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  1. <!DOCTYPE html>
  2. <html lang="en">
  3. <head>
  4.     <meta charset="UTF-8">
  5.     <meta name="viewport" content="width=device-width, initial-scale=1.0">
  6.     <title>LOG // X-LINGUAL ALIGNMENT // EN-HI // PROCRUSTES</title>
  7.     <style>
  8.         /* --- Caves of Qud Inspired Terminal Style --- */
  9.  
  10.         /* Color Palette Variables (Based on Code list) */
  11.         :root {
  12.             --coq-r: #a64a2e; /* dark red */
  13.             --coq-R: #d74200; /* red */
  14.             --coq-o: #f15f22; /* dark orange */
  15.             --coq-O: #e99f10; /* orange */
  16.             --coq-w: #98875f; /* brown */
  17.             --coq-W: #cfc041; /* gold/yellow */
  18.             --coq-g: #009403; /* dark green */
  19.             --coq-G: #00c420; /* green */
  20.             --coq-b: #0048bd; /* dark blue */
  21.             --coq-B: #0096ff; /* blue */
  22.             --coq-c: #40a4b9; /* dark cyan */
  23.             --coq-C: #77bfcf; /* cyan */
  24.             --coq-m: #b154cf; /* dark magenta */
  25.             --coq-M: #da5bd6; /* magenta */
  26.             --coq-k: #0f3b3a; /* dark black/grey */
  27.             --coq-K: #155352; /* dark grey/black */
  28.             --coq-y: #b1c9c3; /* grey */
  29.             --coq-Y: #ffffff; /* white */
  30.             --coq-true-black: #000000;
  31.  
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  34.             --glow-magenta: 0 0 3px var(--coq-M), 0 0 5px var(--coq-m), 0 0 7px var(--coq-b);
  35.             --glow-green: 0 0 3px var(--coq-G), 0 0 5px var(--coq-g), 0 0 7px var(--coq-k);
  36.             --glow-yellow: 0 0 3px var(--coq-W), 0 0 5px var(--coq-O), 0 0 7px var(--coq-w);
  37.             --glow-red: 0 0 3px var(--coq-R), 0 0 5px var(--coq-o), 0 0 7px var(--coq-r);
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  139.  
  140.  
  141.         /* Code Styling */
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  144.             background-color: var(--coq-K); /* Dark Grey bg */
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  148.             color: var(--coq-o); /* Dark Orange text */
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  151.  
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  153.             background-color: var(--coq-true-black); /* True Black bg */
  154.             color: var(--coq-C); /* Cyan text */
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  164.  
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  172.  
  173.         /* Links */
  174.         a {
  175.             color: var(--coq-B); /* Bright Blue */
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  177.             text-decoration-color: var(--coq-b);
  178.             transition: all 0.2s ease-in-out;
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  180.  
  181.         a:hover {
  182.             color: var(--coq-C); /* Cyan on hover */
  183.             background-color: rgba(64, 164, 185, 0.1); /* Faint cyan bg */
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  187.  
  188.         /* Tables */
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  192.             margin: 25px 0;
  193.             font-size: 0.95em;
  194.             border: 1px solid var(--coq-c); /* Dark Cyan border */
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  197.  
  198.         th, td {
  199.             border: 1px dashed var(--coq-K); /* Dark Grey dashed lines */
  200.             padding: 10px 14px;
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  202.             vertical-align: top; /* Align text top */
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  204.  
  205.         th {
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  207.             color: var(--coq-C); /* Cyan header text */
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  211.  
  212.         tr:nth-child(even) td {
  213.             background-color: rgba(15, 59, 58, 0.3); /* Very faint Dark Black bg for even rows */
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  215.         tr:hover td {
  216.              background-color: rgba(119, 191, 207, 0.1); /* Faint Cyan hover */
  217.              color: var(--coq-Y);
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  219.  
