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Update type annotations
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@Limmen
Limmen committed Feb 9, 2025
1 parent 439f8a0 commit 7491176
Showing 1 changed file with 22 additions and 22 deletions.
44 changes: 22 additions & 22 deletions ...-system/libs/gym-csle-stopping-game/src/gym_csle_stopping_game/util/stopping_game_util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -390,7 +390,7 @@ def pomdp_solver_file(config: StoppingGameConfig, discount_factor: float, pi2: n
return file_str

@staticmethod
def reduce_T_attacker(T: npt.NDArray[np.float_], strategy: Policy) -> npt.NDArray[np.float_]:
def reduce_T_attacker(T: npt.NDArray[np.float64], strategy: Policy) -> npt.NDArray[np.float64]:
"""
Reduces the transition tensor based on a given attacker strategy
Expand All @@ -415,7 +415,7 @@ def reduce_T_attacker(T: npt.NDArray[np.float_], strategy: Policy) -> npt.NDArra
return reduced_T

@staticmethod
def reduce_R_attacker(R: npt.NDArray[np.float_], strategy: Policy) -> npt.NDArray[np.float_]:
def reduce_R_attacker(R: npt.NDArray[np.float64], strategy: Policy) -> npt.NDArray[np.float64]:
"""
Reduces the reward tensor based on a given attacker strategy
Expand All @@ -433,7 +433,7 @@ def reduce_R_attacker(R: npt.NDArray[np.float_], strategy: Policy) -> npt.NDArra
return reduced_R

@staticmethod
def reduce_Z_attacker(Z: npt.NDArray[np.float_], strategy: Policy) -> npt.NDArray[np.float_]:
def reduce_Z_attacker(Z: npt.NDArray[np.float64], strategy: Policy) -> npt.NDArray[np.float64]:
"""
Reduces the observation tensor based on a given attacker strategy
Expand All @@ -450,7 +450,7 @@ def reduce_Z_attacker(Z: npt.NDArray[np.float_], strategy: Policy) -> npt.NDArra
return reduced_Z

@staticmethod
def reduce_T_defender(T: npt.NDArray[np.float_], strategy: Policy) -> npt.NDArray[np.float_]:
def reduce_T_defender(T: npt.NDArray[np.float64], strategy: Policy) -> npt.NDArray[np.float64]:
"""
Reduces the transition tensor based on a given defender strategy
Expand All @@ -469,7 +469,7 @@ def reduce_T_defender(T: npt.NDArray[np.float_], strategy: Policy) -> npt.NDArra
return reduced_T

@staticmethod
def reduce_R_defender(R: npt.NDArray[np.float_], strategy: Policy) -> npt.NDArray[np.float_]:
def reduce_R_defender(R: npt.NDArray[np.float64], strategy: Policy) -> npt.NDArray[np.float64]:
"""
Reduces the reward tensor based on a given defender strategy
Expand All @@ -487,10 +487,10 @@ def reduce_R_defender(R: npt.NDArray[np.float_], strategy: Policy) -> npt.NDArra
return reduced_R

@staticmethod
def aggregate_belief_mdp_defender(aggregation_resolution: int, T: npt.NDArray[np.float_],
R: npt.NDArray[np.float_], Z: npt.NDArray[np.float_],
def aggregate_belief_mdp_defender(aggregation_resolution: int, T: npt.NDArray[np.float64],
R: npt.NDArray[np.float64], Z: npt.NDArray[np.float64],
S: npt.NDArray[np.int_], A: npt.NDArray[np.int_], O: npt.NDArray[np.int_]) \
-> Tuple[npt.NDArray[np.float_], npt.NDArray[np.int_], npt.NDArray[np.float_], npt.NDArray[np.float_]]:
-> Tuple[npt.NDArray[np.float64], npt.NDArray[np.int_], npt.NDArray[np.float64], npt.NDArray[np.float64]]:
"""
Generates an aggregate belief MDP from a given POMDP specification and aggregation resolution
Expand All @@ -512,7 +512,7 @@ def aggregate_belief_mdp_defender(aggregation_resolution: int, T: npt.NDArray[np
return aggregate_belief_space, A, belief_T, belief_R

@staticmethod
def generate_aggregate_belief_space(n: int, belief_space_dimension: int) -> npt.NDArray[np.float_]:
def generate_aggregate_belief_space(n: int, belief_space_dimension: int) -> npt.NDArray[np.float64]:
"""
Generate an aggregate belief space B_n.
Expand All @@ -534,8 +534,8 @@ def generate_aggregate_belief_space(n: int, belief_space_dimension: int) -> npt.

