seed for random model

jajapy/base/Base_MC.py

@@ -1,6 +1,6 @@
 from .Model import Model, MC_ID, MDP_ID, CTMC_ID
 from numpy import ndarray, where
-from random import choices
+from random import choices, seed
 
 class Base_MC(Model):
 	"""
@@ -123,7 +123,7 @@ def toStormpy(self):
 		except ModuleNotFoundError:
 			raise RuntimeError("Stormpy is not installed on this machine.")
 
-def labelsForRandomModel(nb_states: int, labelling: list) -> list:
+def labelsForRandomModel(nb_states: int, labelling: list, sseed:int=None) -> list:
 	if 'init' in labelling:
 		msg =  "The label 'init' cannot be used: it is reserved for initial states."
 		raise SyntaxError(msg)
@@ -133,12 +133,14 @@ def labelsForRandomModel(nb_states: int, labelling: list) -> list:
 		print("number of states. The last labels will not be assigned to",end=" ")
 		print("any states.")
 
-
 	if nb_states > len(labelling):
 		print("WARNING: the size of the labelling is lower than the",end=" ")
 		print("number of states. The labels for the last states will",end=" ")
 		print("be chosen randomly.")
-
+
+	if sseed != None:
+		seed(sseed)
 	labelling = labelling[:min(len(labelling),nb_states)] + choices(labelling,k=nb_states-len(labelling))
 	labelling.append("init")
+	seed()
 	return labelling

jajapy/base/tools.py

@@ -114,7 +114,8 @@ def randomProbabilities(size: int) -> ndarray:
 		raise ValueError("The size parameter should be higher than 0.")
 	if type(size) != int:
 		raise TypeError("The size parameter should be an int.")
-	return normalize(randint(1,11,size))
+	res = normalize(randint(1,11,size))
+	return res
 
 def checkProbabilities(l: ndarray) -> bool:
 	"""

jajapy/ctmc/CTMC.py

@@ -1,4 +1,4 @@
-from numpy.random import exponential
+import numpy.random
 from ast import literal_eval
 from ..base.tools import resolveRandom, randomProbabilities
 from ..base.Set import Set
@@ -7,7 +7,7 @@
 from ..base.Base_MC import *
 from math import exp, log
 from random import randint
-from numpy import array, zeros, dot, ndarray, vstack, hstack, newaxis, append, delete, where, insert
+from numpy import array, zeros, dot, vstack, hstack, newaxis, delete, insert
 from sys import platform
 from multiprocessing import cpu_count, Pool
 from sympy import sympify
@@ -175,7 +175,7 @@ def next(self,state: int) -> tuple:
 			if exp_lambda <= 0.0:
 				exps.append(1024)
 			else:
-				exps.append(exponential(1/exp_lambda))
+				exps.append(numpy.random.exponential(1/exp_lambda))
 		next_state= exps.index(min(exps))
 		next_obs = self.labelling[state]
 		return (next_obs, next_state, min(exps))
@@ -346,7 +346,8 @@ def loadCTMC(file_path: str) -> CTMC:
 
 def CTMC_random(nb_states: int, labelling: list, min_exit_rate_time : int,
 				max_exit_rate_time: int, self_loop: bool = True,
-				random_initial_state: bool=True) -> CTMC:
+				random_initial_state: bool=True,
+				sseed:int=None) -> CTMC:
 	"""
 	Generates a random CTMC. All the rates will be between 0 and 1.
 	All the exit rates will be integers.
@@ -367,6 +368,8 @@ def CTMC_random(nb_states: int, labelling: list, min_exit_rate_time : int,
 	random_initial_state: bool, optional
 		If set to True we will start in each state with a random probability, otherwise we will always start in state 0.
 		Default is True.
+	sseed : int, optional
+		the seed value.
 	
