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45 lines
1.9 KiB
Python
45 lines
1.9 KiB
Python
import logging
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import numpy as np
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from logging import getLogger
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logger = getLogger(__name__)
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def generate_demand_for_actor(prices: np.ndarray, params: tuple, noise_std: float = 1.0, distribution_method=np.random.normal) -> np.ndarray:
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"""d(p;0) = max(0, valuation - price) + epsi for single actor type
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params: (mean, std) for valuation distribution D_H or D_A"""
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val = distribution_method(*params, size=len(prices))
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noise = distribution_method(0, noise_std, len(prices))
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demand = np.maximum(0, val - prices + noise)
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total = np.sum(demand)
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return demand / total * 100 if total > 0 else demand
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def estimate_demand(trajectories):
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demand_estimate = {}
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for traj in trajectories:
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for event in traj:
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if 'view_product' in event:
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product_id = int(event.split('_')[-1].replace('product', ''))
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demand_estimate[product_id] = demand_estimate.get(product_id, 0) + 1
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total_views = sum(demand_estimate.values())
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for product_id in demand_estimate:
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demand_estimate[product_id] = (demand_estimate[product_id] / total_views) * 100 # normalize to percentage
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return demand_estimate
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# Example usage
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if __name__ == "__main__":
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np.random.seed(42)
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prices = np.array([20.0, 35.0, 50.0, 65.0])
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# demo actor-specific demands
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human_params, agent_params = (50, 10), (45, 15)
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demand_h = generate_demand_for_actor(prices, human_params)
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demand_a = generate_demand_for_actor(prices, agent_params)
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print("Human Demand:", demand_h)
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print("Agent Demand:", demand_a)
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from .behavior import sample_behavior
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N, alpha = 200, 0.3
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n_h, n_a = int(N * (1 - alpha)), int(N * alpha)
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human_t = [sample_behavior(demand_h, human=True) for _ in range(n_h)]
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agent_t = [sample_behavior(demand_a, human=False) for _ in range(n_a)]
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demand_estimate = estimate_demand(human_t + agent_t)
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print("Estimated Demand from Behavior:", demand_estimate)
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