def test_exponential_variable(self):
# to avoid static method warnings in tests,
# that by construction of the unittest package have to be expressed in such way
self.dummy_variable = "dummy_value"
loc = 0
scale = 5
tol = 0.2
exp = Exponential(loc=loc, scale=scale)
values = exp.get_values(no_values=400)
rand = RandomVariable()
rand.calculate_parameters(values)
if not rand.get_distribution_type() == "EXPONENTIAL":
raise Exception("Expected an exponential!")
loc_r = rand.random_variable.loc
scale_r = rand.random_variable.scale
diff_value_loc = abs(loc - loc_r) / (max(abs(loc), abs(loc_r)) + 0.000001)
diff_value_scale = abs(scale - scale_r) / (max(abs(scale), abs(scale_r)) + 0.000001)
if diff_value_loc > tol or diff_value_scale > tol:
raise Exception("parameters found outside tolerance")
class RandomVariable(object):
def __init__(self):
self.random_variable = None
def read_from_string(self, distribution_type, distribution_parameters):
"""
Read the random variable from string
Parameters
-----------
distribution_type
Distribution type
distribution_parameters
Distribution parameters splitted by ;
"""
if distribution_type == "NORMAL":
self.random_variable = Normal()
self.random_variable.read_from_string(distribution_parameters)
elif distribution_type == "UNIFORM":
self.random_variable = Uniform()
self.random_variable.read_from_string(distribution_parameters)
elif distribution_type == "EXPONENTIAL":
self.random_variable = Exponential()
self.random_variable.read_from_string(distribution_parameters)
elif distribution_type == "IMMEDIATE":
self.random_variable = Constant0()
def get_distribution_type(self):
"""
Get current distribution type
Returns
-----------
distribution_type
String representing the distribution type
"""
if self.random_variable is not None:
return self.random_variable.get_distribution_type()
def get_transition_type(self):
"""
Get the type of transition associated to the current distribution
Returns
-----------
transition_type
String representing the type of the transition
"""
if self.random_variable is not None:
return self.random_variable.get_transition_type()
def get_distribution_parameters(self):
"""
Get a string representing distribution parameters
Returns
-----------
distribution_parameters
String representing distribution parameters
"""
if self.random_variable is not None:
return self.random_variable.get_distribution_parameters()
def calculate_loglikelihood(self, values):
"""
Calculate log likelihood
Parameters
------------
values
Empirical values to work on
Returns
------------
likelihood
Log likelihood that the values follows the distribution
"""
if self.random_variable is not None:
return self.random_variable.calculate_loglikelihood(values)
def calculate_parameters(self,
values,
parameters=None,
force_distribution=None):
"""
Calculate parameters of the current distribution
Parameters
-----------
values
Empirical values to work on
parameters
Possible parameters of the algorithm
force_distribution
If provided, distribution to force usage (e.g. EXPONENTIAL)
"""
if parameters is None:
parameters = {}
debug_mode = parameters["debug"] if "debug" in parameters else False
if self.random_variable is not None:
self.random_variable.calculate_parameters(values)
else:
norm = Normal()
unif = Uniform()
expon = Exponential()
constant = Constant0()
if not force_distribution or not force_distribution == "EXPONENTIAL":
likelihoods = list()
likelihoods.append(
[constant,
constant.calculate_loglikelihood(values)])
if force_distribution == "NORMAL" or force_distribution is None:
norm.calculate_parameters(values)
likelihoods.append(
[norm, norm.calculate_loglikelihood(values)])
if force_distribution == "UNIFORM" or force_distribution is None:
unif.calculate_parameters(values)
likelihoods.append(
[unif, unif.calculate_loglikelihood(values)])
if force_distribution == "EXPONENTIAL" or force_distribution is None:
expon.calculate_parameters(values)
likelihoods.append(
[expon, expon.calculate_loglikelihood(values)])
likelihoods = [x for x in likelihoods if str(x[1]) != 'nan']
likelihoods = sorted(likelihoods,
key=lambda x: x[1],
reverse=True)
if debug_mode:
print("likelihoods = ", likelihoods)
self.random_variable = likelihoods[0][0]
else:
avg_values = np.average(values)
if values and avg_values > 0.00000:
expon.scale = avg_values
self.random_variable = expon
else:
self.random_variable = constant
def get_value(self):
"""
Get a random value following the distribution
Returns
-----------
value
Value obtained following the distribution
"""
if self.random_variable is not None:
return self.random_variable.get_value()
def get_values(self, no_values=400):
"""
Get some random values following the distribution
Parameters
-----------
no_values
Number of values to return
Returns
----------
values
Values extracted according to the probability distribution
"""
if self.random_variable is not None:
return self.random_variable.get_values(no_values=no_values)
def get_weight(self):
"""
Getter of weight
Returns
----------
weight
Weight of the transition
"""
if self.random_variable is not None:
return self.random_variable.get_weight()
def set_weight(self, weight):
"""
Setter of the weight
Parameters
-----------
weight
Weight of the transition
"""
if self.random_variable is not None:
self.random_variable.set_weight(weight)
def get_priority(self):
"""
Getter of the priority
Returns
-----------
priority
Priority of the transition
"""
if self.random_variable is not None:
return self.random_variable.get_priority()
def set_priority(self, priority):
"""
Setter of the priority variable
Parameters
------------
priority
Priority of the transition
"""
if self.random_variable is not None:
self.random_variable.set_priority(priority)
def __str__(self):
"""
Returns a representation of the current object
Returns
----------
repr
Representation of the current object
"""
if self.random_variable is not None:
return str(self.random_variable)
else:
return "UNINITIALIZED"
def __repr__(self):
"""
Returns a representation of the current object
Returns
----------
repr
Representation of the current object
"""
if self.random_variable is not None:
return repr(self.random_variable)
else:
return "UNINITIALIZED"