 	Returns
 	-------
@@ -392,6 +395,10 @@ def CTMC_random(nb_states: int, labelling: list, min_exit_rate_time : int,
 	s2 -> s0 : lambda = 0.2
 	s2 -> s1 : lambda = 0.8
 	"""
+	if sseed != None:
+		seed(sseed)
+		numpy.random.seed(sseed)
+
 	matrix = zeros((nb_states+1,nb_states))
 	for i in range(nb_states):
 		if self_loop:
@@ -411,6 +418,9 @@ def CTMC_random(nb_states: int, labelling: list, min_exit_rate_time : int,
 	matrix = hstack((matrix,zeros(len(matrix))[:,newaxis]))
 
 	labelling = labelsForRandomModel(nb_states,labelling)
+
+	seed()
+	numpy.random.seed()
 	return CTMC(matrix, labelling,"CTMC_random_"+str(nb_states)+"_states")
 
 def createCTMC(transitions: list, labelling: list, initial_state,

jajapy/gohmm/GoHMM.py

@@ -1,10 +1,10 @@
-from numpy.random import normal
+import numpy.random
+import random
 from numpy import array, zeros, ndarray
 from ast import literal_eval
 from ..base.tools import randomProbabilities
 from ..base.Base_HMM import Base_HMM, GOHMM_ID
 from math import sqrt, exp, pi
-from random import uniform
 
 class GoHMM(Base_HMM):
 	"""
@@ -141,7 +141,7 @@ def next_obs(self, s:int) -> list:
 		output : str
 			An observation.
 		"""
-		return [normal(parameter[0],parameter[1],1)[0] for parameter in self.output[s]]
+		return [numpy.random.normal(parameter[0],parameter[1],1)[0] for parameter in self.output[s]]
 
 	def tau(self,s1:int,s2:int,obs: list) -> float:
 		"""
@@ -219,7 +219,7 @@ def loadGoHMM(file_path: str) -> GoHMM:
 def GoHMM_random(nb_states:int,nb_distributions:int,
 				  random_initial_state:bool=False,
 				  min_mu: float=0.0,max_mu: float=2.0,
-				  min_sigma: float=0.5,max_sigma: float=2.0) -> GoHMM:
+				  min_sigma: float=0.5,max_sigma: float=2.0, sseed: int = None) -> GoHMM:
 	"""
 	Generates a random GoHMM.
 
@@ -243,6 +243,8 @@ def GoHMM_random(nb_states:int,nb_distributions:int,
 		lower bound for sigma. By default 0.5
 	max_sigma : float, optional
 		upper bound for sigma. By default 2.0
+	sseed : int, optional
+		the seed value.
 
 	Returns
 	-------
@@ -251,10 +253,13 @@ def GoHMM_random(nb_states:int,nb_distributions:int,
 	"""
 	matrix = []
 	output = []
+	if sseed != None:
+		random.seed(sseed)
+		numpy.random.seed(sseed)
 	for s in range(nb_states):
 		p1 = array(randomProbabilities(nb_states))
 		matrix.append(p1)
-		p2 = [[round(uniform(min_mu,max_mu),3),round(uniform(min_sigma,max_sigma),3)] for i in range(nb_distributions)]
+		p2 = [[round(random.uniform(min_mu,max_mu),3),round(random.uniform(min_sigma,max_sigma),3)] for i in range(nb_distributions)]
 		p2 = array(p2)
 		output.append(p2)
 	matrix = array(matrix)
@@ -264,6 +269,8 @@ def GoHMM_random(nb_states:int,nb_distributions:int,
 		init = randomProbabilities(nb_states)
 	else:
 		init = 0
+	numpy.random.seed()
+	random.seed()
 	return GoHMM(matrix, output, init,"GoHMM_random_"+str(nb_states)+"_states")
 
 

jajapy/hmm/HMM.py

@@ -2,6 +2,7 @@
 from ..base.Base_HMM import Base_HMM,HMM_ID
 from ast import literal_eval
 from numpy import array, where, zeros
+from numpy.random import seed
 
 class HMM(Base_HMM):
 
@@ -180,7 +181,9 @@ def loadHMM(file_path: str) -> HMM:
 	return HMM(matrix, output, alphabet, initial_state, name)
 
 
-def HMM_random(nb_states: int, alphabet: list, random_initial_state: bool = False) -> HMM:
+def HMM_random(nb_states: int, alphabet: list,
+	    	   random_initial_state: bool = False,
+			   sseed: int = None) -> HMM:
 	"""
 	Generates a random HMM.
 
