Run3 calibrate glm
Performs efficient parameter optimization using a heuristic search or scipy.optimize to satisfy RMSE constraints within fixed parameter ranges.From its SKILL.md
npx -y skills add cxcscmu/SkillLearnBench --skill run3_calibrate_glmAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
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Since a full grid search is computationally expensive, implement an optimization routine (e.g., scipy.optimize.minimize with bounds) to minimize the overall_rmse. Ensure all parameters stay within defined constraints: Kw [0.1, 0.5], coef_mix_hyp [0.3, 0.7], wind_factor [0.7, 1.3], lw_factor [0.7, 1.3], ch [0.0005, 0.002].
from scipy.optimize import minimize
def objective_function(x):
params = {'Kw': x[0], 'coef_mix_hyp': x[1], 'wind_factor': x[2], 'lw_factor': x[3], 'ch': x[4]}
update_glm_parameters(params)
run_simulation()
metrics = calculate_metrics(...)
return metrics['overall_rmse']
# Use Bounds to enforce calibration ranges
bounds = [(0.1, 0.5), (0.3, 0.7), (0.7, 1.3), (0.7, 1.3), (0.0005, 0.002)]
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