Datasets:

vykrum
/

hywe-training-data

	---
	license: cc-by-4.0
	pretty_name: HYWE Architectural Training Data
	language:
	- en
	tags:
	- architecture
	- spatial-design
	- graph
	- topology
	- design-computation
	- layout-generation
	- text2layout
	task_categories:
	- text-generation
	- feature-extraction
	size_categories:
	- n<1K
	homepage: https://github.com/vykrum/Hywe
	citation: \|
	@misc{hywe2026dataset,
	author = {Subbaiah, Vikram},
	title = {HYWE Architectural Training Data: A Structured Dataset of Procedural Architectural Programming and Topological Layouts},
	year = {2026},
	publisher = {Hugging Face},
	journal = {Hugging Face Repository},
	howpublished = {\url{https://huggingface.co/datasets/vykrum/hywe-training-data}}
	}
	---

	![HYWE Banner](https://vykrum.github.io/Hywe/images/hyweLogoBanner.png)

	---

	# HYWE Architectural Training Data

	[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/) [![Dataset: Hugging Face](https://img.shields.io/badge/Dataset-%F0%9F%A4%97%20Hugging%20Face-ffd21e)](https://huggingface.co/datasets/vykrum/hywe-training-data) [![Engine: HYWE](https://img.shields.io/badge/Engine-HYWE-654FF0.svg)](https://github.com/vykrum/Hywe) [![Generated By: F#](https://img.shields.io/badge/Generated%20By-F%23-30B0C7.svg)](https://fsharp.org/)

	A highly structured, synthetic dataset of procedural architectural programming, design intent, and topological layouts. This dataset is generated client-side by the [HYWE Core Engine](https://github.com/vykrum/Hywe) and curated via the Hynteract serverless pipeline to provide a robust, logic-driven substrate for machine learning models in architectural design (AEC) and spatial reasoning.

	* Ecosystem Repository: [github.com/vykrum/Hywe](https://github.com/vykrum/Hywe)
	* Interactive Sandbox: [hywe.in](https://hywe.in/)
	* Format: JSON Lines (`.jsonl`)

	---

	## Technical Essence & Philosophy

	Traditional generative ML models for architecture rely on heavy boundary representation formats (like OBJ, IFC, or DXF) or unstructured pixel matrices, which fail to capture functional spatial relationships.

	HYWE (Hygrid Woven Ensemble) models design space as a function of discrete computational logic. It rejects manual drafting and continuous geometric solvers in favor of a hybrid orthogonal-hexagonal grid (Hygrid). In this system, spatial adjacency is a direct mathematical consequence of defined architectural programming constraints rather than absolute geometric coordinates.

	This dataset provides the bridging data to train AI models that can generate deterministic topologies directly from linguistic design narratives.

	---

	### System Architecture Flow
	`Designer Intent` ➔ `HYWE Syntax` ➔ `Deterministic Topology` ➔ `Spatial Configuration` ➔ `Hynteract Structuring` ➔ `JSONL Dataset` ➔ `AI Training`

	---

	## Data Curation & Architecture Flow

	The data is captured through a closed-loop computational pipeline where the deterministic geometry logic of HYWE remains isolated from the probabilistic data pipeline of Hynteract.

	```mermaid
	graph TD
	A1[Interactive Node Tree Input] --> B[HYWE Syntax]
	A2[Interactive Boundary Editor] --> B[HYWE Syntax]
	B --> C(Lexel: Architectural Programming and Flow Parsing)
	C --> D(Hexel: Atomic Spatial Primitive)
	D --> E(Coxel: Simultaneously Evolving Hexel Clusters)
	E --> F(Xyxel: Coxel Configuration and Planar Layout)
	F --> G(Zaxel: Xyxel Stacking and Volumetric Massing)

	F --> F1[SVG Rendering]
	G --> G1[WebGPU Massing]

	F --> H[Spatial Analysis]
	F --> I[Batch Processing]

	I -.-> DatasetLabel((Hynteract: AI Dataset))
	DesignIntent[Design Intent Narrative] --> DatasetLabel
	```

	1. Procedural Variation: The designer builds or batch-generates structural arrangements within HYWE.
	2. Topological Compression: HYWE encodes the hierarchical spatial configurations into an ultra-dense Base34 alphanumeric token (`HYWE Syntax`).
	3. Pipeline Commitment: The Hynteract Vercel API endpoint securely captures the Base34 token, pairs it with the natural language description, and commits structured JSON Lines (`.jsonl`) files directly to this Hugging Face repository.

