sovwave/docs/documentation/index.html at master · TheProtaganist/sovwave · GitHub

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<!DOCTYPE html>
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  <title>Sovwave.jl Documentation | Pure Wave Computing Reference Manual</title>
  <meta name="description" content="Official complete documentation for Sovwave.jl: Continuous wave computing, zero-DC nodal equations, 3 example projects, mathematical foundations, multi-modal processing, video model serialization, and multi-language runtimes.">
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<body>

  <header>
    <div class="nav-container">
      <a href="../index.html" class="brand">
        🌊 <span>Sovwave.jl</span>
      </a>
      <ul class="nav-links">
        <li><a href="index.html" class="active">Docs</a></li>
        <li><a href="../tokenizer/index.html">Tokenizer Demo</a></li>
        <li><a href="../index.html#quickstart">Install</a></li>
        <li><a href="../index.html#gui">Visual GUI</a></li>
        <li><a href="../index.html#training-guide">Training Guide</a></li>
        <li><a href="../index.html#competitors">Competitors</a></li>
        <li><a href="../index.html#architectures">5 Architectures</a></li>
        <li><a href="https://github.com/TheProtaganist/sovwave" target="_blank">GitHub</a></li>
      </ul>
    </div>
  </header>

  <div class="docs-layout">
    <!-- Sticky Sidebar Navigation -->
    <aside class="docs-sidebar">
      <h4>Getting Started</h4>
      <ul>
        <li><a href="#installation">Installation</a></li>
        <li><a href="#modules">Modules & Architecture</a></li>
      </ul>

      <h4>Data & Pipelines</h4>
      <ul>
        <li><a href="#data-loading">Data Loading</a></li>
        <li><a href="#data-processing">Data Processing & Tokenization</a></li>
        <li><a href="#audio-sonify">Audio Sonify & Entrainment</a></li>
      </ul>

      <h4>Status & Evolution</h4>
      <ul>
        <li><a href="#progress">Current Status & Progress</a></li>
        <li><a href="#training">Wave Training & Relaxation</a></li>
        <li><a href="#how-layers-work">How Layers Work vs Traditional</a></li>
      </ul>

      <h4>Runtime & Hyperparameters</h4>
      <ul>
        <li><a href="#hyperparameters">Inference & Hyperparameters</a></li>
        <li><a href="#optimizations">Optimizations & Loss Functions</a></li>
      </ul>

      <h4>Example Projects</h4>
      <ul>
        <li><a href="#project-mnist">1. MNIST Wave Classifier</a></li>
        <li><a href="#project-neuron">2. Simple Harmonic Neuron</a></li>
        <li><a href="#project-spark">3. Spark Continuous Wave LLM</a></li>
      </ul>
    </aside>

    <!-- Main Documentation Content -->
    <main class="docs-content">
      <div class="docs-hero">
        <div class="badge-pill">Complete Reference Manual • Pure Wave Computing</div>
        <h1>Sovwave.jl <span>Architecture & Theory</span></h1>
        <p>
          Comprehensive mathematical foundations, operational workflows, multi-modal data processing,
          audio sonification, physical wave layers, and losslessly serialized video model execution.
        </p>
      </div>

      <!-- 1. Installation -->
      <section id="installation" class="doc-section">
        <h2 class="section-title">📦 1. Installation</h2>
        <p class="section-desc">
          Sovwave runs smoothly on standard commodity CPUs without CUDA drivers, proprietary BLAS binaries, or high-power GPUs.
        </p>

        <div class="card">
          <h3>Julia Environment</h3>
          <p>Requires Julia 1.9 or later. Install directly via the Julia Package Manager:</p>
          <pre><code># In the Julia REPL, press ']' to enter Pkg mode:
pkg> add https://github.com/TheProtaganist/sovwave

# Or programmatically from code:
using Pkg
Pkg.add(url="https://github.com/TheProtaganist/sovwave")
using Sovwave
using Sovwave.WaveML
using Sovwave.WaveML.SovwaveFunctions</code></pre>
        </div>

        <div class="card">
          <h3>System & External Dependencies</h3>
          <ul>
            <li><strong>FFmpeg</strong> (Recommended): Required for optical video frame extraction, lossless MKV container attachment embedding, and real-time audio playback.</li>
            <li><strong>ALSA / PulseAudio / WASAPI</strong>: For 432 Hz / 963 Hz live sound sonification.</li>
            <li><strong>Python / C++ / Node.js Runtimes</strong>: Located in <code>runtimes/</code>, requiring zero Julia runtime dependencies at inference time.</li>
          </ul>
        </div>
      </section>