  220.         td code { /* Make code in tables slightly different */
  221.              color: var(--coq-R); /* Red code in tables */
  222.              border-color: var(--coq-r);
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  224.  
  225.         /* Step Cards */
  226.         .step-card {
  227.             border: 1px solid var(--coq-m); /* Dark Magenta border */
  228.             padding: 18px;
  229.             margin-bottom: 20px;
  230.             border-radius: 0; /* Sharp corners */
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  234.         .step-card h3 {
  235.             margin-top: 0;
  236.             color: var(--coq-M); /* Keep Magenta */
  237.             border-bottom: 1px dotted var(--coq-m);
  238.             padding-bottom: 5px;
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  240.  
  241.         /* Footer */
  242.         footer {
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  244.             margin-top: 40px;
  245.             padding: 15px;
  246.             border-top: 1px solid var(--coq-g); /* Dark Green top border */
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  248.             color: var(--coq-G); /* Green text */
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  250.         footer::before, footer::after {
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  252.             color: var(--coq-g); /* Dark Green slashes */
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  254.  
  255.         /* Keyword Spans (will be generated by JS) */
  256.         .keyword span {
  257.             /* Add transition for potential future hover effects */
  258.             /* transition: text-shadow 0.2s ease-in-out; */
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  260.          /* Add a subtle glow to keywords */
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  262.              text-shadow: 0 0 4px rgba(255, 255, 255, 0.1);
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  264.  
  265.  
  266.         /* Responsive Design */
  267.         @media (max-width: 768px) {
  268.             body { font-size: 15px; }
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  274.             h1 { font-size: 1.8em; }
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  283.  
  284.     </style>
  285. </head>
  286. <body>
  287.     <div class="container">
  288.         <h1>X-Lingual Vector Space Alignment // PROC LOG</h1>
  289.  
  290.         <section id="background">
  291.             <h2>Initiate // Context Sync</h2>
  292.             <p>
  293.                 <span class="keyword" data-template="hologram">Word embeddings</span> manifest as numerical vectors, encoding semantic resonance. Proximal vectors imply analogous meanings. Standard methodologies yield discrete vector manifolds per language substrate (e.g., English, Hindi).
  294.             </p>
  295.             <p>
  296.                 <span class="keyword" data-template="prismatic">Cross-lingual word embeddings</span> endeavor to reconcile these disparate spaces, projecting vectors into a unified continuum. Within this shared manifold, translation cognates (e.g., "<span class="keyword" data-template="rainbow">cat</span>" :: "<span class="keyword" data-template="qon">बिल्ली</span>") should exhibit high vector congruence. This paradigm unlocks potential for <span class="keyword" data-template="zetachrome">multilingual NLP</span> strata: machine translation, x-lingual retrieval, knowledge transference.
  297.             </p>
  298.         </section>
  299.  
  300.         <section id="objective">
  301.             <h2>Directive // Project Goal</h2>
  302.             <p>
  303.                 Primary objective: Implement and scrutinize a <span class="keyword" data-template="overloaded">supervised</span> methodology for aligning monolingual English & Hindi vector spaces. Employed technique: The <span class="keyword" data-template="psychalflesh">Procrustes</span> algorithm, learning a linear transformation matrix via a <span class="keyword" data-template="patchwork">bilingual lexicon</span>.
  304.            </p>
  305.        </section>
  306.  
  307.        <section id="methodology">
  308.            <h2>Algorithm // Procrustes Alignment Protocol</h2>
  309.            <p>
  310.                The <span class="keyword" data-template="metamorphic">Procrustes</span> problem seeks an optimal <span class="keyword" data-template="crystalline">orthogonal linear transformation</span> matrix <code>W</code>, mapping source vectors (English, <code>X</code>) to maximally approximate target vectors (Hindi, <code>Y</code>), anchored by known translation pairings from the lexicon.
