// Wine: Aeberhard & Forina (1992), UCI, CC BY 4.0. See THIRD_PARTY_DATA.md.
// An educational configuration, not an accuracy or performance benchmark.
import aner.dataset
import aner.nn
import aner.ml

let wine = dataset.wine()
let split = wine.split(test: 0.2, seed: 2026)
let network = nn.sequential(seed: 42)
    .dense(inputs: 13, outputs: 16)
    .tanh()
    .dense(inputs: 16, outputs: 3)
let pipeline = ml.classifier(network, standardize: true)
let fitted = pipeline.fit(split.train(), epochs: 1000, rate: 0.05)
print(fitted.evaluate(split.test()).summary())