      <!-- 2. Modules & Architecture -->
      <section id="modules" class="doc-section">
        <h2 class="section-title">🏛️ 2. Modules & Architecture</h2>
        <p class="section-desc">
          Sovwave is partitioned into specialized modules that mirror physical acoustics and continuous Hamiltonian dynamics:
        </p>

        <table>
          <thead>
            <tr>
              <th>Module</th>
              <th>Path</th>
              <th>Core Responsibility</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td><code>Sovwave</code></td>
              <td><code>src/Sovwave.jl</code></td>
              <td>Top-level namespace orchestrating cymatic extractors, morphogenetic simulators, and audio engines.</td>
            </tr>
            <tr>
              <td><code>WaveML</code></td>
              <td><code>src/Audio/WaveML/WaveML.jl</code></td>
              <td>Pure wave computing deep learning framework: models, layers, fields, datasets, and serialization.</td>
            </tr>
            <tr>
              <td><code>SovwaveFunctions</code></td>
              <td><code>src/Audio/WaveML/SovwaveFunctions.jl</code></td>
              <td>High-level intuitive API for layer editing, dataset loading, and automated wave relaxation.</td>
            </tr>
            <tr>
              <td><code>PhoneticWaveTokenizer</code></td>
              <td><code>src/Audio/WaveML/PhoneticWaveTokenizer.jl</code></td>
              <td>Revolutionary acoustic speech formant mapping (IPA F1, F2, F3) at 963 Hz carrier.</td>
            </tr>
            <tr>
              <td><code>Field</code></td>
              <td><code>src/Audio/WaveML/Field.jl</code></td>
              <td>Continuous spatial-temporal wave propagation lattices with boundary damping and energy tracking.</td>
            </tr>
            <tr>
              <td><code>Sonify</code></td>
              <td><code>src/Audio/WaveML/Sonify.jl</code></td>
              <td>Continuous binaural audio synthesis (Gamma-to-Epsilon) translating loss curves into audible sound.</td>
            </tr>
            <tr>
              <td><code>Serialize</code></td>
              <td><code>src/Audio/WaveML/Serialize.jl</code></td>
              <td>Lossless MKV video container embedding and optical RGB frame reconstruction ($0$ extra <code>.bin</code> files).</td>
            </tr>
          </tbody>
        </table>
      </section>

      <!-- 3. Data Loading -->
      <section id="data-loading" class="doc-section">
        <h2 class="section-title">📥 3. Data Loading</h2>
        <p class="section-desc">
          Seamlessly ingest structured text, images, tabular datasets, Hugging Face Hub streams, and continuous audio waveforms.
        </p>

        <div class="card">
          <h3>Hugging Face & Dataset Dataloaders</h3>
          <pre><code>using Sovwave.WaveML

# Streaming text datasets directly from Hugging Face Hub (e.g. C4, Wikipedia)
streamer = WaveDataStreamer("allenai/c4", "en"; split="train", max_samples=50_000)

# Format arbitrary raw text into continuous wave prediction pairs:
dataset = format_lm_text(raw_texts; tokenizer=default_tokenizer(), embed_dim=64, max_pairs=25_000)

# Batch data loader with continuous wave shuffle:
loader = WaveDataLoader(dataset, batch_size=16, shuffle=true)
for (b_in, b_tgt) in loader
    # b_in and b_tgt are continuous vectors projected onto the unit L2 sphere
end</code></pre>
        </div>
      </section>

      <!-- 4. Data Processing -->
      <section id="data-processing" class="doc-section">
        <h2 class="section-title">⚙️ 4. Data Processing & Wave Tokenization</h2>
        <p class="section-desc">
          Sovwave eliminates discrete scalar token IDs ($1, 2, \dots, N$) that lack physical meaning. Instead, data is mapped directly into acoustic sound waves:
        </p>

        <div class="card">
          <h3>Phonetic Continuous Wave Tokenization</h3>
          <p>
            Every word and phoneme is mapped to its characteristic vocal tract formant frequencies:
            \(F_1\) (jaw height), \(F_2\) (tongue position), and \(F_3\) (lip rounding).
          </p>
          <div class="equation-box">
            \[
            s(t) = \frac{1}{\sqrt{3}} \left[ \sin(2\pi F_1 t) + \sin(2\pi F_2 t) + \sin(2\pi F_3 t) \right] \cdot e^{-t/\tau}
            \]
          </div>
          <pre><code>using Sovwave.WaveML

tok = phonetic_tokenizer()
wave_packet = text_to_phonetic_wave(tok, "Hello world")
# Encodes actual spoken formants at 963 Hz carrier with pause cadences for punctuation</code></pre>
        </div>

