memlnaut-nisps

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Author	SHA1	Message	Date
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

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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

3 commits