  311.            </p>
  312.            <p>
  313.                Optimization target: Minimize the squared Frobenius norm divergence:
  314.            </p>
  315.            <pre><code class="language-math">argmin ||XW - Y||²   s.t. WᵀW = I</code></pre>
  316.            <p>
  317.                Here, <code>X</code>, <code>Y</code> represent matrices of embeddings for anchor pairs. Constraint <code>WᵀW = I</code> enforces <span class="keyword" data-template="phase-harmonic">orthogonality</span>, preserving Euclidean distances and relative vector orientations – the internal geometry of the source space remains invariant post-transformation.
  318.            </p>
  319.            <p>
  320.                Analytic solution achieved via <span class="keyword" data-template="mercurial">Singular Value Decomposition (SVD)</span>. Compute SVD of <code>YᵀX</code> as <code>UΣVᵀ</code>. The optimal orthogonal map <code>W</code> emerges as:
  321.            </p>
  322.            <pre><code class="language-math">W = UVᵀ</code></pre>
  323.            <p>
  324.                Learned matrix <code>W</code>, derived from the training lexicon, is then broadcast across *all* source language vectors, projecting them into the shared alignment space: <code>X_aligned = X_source @ W</code>.
  325.            </p>
  326.        </section>
  327.  
  328.        <section id="implementation">
  329.            <h2>Execution Log // Pipeline Steps</h2>
  330.            <p>Systematic procedure enacted via <span class="keyword" data-template="agolgot">Python</span>:</p>
  331.  
  332.            <div class="step-card">
  333.                <h3>1. Data Ingestion & Prep</h3>
  334.                <ul>
  335.                    <li>Acquired initial 10k article fragments per language substrate (EN:<code>20220301.en</code>, HI:<code>20220301.hi</code>) from <span class="keyword" data-template="starry">Wikipedia</span> archives via Hugging Face <code>datasets</code> interface.</li>
  336.                    <li>Applied text normalization routines (case folding, non-alphanumeric purge) -> temporary data streams.</li>
  337.                    <li>Retrieved EN-HI bilingual lexicon (<a href="https://github.com/facebookresearch/MUSE#ground-truth-bilingual-dictionaries" target="_blank" rel="noopener noreferrer"><span class="keyword" data-template="bethsaida">MUSE</span></a> artifact), bifurcated into train/test partitions.</li>
  338.                </ul>
  339.            </div>
  340.  
  341.            <div class="step-card">
  342.                <h3>2. Monolingual Vector Synthesis</h3>
  343.                <ul>
  344.                    <li>Synthesized 300-D <span class="keyword" data-template="plasma">FastText</span> (skipgram config) models independently for EN & HI streams.</li>
  345.                    <li>Parameters: <code>minCount</code>=5, <code>epochs</code>=5.</li>
  346.                    <li>Extracted vector representations for top 100k lexical items per language manifold based on frequency heuristics.</li>
  347.                </ul>
  348.            </div>
  349.  
  350.             <div class="step-card">
  351.                <h3>3. Vector Space Conditioning</h3>
  352.                <ul>
  353.                    <li>Applied standard pre-alignment vector conditioning:
  354.                        <ol>
  355.                           <li><span class="keyword" data-template="ironshank">Mean Centering:</span> Nullified mean vector bias within each set.</li>
  356.                           <li><span class="keyword" data-template="refractive">Unit Length Normalization:</span> Scaled all vectors to L2 norm = 1.</li>
  357.                        </ol>
  358.                        Critical for Procrustes stability and cosine similarity validity.
  359.                    </li>
  360.                </ul>
  361.            </div>
  362.  