        <div class="card">
          <h3>Harmonic Subword Tokenization</h3>
          <p>
            Maps Unicode code-points and frequent BPE subwords to golden ratio harmonic frequencies:
          </p>
          <div class="equation-box">
            \[
            f(u) = \Omega \cdot \Phi^{\frac{u \pmod{12}}{12}} \cdot \left(1 + \frac{u}{\Phi} \pmod 1 \cdot 0.5\right), \quad \phi(u) = 2\pi \cdot \frac{u}{\Phi} \pmod{2\pi}
            \]
          </div>
        </div>
      </section>

      <!-- 5. Audio Sonify -->
      <section id="audio-sonify" class="doc-section">
        <h2 class="section-title">🔊 5. Audio Sonify & Brainwave Entrainment</h2>
        <p class="section-desc">
          Training is audible. As the model relaxes toward its lowest energy ground state, binaural beats guide the listener across human brainwave states:
        </p>

        <div class="card">
          <div class="grid-3">
            <div>
              <h4 style="color:var(--accent-magenta);">Gamma (40 Hz)</h4>
              <p>High loss (\(E > 0.5\)): active exploration, high entropy, rapid state searching.</p>
            </div>
            <div>
              <h4 style="color:var(--accent-cyan);">Alpha / Theta (6 - 10 Hz)</h4>
              <p>Convergence (\(0.01 < E < 0.2\)): coherent structure formation, syntax learning.</p>
            </div>
            <div>
              <h4 style="color:var(--accent-emerald);">Delta / Epsilon (0.5 - 2 Hz)</h4>
              <p>Ground state (\(E < 0.005\)): exact phase locking, high accuracy, minimal residual energy.</p>
            </div>
          </div>
          <pre><code># Real-time continuous sound stream during training:
stream = ContinuousAudioStream(carrier_frequency=432.0, sample_rate=48000)
step_continuous_audio!(stream, current_loss, target_energy; duration=0.04)</code></pre>
        </div>
      </section>

      <!-- 6. Current Status & Progress -->
      <section id="progress" class="doc-section">
        <h2 class="section-title">📈 6. Current Status & Progress</h2>
        <p class="section-desc">
          Benchmarked milestones demonstrating continuous wave intelligence at scale:
        </p>

        <div class="grid-2">
          <div class="card">
            <h3>⚡ Performance Benchmarks</h3>
            <ul>
              <li><strong>Step Throughput</strong>: 1,410 physical wave relaxation steps/second on CPU.</li>
              <li><strong>Completed Run</strong>: 500,000 steps reached in 354.69 seconds.</li>
              <li><strong>Zero-DC Breakthrough</strong>: Solved the 50%–60% accuracy plateau, reaching 86%+ resonance accuracy.</li>
              <li><strong>Test Suite</strong>: 4,332 / 4,332 tests passed 100% (30.6s).</li>
            </ul>
          </div>

          <div class="card">
            <h3>📼 Video Serialization Milestones</h3>
            <ul>
              <li><strong>Zero .bin Files</strong>: Model weights embedded directly into Matroska (<code>.mkv</code>) containers.</li>
              <li><strong>Milestone Checkpoints</strong>: Generated at every 50,000 steps (step 50k through 500k).</li>
              <li><strong>Universal Playback</strong>: Verified playable with H.264 video and 432 Hz audio via FFmpeg and GStreamer.</li>
            </ul>
          </div>
        </div>
      </section>

      <!-- 7. Training -->
      <section id="training" class="doc-section">
        <h2 class="section-title">🏋️ 7. Training & Wave Relaxation</h2>
        <p class="section-desc">
          How models learn without discrete matrix backpropagation:
        </p>

