Interactive hit-and-run sampling#

Runs in the browser via numbl — no install.

🎛 Just the figure →#

The figure-only view runs the sampler and drops you straight into the interactive figure (it shows the run's output first, then the figure). The full developer view — file tree, editable code, console — is below.

▶ Open hitandrun_demo.m and click Run#

A random 2D convex region is generated and N points are drawn uniformly from it by hit-and-run: from the current point, pick a random direction, take the chord where that line crosses the region, and jump to a uniform point on it. Repeat. The figure shows the region and the samples.

Controls:

• Samples — set N (re-runs the sampler).
• Resample — new samples, same region.
• New region — a fresh region.
• Play movie — step through the algorithm: each step draws the chord and the point that landed on it.

How it works#

• hitandrun_demo.m — driver: addpath('helpers'), seed, call the sampler.
• hitandrun_sampler.m — opens the figure, sends data, handles resample requests.
• helpers/ — make_region.m and hit_and_run.m.
• app/ — a single-file React app that draws the region and samples on a canvas.

The script and figure talk both ways: the script sends the region + samples via uihtml(..., 'Data', ...), and the controls call back with sendToMATLAB('resample' | 'newRegion', ...), which re-runs the sampler and returns new points via sendEventToHTMLSource. The script is stateless — the figure owns the region and passes it back with each request.

JIT-compiled kernel#

The loop in hit_and_run.m runs once per sample, so numbl JS-JIT-compiles it to JavaScript — about 30× faster than its interpreter, which is what keeps large N instant. The %!numbl:assert_jit directive asserts this happens (it errors rather than silently falling back). It relies on numbl's scalar-rand() JIT support; rng(seed) still controls the shared PRNG.

Deploy#

Pushing to main builds the app and publishes the project to GitHub Pages via the deploy workflow.