Add Dataset.computeWeights() with three modes:
- global: exponential recency decay (newest examples weighted higher)
- local: spatial suppression of older examples near the current input
- combined: both applied together
IML and WasmIML now compute weights on every train() call using the
active mode. Exposes recencyBias, weightingMode, localRadius properties.
WASM worker path passes sampleWeights through to C++ via the new binding.
Faithful JS port of nisps-core MLP + IML engine with a touch-friendly
UI for exploring neural parameter mapping visually. Two learning modes:
example-based (set slider targets) and RL feedback (thumbs up/down with
exploration noise). Flow field particle system controlled by 8 MLP outputs.
