naoice COI implementation

engine/engine.py

@@ -3,20 +3,23 @@ import numpy as np
 from .lib.demand import generate_demand_for_actor, estimate_demand
 from .lib.behavior import sample_behavior
 from logging import INFO, getLogger
+
 logger = getLogger(__name__)
 logger.setLevel(INFO)
 
 
-class MarketEngine():
+class MarketEngine:
     """implements separate demand distributions for humans and agents per Section 3.1.1"""
 
-    def __init__(self,
-                 alpha: float,
-                 N: int,
-                 human_params: tuple,
-                 agent_params: tuple,
-                 demand_distribution = np.random.normal,
-                 noise_std: float = 1.0):
+    def __init__(
+        self,
+        alpha: float,
+        N: int,
+        human_params: tuple,
+        agent_params: tuple,
+        demand_distribution=np.random.normal,
+        noise_std: float = 1.0,
+    ):
         # no defaults for D_H, D_A - force explicit experiment design
         self.alpha = alpha
         self.Nagents = int(N * alpha)
@@ -28,31 +31,41 @@ class MarketEngine():
 
     def act(self, prices):
         # generate separate demands d() per actor type
-        demand_h = generate_demand_for_actor(prices, self.human_params, self.noise_std, distribution_method = self.demand_dist)
-        demand_a = generate_demand_for_actor(prices, self.agent_params, self.noise_std, distribution_method = self.demand_dist)
+        demand_h = generate_demand_for_actor(
+            prices,
+            self.human_params,
+            self.noise_std,
+            distribution_method=self.demand_dist,
+        )
+        demand_a = generate_demand_for_actor(
+            prices,
+            self.agent_params,
+            self.noise_std,
+            distribution_method=self.demand_dist,
+        )
         # sample behavior trajectories from each demand distribution
         human_t = [sample_behavior(demand_h, human=True) for _ in range(self.Nhumans)]
         agent_t = [sample_behavior(demand_a, human=False) for _ in range(self.Nagents)]
-        return estimate_demand(human_t + agent_t)
+        # store trajectories for agent probability calculation
+        self.last_trajectories = human_t + agent_t
+        return estimate_demand(self.last_trajectories)
 
     def measure(self):
         pass
 
-class PricingEngine():
-    def __init__(self,
-                 ) -> None:
+
+class PricingEngine:
+    def __init__(
+        self,
+    ) -> None:
         pass
 
     def act(self, demand):
         return np.random.uniform(low=25, high=100, size=10)
 
 
-
-class Limbo():
-    def __init__(self,
-                 platform,
-                 market
-                 ) -> None:
+class Limbo:
+    def __init__(self, platform, market) -> None:
         self.platform_turn = True
         self.platform = platform
         self.market = market
@@ -67,9 +80,12 @@ class Limbo():
         print(self.output)
         self.platform_turn = not self.platform_turn
 
+
 if __name__ == "__main__":
     platform = PricingEngine()
-    market = MarketEngine(alpha=0.3, N=100, human_params=(50, 10), agent_params=(45, 15))
+    market = MarketEngine(
+        alpha=0.3, N=100, human_params=(50, 10), agent_params=(45, 15)
+    )
     limbo = Limbo(platform, market)
     for _ in range(10):
         limbo.step()