@@ -193,6 +196,8 @@ def HMM_random(nb_states: int, alphabet: list, random_initial_state: bool = Fals
 	random_initial_state: bool, optional
 		If set to True we will start in each state with a random probability, otherwise we will always start in state 0.
 		Default is False.
+	sseed : int, optional
+		the seed value.
 	
 	Returns
 	-------
@@ -205,6 +210,8 @@ def HMM_random(nb_states: int, alphabet: list, random_initial_state: bool = Fals
 	"""
 	matrix = []
 	output = []
+	if sseed != None:
+		seed(sseed)
 	for s in range(nb_states):
 		p1 = array(randomProbabilities(nb_states))
 		matrix.append(p1)
@@ -217,6 +224,7 @@ def HMM_random(nb_states: int, alphabet: list, random_initial_state: bool = Fals
 		init = randomProbabilities(nb_states)
 	else:
 		init = 0
+	seed()
 	return HMM(matrix, output, alphabet, init,"HMM_random_"+str(nb_states)+"_states")
 
 

jajapy/mc/MC.py

@@ -2,6 +2,7 @@
 from ..base.Base_MC import *
 from ast import literal_eval
 from numpy import ndarray, array, zeros, vstack, hstack, newaxis, append, where
+from numpy.random import seed
 
 class MC(Base_MC):
 	def __init__(self, matrix: ndarray, labelling: list, name: str ="unknown_MC") -> None:
@@ -225,7 +226,7 @@ def loadMC(file_path: str) -> MC:
 	f.close()
 	return MC(matrix, labelling, name)
 
-def MC_random(nb_states: int, labelling: list, random_initial_state: bool=True) -> MC:
+def MC_random(nb_states: int, labelling: list, random_initial_state: bool=True, sseed:int=None) -> MC:
 	"""
 	Generate a random MC.
 
@@ -239,6 +240,8 @@ def MC_random(nb_states: int, labelling: list, random_initial_state: bool=True)
 		If set to True we will start in each state with a random probability,
 		otherwise we will always start in state 0.
 		Default is True.
+	sseed : int, optional
+		the seed value.
 	
 	Returns
 	-------
@@ -260,6 +263,8 @@ def MC_random(nb_states: int, labelling: list, random_initial_state: bool=True)
 	----STATE 2--init----
 	s2 -> s0 : 1.0
 	"""
+	if sseed != None:
+		seed(sseed)
 	matrix = []
 	for _ in range(nb_states):
 		matrix.append(append(randomProbabilities(nb_states),0.0))
@@ -269,7 +274,8 @@ def MC_random(nb_states: int, labelling: list, random_initial_state: bool=True)
 	else:
 		matrix.append(array([1.0]+[0.0 for i in range(nb_states)]))
 	matrix = array(matrix)
-	labelling = labelsForRandomModel(nb_states,labelling)
+	labelling = labelsForRandomModel(nb_states,labelling,sseed)
+	seed()
 	return MC(matrix, labelling,"MC_random_"+str(nb_states)+"_states")
 
 def createMC(transitions: list, labelling: list, initial_state, name: str ="unknown_MC") -> MC:

jajapy/mdp/MDP.py

@@ -3,11 +3,10 @@
 from ..base.Base_MC import *
 from ..base.Set import Set
 from .Scheduler import Scheduler
-from numpy.random import geometric
-from numpy import array, append, dot, zeros, vsplit, ndarray, where, reshape, vstack, append, concatenate
+import numpy.random
+from numpy import array, append, dot, zeros, vsplit, reshape, vstack, append, concatenate
 from ast import literal_eval
 from multiprocessing import cpu_count, Pool
-from random import choices
 
 class MDP(Base_MC):
 	"""
@@ -253,7 +252,7 @@ def generateSet(self, set_size: int, param, scheduler: Scheduler, distribution=N
 		val = []
 		for i in range(set_size):
 			if distribution == 'geo':
-				curr_size = min_size + int(geometric(param))
+				curr_size = min_size + int(numpy.random.geometric(param))
 			else:
 				if type(param) == list:
 					curr_size = param[i]
@@ -473,7 +472,9 @@ def loadMDP(file_path: str) -> MDP:
 	return MDP(matrix, labelling, actions, name)
 
