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41 lines
1.8 KiB
Python
41 lines
1.8 KiB
Python
import numpy as np
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def generate_demand(prices):
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# assumption 1: each product has an intrinsic valuation drawn from a normal distribution centered at 50
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product_valuations = np.random.normal(loc=50.0, scale=10.0, size=len(prices))
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# assumption 2: demand decreases as price increases, following a simple linear model
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demand = np.maximum(0, product_valuations - prices) # demand cannot be negative
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demand = demand / np.sum(demand) * 100 # normalize to total demand of 1000 units so demand output is within [0, 100]
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return 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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demand = generate_demand(prices)
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print("Generated Demand:", demand)
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from .behavior import sample_behavior
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N, alphat =200, 0.1
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trajectories = []
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for _ in range(int(N*(1 - alphat))):
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trajectories.append(sample_behavior(demand, human=True))
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for _ in range(int(N*alphat)):
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trajectories.append(sample_behavior(demand, human=False))
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demand_estimate = estimate_demand(trajectories)
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print("Estimated Demand from Behavior:", demand_estimate)
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delta = {k: demand_estimate.get(k, 0) - demand[i] for i, k in enumerate(range(len(prices)))}
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delta = np.mean([np.abs(v) for v in delta.values()])
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print("Demand Delta:", delta)
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