"""
Classical machine learning predictors for return forecasting
Combines Transformer and LSTM models from the original implementation
"""

# Re-export from original implementation
import sys
import os

# Add parent directory to path for imports
parent_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
original_models = os.path.join(parent_dir, '..', 'quantum-portfolio-optimizer', 'models', 'classical')
if os.path.exists(original_models):
    sys.path.insert(0, original_models)

try:
    from transformer_predictor import TransformerPredictor
    from lstm_predictor import LSTMPredictor
except ImportError:
    # Fallback: minimal implementation
    import warnings
    warnings.warn("Could not import classical models. Using fallback implementation.")
    
    class TransformerPredictor:
        """Fallback Transformer implementation"""
        def __init__(self, *args, **kwargs):
            pass
    
    class LSTMPredictor:
        """Fallback LSTM implementation"""
        def __init__(self, *args, **kwargs):
            pass

__all__ = ['TransformerPredictor', 'LSTMPredictor']
