(.venv) impactlab@en4230651l:/drive2$ python /drive2/Kuntal/Pysindy-experiment/RHO_exp/eyepac.py
CuPy not available. Using NumPy for array operations.
Using GPU: NVIDIA RTX 6000 Ada Generation
Loading theta data from /drive2/Kuntal/Pysindy-experiment/eyepacs_theta_data/eyepacs_all_thetas.npy
Loading theta IDs from /drive2/Kuntal/Pysindy-experiment/eyepacs_theta_data/eyepacs_theta_ids.npy
Loaded 35122 theta vectors with shape (35122, 3, 20)
Training set: 21073 samples
Validation set: 14049 samples
Running experiment with CPU...
Will process 21073 training samples as Q vectors
Each Q will be evaluated against 14049 validation samples
Processing theta 1/21073...
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Total number of subtracted sigma_j_m values for confidence interval: 444071329
Selected index using formula: 210933882 (of 444071329 values)
Selected σ_m value: -0.04568944923551152
Confidence interval: [-0.04568944923551152, 0.04568944923551152]

Experiment Settings:
- Using CPU acceleration
- Theta vectors normalized to unit length

Results (averaged over 21073 theta vectors):
- Average ρ_avg (training): 0.01288287374567551
- Overall confidence interval: [-0.04568944923551152, 0.04568944923551152]

Experiment Results Summary:
Total runtime: 2295.09 seconds
Average ρ_avg (training): 0.01288287374567551
Overall confidence interval: [-0.04568944923551152, 0.04568944923551152]

Conclusion: No significant difference between training and validation performance.
(.venv) impactlab@en4230651l:/drive2$ 