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d0ba1faaea
Phase 0 — spread-aware API ported to nisps-core C++: - MLP::DrawWeightsSpread(T spread) — interpolate uniform↔Xavier per layer - MLP::MoveWeightsSpread(T speed, T spread) — per-layer noise + weight decay - IML::randomise_weights(Float spread) and IML::move_weights(speed, spread) - 5 unit tests (10/10 total pass) Phase 1 — VCV Rack 2 plugin skeleton: - Makefile with C++20, nisps-core include path - plugin.json manifest - Empty MEMLNaut module: 2 inputs, 12 outputs, placeholder SVG panel - static_assert verifies nisps-core headers resolve - C++20 confirmed working in VCV SDK (8 existing plugins use it) |
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| .beads | ||
| .github/workflows | ||
| .vscode | ||
| data | ||
| modes | ||
| nisps-core | ||
| playground | ||
| src | ||
| vcv | ||
| voicespaces | ||
| .gitignore | ||
| .gitmodules | ||
| AGENTS.md | ||
| ChannelStripAudioApp.hpp | ||
| CLAUDE.md | ||
| IMLInterface.hpp | ||
| LICENSE | ||
| MEMLNaut-NISPS.ino | ||
| NISPS_CORE_EXTRACTION_PLAN.md | ||
| NISPS_CORE_TASKS.md | ||
| PAFSynthAudioApp.hpp | ||
| README.md | ||
| ThruAudioApp.hpp | ||
| XiasriAnalysis.cpp | ||
| XiasriAnalysis.hpp | ||
| XIASRIAudioApp.hpp | ||
Neural Interactive Shaping of Parameter Spaces
https://musicallyembodiedml.github.io/memlnaut/approaches/nisps
Web Playground
Try NISPS in your browser — no hardware required:
cd playground
python3 -m http.server
# Open http://localhost:8000
Train a neural network to map joystick positions to generative visuals through interactive machine learning. Two learning modes: direct example mapping and reinforcement learning with thumbs up/down feedback.
The playground UI includes an Expand toggle on the visual surface so you can make the canvas nearly full-screen while compressing parameter/control panels into a minimal strip beneath it.