Apply automatic ruff fixes. (bf81dc3d) · Commits · Formela / Multi-agent Steady State Synthesis

src/multiagent_steady_state_synthesis/main.py

+4 −3

Original line number	Diff line number	Diff line
		import argparse
		from pathlib import Path

		import multiagent_steady_state_synthesis.graph as graph
		import multiagent_steady_state_synthesis.objectives as objectives
		from multiagent_steady_state_synthesis.strategy_synthesis.multi import synthesize_strategy
		from multiagent_steady_state_synthesis import graph, objectives
		from multiagent_steady_state_synthesis.strategy_synthesis.multi import (
		synthesize_strategy,
		)


		def get_opts() -> argparse.Namespace:

src/multiagent_steady_state_synthesis/strategy.py

+1 −2

Original line number	Diff line number	Diff line
		from dataclasses import dataclass
		from pathlib import Path

		from math import isclose
		from pathlib import Path

		from multiagent_steady_state_synthesis.graph import Node

src/multiagent_steady_state_synthesis/strategy_evaluator.py

+7 −5

Original line number	Diff line number	Diff line
		@@ -2,13 +2,15 @@ from copy import deepcopy

		from multiagent_steady_state_synthesis.graph import Graph, Node
		from multiagent_steady_state_synthesis.graph_utils.cyclic_classes import (
		CyclicClasses,
		compute_cyclic_classes,
		node_to_cyclic_class_index,
		CyclicClasses
		)

		from multiagent_steady_state_synthesis.objectives import Objectives
		from multiagent_steady_state_synthesis.strategy import MultiAgentStrategy, SingleAgentStrategy
		from multiagent_steady_state_synthesis.strategy import (
		MultiAgentStrategy,
		SingleAgentStrategy,
		)

		ColorCyclicClassIntersections = list[dict[int, list[Node]]]

		@@ -30,7 +32,7 @@ def compute_color_cc_nodes(objectives: Objectives, node_to_cc_idx: dict[Node, in
		return color_cc_intersections


		class StrategyValue(object):
		class StrategyValue:
		def __init__(
		self,
		graph: Graph,
		@@ -53,7 +55,7 @@ class StrategyValue(object):
		for _ in range(self.num_of_colors)
		]

		def copy(self) -> 'StrategyValue':
		def copy(self) -> "StrategyValue":
		return StrategyValue(
		graph=self.graph,
		num_of_colors=self.num_of_colors,

src/multiagent_steady_state_synthesis/strategy_synthesis/multi.py

+9 −5

Original line number	Diff line number	Diff line
		import multiagent_steady_state_synthesis.strategy_synthesis.single as single

		from multiagent_steady_state_synthesis.graph_utils.cyclic_classes import compute_cyclic_classes
		from multiagent_steady_state_synthesis.graph import Graph
		from multiagent_steady_state_synthesis.graph_utils.cyclic_classes import (
		compute_cyclic_classes,
		)
		from multiagent_steady_state_synthesis.objectives import Objectives
		from multiagent_steady_state_synthesis.strategy import MultiAgentStrategy, SingleAgentStrategy
		from multiagent_steady_state_synthesis.strategy import (
		MultiAgentStrategy,
		SingleAgentStrategy,
		)
		from multiagent_steady_state_synthesis.strategy_evaluator import create_strategy_value
		from multiagent_steady_state_synthesis.strategy_synthesis import single


		def is_satisfied(objectives: Objectives, values: list[float]) -> bool:
		@@ -32,7 +36,7 @@ def synthesize_strategy(
		num_agents = len(multi_strategy.agent_strategies)
		print(f"Finding best strategy for agent {num_agents}")

		best_distance = float('inf')
		best_distance = float("inf")
		best_value = value
		best_strategy: None \| SingleAgentStrategy = None

src/multiagent_steady_state_synthesis/strategy_synthesis/single.py

+9 −6

Original line number	Diff line number	Diff line
		@@ -6,7 +6,10 @@ from gurobipy import GRB
		from multiagent_steady_state_synthesis.graph import Graph, Node
		from multiagent_steady_state_synthesis.objectives import Objectives
		from multiagent_steady_state_synthesis.strategy import SingleAgentStrategy
		from multiagent_steady_state_synthesis.strategy_evaluator import StrategyValue, ColorCyclicClassIntersections
		from multiagent_steady_state_synthesis.strategy_evaluator import (
		ColorCyclicClassIntersections,
		StrategyValue,
		)


		class GurobiStrategySynthesizer:
		@@ -29,7 +32,7 @@ class GurobiStrategySynthesizer:
		def _declare_color_time_vars(self, num_of_colors: int, period: int) -> None:
		for color in range(num_of_colors):
		for time in range(period):
		name = f'{color=},{time=}'
		name = f"{color=},{time=}"
		var = self._model.addVar(vtype=GRB.CONTINUOUS, name=name)
		self._color_time_vars[(color, time)] = var

		@@ -95,14 +98,14 @@ class GurobiStrategySynthesizer:
		current_value = value.get_color_value(color_idx=color_index)

		if current_value >= target_value:
		return
		continue

		cur_vals = value.get_probs_no_visit_of_color(color_idx=color_index)
		new_vals = [self._color_time_vars[(color_index, (time + cc_index) % period)] for time in range(period)]

		self._model.addConstr(
		1 - sum(
		cur_val * (1 - period * new_val) / period for cur_val, new_val in zip(cur_vals, new_vals)
		cur_val * (1 - period * new_val) / period for cur_val, new_val in zip(cur_vals, new_vals, strict=False)
		) <= target_value,
		"objective_{color_index}",
		)
		@@ -119,13 +122,13 @@ class GurobiStrategySynthesizer:
		current_value = value.get_color_value(color_idx=color_index)

		if current_value >= target_value:
		return
		continue

		cur_vals = value.get_probs_no_visit_of_color(color_idx=color_index)
		new_vals = [self._color_time_vars[(color_index, (time + cc_index) % period)] for time in range(period)]

		result += target_value - (
		1 - sum(cur_val * (1 - period * new_val) / period for cur_val, new_val in zip(cur_vals, new_vals))
		1 - sum(cur_val * (1 - period * new_val) / period for cur_val, new_val in zip(cur_vals, new_vals, strict=False))
		)

		self._model.setObjective(result, GRB.MINIMIZE)