docs: new tutorials - #38
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Rework the "Introduction to HDC" tutorial to teach the operations through a cooking metaphor: map ingredient emojis to hypervectors, then mix (bundle), associate (bind), and order (permute) them to "cook" a taco, a hamburger, and a chicken club sandwich. Uses BinaryHV throughout, aliased to H so the whole tutorial can be rerun in another VSA by changing one line. Covers similarity (Jaccard baseline), the multiset/bundlesequence/ hashtable encoders, ambiguous ingredients via superposition, role querying, and cross-dish analogy. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Fix the Iris example so it compiles and renders end-to-end: - add the missing `using Statistics` (mean) and the Statistics dep - write in-body comments in `traintest` as `##` so Literate keeps them in the code block (indented `#` comments were splitting the function across @example blocks, causing ParseError/undefined-variable cascades) - flesh out the narrative (level encoding rationale, encode/decode round-trip, few-shot framing) and fix the empty API reference link - add callouts/links to the functions used (hashtable, level, encodelevel, decodelevel, convertlevel, nearest_neighbor) Also fix malformed @docs fences in api.md (the code-fence and @docs tag were transposed), so docstrings register and @ref links resolve. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Rework the intro tutorial's mapping section so each ingredient is its own
random hypervector bound directly to an emoji variable (🥩, 🧀, 🧅, ...),
used consistently throughout the bundling/binding/permutation demos, the
taco/burger/sandwich recipes, and the algebra queries.
Drop the "hypervectors are seeded from their emoji" framing from the main
narrative (ingredients are now plain random draws) and instead present
seeding as a dedicated "Seeding hypervectors" tip callout, noting it is
how you get reproducible / object-determined vectors (H("🥩"), H(:beef)).
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July 13, 2026 14:03
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@MichielStock Can you check the tutorials? I implemented a "cooking with HDC" introductory tutorial and a classical Iris dataset prediction tutorial (to highlight continuous encodings mainly) |
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Partly carried into #66 — thanks @cvigilv. #66 is based on For the record on |
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This PR contains the new tutorials found in the documentation, based on #32.
Closes #32
Closes #36