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.
Compile nisps-core C++ MLP to WASM (36KB) and use it as the ML engine
in the playground, replacing the JavaScript port for inference, training,
and weight manipulation.
- Add extern "C" WASM bindings with spread-aware drawWeights/moveWeights
- WasmIML class is a drop-in replacement for the JS IML
- Inference runs on main thread via WASM (fast, synchronous)
- Training runs in a Web Worker with its own WASM instance (non-blocking)
- Interactive training (thumbs-up, train button) no longer freezes UI/audio
- Preset loading and state restore still use sync training