@staticmethod
def generate_aggregate_belief_reward_tensor(
aggregate_belief_space: npt.NDArray[np.float_], S: npt.NDArray[np.int_], A: npt.NDArray[np.int_],
R: npt.NDArray[np.float_]) -> npt.NDArray[np.float_]:
aggregate_belief_space: npt.NDArray[np.float64], S: npt.NDArray[np.int_], A: npt.NDArray[np.int_],
R: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]:
"""
Generates an aggregate reward tensor for the aggregate belief MDP
Expand All @@ -557,8 +557,8 @@ def generate_aggregate_belief_reward_tensor(

@staticmethod
def generate_aggregate_belief_transition_operator(
aggregate_belief_space: npt.NDArray[np.float_], S: npt.NDArray[np.int_], A: npt.NDArray[np.int_],
O: npt.NDArray[np.int_], T: npt.NDArray[np.float_], Z: npt.NDArray[np.float_]) -> npt.NDArray[np.float_]:
aggregate_belief_space: npt.NDArray[np.float64], S: npt.NDArray[np.int_], A: npt.NDArray[np.int_],
O: npt.NDArray[np.int_], T: npt.NDArray[np.float64], Z: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]:
"""
Generates an aggregate belief space transition operator
Expand All @@ -581,11 +581,11 @@ def generate_aggregate_belief_transition_operator(
return belief_T

@staticmethod
def aggregate_belief_transition_probability(b1: npt.NDArray[np.float_], b2: npt.NDArray[np.float_], a: int,
def aggregate_belief_transition_probability(b1: npt.NDArray[np.float64], b2: npt.NDArray[np.float64], a: int,
S: npt.NDArray[np.int_], O: npt.NDArray[np.int_],
A: npt.NDArray[np.int_],
T: npt.NDArray[np.float_], Z: npt.NDArray[np.float_],
aggregate_belief_space: npt.NDArray[np.float_]) -> float:
T: npt.NDArray[np.float64], Z: npt.NDArray[np.float64],
aggregate_belief_space: npt.NDArray[np.float64]) -> float:
"""
Calculates the probability of transitioning from belief b1 to belief b2 when taking action a
Expand Down Expand Up @@ -616,8 +616,8 @@ def aggregate_belief_transition_probability(b1: npt.NDArray[np.float_], b2: npt.

@staticmethod
def pomdp_next_belief(o: int, a: int, b: npt.NDArray[np.float64], states: npt.NDArray[np.int_],
observations: npt.NDArray[np.int_], observation_tensor: npt.NDArray[np.float_],
transition_tensor: npt.NDArray[np.float_]) \
observations: npt.NDArray[np.int_], observation_tensor: npt.NDArray[np.float64],
transition_tensor: npt.NDArray[np.float64]) \
-> npt.NDArray[np.float64]:
"""
Computes the next belief of the POMDP using a Bayesian filter
Expand All @@ -643,8 +643,8 @@ def pomdp_next_belief(o: int, a: int, b: npt.NDArray[np.float64], states: npt.ND

@staticmethod
def pomdp_bayes_filter(s_prime: int, o: int, a: int, b: npt.NDArray[np.float64], states: npt.NDArray[np.int_],
observations: npt.NDArray[np.int_], observation_tensor: npt.NDArray[np.float_],
transition_tensor: npt.NDArray[np.float_]) -> float:
observations: npt.NDArray[np.int_], observation_tensor: npt.NDArray[np.float64],
transition_tensor: npt.NDArray[np.float64]) -> float:
"""
A Bayesian filter to compute b[s_prime] of the POMDP
Expand Down Expand Up @@ -679,8 +679,8 @@ def pomdp_bayes_filter(s_prime: int, o: int, a: int, b: npt.NDArray[np.float64],
return b_prime_s_prime

@staticmethod
def find_nearest_neighbor_belief(belief_space: npt.NDArray[np.float_], target_belief: npt.NDArray[np.float_]) \
-> npt.NDArray[np.float_]:
def find_nearest_neighbor_belief(belief_space: npt.NDArray[np.float64], target_belief: npt.NDArray[np.float64]) \
-> npt.NDArray[np.float64]:
"""
Finds the nearest neighbor (in the Euclidean sense) of a given belief in a certain belief space
Expand Down

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