Source code for pm4py.objects.random_variables.deterministic.random_variable
'''
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
import sys
from pm4py.objects.random_variables.basic_structure import (
BasicStructureRandomVariable,
)
[docs]
class Deterministic(BasicStructureRandomVariable):
"""
Describes a deterministic random variable
"""
def __init__(self, value=0):
"""
Constructor
Parameters
----------
value
Constant value of the distribution
"""
BasicStructureRandomVariable.__init__(self)
self.value = value
self.priority = 1
[docs]
def read_from_string(self, distribution_parameters):
"""
Initialize distribution parameters from string
Parameters
-----------
distribution_parameters
Current distribution parameters as exported on the Petri net
"""
self.value = distribution_parameters
[docs]
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
"""
return "DETERMINISTIC"
[docs]
def get_distribution_type(self):
"""
Get current distribution type
Returns
-----------
distribution_type
String representing the distribution type
"""
return "DETERMINISTIC"
[docs]
def get_distribution_parameters(self):
"""
Get a string representing distribution parameters
Returns
-----------
distribution_parameters
String representing distribution parameters
"""
return str(self.value)
[docs]
def get_value(self):
"""
Get a random value following the distribution
Returns
-----------
value
Value obtained following the distribution
"""
return self.value
[docs]
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
"""
return [self.get_value() for i in range(no_values)]
[docs]
def calculate_loglikelihood(self, values, tol=0.0001):
"""
Calculate log likelihood
Parameters
------------
values
Empirical values to work on
tol
Tolerance about float values (consider them 0?)
Returns
------------
likelihood
Log likelihood that the values follows the distribution
"""
values_0 = [x for x in values if abs(x - self.value) < tol]
if len(values) == len(values_0):
return sys.float_info.max
return -sys.float_info.max
