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34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
"""shared factor definitions for experimental designs"""
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import numpy as np
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from dataclasses import dataclass
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@dataclass
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class Factor:
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name: str
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levels: list
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primary: bool = True # full cross vs sampled
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# demand functions with compatible signatures
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def demand_linear(mu, sigma, size): return np.maximum(0, np.random.normal(mu, sigma, size))
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def demand_uniform(mu, sigma, size): return np.random.uniform(mu - sigma, mu + sigma, size)
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def demand_exponential(mu, sigma, size): return np.random.exponential(mu, size)
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def demand_logistic(mu, sigma, size): return np.random.logistic(mu, sigma, size)
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DEMAND_FUNCTIONS = {
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"linear": demand_linear,
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"uniform": demand_uniform,
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"exponential": demand_exponential,
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"logistic": demand_logistic,
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}
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FACTORS = [
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Factor("demand_fn", list(DEMAND_FUNCTIONS.keys()), primary=True),
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Factor("alpha", [0.1, 0.3, 0.5, 0.7], primary=True),
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Factor("n_products", [5, 15, 30, 50], primary=True),
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Factor("demand_mu", [30.0, 50.0, 70.0], primary=False),
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Factor("demand_sigma", [5.0, 10.0, 20.0], primary=False),
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Factor("N", [100, 500, 1000], primary=False),
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]
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SEEDS_PER_CONFIG = 5
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