  363.            <div class="step-card">
  364.                <h3>4. Procrustes Transformation Learning</h3>
  365.                <ul>
  366.                    <li>Loaded MUSE training lexicon, filtering pairs absent from the top-100k vocabulary strata.</li>
  367.                    <li>Constructed parallel matrices <code>X_train</code>, <code>Y_train</code> from normalized vectors corresponding to filtered lexicon entries.</li>
  368.                    <li>Computed optimal orthogonal transformation <code>W</code> via <span class="keyword" data-template="ubernostrum">SVD</span> solution (<code>W = UVᵀ</code> where <code>UΣVᵀ = SVD(Y_trainᵀ @ X_train)</code>).</li>
  369.                    <li>Projected the entire normalized English vector space using the learned map: <code>embeddings_src_aligned = embeddings_src_norm @ W</code>.</li>
  370.                </ul>
  371.            </div>
  372.  
  373.            <div class="step-card">
  374.                <h3>5. Alignment Quality Assessment</h3>
  375.                <ul>
  376.                    <li><strong><span class="keyword" data-template="fiery">Word Translation Task</span>:</strong>
  377.                        <ul>
  378.                            <li>Utilized filtered MUSE test lexicon.</li>
  379.                            <li>For each EN source term, retrieved its aligned vector representation.</li>
  380.                            <li>Computed <span class="keyword" data-template="watery">cosine similarity</span> against *all* normalized HI target vectors.</li>
  381.                            <li>Identified Top-1/Top-5 HI nearest neighbors based on similarity maxima.</li>
  382.                            <li>Calculated <span class="keyword" data-template="sunslag">Precision@1 (P@1)</span> & <span class="keyword" data-template="sunslag">Precision@5 (P@5)</span>: % concordance of predicted neighbors with ground-truth translation within k={1, 5}.</li>
  383.                        </ul>
  384.                    </li>
  385.                    <li><strong><span class="keyword" data-template="chiral">Semantic Congruence Probe</span>:</strong>
  386.                        <ul>
  387.                            <li>Calculated cosine similarities for curated pairs (translations, related concepts, unrelated concepts) between aligned EN vectors and original HI vectors for qualitative validation.</li>
  388.                        </ul>
  389.                    </li>
  390.                    <li><strong><span class="keyword" data-template="metachrome">Ablation Analysis (Dictionary Size)</span>:</strong>
  391.                        <ul>
  392.                            <li>Re-executed alignment pipeline (Steps 4 & 5) with incrementally smaller subsets of the training lexicon (e.g., 500, 1k, 2k, 5k pairs).</li>
  393.                            <li>Tracked P@1/P@5 sensitivity to the volume of supervisory signal (lexicon size).</li>
  394.                        </ul>
  395.                    </li>
  396.                </ul>
  397.            </div>
  398.        </section>
  399.  
  400.        <section id="results">
  401.            <h2>Output // Assessment Metrics</h2>
  402.  
  403.            <h3>Word Translation Accuracy (Full Training Lexicon)</h3>
  404.            <p>Alignment efficacy derived from complete filtered MUSE training data, evaluated on test partition:</p>
  405.            <!-- NOTE: Replace these placeholder values with your actual results -->
  406.            <table>
  407.                <thead>
  408.                    <tr>
  409.                        <th>Metric</th>
  410.                        <th>Score (%)</th>
  411.                    </tr>
  412.                </thead>
  413.                <tbody>
  414.                    <tr>
  415.                        <td>Precision@1</td>
  416.                        <td><span class="keyword" data-template="rocket">XX.XX</span> %</td> <!-- Placeholder -->
  417.                    </tr>
  418.                    <tr>
  419.                        <td>Precision@5</td>
  420.                        <td><span class="keyword" data-template="rocket">YY.YY</span> %</td> <!-- Placeholder -->
  421.                    </tr>
  422.                </tbody>
  423.            </table>
  424.            <p><em>(System Note: Values contingent on vector synthesis quality & vocab/lexicon overlap. Placeholders active.)</em></p>
  425.  
  426.  
