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Author SHA1 Message Date
monkey-w1n5t0n
8a19e5b52c refactor(ml)!: P2.1 storage-policy split — MLPCore<Storage>, fixed + dynamic models
Algorithms (forward, backprop/SGD, init, move_weights, diagnostics) now live
once in MLPCore<Storage> (nisps/ml/mlp.hpp). Storage models:

- FixedStorage (storage.hpp): template-sized std::array, zero heap. The
  classic MLP<NIn,H1,H2,H3,NOut,...> is an alias preserving kInput/kHidden*/
  kOutput/kNumLayers/weight_count() constexpr — firmware + bindings + modes
  compile unchanged.
- DynamicStorage (dynamic_storage.hpp): runtime dims, ONE arena allocation
  at construction, nothing per-call. #error under NISPS_TARGET_EMBEDDED
  (new macro in core/perf.hpp); sole lint-cpp.sh heap-allowlist entry, plus
  a lint check that fails if the #error guard disappears.

Verification:
- new ctest test_mlp_storage_parity: fixed↔dynamic BIT-identical across
  init/draw/inference/train(FIFO)/move_weights(pin mask)/eval_loss/
  layer_stats/set_weights/infer_batch/reset; invalid+moved-from inert
- golden ML vectors (pre-refactor constants) pass → bit-stable refactor
- native↔WASM parity PASS, max delta unchanged (2.4e-7)
- chokepoint B compile: PAFSynth .text 122324→122692 (+0.30%, ±1% budget);
  RAM +416B (eval scratch)
- fix: firmware-common.sh used bare 'python' (absent here) → ${PYTHON:-python3}

Part of one-core-engine-refactor P2. nisps_ml_create ABI untouched (P2.2 is
an operator stop-point).
2026-07-13 23:47:03 +02:00
monkey-w1n5t0n
c936bf75c9 docs(ergo): update task guidance 2026-07-13 22:58:26 +02:00
monkey-w1n5t0n
c986377b4c feat(firmware): reposition gesture — relocate an existing example to a new input
Add a "grab → move → drop" gesture that moves an existing positive
example's output to a new input position, preserving the output. This is
the new core's home for upstream InterfaceRL's drag-store/reposition-commit,
distinct from Explore→Place (which places newly-auditioned scratchpad sounds).

- nisps/ml/feedback.hpp: begin_reposition()/commit_reposition()/repositioning().
  Reuses the Placing state (static_output holds the carried vector) but a
  reposition_ flag makes commit AND the mode-switch teardown SKIP the weight
  restore — the real net is never set aside here, so restoring snapshot_ would
  clobber the live trained net. Guards cancel_place + abort_explore_place.
- firmware glue: state-gate Toggle B. Exploring → reroll/nudge (unchanged);
  Idle → MomB1 grab, MomB2 drop (commit + add_example + train). The 4D variant
  has no joystick button, so the gesture lives on the momentary toggle. Also
  fix a stale top-of-file control-map comment that contradicted the bindings.
- tests: 4 reposition cases (hold without snapshot; commit stores carried
  output with no restore; mode-switch aborts without clobber; begin-only-Idle).
- MAP.md: document the full ExploreAndPlace lifecycle + reposition wiring.

Audio-hold (carrying the sound audibly during the move) remains the existing
unwired static_output() TODO and affects Explore→Place identically.
Firmware compile unverified (no arduino-cli); host tests + lint pass.
2026-06-28 23:45:27 +02:00
monkey-w1n5t0n
4e60d0192a feat(slp-workshop): new MEMLCelium-based mode + port Jolt & OU-noise RL learning
New SLP-Workshop firmware variant (Synth Library Portland), built on the
MEMLCelium engine + MLP shape. Ports the two post-fork learning-algorithm
changes from upstream memllib InterfaceRL into the shared nisps/ml core,
runtime-configurable (no compile-time switch), inert by default:

- nisps/ml/jolt.hpp: Jolt — held continuous weight morph over the flat
  weight buffer + post-release LR ramp (kJolt* constants verbatim).
- nisps/ml/ou_noise.hpp: OUNoise<N> — Ornstein-Uhlenbeck exploration walk
  on the output vector (theta=0.02, dt=0.001, kMaxAmplitude=0.65).

