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matrix_operations

Tasks:
Reinforcement Learning
Tabular Classification
Tabular Regression
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math
linear-algebra
matrix-calculus
mathematics
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text
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Dataset Card for matrix_operations

Dataset Description

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Dataset Summary

Synthetic matrix operations dataset for ML training

This dataset was automatically generated using the HF Dataset Generator v2.2 with TensorFlow.js backend (webgl).

Supported Tasks

  • Matrix operation prediction
  • Computational performance benchmarking
  • Synthetic data for ML training
  • Algorithm validation and testing

Languages

English

Dataset Structure

Data Instances

Each instance contains:

  • Unique sample ID
  • Generation timestamp
  • Matrix size (n×n)
  • List of operations performed with:
    • Operation type
    • Execution time in milliseconds
    • Input matrices
    • Result matrices
    • Error messages (if any)

Data Fields

  • id: Unique identifier (string)
  • timestamp: Generation timestamp (string)
  • matrix_size: Dimension of matrices (int32)
  • operations: List of operations performed (list of dicts)

Data Splits

  • Train: 400 samples
  • Test: 100 samples

Dataset Creation

Curation Rationale

This dataset was created to provide synthetic matrix operation data for machine learning research, benchmarking computational kernels, and testing numerical algorithms.

Source Data

Synthetically generated using TensorFlow.js matrix operations with random normal distributions.

Annotations

No human annotations.

Personal and Sensitive Information

None. All data is synthetically generated.

Considerations for Using the Data

Social Impact

This dataset enables research in computational mathematics, machine learning optimization, and numerical analysis education.

Discussion of Biases

Matrices are randomly generated from normal distributions (mean=0, std=1). Real-world matrices may have different distributions.

Other Known Limitations

  1. Matrix inverse may fail for singular matrices
  2. Performance timing varies by hardware (webgl backend)
  3. Limited to square matrices

Additional Information

Dataset Curators

Generated automatically by HF Dataset Generator v2.2

Licensing Information

apache-2.0 License

Contributions

Thanks to TensorFlow.js and Hugging Face communities.