// Iris: Fisher (1936), UCI, CC BY 4.0. See THIRD_PARTY_DATA.md.
// MSE averages squared softmax-probability errors over rows × classes.
// Split first; the fitted pipeline learns preprocessing only from training rows.
import aner.dataset
import aner.nn
import aner.ml

let flowers = dataset.iris()
let split = flowers.split(test: 0.2, seed: 2026)
let network = nn.sequential(seed: 42)
    .dense(inputs: 4, outputs: 8)
    .tanh()
    .dense(inputs: 8, outputs: 3)
let pipeline = ml.classifier(network, standardize: true, loss: "mse")
let fitted = pipeline.fit(split.train(), epochs: 2000, rate: 0.1)
print(fitted.evaluate(split.test()).summary())