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# Epsilon-Transformers Belief Analysis Dataset
This dataset contains trained neural network models and their corresponding belief state regression analysis from the Epsilon-Transformers project. The models were trained on four different stochastic processes and analyzed for their ability to learn and represent belief states.
## Dataset Structure
```
epsilon-transformers-belief-analysis/
├── README.md
├── models/ # Model checkpoints and configurations from S3
│ ├── {sweep_id}_{run_id}/
│ │ ├── 0.pt # Initial checkpoint
│ │ ├── {final}.pt # Final checkpoint
│ │ ├── run_config.yaml # Training configuration
│ │ └── loss.csv # Training loss data
│ └── ...
└── analysis/ # Belief state regression analysis results
├── {sweep_id}_{run_id}/
│ ├── checkpoint_0.joblib # Initial checkpoint analysis
│ ├── checkpoint_{final}.joblib # Final checkpoint analysis
│ ├── ground_truth_data.joblib # Neural network ground truth
│ ├── markov3_checkpoint_*.joblib # Classical Markov comparisons
│ └── markov3_ground_truth_data.joblib # Classical ground truth
└── ...
```
## Model Mappings
| Sweep ID | Run ID | Architecture | Process | Description |
|----------|--------|--------------|---------|-------------|
### Mess3
| 20241121152808 | 55 | LSTM | Mess3 | LSTM trained on Mess3 |
| 20241121152808 | 63 | GRU | Mess3 | GRU trained on Mess3 |
| 20241121152808 | 71 | RNN | Mess3 | RNN trained on Mess3 |
| 20241205175736 | 23 | Transformer | Mess3 | Transformer trained on Mess3 |
### FRDN
| 20241121152808 | 53 | LSTM | FRDN | LSTM trained on FRDN |
| 20241121152808 | 61 | GRU | FRDN | GRU trained on FRDN |
| 20241121152808 | 69 | RNN | FRDN | RNN trained on FRDN |
| 20250422023003 | 1 | Transformer | FRDN | Transformer trained on FRDN |
### Bloch Walk
| 20241121152808 | 49 | LSTM | Bloch Walk | LSTM trained on Bloch Walk |
| 20241121152808 | 57 | GRU | Bloch Walk | GRU trained on Bloch Walk |
| 20241121152808 | 65 | RNN | Bloch Walk | RNN trained on Bloch Walk |
| 20241205175736 | 17 | Transformer | Bloch Walk | Transformer trained on Bloch Walk |
### Moon Process
| 20241121152808 | 48 | LSTM | Moon Process | LSTM trained on Moon Process |
| 20241121152808 | 56 | GRU | Moon Process | GRU trained on Moon Process |
| 20241121152808 | 64 | RNN | Moon Process | RNN trained on Moon Process |
| 20250421221507 | 0 | Transformer | Moon Process | Transformer trained on Moon Process |
## Process Descriptions
### Mess3 (Classical Process)
A classical stochastic process used as a baseline for comparison with quantum processes.
### FRDN (Finite Random Dynamics Networks)
A quantum process representing finite random dynamics networks, modeling quantum systems with specific structural properties.
### Bloch Walk
A quantum random walk process on the Bloch sphere, representing quantum state evolution in a geometric framework.
### Moon Process
A post-quantum stochastic process that explores computational mechanics beyond standard quantum frameworks.
## Model Architectures
### RNN Models (LSTM, GRU, RNN)
- **Layers**: 4
- **Hidden Units**: 64
- **Direction**: Unidirectional
- **Configuration**: L4_H64_uni
### Transformer Models
- **Layers**: 4
- **Attention Heads**: 4
- **Head Dimension**: 16
- **Model Dimension**: 64
- **Configuration**: L4_H4_DH16_DM64
## File Formats
### Model Files (.pt)
PyTorch model checkpoints containing trained model weights and optimizer states.
### Analysis Files (.joblib)
Joblib-serialized files containing:
- **checkpoint_*.joblib**: Regression analysis results mapping activations to belief states
- **ground_truth_data.joblib**: True belief states and probabilities for the neural network data
- **markov3_*.joblib**: Classical Markov model comparisons and baselines
## Usage
### Loading Models
```python
import torch
from pathlib import Path
# Load a model checkpoint
model_path = Path("models/20241121152808_57/4075724800.pt")
checkpoint = torch.load(model_path, map_location='cpu')
```
### Loading Analysis Data
```python
import joblib
from pathlib import Path
# Load regression analysis results
analysis_path = Path("analysis/20241121152808_57/checkpoint_4075724800.joblib")
analysis_data = joblib.load(analysis_path)
# Access layer-wise regression metrics
for layer, metrics in analysis_data.items():
print(f"Layer {layer} RMSE: {metrics['rmse']}")
```
## Citation
If you use this dataset in your research, please cite:
```bibtex
@misc{epsilon-transformers-belief-analysis,
title={Epsilon-Transformers Belief Analysis Dataset},
author={[Your Name]},
year={2024},
howpublished={Hugging Face Datasets},
url={https://huggingface.co/datasets/[your-username]/epsilon-transformers-belief-analysis}
}
```
## License
[Specify your license here]
## Contact
[Your contact information]
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