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function-approximation

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YAML Metadata Warning: The task_categories "symbolic-regression" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

YAML Metadata Warning: The task_categories "function-approximation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

YAML Metadata Warning: The task_categories "3d-modeling" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

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/
    └── ...

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

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:

@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

MIT License

Contact

[Your contact information]

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