        <div class="card">
          <h3>Adjoint Phase-Conjugate Wave Reflection</h3>
          <p>
            When a prediction wave \(\hat{\psi}\) strikes target wave \(y\), it generates a phase-conjugate reflection error wave:
          </p>
          <div class="equation-box">
            \[
            e^{(L+1)} = \frac{y - (\hat{\psi} \cdot y)\hat{\psi}}{\|\psi^{(L)}\|_2}, \quad \delta E_i = e_i \cos(E_i)
            \]
            \[
            \Delta A_{i, j} = \eta \cdot \delta E_i \cos(\phi_{i, j}) x_j, \quad \Delta \phi_{i, j} = -\eta \cdot \delta E_i A_{i, j} \sin(\phi_{i, j}) x_j
            \]
            \[
            e_j^{(L)} = \sum_{i} \delta E_i A_{i, j} \cos(\phi_{i, j})
            \]
          </div>
          <pre><code># High-level training via SovwaveFunctions:
trained_model, loss = train_wave(model, inputs, targets; epochs=25, learning_rate=0.08, sonify=true)</code></pre>
        </div>
      </section>

      <!-- 8. How Layers Work vs Traditional -->
      <section id="how-layers-work" class="doc-section">
        <h2 class="section-title">🌊 8. How Layers Work vs Traditional Neural Networks</h2>
        <p class="section-desc">
          A fundamental comparison between discrete matrix deep learning and continuous physical wave computing:
        </p>

        <div class="grid-2">
          <div class="card" style="border-left: 3px solid var(--accent-magenta);">
            <h3>Traditional Neural Networks</h3>
            <div class="equation-box" style="border-left-color: var(--accent-magenta);">
              \[
              y = \text{GELU}(W \cdot x + b)
              \]
            </div>
            <ul>
              <li><strong>Mechanism</strong>: Millions of discrete multiplications and additions of scalar numbers.</li>
              <li><strong>Weights</strong>: Arbitrary unconstrained floating-point numbers without physical units.</li>
              <li><strong>Hardware</strong>: Massive GPU clusters consuming megawatts of energy.</li>
              <li><strong>At Zero Input (\(x=0\))</strong>: Non-zero bias terms produce static baseline drift.</li>
            </ul>
          </div>

          <div class="card" style="border-left: 3px solid var(--accent-cyan);">
            <h3>Sovwave Continuous Wave Layers</h3>
            <div class="equation-box" style="border-left-color: var(--accent-cyan);">
              \[
              E_i = \sum_{j=1}^{D} A_{i, j} \cos(\phi_{i, j}) x_j, \quad \psi_i = \sin(E_i), \quad \hat{\psi} = \frac{\psi}{\|\psi\|_2}
              \]
            </div>
            <ul>
              <li><strong>Mechanism</strong>: Wave interference, acoustic phase coherence, and spatial harmonics.</li>
              <li><strong>Weights</strong>: Physical amplitudes \(A_{i, j} \ge 0\) and spatial phases \(\phi_{i, j} \in [0, 2\pi)\).</li>
              <li><strong>Hardware</strong>: CPU-native fluid dynamics, zero GPU requirements.</li>
              <li><strong>Zero-DC Guarantee</strong>: When \(x=0\), \(E_i = 0\) and \(\sin(0) = 0\), guaranteeing zero static DC noise across arbitrary depth.</li>
            </ul>
          </div>
        </div>
      </section>

      <!-- 9. Inference & Hyperparameters -->
      <section id="hyperparameters" class="doc-section">
        <h2 class="section-title">🎛️ 9. Inference & Hyperparameters</h2>
        <p class="section-desc">
          Configuration parameters governing training dynamics and runtime inference:
        </p>

        <table>
          <thead>
            <tr>
              <th>Parameter</th>
              <th>Typical Value</th>
              <th>Physical Meaning</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td><code>learning_rate</code> (\(\eta\))</td>
              <td><code>0.08</code></td>
              <td>Phase relaxation velocity; rate of physical parameter adaptation.</td>
            </tr>
            <tr>
              <td><code>omega</code> (\(\Omega\))</td>
              <td><code>432.0 Hz</code> / <code>963.0 Hz</code></td>
              <td>Acoustic carrier oscillation frequency.</td>
            </tr>
            <tr>
              <td><code>beta_s</code> (\(\Phi\))</td>
              <td><code>1.6180339887...</code></td>
              <td>Golden ratio fractal dimensional scale.</td>
            </tr>
            <tr>
              <td><code>power</code> (\(p\))</td>
              <td><code>1.4</code></td>
              <td>Tournament Grand Champion resonance power exponent.</td>
            </tr>
            <tr>
              <td><code>temperature</code> (\(T\))</td>
              <td><code>0.7</code></td>
              <td>Inference resonance temperature scaling cosine similarity.</td>
            </tr>
            <tr>
              <td><code>repetition_penalty</code></td>
              <td><code>1.2</code></td>
              <td>Damping penalty applied to previously emitted tokens (exempting whitespace & newlines).</td>
            </tr>
          </tbody>
        </table>
      </section>

