The real story is the metric

The palettes only improved because my yardstick did. Here's how it evolved.

The first sampler ranked colours by a learned harmony and chose with MMR. Before leaning on it, I ran a simple test: show the score one real Wada combination and one random pairing of his colours — can it tell which is real? A perfect judge is right 100% of the time; a useless one, 50% — a coin flip.

Harmony scored a coin flip. Another way to see it: plot the scores it gives to real combinations versus random pairings. A good metric pulls them apart; harmony piles them on top of each other. The gamut (which colours, not how they're combined) is the signal that actually separates.

If I can't score harmony, lean on structure: a Determinantal Point Process, the canonical diverse-subset picker. But my kernel weighted "quality" so hard that the DPP huddled in the dense, muted core of the gamut. Every palette became three mid-tones at the same lightness — flatter than random.

I measured Wada's real contrast (mean ΔE ≈ 0.28) and tuned the kernel to hit it. The number matched; the palettes turned into loud primaries — because a single contrast number doesn't care how you reach it, and the cheapest contrast to buy is raw saturation.

Act IV · the diagnosis

I stopped tuning and looked. Take every pair of colours inside a combination and ask how one turns into the other: does it change in value (lighter/darker) or in saturation? Plotted, the answer is a tall, thin cloud — Wada moves up and down, almost never sideways. His contrast is ~13× more value than chroma.

My isotropic kernel treated both directions the same, so it happily traded value for the saturation it should have left alone. The fix had to make the algorithm see colour the way the cloud is shaped.

The gamut, and why it matters

Plot all 159 colours by lightness and saturation. They hug the left — low chroma — at every lightness. That muted band is the gamut. The whole job is: stay in the band, but travel up and down it.

Left, the garish attempt breaks right out of the band into saturation. Right, the final sampler stays in the band and spreads top-to-bottom — contrast from value, exactly like Wada.

The anisotropy is real — but baking it into a bespoke distance metric had a side-effect the two-sample test caught: it skewed palettes warm and clustered their hues. The cleaner fix puts the intelligence in the quality: a multidimensional, Optuna-tuned vector of Wada-ness — manifold proximity, value-conditional typicality (so dark colours aren't punished for being dark), mutedness, hue-fit. With that, diversity can be measured plainly (isotropic), and drawing a k-DPP sample instead of the single greedy-best yields the dark tail, the muted chroma, and a naturally graded saturated accent — no hand-tuned metric required.

The whole journey, on one map

Each attempt placed by what it actually made: saturation across, value contrast up. Timid sinks to the floor; the contrast-by-number fix overshoots into saturation; only the final recipe — isotropic diversity + the learned quality vector — lands on Wada: high value contrast at low chroma.

One more fix underneath

My candidate colours had been blobs jittered off the 159 named points — which sprayed a third of them out past the muted band, into saturation. I replaced it with sampling the gamut region directly.

Alternatives considered & discarded

Did I actually become Wada?

Matching a few averages is not the same as matching the distribution. The real check: train a classifier to tell generated palettes from real Wada combinations on perceptual (OKLab) features. If it can't — AUC near 0.5 — they're indistinguishable; near 1.0 it has found an obvious tell.

At first it was trivial (AUC ≈ 0.95): my palettes ran too warm, their hues clustered, their colours washed. The fixes — make colour quality a learned vector of Wada-ness (Optuna-weighted, with a chroma term), measure diversity isotropically, and sample the dark tail — pulled it to 0.71, with every marginal landing on Wada:

AUC is just this: line every palette up by the classifier's score and ask how often a real Wada outranks one of mine. At 0.71 the two populations overlap heavily — and the paired examples below, which the classifier scored the same, are genuinely hard to call (top of each pair = the book, bottom = mine).

Not a perfect 0.5 — a from-scratch generator still leaves a faint fingerprint (a slightly narrower chroma range) — but mine now sits inside Wada's distribution rather than beside it.

How close can I get? The honest ceiling

Every marginal matches, but AUC ~0.70 means a joint-structure residual remains: which lightness pairs with which chroma and hue. To close it I modelled the joint directly — a Gaussian copula (Sklar's theorem: keep the exact marginals, add only the dependence — the covariance idea a Gaussian process is built on, aimed at generation) and a GMM — and validated honestly with a train/test holdout.

The trap is the last two columns. Perturbing real combos hits AUC 0.45 — but novelty 0.03 means it's just copying. The copula dazzles in-sample (0.61), but held-out it overfits: a 9–12-dimensional joint estimated from ~160 training combinations memorises their quirks. A held-out global Optuna found no real gain over the defaults either.

So the honest verdict: with 348 curated combinations, AUC→0.5 is reachable only by memorising — ~0.70 is the ceiling for genuinely novel generation. The one lever that moves it is more data (Wada's Volume 2 and the original six), at which point the copula and a global tune stop overfitting and become real wins. Until then, the controllable sampler is the right answer.

Where we landed

Choose a hue from the wheel. The sampler builds a palette centred on it — your colour forced in, surrounded by its Wada-fit companions. Beside it, the closest real combination from the book: the one whose nearest colour matches your pick.