  427.            <h3>Cross-Lingual Semantic Similarity Samples</h3>
  428.            <p>Cosine similarity readouts between selected aligned-EN :: original-HI vector pairs:</p>
  429.            <!-- NOTE: Replace these placeholder values with your actual results -->
  430.            <table>
  431.                <thead>
  432.                    <tr>
  433.                        <th>EN Term (Aligned)</th>
  434.                        <th>HI Term (Original)</th>
  435.                        <th>Cosine Sim.</th>
  436.                        <th>Expected Relation</th>
  437.                    </tr>
  438.                </thead>
  439.                <tbody>
  440.                    <tr>
  441.                        <td><code>king</code></td>
  442.                        <td><code>राजा</code></td>
  443.                        <td><span class="keyword" data-template="lovesickness">0.XXX</span></td> <!-- Placeholder -->
  444.                        <td>Translation</td>
  445.                    </tr>
  446.                     <tr>
  447.                        <td><code>queen</code></td>
  448.                        <td><code>रानी</code></td>
  449.                        <td><span class="keyword" data-template="lovesickness">0.XXX</span></td> <!-- Placeholder -->
  450.                        <td>Translation</td>
  451.                    </tr>
  452.                     <tr>
  453.                        <td><code>water</code></td>
  454.                        <td><code>पानी</code></td>
  455.                        <td><span class="keyword" data-template="watery">0.XXX</span></td> <!-- Placeholder -->
  456.                        <td>Translation</td>
  457.                    </tr>
  458.                    <tr>
  459.                        <td><code>king</code></td>
  460.                        <td><code>queen</code></td>
  461.                        <td><span class="keyword" data-template="amorous">0.XXX</span></td> <!-- Placeholder -->
  462.                        <td>Related (EN)</td>
  463.                    </tr>
  464.                     <tr>
  465.                        <td><code>राजा</code></td>
  466.                        <td><code>रानी</code></td>
  467.                        <td><span class="keyword" data-template="amorous">0.XXX</span></td> <!-- Placeholder -->
  468.                        <td>Related (HI via aligned EN)</td>
  469.                    </tr>
  470.                    <tr>
  471.                        <td><code>king</code></td>
  472.                        <td><code>पानी</code></td>
  473.                        <td><span class="keyword" data-template="glotrot">0.XXX</span></td> <!-- Placeholder -->
  474.                        <td>Unrelated</td>
  475.                    </tr>
  476.                </tbody>
  477.            </table>
  478.             <p>Elevated similarity scores for cognates/related concepts signal successful spatial reconciliation.</p>
  479.        </section>
  480.  
  481.        <section id="ablation">
  482.            <h2>Sensitivity Analysis // Lexicon Size Impact</h2>
  483.            <p>
  484.                Investigating alignment fidelity dependence on supervisory signal volume (training pair count). Measured via word translation accuracy on static test set.
  485.            </p>
  486.            <!-- NOTE: Replace these placeholder values with your actual results -->
  487.            <table>
  488.                 <thead>
  489.                    <tr>
  490.                        <th>Training Pairs</th>
  491.                        <th>Precision@1 (%)</th>
  492.                        <th>Precision@5 (%)</th>
  493.                    </tr>
  494.                </thead>
  495.                <tbody>
  496.                    <tr>
  497.                        <td>500</td>
  498.                        <td><span class="keyword" data-template="nervous">AA.AA</span> %</td> <!-- Placeholder -->
  499.                        <td><span class="keyword" data-template="nervous">BB.BB</span> %</td> <!-- Placeholder -->
  500.                    </tr>
  501.                     <tr>
  502.                        <td>1000</td>
  503.                        <td><span class="keyword" data-template="gaslight">CC.CC</span> %</td> <!-- Placeholder -->
  504.                        <td><span class="keyword" data-template="gaslight">DD.DD</span> %</td> <!-- Placeholder -->
  505.                    </tr>
  506.                     <tr>
  507.                        <td>2000</td>
  508.                        <td><span class="keyword" data-template="brainbrine">EE.EE</span> %</td> <!-- Placeholder -->
  509.                        <td><span class="keyword" data-template="brainbrine">FF.FF</span> %</td> <!-- Placeholder -->
  510.                    </tr>
  511.                     <tr>
  512.                        <td>5000 (Full)</td>
  513.                        <td><span class="keyword" data-template="overloaded">XX.XX</span> %</td> <!-- Placeholder -->
  514.                        <td><span class="keyword" data-template="overloaded">YY.YY</span> %</td> <!-- Placeholder -->
  515.                    </tr>
  516.                </tbody>
  517.            </table>
  518.            <p>
  519.                Observation: Augmenting training pairs generally enhances alignment quality, yielding superior translation metrics, though returns may diminish asymptotically.
