update numpy typing to be compatible with v2.0

simulation-system/libs/gym-csle-apt-game/src/gym_csle_apt_game/dao/apt_game_config.py

@@ -13,7 +13,7 @@ class AptGameConfig(SimulationEnvInputConfig):
  def __init__(self, env_name: str,
  T: npt.NDArray[Any], O: npt.NDArray[np.int_], Z: npt.NDArray[Any],
  C: npt.NDArray[Any], S: npt.NDArray[np.int_], A1: npt.NDArray[np.int_],
- A2: npt.NDArray[np.int_], b1: npt.NDArray[np.float_], N: int, p_a: float,
+ A2: npt.NDArray[np.int_], b1: npt.NDArray[np.float64], N: int, p_a: float,
  save_dir: str, checkpoint_traces_freq: int, gamma: float = 1) -> None:
  """
  Initializes the DTO

simulation-system/libs/gym-csle-apt-game/src/gym_csle_apt_game/dao/apt_game_state.py

@@ -10,7 +10,7 @@ class AptGameState(JSONSerializable):
  Represents the state of the optimal APT game
  """
 
- def __init__(self, b1: npt.NDArray[np.float_]) -> None:
+ def __init__(self, b1: npt.NDArray[np.float64]) -> None:
  """
  Intializes the state
 

simulation-system/libs/gym-csle-apt-game/src/gym_csle_apt_game/envs/apt_game_mdp_attacker_env.py

@@ -47,7 +47,7 @@ def __init__(self, config: AptGameAttackerMdpConfig):
  self.reset()
  super().__init__()
 
- def step(self, pi2: Union[npt.NDArray[Any], int, float, np.int_, np.float_]) \
+ def step(self, pi2: Union[npt.NDArray[Any], int, float, np.int_, np.float64]) \
  -> Tuple[npt.NDArray[Any], int, bool, bool, Dict[str, Any]]:
  """
  Takes a step in the environment by executing the given action

simulation-system/libs/gym-csle-apt-game/src/gym_csle_apt_game/util/apt_game_util.py

@@ -12,7 +12,7 @@ class AptGameUtil:
  """
 
  @staticmethod
- def b1(N: int) -> npt.NDArray[np.float_]:
+ def b1(N: int) -> npt.NDArray[np.float64]:
  """
  Gets the initial belief
 
@@ -177,7 +177,7 @@ def sample_next_state(T: npt.NDArray[Any], s: int, a1: int, a2: int, S: npt.NDAr
  return int(np.random.choice(np.arange(0, len(S)), p=state_probs))
 
  @staticmethod
- def sample_initial_state(b1: npt.NDArray[np.float_]) -> int:
+ def sample_initial_state(b1: npt.NDArray[np.float64]) -> int:
  """
  Samples the initial state
 
@@ -202,7 +202,7 @@ def sample_next_observation(Z: npt.NDArray[Any], s_prime: int, O: npt.NDArray[np
  return int(o)
 
  @staticmethod
- def bayes_filter(s_prime: int, o: int, a1: int, b: npt.NDArray[np.float_], pi2: npt.NDArray[Any],
+ def bayes_filter(s_prime: int, o: int, a1: int, b: npt.NDArray[np.float64], pi2: npt.NDArray[Any],
  config: AptGameConfig) -> float:
  """
  A Bayesian filter to compute the belief of player 1
@@ -236,8 +236,8 @@ def bayes_filter(s_prime: int, o: int, a1: int, b: npt.NDArray[np.float_], pi2:
  return b_prime_s_prime
 
  @staticmethod
- def next_belief(o: int, a1: int, b: npt.NDArray[np.float_], pi2: npt.NDArray[Any],
- config: AptGameConfig, a2: int = 0, s: int = 0) -> npt.NDArray[np.float_]:
+ def next_belief(o: int, a1: int, b: npt.NDArray[np.float64], pi2: npt.NDArray[Any],
+ config: AptGameConfig, a2: int = 0, s: int = 0) -> npt.NDArray[np.float64]:
  """
  Computes the next belief using a Bayesian filter
 

simulation-system/libs/gym-csle-intrusion-response-game/src/gym_csle_intrusion_response_game/dao/intrusion_response_game_state_local.py

@@ -11,7 +11,7 @@ class IntrusionResponseGameStateLocal(JSONSerializable):
  with public observations)
  """
 
