9e2fe0cae6
- Create python directory with data/, model/ subdirectories - Implement LinearEval(61072->1) model - Add config, constants, feature_extractor - Add tests with 4 passing test cases
40 lines
1006 B
Python
40 lines
1006 B
Python
"""Main entry point for training"""
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import numpy as np
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from python.model.nnue_linear import LinearEval
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from python.data.generate_data import generate_data_from_pgn
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from python.data.preprocessing import normalize_features
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from python.train import train
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def main():
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"""Training pipeline"""
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# Generate data (placeholder - replace with real PGN loading)
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print("Generating data...")
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features, evals = generate_data_from_pgn(
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"rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"
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)
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# Normalize
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print("Normalizing features...")
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features = np.array(features, dtype=np.float32)
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evals = np.array(evals, dtype=np.float32)
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features = normalize_features(features)
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# Train
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print("Training...")
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model = train(features, evals)
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# Test
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print("Testing...")
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x = torch.randn(1, 61072)
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with torch.no_grad():
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pred = model(x)
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print(f"Sample prediction: {pred.item():.4f}")
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if __name__ == "__main__":
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import torch
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main()
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