Source code for pm4py.objects.random_variables.random_variable

'''
    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

    PM4Py is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    PM4Py 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 General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.
'''
import numpy as np

from pm4py.objects.random_variables.constant0.random_variable import Constant0
from pm4py.objects.random_variables.exponential.random_variable import Exponential
from pm4py.objects.random_variables.normal.random_variable import Normal
from pm4py.objects.random_variables.uniform.random_variable import Uniform
from pm4py.objects.random_variables.lognormal.random_variable import LogNormal
from pm4py.objects.random_variables.gamma.random_variable import Gamma


[docs]class RandomVariable(object): def __init__(self): self.random_variable = None
[docs] 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 == "LOGNORMAL": self.random_variable = LogNormal() self.random_variable.read_from_string(distribution_parameters) elif distribution_type == "GAMMA": self.random_variable = Gamma() self.random_variable.read_from_string(distribution_parameters) elif distribution_type == "IMMEDIATE": self.random_variable = Constant0()
[docs] 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()
[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 """ if self.random_variable is not None: return self.random_variable.get_transition_type()
[docs] 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()
[docs] 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)
[docs] 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() lognormal = LogNormal() gamma = Gamma() 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
[docs] 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()
[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 """ if self.random_variable is not None: return self.random_variable.get_values(no_values=no_values)
[docs] 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()
[docs] 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)
[docs] 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()
[docs] 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"