- def __init__(self, d_b1: npt.NDArray[np.float_], a_b1: npt.NDArray[np.float_], s_1_idx: int,
+ def __init__(self, d_b1: npt.NDArray[np.float64], a_b1: npt.NDArray[np.float64], s_1_idx: int,
  S: npt.NDArray[Any], S_A: npt.NDArray[Any], S_D: npt.NDArray[Any]) -> None:
  """
  Initializes the DTO

simulation-system/libs/gym-csle-intrusion-response-game/src/gym_csle_intrusion_response_game/dao/local_intrusion_response_game_config.py

@@ -14,8 +14,8 @@ class LocalIntrusionResponseGameConfig(JSONSerializable):
  def __init__(self, env_name: str, T: npt.NDArray[Any], O: npt.NDArray[np.int_], Z: npt.NDArray[Any],
  R: npt.NDArray[Any], S: npt.NDArray[np.int_], S_A: npt.NDArray[np.int_],
  S_D: npt.NDArray[np.int_], s_1_idx: int, zones: npt.NDArray[np.int_],
- A1: npt.NDArray[np.int_], A2: npt.NDArray[np.int_], d_b1: npt.NDArray[np.float_],
- a_b1: npt.NDArray[np.float_], gamma: float,
+ A1: npt.NDArray[np.int_], A2: npt.NDArray[np.int_], d_b1: npt.NDArray[np.float64],
+ a_b1: npt.NDArray[np.float64], gamma: float,
  beta: float, C_D: npt.NDArray[Any], eta: float, A_P: npt.NDArray[Any],
  Z_D_P: npt.NDArray[Any], Z_U: npt.NDArray[Any]) -> None:
  """

simulation-system/libs/gym-csle-intrusion-response-game/src/gym_csle_intrusion_response_game/util/intrusion_response_game_util.py

@@ -140,7 +140,7 @@ def local_initial_state_idx(initial_zone: int, S: npt.NDArray[Any]) -> int:
  raise ValueError("Initial state not recognized")
 
  @staticmethod
- def local_initial_state_distribution(initial_state_idx, S: npt.NDArray[Any]) -> npt.NDArray[np.float_]:
+ def local_initial_state_distribution(initial_state_idx, S: npt.NDArray[Any]) -> npt.NDArray[np.float64]:
  """
  Gets the initial state distribution
 
@@ -153,7 +153,7 @@ def local_initial_state_distribution(initial_state_idx, S: npt.NDArray[Any]) ->
  return rho
 
  @staticmethod
- def local_initial_defender_belief(S_A: npt.NDArray[Any]) -> npt.NDArray[np.float_]:
+ def local_initial_defender_belief(S_A: npt.NDArray[Any]) -> npt.NDArray[np.float64]:
  """
  Gets the initial defender belief for a local version of the game
 
@@ -165,7 +165,7 @@ def local_initial_defender_belief(S_A: npt.NDArray[Any]) -> npt.NDArray[np.float
  return d_b1
 
  @staticmethod
- def local_initial_attacker_belief(S_D: npt.NDArray[Any], initial_zone) -> npt.NDArray[np.float_]:
+ def local_initial_attacker_belief(S_D: npt.NDArray[Any], initial_zone) -> npt.NDArray[np.float64]:
  """
  Gets the initial attacker belief for a local version of the game
 
@@ -270,7 +270,7 @@ def local_workflow_utility(beta: float, reachable: bool, s: npt.NDArray[Any], in
  return beta * impact * int(not IntrusionResponseGameUtil.is_local_state_shutdown_or_redirect(s=s))
 
  @staticmethod
- def constant_defender_action_costs(A1: npt.NDArray[np.int_], constant_cost: float) -> npt.NDArray[np.float_]:
+ def constant_defender_action_costs(A1: npt.NDArray[np.int_], constant_cost: float) -> npt.NDArray[np.float64]:
  """
  Returns a vector with the local defender action costs where each action has the same constant cost
 
@@ -297,7 +297,7 @@ def zones(num_zones: int) -> npt.NDArray[Any]:
  return np.array(list(range(1, num_zones + 1)))
 
  @staticmethod
- def constant_zone_utilities(zones: npt.NDArray[np.int_], constant_utility: float) -> npt.NDArray[np.float_]:
+ def constant_zone_utilities(zones: npt.NDArray[np.int_], constant_utility: float) -> npt.NDArray[np.float64]:
  """
  Returns a vector with the zone utilities where each zone has the same constant utility
 
@@ -312,7 +312,7 @@ def constant_zone_utilities(zones: npt.NDArray[np.int_], constant_utility: float
 