Both wired into ModeBase so every mode gains jolt_press/jolt_release/
jolt_lr_scale + set_explore_intensity; gated so existing modes stay
bit-identical (parity + golden tests green). Firmware surfaces them on
TogB1 (Jolt) and RVX1 (explore). New SLPWorkshopMode mode + schema +
codegen; firmware alias + .ino variant; playground mode registration.

Tests: jolt + OU unit tests, ModeBase learning integration incl. an
inert-parity test proving SLP-Workshop == MEMLCelium with features off.
Verified: cpp tests, wasm build, native↔wasm parity, lint, codegen
golden, playground typecheck. Firmware compile/e2e/hardware are
environment-bound (no arduino-cli/submodules/browser here).

Refs ergo 019f0fca.
2026-06-28 22:15:36 +02:00
monkey-w1n5t0n
22efb1c411 feat(nisps/ml): crystallise Explore-and-place into shared FeedbackController
Add FeedbackMode::ExploreAndPlace + Idle/Exploring/Placing state machine
(no-heap, deterministic nisps::Rng): enter/exit_explore, reroll, nudge, undo,
begin_place, commit_place, cancel_place + on_down/on_up browser policy. Wire
the nisps_ml_feedback_* C API + EXPORTED_FUNCTIONS, extend parity Stage 5d.
Fix set_mode(3) falling back to Avoid. Native ctest 4/4; parity native==WASM
within 1e-5 (max delta 2.4e-7). Rebuilt nisps.{js,wasm}.
2026-06-28 04:14:12 +02:00
w1n5t0n
825ed6ad33 feat(nisps/ml): MLP library with fixed-architecture template + spread-aware RL (meml-wmh)
Stream 2 of the clean-slate rewrite: nisps/ml/ replaces src/memlp/ with a
header-only, heap-free MLP that satisfies nisps::core::MLEngine.

Files (nisps/ml/):
- activations.hpp — ReLU (leaky 0.01 for parity), sigmoid, tanh
- loss.hpp — MSE per-sample (fixes meml-ues double-scaling: returns the
  sample's MSE without an extra 1/N multiplication; the training loop
  averages explicitly)
- init.hpp — uniform/Xavier/spread-aware weight init
- training.hpp — gradient clip helper (±10.0 matches legacy)
- rl.hpp — move_weights with per-layer Xavier scaling, weight decay
  (10% * spread), gaussian noise via the deterministic Rng (matches the
  legacy JS sum-of-three-uniforms shape); draw_weights also spread-aware
- stats.hpp — per-layer mean/max/dead/saturating diagnostics
- mlp.hpp — 4-layer (3 hidden + sigmoid output) MLP class with
  std::array-backed weights, biases, gradient accumulators, dataset
  ring buffer (default 128 examples), loss history (default 4096 iters).
  Bias is a separate per-layer parameter — no input-vector mutation.
  Flat get_weights/set_weights layout: weights all layers (row-major,
  layer order), then biases all layers.

Tests (tests/cpp/, all 50 passing under -Wall -Wextra -Werror -Wpedantic):
- test_mlp_init.cpp — deterministic seeding, spread regimes,
  static_assert MLEngine concept satisfied
- test_mlp_inference.cpp — golden hand-computed forward pass match,
  sigmoid output range, set_input bounds
- test_mlp_training.cpp — XOR convergence (loss < 0.01 in <2k iters),
  ring-buffer eviction
- test_mlp_loss.cpp — meml-ues regression test: reported loss equals
  hand-computed average MSE without extra 1/N scaling; sample weights
  honoured
- test_mlp_rl.cpp — move_weights respects output_pin_mask (final-layer
  rows + biases preserved); spread regimes; grad clear after draw_weights
- test_mlp_serialize.cpp — get_weights/set_weights round-trip preserves
  inference exactly; eval_loss is non-mutating; infer_batch matches
  individual inference

Verification:
- Clean build, no warnings
- 50 tests pass (22 prior + 28 new)
- No std::vector / new / malloc in nisps/ml/
- All float literals .f-suffixed in code (comments excepted)
2026-04-29 15:55:43 +03:00