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chore: e2e is done with new pipeline
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@@ -26,8 +26,12 @@ class ElasticityBasedPricer(PricingFunction):
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raise ValueError("historical_data must contain 'elasticity' column")
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self.elasticity = historical_data['elasticity'].values
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self.base_prices = historical_data.get('base_price', np.ones(len(historical_data)) * 100).values
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self.mean_demand = historical_data.get('mean_demand', np.ones(len(historical_data)) * 10).values
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self.base_prices = (historical_data['base_price'].values
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if 'base_price' in historical_data.columns
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else np.ones(len(historical_data)) * 100)
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self.mean_demand = (historical_data['mean_demand'].values
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if 'mean_demand' in historical_data.columns
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else np.ones(len(historical_data)) * 10)
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return self
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def predict(self, state_space) -> np.ndarray:
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