IRCP Training Module

This module provides comprehensive training functionality for the Enhanced Inverse Ring Contextual Propagation (IRCP) framework. The `ICPTrainer` class implements a sophisticated training pipeline with the following capabilities: #### Multi-Component Loss Function 1. **Coordinate Prediction Loss**: MSE loss for predicting 4D coordinates (x, y, z, t) 2. **Embedding Consistency Loss**: Ensures similar coordinates have similar embeddings 3. **Conservation Constraint Loss**: Implements measure preservation constraints 4. **Topological Consistency Loss**: Preserves local neighborhood structure 5. **L2 Regularization**: Prevents overfitting #### Advanced Training Features - **Adaptive Learning Rate Scheduling**: Cosine, step, and exponential schedulers - **Gradient Clipping**: Prevents gradient explosion - **Comprehensive Logging**: Detailed loss component tracking - **Checkpoint Management**: Automatic saving of best models - **Progress Visualization**: Real-time training progress with component losses #### Model Compatibility - **PyTorch Models**: Standard `forward()` method support - **Custom ICP Models**: `predict_coordinates()` method support - **Fallback Architecture**: Automatic linear layer creation for unsupported models