Rbm/CLAUDE.md

# CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

## Overview

C++11 library and set of executables implementing Restricted Boltzmann Machines (RBMs), stacked into
deep belief networks (`DeepStack`) or recurrent structures (`RnnStack`) for character-level text generation
("poet"). There are three build targets sharing the same core sources: a JUCE-based desktop GUI (`GUI`), a
console test harness (`TEST`), and a text-generation CLI (`POET`).

## Build

Build system is a hand-written `Makefile` (no CMake). Select the target with `PRJ` and the build flavor with
`CONFIG`:

```sh
make PRJ=POET CONFIG=release   # builds build/release/poet.elf (default PRJ=POET, CONFIG=release)
make PRJ=TEST CONFIG=debug     # builds build/debug/test.elf
make PRJ=GUI                   # builds build/release/rbm.elf, also builds the JUCE static lib first
make clean                     # removes build/${CONFIG}
```

The `GUI` target additionally depends on `juce/build/${CONFIG}/libjuce.a`, built via `make -C juce` (uses
`juce/Makefile`, `juce/pkg.mk`, `juce/JUCE-3.1.1.mk` against the vendored `juce/JUCE-3.1.1` source tree).
`install` copies `build/release/rbm.elf` to `${HOME}/bin`.

Object/dependency files land in `build/${CONFIG}/`; `.d` files are auto-included for header dependency
tracking. There is no separate lint or test-runner command — `TEST_CXX_SRCS` builds `main.cpp` into
`test.elf`, which is a manual smoke-test program (load a project, train, print matrices), not a unit test
suite. Run it directly to sanity-check core behavior:

```sh
make PRJ=TEST CONFIG=debug && ./build/debug/test.elf
```

### External dependencies (system-installed, not vendored)

- **Armadillo** (`<armadillo>`, linked `-larmadillo`) — all matrix/vector math goes through `arma::mat`.
- **JsonCpp** (`<jsoncpp/json/json.h>`, linked `-ljsoncpp`) — project files (`*.prj`) and layer weight
  metadata serialize to/from `Json::Value`.
- JUCE (vendored under `juce/`, only needed for the `GUI` target) plus its Linux deps: freetype, X11,
  Xinerama, Xext, GL, pthread, dl, rt.

## Architecture

### Core RBM hierarchy

- **`Rbm`** (`Rbm.hpp/cpp`) — a single restricted Boltzmann machine: visible/hidden units, weights (`m_whv`),
  biases (`m_bh`, `m_bv`), `Params` (learning rate, momentum, Gibbs sampling options, etc.), and the
  contrastive-divergence training step (`cd`, `cd_hinton`, `cd_jens`, ...). Params and trained state
  round-trip through `toJson()`/`fromJson()`.
- **`Layer`** (`Layer.hpp/cpp`) — wraps an `Rbm` with 2D visible geometry (`numVisibleX/Y`, for image-like
  data) plus an optional context block (`numContext`, used by the RNN stack to feed in previous state).
  Layers form an intrusive doubly-linked list via raw `next`/`prev` pointers (not `std::vector`); `root()`
  walks to the first layer. Per-layer weights persist as separate `<prj>.Layer.<id>.{w,bh,bv}.dat` files
  (`weightsLoad`/`weightsSave`), independent from the JSON project file.
- **`AStack`** (`AStack.hpp/cpp`) — abstract base owning the `Layer` linked list for a named project
  ("stack"). Handles layer add/remove, weight init/load/save for the whole stack, and the training-batch
  matrix (`m_trainingBatch`, loaded from `*.training.dat`). Declares `train()` as pure virtual — subclasses
  decide how data flows between layers.
  - **`DeepStack`** — classic layer-wise DBN: `upPass`/`downPass`/`upDownPass` propagate a sample through
    the full stack; `train()` greedily trains each layer on the representation produced by the layers below.
  - **`RnnStack`** — treats the stack as a recurrent cell operating on one-hot character codes
    (`NUM_CODES = 37`: space + a-z + 0-9). `step_forward` advances the recurrent state one timestep;
    `v_to_vc`/`vc_to_v` pack/unpack the visible+context vector. Used by `poet.cpp` for text generation.
- **`StackCreator`** (`StackCreator.hpp/cpp`) — serializes/deserializes an `AStack` (and its `Layer`s) to/from
  a `<name>.prj` JSON file. Reads `stack.type` to decide whether to instantiate a `DeepStack` or `RnnStack`.
  Accepts an optional `LayerConstructor` callback so callers (e.g. the GUI) can substitute their own
  `Layer` subclass (see `RbmComponent`) when building layers from JSON.
- **`RnnTextHelper`** / **`Matutils`** (`matutils.hpp`) — character <-> one-hot vector encoding
  (`char2vec`/`vec2char`, `ch2idx`/`idx2ch`), training-batch construction from text files, and small
  Armadillo helpers (`sample`, `prob`, `normalize`, `uniform`) used by the CD training routines.

### Entry points

- **`source/main.cpp`** — `TEST` target; exercises `DeepStack` directly (build/train/save a small DBN,
  or load an existing one, depending on `CREATE_TEST`/`TRAIN_TEST` compile-time flags).
- **`source/poet.cpp`** — `POET` target; loads an `RnnStack` project (default `poet_2v_5s`) and dispatches
  on `argv[1]`: `c`(reate)/`r`(eset weights)/`t`(rain, optionally with a training-text path in `argv[2]`)/
  `f`(orward-generate text, optional seed string in `argv[2]`). Training periodically calls back into
  `forward()` to sample generated text as a progress check.
- **`source/gui.cpp`** + **`MainComponent`** — JUCE app entry point; `MainComponent` implements
  `LayerConstructor` to build `RbmComponent` layers (interactive `Layer` subclass with sliders/toggles bound
  to `Rbm::Params`) and drives training via the `Rbm::IListener` progress callback, mirroring what
  `RbmListener` does in the CLI tools.

### Project files on disk

Each named project `<name>` (e.g. `mnist`, `poet_2v_5s`, `context99` — see the many `*.prj` files at repo
root, mostly saved experiment artifacts) consists of:
- `<name>.prj` — JSON describing the stack type and layer geometry (via `StackCreator`).
- `<name>.training.dat` — the training batch matrix.
- `<name>.Layer.<id>.w.dat` / `.bh.dat` / `.bv.dat` — per-layer weights/biases (Armadillo binary/text format).

These root-level `.dat`/`.prj`/`.txt` files are experiment data and generated artifacts, not something to
edit by hand; treat them as fixtures unless a task specifically concerns training data or saved models.