4e60d0192a
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.
94 lines
2.7 KiB
C++
94 lines
2.7 KiB
C++
// tests/cpp/test_mlp_jolt.cpp — Jolt (held continuous weight morph) behavior.
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//
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// - default state is inert: inactive, step() is a no-op, lr_scale()==1.
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// - press() activates and pauses learning (lr_scale()==0).
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// - step() morphs selected weights toward bounded targets, staying within
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// the [target_min, target_max] envelope.
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// - release() freezes and re-arms the LR ramp (0 → 1 via tick_lr_ramp).
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// - same seed ⇒ identical morph (determinism / parity).
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#include <array>
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#include <cmath>
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#include <cstdint>
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#include <span>
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#include "test_helpers.hpp"
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#include "../../nisps/ml/jolt.hpp"
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namespace {
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using nisps::ml::Jolt;
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using nisps::ml::JoltParams;
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NISPS_TEST(jolt_inert_by_default) {
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Jolt j(0ull);
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NISPS_EXPECT(!j.active());
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NISPS_EXPECT(j.lr_scale() == 1.f);
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std::array<float, 32> w{};
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w.fill(0.5f);
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j.step(std::span<float>(w)); // no-op while inactive
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for (float v : w) NISPS_EXPECT(v == 0.5f);
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}
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NISPS_TEST(jolt_press_activates_and_pauses_learning) {
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Jolt j(1ull);
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j.press(100u);
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NISPS_EXPECT(j.active());
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NISPS_EXPECT(j.lr_scale() == 0.f);
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}
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NISPS_TEST(jolt_step_morphs_weights_within_envelope) {
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Jolt j(7ull);
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JoltParams p;
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p.num_weights = 16u;
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j.set_params(p);
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std::array<float, 64> w{};
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w.fill(0.f);
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j.press(w.size());
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for (int i = 0; i < 300; ++i) j.step(std::span<float>(w));
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int changed = 0;
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for (float v : w) {
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if (std::fabs(v) > 1e-4f) ++changed;
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// Targets are bounded to [target_min, target_max] = [-1.2, 0.9];
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// EMA interpolation keeps weights inside that envelope.
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NISPS_EXPECT(v >= p.target_min - 0.01f);
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NISPS_EXPECT(v <= p.target_max + 0.01f);
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}
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NISPS_EXPECT(changed > 0);
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NISPS_EXPECT(changed <= 16); // at most num_weights distinct indices
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}
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NISPS_TEST(jolt_release_ramps_lr_back_to_full) {
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Jolt j(3ull);
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j.press(50u);
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NISPS_EXPECT(j.lr_scale() == 0.f);
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j.release();
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NISPS_EXPECT(!j.active());
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NISPS_EXPECT(j.lr_scale() == 0.f); // ramp re-armed at 0
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for (int i = 0; i < 10; ++i) j.tick_lr_ramp();
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NISPS_EXPECT(j.lr_scale() > 0.f && j.lr_scale() <= 1.f);
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for (int i = 0; i < 2000; ++i) j.tick_lr_ramp(); // default step 0.001
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NISPS_EXPECT_NEAR(j.lr_scale(), 1.f, 1e-6);
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}
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NISPS_TEST(jolt_deterministic_same_seed) {
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Jolt a(99ull), b(99ull);
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std::array<float, 48> wa{}, wb{};
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wa.fill(0.25f);
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wb.fill(0.25f);
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a.press(wa.size());
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b.press(wb.size());
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for (int i = 0; i < 150; ++i) {
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a.step(std::span<float>(wa));
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b.step(std::span<float>(wb));
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}
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for (std::size_t i = 0; i < wa.size(); ++i) NISPS_EXPECT(wa[i] == wb[i]);
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}
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} // namespace
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