 
-def MDP_random(nb_states: int,labelling: list, actions: list,random_initial_state: bool = True, deterministic: bool = False) -> MDP:
+def MDP_random(nb_states: int,labelling: list, actions: list,
+	    	   random_initial_state: bool = True, deterministic: bool = False,
+			   sseed: int = None) -> MDP:
 	"""
 	Generate a random MDP.
 
@@ -492,12 +493,17 @@ def MDP_random(nb_states: int,labelling: list, actions: list,random_initial_stat
 		If True, the model will be determinstic: in state `s`, with action `a`, for all label `o`,
 		there is at most one transition to a state labelled with `o`.
 		Default is False.
+	sseed : int, optional
+		the seed value.
 	
 	Returns
 	-------
 	MDP
 		A pseudo-randomly generated MDP.
 	"""
+	if sseed != None:
+		seed(sseed)
+		numpy.random.seed(sseed)
 	alphabet = list(set(labelling))
 	matrix = []
 	for s in range(nb_states):
@@ -523,6 +529,10 @@ def MDP_random(nb_states: int,labelling: list, actions: list,random_initial_stat
 	matrix = array(matrix)
 
 	labelling = labelsForRandomModel(nb_states,labelling)
+
+	seed()
+	numpy.random.seed()
+
 	return MDP(matrix, labelling, actions, "MDP_random_"+str(nb_states)+"_states")
 
 

jajapy/pctmc/PCTMC.py

@@ -1,12 +1,10 @@
 from ..base.tools import resolveRandom
 from ..base.Parametric_Model import *
-from ..base.Set import Set
-from numpy import ndarray, array, zeros, dot, newaxis, hstack, inf, vstack, delete
-from numpy.random import exponential, rand
+from numpy import  zeros, dot, newaxis, hstack, inf, vstack, delete
+from numpy.random import exponential, rand, seed
 from math import exp, log
 from multiprocessing import cpu_count, Pool
 from sys import platform
-from sympy import sympify
 
 class PCTMC(Parametric_Model):
 	"""
@@ -207,7 +205,8 @@ def instantiate(self, parameters: list, values: list) -> bool:
 			return True
 		return False
 
-	def randomInstantiation(self, parameters: list = None,min_val:float = None,max_val:float = None) -> None:
+	def randomInstantiation(self, parameters: list = None,min_val:float = None,
+			 				max_val:float = None, sseed: int = None) -> None:
 		"""
 		Randomly instantiated the parameters given in `parameters`.
 		If `parameters` is not set it instantiates all the non-instantiated
@@ -229,6 +228,8 @@ def randomInstantiation(self, parameters: list = None,min_val:float = None,max_v
 			this value is equal to the parameters with the highest instantiation.
 			If not set and if the model has less than two instantiated parameters,
 			this value is equal to 5.0.
+		sseed : int, optional
+			the seed value.
 		"""
 		if parameters == None:
 			parameters = []
@@ -247,10 +248,15 @@ def randomInstantiation(self, parameters: list = None,min_val:float = None,max_v
 				max_val = max(self.parameter_values.values())
 			else:
 				max_val = 0.3
+
+		if sseed != None:
+			seed(sseed)
 		val = rand(len(parameters))*(max_val-min_val)+min_val
 		while not self.instantiate(parameters,val):
 			val = rand(len(parameters))*(max_val-min_val)+min_val
-
+
+		seed()
+
 	def _stateToString(self,state:int) -> str:
 		res = "----STATE "+str(state)+"--"+self.labelling[state]+"----\n"
 		if self.isInstantiated(state):