1. AdaptiveDataMapper Class
The `AdaptiveDataMapper` class has been significantly improved to enhance flexibility, robustness, and user experience in dimensionality reduction tasks. Key updates include:
- **Support for PCA and t-SNE:** The class now supports both PCA and t-SNE, allowing users to choose between these popular dimensionality reduction techniques.
- **Customizable Parameters:** The addition of a `random_state` parameter ensures reproducibility, while `**kwargs` allows for further customization of the underlying models.
- **Enhanced Parameter Validation:** Improved input validation ensures that `n_components` is a positive integer and that the method selected is valid, reducing potential runtime errors.
- **Method Chaining:** The `fit` method now returns `self`, enabling method chaining for a more intuitive user experience.
- **Improved Error Handling:** The class raises more informative errors when the mapper is used incorrectly, such as when calling `transform` before `fit`.
- **Detailed Documentation:** Each method now includes docstrings for better understanding and ease of use.
2. AdaptiveLossFunction Class
The new `AdaptiveLossFunction` class introduces an innovative approach to dynamically manage loss functions during model training, significantly enhancing the learning process. Key features include:
- **Multiple Loss Functions Supported:** The class supports Mean Squared Error (MSE), Mean Absolute Error (MAE), and Huber loss, with automatic switching between these based on training progress.
- **Dynamic Loss Function Switching:** The class can automatically adapt the loss function being used, guided by the recent training loss history, which optimizes the learning process.
- **Customizable Parameters:** Users can customize delta settings for Huber loss and configure patience and threshold parameters for adaptive mode switching.
- **Traceability and Debugging:** Detailed print statements are incorporated to provide insights into loss values, mode switches, and overall training progress, aiding in traceability and debugging.
- **Modular Design:** The `AdaptiveLossFunction` is modularized into its own file (`AdaptiveLossFunction.py`), improving maintainability and reusability across different projects.
3. ContextualAdapter Class
The `ContextualAdapter` class has been refined to offer robust and flexible contextual adaptation during training. Key updates include:
- **Flexible Contextual Adaptation:** The class supports different types of contextual adaptations, including environmental and task-specific, with an easy-to-extend design.
- **Improved Error Handling:** The class now includes better validation and error messages, ensuring that incorrect types or values are caught early.
- **Efficient Buffering:** If applicable, the class now uses more efficient data structures for managing contextual parameters, improving performance.
4. RealTimeDataIntegrator Class
The `RealTimeDataIntegrator` class has been enhanced to improve the integration and processing of real-time data:
- **Efficient Buffer Management:** The class now uses a `deque` from the `collections` module to efficiently manage the data buffer, with a customizable buffer size.
- **Robust Data Handling:** Added validation ensures that only valid data types are integrated, reducing the risk of errors during processing.
- **Utility Methods:** New methods such as `clear_buffer` and `buffer_length` provide additional control and introspection for users.
General Enhancements
- **Improved Documentation:** All updated classes and methods