	---

	## The Computational Generation Pipeline

	Every spatial layout committed to this dataset is resolved using a zero-dependency, first-principles geometric compiler. Rather than relying on heuristic geometric optimization or probabilistic CAD models, the HYWE Core Engine compiles layout configurations deterministically through five functional stages:

	1. Lexel (Linguistic & Flow Parsing):
	* Role: Translates hierarchical trees and flow connections (defining circulation routes and adjacency rules) into active logical constraints.
	2. Hexel (Atomic Priming):
	* Role: Maps discrete space coordinates onto a hybrid orthogonal-hexagonal spatial lattice (Hygrid) using integer arithmetic, preventing floating-point coordinate drift.
	3. Coxel (Cluster Growth):
	* Role: Groups discrete hexels into simultaneously expanding clusters to represent coherent programmatic zones, rooms, or functional areas.
	4. Xyxel (Planar Subdivision):
	* Role: Subdivides, proportions, and fits the growing coxel boundaries within irregularity constraints to resolve a finalized 2D planar floor layout.
	5. Zaxel (Volumetric Stacking):
	* Role: Stacks planar layout matrices vertically to distribute programmatic rooms across multiple levels, resolving vertical circulation and massing constraints.

	---

	## Data Schema

	Each record is stored as a `.jsonl` file in the `data/` directory. The top-level structure per record:

	\| Field \| Type \| Description \|
	\| :--- \| :--- \| :--- \|
	\| `definition` \| `string` \| The single source of truth HYWE Syntax representation containing design constraints, block attributes, boundaries, and cell specs. Note: Contains a default active sequence query key in its `Q` attribute, but layout variations are not restricted to this query. \|
	\| `description` \| `string` \| A natural language spatial narrative detailing the designer's intent, scale, flow, and typology. \|
	\| `configuration` \| `string[]` \| An array of exactly 24 layout string variations, corresponding to the 24 procedural sequence sweeps. \|

	> [!NOTE]
	> Complete Layout Sweeps: Although the `definition` field contains a default sequence rule in its `Q` attribute (representing the active configuration at the time of export), the dataset itself is not restricted to that single configuration. The accompanying `configuration` field contains the resolved layout strings for all 24 possible sequence permutations, enabling comprehensive training across all sequence rules.

	### Configuration Layout String Format
	Each layout string inside the `configuration` array represents the fully resolved 2D/3D geometry of all levels and rooms.
	* Levels: Separated by `\|`. E.g., `L0[ ... ] \| L3[ ... ]`.
	* Rooms: Separated by `;`. E.g., `1[ ... ] ; 1.1[ ... ]`.
	* Geometry: Space-separated coordinate pairs. E.g., `1[0,0 2,0]`.
	* Nested Variation Sweeps: Host rooms with child configurations wrap their 24 sweeps in curly braces separated by colons. E.g., `1[coords]{1.1[coords];1.2[coords] : 1.1[coords];1.2[coords] : ... }`.

	### Sequence Mappings (Indices 0 to 23)
	The index of each layout string in the array corresponds to the following sequence rule:

	\| Index \| Sequence Name \| Index \| Sequence Name \|
	\| :---: \| :--- \| :---: \| :--- \|
	\| 0 \| `VRCWEE` \| 12 \| `HRCWNN` \|
	\| 1 \| `VRCCEE` \| 13 \| `HRCCNN` \|
	\| 2 \| `VRCWSE` \| 14 \| `HRCWNE` \|
	\| 3 \| `VRCCSE` \| 15 \| `HRCCNE` \|
	\| 4 \| `VRCWSW` \| 16 \| `HRCWSE` \|
	\| 5 \| `VRCCSW` \| 17 \| `HRCCSE` \|
	\| 6 \| `VRCWWW` \| 18 \| `HRCWSS` \|
	\| 7 \| `VRCCWW` \| 19 \| `HRCCSS` \|
	\| 8 \| `VRCWNW` \| 20 \| `HRCWSW` \|
	\| 9 \| `VRCCNW` \| 21 \| `HRCCSW` \|
	\| 10 \| `VRCWNE` \| 22 \| `HRCWNW` \|
	\| 11 \| `VRCCNE` \| 23 \| `HRCCNW` \|

	---

	## Citation

	If you use this dataset or engine in your research or projects, please cite it using the following BibTeX format:

	```bibtex
	@misc{hywe2026dataset,
	author = {Subbaiah, Vikram},
	title = {HYWE Architectural Training Data: A Structured Dataset of Procedural Architectural Programming and Topological Layouts},
	year = {2026},
	publisher = {Hugging Face},
	journal = {Hugging Face Repository},
	howpublished = {\url{https://huggingface.co/datasets/vykrum/hywe-training-data}}
	}
	```

	---

	## License

	To align with the open-science principles of this project while protecting the underlying source code, the HYWE ecosystem utilizes a dual-licensing structure:

	* This Dataset Card and all Datasets inside this Hugging Face repository are licensed under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license. You are free to share and adapt this data, provided you give appropriate credit to the author.
	* The HYWE Core Engine source code (the F# compiler, frontend, and coordinate solvers) is licensed under the highly permissive [MIT License](https://github.com/vykrum/Hywe/blob/main/LICENSE).