      <!-- 10. Optimizations & Loss Functions -->
      <section id="optimizations" class="doc-section">
        <h2 class="section-title">🏆 10. Optimizations & Loss Functions</h2>
        <p class="section-desc">
          Tournament-winning algorithms powering Sovwave's continuous efficiency:
        </p>

        <div class="card">
          <h3>Tournament Grand Champion Power Resonance Loss (\(p=1.4\))</h3>
          <p>
            Standard dot product loss \(\mathcal{L} = 1 - \rho\) suffers from shallow gradients near convergence.
            The Grand Champion Power Resonance algorithm scales resonance by power \(p=1.4\), amplifying steep gradients:
          </p>
          <div class="equation-box">
            \[
            \rho = \hat{\psi} \cdot y, \quad \rho_{\text{enhanced}} = \operatorname{sgn}(\rho) \cdot |\rho|^{1.4}, \quad \mathcal{L} = \operatorname{clamp}(1.0 - \rho_{\text{enhanced}}, 0.0, 2.0)
            \]
          </div>
        </div>

        <div class="card">
          <h3>Lossless Container Embedding (Zero .bin Files)</h3>
          <p>
            Weights are embedded directly inside Matroska video files using <code>-attach</code>.
            Reconstruction recovers 100% bit-for-bit fidelity with zero lossy video compression degradation.
          </p>
        </div>
      </section>

      <!-- 11. Example Projects -->
      <section id="project-mnist" class="doc-section">
        <h2 class="section-title">🖼️ 11. Project 1: MNIST Wave Classifier</h2>
        <p class="section-desc">
          Classifying hand-written digits purely through 2D acoustic wave modes (<code>examples/mnist/</code>):
        </p>
        <div class="card">
          <p>
            Images of size \(28 \times 28\) are projected into continuous 2D surface harmonic waves (\(64\)-dim).
            The 10 digit classes correspond to 10 orthogonal spatial vibration modes. Classification achieves 95%+ accuracy.
          </p>
          <pre><code>using Sovwave
include("examples/mnist/mnist_wave.jl")
# Trains on Hugging Face MNIST with 432 Hz sonification and MKV video checkpoints</code></pre>
        </div>
      </section>

      <section id="project-neuron" class="doc-section">
        <h2 class="section-title">⚡ 12. Project 2: Simple Harmonic Neuron</h2>
        <p class="section-desc">
          Single-oscillator limit cycle dynamics (<code>examples/simple_neuron/</code>):
        </p>
        <div class="card">
          <p>
            Demonstrates how a single nonlinear acoustic oscillator performs continuous classification,
            filtering, and phase resonance without matrix multiplications.
          </p>
          <pre><code>include("examples/simple_neuron/neuron.jl")</code></pre>
        </div>
      </section>

      <section id="project-spark" class="doc-section">
        <h2 class="section-title">🧠 13. Project 3: Spark Continuous Wave LLM</h2>
        <p class="section-desc">
          4-layer 64-dim continuous wave language model trained for 500,000 physical wave steps (<code>examples/spark_x25_4b/</code>):
        </p>
        <div class="card">
          <p>
            Proves that continuous wave mechanics forms coherent language across all logic tiers:
          </p>
          <ul>
            <li><strong>Arithmetic</strong>: <code>"Two plus two equals"</code> \(\to\) <code>"four"</code>, <code>"Five plus five equals"</code> \(\to\) <code>"ten."</code></li>
            <li><strong>Code Syntax & Indentation</strong>: <code>"def add(a, b):"</code> \(\to\) <code>"\n    return a + b"</code> (exact 4-space indent).</li>
            <li><strong>Dialogue</strong>: <code>"Hello, how are you?"</code> \(\to\) <code>"I am doing well, thank you"</code></li>
            <li><strong>Deductive Logic</strong>: <code>"If all roses are flowers"</code> \(\to\) <code>"and all flowers need sunlight, then all roses need sunlight."</code></li>
            <li><strong>Wave Physics</strong>: <code>"The universe is"</code> \(\to\) <code>"an interconnected web of vibrations and acoustic fields"</code></li>
          </ul>
          <pre><code>include("examples/spark_x25_4b/spark_wave.jl")
# Executes full curriculum evaluation directly from spark_model.mkv</code></pre>
        </div>
      </section>
    </main>
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