  520.            </p>
  521.        </section>
  522.  
  523.        <section id="tools">
  524.            <h2>Toolkit // Libraries & Artifacts</h2>
  525.            <ul>
  526.                <li><strong><span class="keyword" data-template="pythonic">Python 3</span>:</strong> Core execution environment.</li>
  527.                <li><strong><span class="keyword" data-template="plasma">FastText</span>:</strong> Monolingual vector synthesis engine.</li>
  528.                <li><strong>Hugging Face <code>datasets</code>:</strong> Corpus acquisition interface (<span class="keyword" data-template="starry">Wikipedia</span>).</li>
  529.                <li><strong><span class="keyword" data-template="nanotech">NumPy</span>:</strong> N-dimensional array manipulation & linear algebra substrate.</li>
  530.                <li><strong><span class="keyword" data-template="qon">SciPy</span>:</strong> <span class="keyword" data-template="mercurial">SVD</span> computation module (<code>linalg.svd</code>).</li>
  531.                <li><strong><span class="keyword" data-template="psionic">Scikit-learn</span>:</strong> Cosine similarity utility (alternative implementation).</li>
  532.                <li><strong><span class="keyword" data-template="webclient">Requests</span>:</strong> HTTP retrieval agent (MUSE lexicon).</li>
  533.                <li><strong><span class="keyword" data-template="bethsaida">MUSE Bilingual Dictionaries</span>:</strong> Ground-truth translation pairs (supervisory signal).</li>
  534.            </ul>
  535.        </section>
  536.  
  537.         <section id="conclusion">
  538.            <h2>Termination // Synopsis</h2>
  539.            <p>
  540.                Project successfully deployed the <span class="keyword" data-template="psychalflesh">Procrustes</span> supervised alignment protocol onto EN/HI <span class="keyword" data-template="plasma">FastText</span> vectors derived from <span class="keyword" data-template="starry">Wikipedia</span> data. Learning an <span class="keyword" data-template="crystalline">orthogonal transformation</span> from a bilingual lexicon yielded a unified vector manifold exhibiting proximity between translation cognates. Efficacy validated via word translation metrics (P@1, P@5) and semantic similarity probes. <span class="keyword" data-template="metachrome">Ablation analysis</span> confirmed positive correlation between supervisory lexicon scale and alignment robustness. System achieved specified objectives.
  541.            </p>
  542.        </section>
  543.  
  544.        <footer>
  545.            Alignment Protocol Log // End Transmission
  546.        </footer>
  547.    </div>
  548.  
  549.    <script>
  550.      document.addEventListener('DOMContentLoaded', () => {
  551.        // --- CoQ Color Definitions ---
  552.        const colors = {
  553.            'r': '#a64a2e', 'R': '#d74200', 'o': '#f15f22', 'O': '#e99f10',
  554.            'w': '#98875f', 'W': '#cfc041', 'g': '#009403', 'G': '#00c420',
  555.            'b': '#0048bd', 'B': '#0096ff', 'c': '#40a4b9', 'C': '#77bfcf',
  556.            'm': '#b154cf', 'M': '#da5bd6', 'k': '#0f3b3a', 'K': '#155352',
  557.            'y': '#b1c9c3', 'Y': '#ffffff'
  558.        };
  559.  
