = 5.1 / (4*pi^2)
Expand All @@ -27,12 +28,21 @@ end
This improved function now incorporates irregularities, variability, and a dynamic aspect. These changes aim to make the optimization landscape more challenging and realistic.

# Using the Improved Branin Function:
After defining the improved Branin function, you can proceed to test different surrogates and visualize their performance using the updated function. Here's an example of using the improved function with the Radial Basis surrogate:

```
# Assuming you've defined 'improved_branin' and imported necessary packages
After defining the improved Branin function, you can proceed to test different surrogates and visualize their performance using the updated function. Here's an example of using the improved function with the Radial Basis surrogate:

radial_surrogate = RadialBasis(xys, [improved_branin(xy, 0.1) for xy in xys], lower_bound, upper_bound)
```@example improved_branin
using Surrogates, Plots

n_samples = 80
lower_bound = [-5, 0]
upper_bound = [10, 15]
xys = sample(n_samples, lower_bound, upper_bound, SobolSample())
zs = [improved_branin(xy, 0.1) for xy in xys]
radial_surrogate = RadialBasis(xys, zs, lower_bound, upper_bound)
x, y = -5.00:10.00, 0.00:15.00
xs = [xy[1] for xy in xys]
ys = [xy[2] for xy in xys]
p1 = surface(x, y, (x, y) -> radial_surrogate([x, y]))
scatter!(xs, ys, marker_z=zs)
p2 = contour(x, y, (x, y) -> radial_surrogate([x, y]))
Expand Down