Experiments on Programmer.ie: Modern AI programming

Cellular Automata From First Principles 58: Capstone — Discover, Measure and Explain a New System

Mon, 10 Aug 2026 21:01:00 +0100

Cellular Automata From First Principles 58: Capstone — Discover, Measure and Explain a New System

The book began with one tiny rule applied to one tiny neighborhood.

We end with a different question:

Can we discover a cellular system, characterize its behavior, test its robustness and explain what we actually know about it?

That is the capstone.

Choose a system family

The capstone can use any family we built:

Cellular Automata From First Principles 57: Build a Cellular Automata Laboratory

Mon, 10 Aug 2026 21:00:00 +0100

Cellular Automata From First Principles 57: Build a Cellular Automata Laboratory

We now have enough pieces to stop thinking in terms of isolated scripts.

A useful cellular-automata laboratory should let us define, run, measure, replay and visualize experiments across many kinds of automata without hiding the mechanisms we spent the whole book learning.

The laboratory is not the model

Keep these concerns separate:

model definition
execution backend
experiment configuration
metrics
artifacts
analysis

A rule should not know where its PNG is saved.

Cellular Automata From First Principles 55: Run Parameter Sweeps and Benchmarks

Mon, 10 Aug 2026 20:58:00 +0100

Cellular Automata From First Principles 55: Run Parameter Sweeps and Benchmarks

Once experiments are reproducible, we can stop treating parameters as one-off choices and start mapping behavior systematically.

A parameter sweep is not merely a convenience.

It is a way to turn a model into a measurable landscape.

Build a grid of experiments

from itertools import product

mus = [0.12, 0.14, 0.16]
sigmas = [0.01, 0.02, 0.03]

configs = [
    {"mu": mu, "sigma": sigma, "seed": seed}
    for mu, sigma, seed in product(mus, sigmas, range(10))
]

Notice the seed axis.

Cellular Automata From First Principles 54: Make Experiments Reproducible

Mon, 10 Aug 2026 20:57:00 +0100

Cellular Automata From First Principles 54: Make Experiments Reproducible

By this stage the book contains many systems whose behavior depends on parameters, seeds and implementation choices.

If we cannot reconstruct a run, we cannot really compare it.

Treat configuration as data

from dataclasses import dataclass, asdict


@dataclass(frozen=True)
class ExperimentConfig:
    model: str
    width: int
    height: int
    steps: int
    seed: int
    backend: str
    dtype: str

Then extend with model-specific parameters rather than hiding them in notebook cells.