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README.md

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+# Comprehensive Symbolic Regression Surface Dataset
+
+## Overview
+
+This dataset contains a comprehensive collection of symbolic regression problems focused on 3D surface modeling. The dataset includes 18 different categories of surface types, each with multiple instances, providing a diverse benchmark for symbolic regression algorithms.
+
+## Dataset Structure
+
+The dataset is organized in HDF5 format with the following structure:
+
+```
+/
+├── Category_1/
+│   ├── Instance_1/
+│   │   ├── train_data (5000, 3) - Training data [x, y, z]
+│   │   ├── test_data (500, 3) - Test data [x, y, z]
+│   │   └── ood_test (500, 3) - Out-of-distribution test data [x, y, z]
+│   └── Instance_2/
+│       └── ...
+└── Category_2/
+    └── ...
+```
+
+## Categories
+
+1. **Algebraic_Manifolds_of_Higher_Degree** (24 instances)
+2. **Bio-Inspired_Morphological_Surfaces** (10 instances)
+3. **Complex_Composite_Surfaces** (10 instances)
+4. **Complex_Morphological_Surfaces** (16 instances)
+5. **Discrete_Symbolic_Grid_Surfaces** (10 instances)
+6. **High_Dimensional_Parametric_Surfaces** (10 instances)
+7. **Hybrid_Multi-Modal_Symbolic_Surfaces** (10 instances)
+8. **Non-Canonical_3D_Geometric_Surfaces** (11 instances)
+9. **Nonlinear_Dynamical_System_Surfaces** (9 instances)
+10. **Piecewise_Regime_Surfaces** (10 instances)
+11. **Procedural_Fractal_Surfaces** (10 instances)
+12. **Quantum_Inspired_Surfaces** (10 instances)
+13. **Stochastic_Process_Surfaces** (10 instances)
+14. **Surrogate_Distilled_Symbolic_Approximations** (9 instances)
+15. **Symbolic-Numeric_Composite_Surfaces** (10 instances)
+16. **Tensor_Field_Surfaces** (10 instances)
+17. **Topologically_Rich_Parametric_Surfaces** (10 instances)
+18. **Transformed_Coordinate_Surfaces** (10 instances)
+
+## Data Format
+
+- **Input**: 2D coordinates (x, y)
+- **Output**: Surface height (z)
+- **Training set**: 5,000 points per instance
+- **Test set**: 500 points per instance
+- **Out-of-distribution test**: 500 points per instance
+- **Data type**: float64
+
+## Usage
+
+```python
+import h5py
+import numpy as np
+
+# Load the dataset
+with h5py.File('dataset.h5', 'r') as f:
+    # Access a specific category and instance
+    category = 'Bio-Inspired_Morphological_Surfaces'
+    instance = 'BIMS1'
+    
+    # Load training data
+    train_data = f[f'{category}/{instance}/train_data'][:]
+    X_train = train_data[:, :2]  # x, y coordinates
+    y_train = train_data[:, 2]   # z values
+    
+    # Load test data
+    test_data = f[f'{category}/{instance}/test_data'][:]
+    X_test = test_data[:, :2]
+    y_test = test_data[:, 2]
+    
+    # Load out-of-distribution test data
+    ood_data = f[f'{category}/{instance}/ood_test'][:]
+    X_ood = ood_data[:, :2]
+    y_ood = ood_data[:, 2]
+```
+
+## Applications
+
+This dataset is designed for:
+- Symbolic regression algorithm benchmarking
+- 3D surface modeling and reconstruction
+- Function approximation research
+- Out-of-distribution generalization studies
+- Multi-modal symbolic learning
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@dataset{symbolic_regression_surfaces_2024,
+  title={Comprehensive Symbolic Regression Surface Dataset},
+  author={[Your Name]},
+  year={2024},
+  url={https://huggingface.co/datasets/[your-username]/[dataset-name]}
+}
+```
+
+## License
+
+[Specify your license here]
+
+## Contact
+
+[Your contact information]