  560.         // --- CoQ Template Definitions (Subset) ---
  561.         const templates = {
  562.             'hologram': { colors: 'b-B-C-c', type: 'sequence' },
  563.             'ydfreehold': { colors: 'r-R-k-c-C-W-W-C-c-r-R', type: 'sequence' },
  564.             'purple': { colors: 'm', type: 'sequence' }, // Effectively solid
  565.             'paisley': { colors: 'm-M-Y-M-m', type: 'sequence' },
  566.             'biomech': { colors: 'w-w-r-r-r-w-r-r', type: 'sequence' },
  567.             'rainbow': { colors: 'r-R-W-G-B-b-m', type: 'alternation' }, // Use alternation for more variance
  568.             'important': { colors: 'W', type: 'sequence' }, // Solid Gold/Yellow
  569.             'metamorphic': { colors: 'y-y-y-Y-Y-Y-M-M-M-m-m-m-m', type: 'sequence' },
  570.             'ubernostrum': { colors: 'c-g-G-W-w-c-C-G-g-w-W', type: 'sequence' },
  571.             'rocket': { colors: 'Y-W-R-R-r-y', type: 'alternation' },
  572.             'visage': { colors: 'R-r-b-B-Y-y', type: 'sequence' },
  573.             'dreamsmoke': { colors: 'b-b-b-b-y-Y-Y-W-w-b-b-b', type: 'sequence' },
  574.             'polarized': { colors: 'K-y-Y-y-K-y-Y-y-K', type: 'alternation' },
  575.             'ironshank': { colors: 'K-y-Y-y', type: 'sequence' },
  576.             'dark fiery': { colors: 'r-R-W-R-r', type: 'alternation' },
  577.             'bethsaida': { colors: 'w-W-C-c-m-c-C-W-w', type: 'sequence' },
  578.             'plasma': { colors: 'g-G-Y-Y-G-g', type: 'sequence' },
  579.             'prismatic': { colors: 'r-R-W-G-B-b-m', type: 'sequence' },
  580.             'lovesickness': { colors: 'r-R-M-m-r-R-M', type: 'sequence' },
  581.             'fiery': { colors: 'R', type: 'sequence' }, // Solid Red
  582.             'qon': { colors: 'm-b-B', type: 'sequence' },
  583.             'agolgot': { colors: 'K-g-w-m-w-g-K', type: 'sequence' },
  584.             'zetachrome': { colors: 'm-M-Y-C-c-c-C-Y-M-m', type: 'alternation' },
  585.             'watery': { colors: 'B-C-Y-C-B', type: 'alternation' },
  586.             'psychalflesh': { colors: 'w-w-w-r-R-M-M-m-M-M-R-r-w-w-w-w', type: 'sequence' },
  587.             'starry': { colors: 'K-Y-K-K-Y-K', type: 'sequence' },
  588.             'blaze': { colors: 'r-r-R-W-Y', type: 'sequence' },
  589.             'amorous': { colors: 'r-R-M-m', type: 'alternation' },
  590.             'mercurial': { colors: 'c-c-C-W-Y-W-C-c-c', type: 'alternation' },
  591.             'shade': { colors: 'y-K-c-b-B-y-C-y-K', type: 'sequence' },
  592.             'crystalline': { colors: 'm-m-m-b-B-Y-B-b-m-m-m', type: 'sequence' },
  593.             'phase-harmonic': { colors: 'Y-y-m-y-K', type: 'sequence' },
  594.             'refractive': { colors: 'y-Y', type: 'sequence' },
  595.             'chiral': { colors: 'B-b-c-C-M-m-k-m-M-C-c-b', type: 'sequence' },
  596.             'patchwork': { colors: 'W-w-r-R-W-w-b-B-W', type: 'sequence' },
  597.             'overloaded': { colors: 'y-y-w-W-R-W-w-y-y', type: 'alternation' },
  598.             'sunslag': { colors: 'r-W-Y-Y-Y-W-r', type: 'sequence' },
  599.             'metachrome': { colors: 'w-W-Y-C-c-c-C-Y-W-w', type: 'alternation' },
  600.             'nanotech': { colors: 'K-K-y-K', type: 'sequence' },
  601.             'qon': { colors: 'm-b-B', type: 'sequence' }, // Hindi word example
  602.             'pythonic': { colors: 'B-b-W-y-Y-W', type: 'sequence'}, // For Python
