# GP Attention Library Documentation

The GP Attention Library is a versatile library designed to facilitate the implementation and usage of Generalized Product (GP) Attention mechanisms. This library provides a wide range of functionalities to enhance attention mechanisms, making them more flexible and adaptable to different tasks.

## Installation

To install the GP Attention Library, you can use `pip`:

```bash
pip install gp-attention-library
```

Make sure you have Python and `pip` installed on your system.

## Getting Started

Here is an example of how to use the GP Attention Library:

```python
# Example usage of the GP Attention Library

from gp_attention_library import GPAttention
import numpy as np

# Create a sample context and modalities
context = np.random.randn(10, 128)  # Context with shape (num_samples, context_size)
modality1 = np.random.randn(10, 64)  # Modality 1 with shape (num_samples, input_size1)
modality2 = np.random.randn(10, 32)  # Modality 2 with shape (num_samples, input_size2)

# Create an instance of GPAttention
modalities = [modality1, modality2]
gp_attention = GPAttention(modalities, context_size=128, memory_size=10)

# Forward pass
output = gp_attention.forward(modalities, context)
print(output)
```

This example demonstrates a basic usage of the GP Attention Library. You can replace the sample context and modalities with your actual data.

## Parameters and Customization

The `GPAttention` class in the library provides various parameters that you can customize:

- `modalities`: List of modalities (input data) for attention processing.
- `context_size`: Size of the context vector.
- `memory_size`: Size of the external memory.
- `num_attention_layers`: Number of attention layers.
- `dropout_rate`: Dropout rate for regularization.
- `uncertainty_factor`: Factor for uncertainty quantification.
- `error_correction_factor`: Factor for error correction.
- `temporal_aggregation`: Flag for temporal aggregation.
- `use_lstm`: Flag to include an LSTM cell.
- `head_scaling_factor`: Scaling factor for the number of attention heads.

You can adjust these parameters based on your specific use case and requirements.

## Advanced Features

The GP Attention Library incorporates several advanced features:

- **Multimodal Attention:** The library supports handling multimodal data, allowing attention mechanisms to integrate information from different modalities.

- **Dynamic Attention with Adaptive Parameters:** Adaptive parameters are introduced in the attention mechanism, enabling dynamic adjustments based on the task and input characteristics.

- **Attention with Uncertainty Quantification:** Uncertainty quantification is incorporated into the attention mechanism, providing insights into the confidence of attention weights and outputs.

- **Attention with Uncertainty Quantification and Error Correction:** The attention mechanism assesses the reliability of its attention weights and outputs and makes corrections when necessary.

- **Attention with Temporal Aggregation:** Temporal aggregation is included, allowing the attention mechanism to process sequential data by considering temporal context.

- **Attention with Long Short-Term Memory (LSTM):** LSTMs are leveraged in the attention mechanism to capture long-range dependencies and temporal patterns in the input data.

- **Attention with Multi-Head Attention:** Multi-head attention is utilized, enabling the attention mechanism to focus on different aspects of the input from multiple perspectives.

- **Attention with Attention Distillation:** Attention distillation is introduced, transferring knowledge from a larger, more complex attention mechanism to a smaller, more efficient one.

- **Attention with Graph Neural Networks (GNNs):** Graph Neural Networks are integrated into the attention mechanism, enabling it to handle graph-structured data.

Feel free to explore and combine these features based on your specific use cases and requirements.

## Additional Notes

- Ensure that you have the required dependencies installed (`numpy`, etc.). If not, you can install them separately.

- Check the library's documentation for more details on available parameters, methods, and advanced functionalities.

Feel free to customize and extend the usage based on the specific needs and features of your project!