  @staticmethod
  def constant_zone_detection_probabilities(zones: npt.NDArray[np.int_], constant_detection_prob: float) \
- -> npt.NDArray[np.float_]:
+ -> npt.NDArray[np.float64]:
  """
  Returns a vector with the zone detection probabilities where each zone as the same uniform detection
  probability

simulation-system/libs/gym-csle-stopping-game/src/gym_csle_stopping_game/dao/stopping_game_config.py

@@ -14,7 +14,7 @@ def __init__(self, env_name: str,
  T: npt.NDArray[Any], O: npt.NDArray[np.int_], Z: npt.NDArray[Any],
  R: npt.NDArray[Any], S: npt.NDArray[np.int_], A1: npt.NDArray[np.int_],
  A2: npt.NDArray[np.int_], L: int, R_INT: int, R_COST: int, R_SLA: int, R_ST: int,
- b1: npt.NDArray[np.float_],
+ b1: npt.NDArray[np.float64],
  save_dir: str, checkpoint_traces_freq: int, gamma: float = 1, compute_beliefs: bool = True,
  save_trace: bool = True) -> None:
  """

simulation-system/libs/gym-csle-stopping-game/src/gym_csle_stopping_game/dao/stopping_game_state.py

@@ -10,7 +10,7 @@ class StoppingGameState(JSONSerializable):
  Represents the state of the optimal stopping game
  """
 
- def __init__(self, b1: npt.NDArray[np.float_], L: int) -> None:
+ def __init__(self, b1: npt.NDArray[np.float64], L: int) -> None:
  """
  Intializes the state
 

simulation-system/libs/gym-csle-stopping-game/src/gym_csle_stopping_game/envs/stopping_game_mdp_attacker_env.py

@@ -48,7 +48,7 @@ def __init__(self, config: StoppingGameAttackerMdpConfig):
  self.reset()
  super().__init__()
 
- def step(self, pi2: Union[npt.NDArray[Any], int, float, np.int_, np.float_]) \
+ def step(self, pi2: Union[npt.NDArray[Any], int, float, np.int_, np.float64]) \
  -> Tuple[npt.NDArray[Any], int, bool, bool, Dict[str, Any]]:
  """
  Takes a step in the environment by executing the given action

simulation-system/libs/gym-csle-stopping-game/src/gym_csle_stopping_game/util/stopping_game_util.py

@@ -11,7 +11,7 @@ class StoppingGameUtil:
  """
 
  @staticmethod
- def b1() -> npt.NDArray[np.float_]:
+ def b1() -> npt.NDArray[np.float64]:
  """
  Gets the initial belief
 
@@ -233,7 +233,7 @@ def sample_next_state(T: npt.NDArray[Any], l: int, s: int, a1: int, a2: int, S:
  return int(np.random.choice(np.arange(0, len(S)), p=state_probs))
 
  @staticmethod
- def sample_initial_state(b1: npt.NDArray[np.float_]) -> int:
+ def sample_initial_state(b1: npt.NDArray[np.float64]) -> int:
  """
  Samples the initial state
 
@@ -264,7 +264,7 @@ def sample_next_observation(Z: npt.NDArray[Any], s_prime: int, O: npt.NDArray[np
  return int(o)
 
  @staticmethod
- def bayes_filter(s_prime: int, o: int, a1: int, b: npt.NDArray[np.float_], pi2: npt.NDArray[Any], l: int,
+ def bayes_filter(s_prime: int, o: int, a1: int, b: npt.NDArray[np.float64], pi2: npt.NDArray[Any], l: int,
  config: StoppingGameConfig) -> float:
  """
  A Bayesian filter to compute the belief of player 1
@@ -302,8 +302,8 @@ def bayes_filter(s_prime: int, o: int, a1: int, b: npt.NDArray[np.float_], pi2:
  return float(b_prime_s_prime)
 
  @staticmethod
- def next_belief(o: int, a1: int, b: npt.NDArray[np.float_], pi2: npt.NDArray[Any],
- config: StoppingGameConfig, l: int, a2: int = 0, s: int = 0) -> npt.NDArray[np.float_]:
+ def next_belief(o: int, a1: int, b: npt.NDArray[np.float64], pi2: npt.NDArray[Any],
+ config: StoppingGameConfig, l: int, a2: int = 0, s: int = 0) -> npt.NDArray[np.float64]:
  """
  Computes the next belief using a Bayesian filter