README.md

---
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/) [![Format: JSONL](https://img.shields.io/badge/Format-JSONL-2ea44f.svg)](https://jsonlines.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/)

---

## 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 **Base36 alphanumeric token (`HYWE Syntax`)**.
3. **Pipeline Commitment**: The **Hynteract** Vercel API endpoint securely captures the Base36 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

> [!TIP]
> For a deep dive into the engine-specific terminology used below (Lexel, Hexel, Coxel, etc.), refer to the **[HYWE Architecture Wiki](https://github.com/vykrum/Hywe/wiki)**.

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, nodes, 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 nodes 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. |
| `description` | `string` | A natural language spatial narrative detailing the designer's intent, scale, flow, and typology. |
| `configuration` | `string[]` | An array of container-scoped geometry strings, each encoding exactly 24 layout string variations in a Base36-compressed format. |

> [!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 string in `configuration` encodes one architectural container (Level or Nest) along with all its 24 sweeps:

```
Marker(ID_1;ID_2;...;ID_N | Variation_0 | Variation_1 | ... | Variation_23)
```

- **Marker**: `L0`, `L1`, `N1`, `N2`, etc.
- **IDs**: Semicolon-separated list of prefix-stripped local IDs (e.g. `1;1.1;1.2`).
- **Variations**: 24 blocks separated by ` | `. Each block contains semicolon-separated node coordinates.
- **Coordinates**: Flat, comma-separated Base36 X,Y pairs per hexel (e.g. `F,5,H,5` = cells at (15,5) and (17,5)).
- **Positional Alignment**: The coordinate groups within each variation block are **strictly positionally aligned** with the `IDs` list in the header. (e.g., the 3rd coordinate string in Variation 5 belongs to the 3rd node listed in the header).

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

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