  603.             'webclient': { colors: 'C-c-B-b-Y', type: 'alternation'}, // For Requests
  604.             'psionic': { colors: 'b-B-C-c-b-B-C', type: 'alternation'}, // Scikit-learn
  605.              'nervous': { colors: 'g-g-w-W-w-g-g', type: 'sequence'},
  606.              'gaslight': { colors: 'g-g-w-W-w-g-g', type: 'alternation'},
  607.              'brainbrine': { colors: 'g-g-g-w-W-W-W-w-g-g-g', type: 'sequence'},
  608.              'glotrot': { colors: 'K-K-r-R-r', type: 'sequence'},
  609.             // Add more templates if needed
  610.         };
  611.  
  612.         function applyColorTemplate(element, templateName) {
  613.             const template = templates[templateName] || templates['rainbow']; // Default to rainbow
  614.             if (!template) return; // Safety check
  615.  
  616.             const colorCodes = template.colors.split('-').map(code => colors[code] || colors['y']); // Get hex codes, default to grey
  617.             if (colorCodes.length === 0) return;
  618.  
  619.             const text = element.innerText;
  620.             const letters = text.split('');
  621.             let html = '';
  622.             let colorIndex = 0;
  623.  
  624.             letters.forEach((letter, i) => {
  625.                 if (letter.trim() === '') { // Keep spaces uncolored
  626.                     html += letter;
  627.                 } else {
  628.                     if (template.type === 'sequence') {
  629.                         colorIndex = i % colorCodes.length;
  630.                     } else if (template.type === 'alternation') {
  631.                          // Simple alternation cycle - can be made more complex
  632.                         colorIndex = i % colorCodes.length;
  633.                     } else { // Default to sequence if type is unknown
  634.                         colorIndex = i % colorCodes.length;
  635.                     }
  636.                     const color = colorCodes[colorIndex];
  637.                     html += `<span style="color: ${color};">${letter}</span>`;
  638.                 }
  639.             });
  640.  
  641.             element.innerHTML = html;
  642.         }
  643.  
  644.         // --- Apply Templates ---
  645.         const keywords = document.querySelectorAll('.keyword');
  646.         keywords.forEach(el => {
  647.             const templateName = el.dataset.template;
  648.             if (templateName) {
  649.                 applyColorTemplate(el, templateName);
  650.             } else {
  651.                 applyColorTemplate(el, 'rainbow'); // Apply default if no template specified
  652.             }
  653.         });
  654.  
  655.         // Add some dynamic effect (optional - e.g., slight glow pulse on h1)
  656.         const mainTitle = document.querySelector('h1');
  657.          if (mainTitle) {
  658.              // Example: simple pulse (can be much more elaborate)
  659.              // mainTitle.style.animation = 'pulseGlow 3s infinite alternate';
  660.          }
  661.          // Define pulse animation if used:
  662.          // @keyframes pulseGlow { from { text-shadow: var(--glow-cyan); } to { text-shadow: 0 0 5px var(--coq-C), 0 0 8px var(--coq-c), 0 0 12px var(--coq-b), 0 0 15px var(--coq-B); } }
  663.  
  664.       });
  665.     </script>
  666.  
  667. </body>
  668. </html>
  669.  
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