Source code for pm4py.objects.random_variables.uniform.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 sys

import numpy as np

from pm4py.objects.random_variables.basic_structure import BasicStructureRandomVariable


[docs]class Uniform(BasicStructureRandomVariable): """ Describes an uniform variable """ def __init__(self, loc=0, scale=1): """ Constructor Parameters ----------- loc Start of the interval scale Scale of the interval """ self.loc = loc self.scale = scale self.priority = 0 BasicStructureRandomVariable.__init__(self)
[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.loc = distribution_parameters.split(";")[0] self.scale = distribution_parameters.split(";")[1]
[docs] def get_distribution_type(self): """ Get current distribution type Returns ----------- distribution_type String representing the distribution type """ return "UNIFORM"
[docs] def get_distribution_parameters(self): """ Get a string representing distribution parameters Returns ----------- distribution_parameters String representing distribution parameters """ return str(self.loc) + ";" + str(self.scale)
[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 """ from scipy.stats import uniform if len(values) > 0: somma = 0 for value in values: somma = somma + np.log(uniform.pdf(value, self.loc, self.scale)) return somma return -sys.float_info.max
[docs] def calculate_parameters(self, values): """ Calculate parameters of the current distribution Parameters ----------- values Empirical values to work on """ from scipy.stats import uniform if len(values) > 0: self.loc, self.scale = uniform.fit(values)
[docs] def get_value(self): """ Get a random value following the distribution Returns ----------- value Value obtained following the distribution """ from scipy.stats import uniform return uniform.rvs(self.loc, self.scale)