Source code for pm4py.objects.random_variables.lognormal.random_variable

import sys

import numpy as np

from pm4py.objects.random_variables.basic_structure import (
    BasicStructureRandomVariable,
)




class LogNormal(BasicStructureRandomVariable):
    """
    Describes a normal variable
    """

    def __init__(self, s=1, loc=0, scale=1):
        """
        Constructor
        """
        self.s = s
        self.loc = loc
        self.scale = scale
        BasicStructureRandomVariable.__init__(self)



    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.s = float(distribution_parameters.split(";")[0])
        self.loc = float(distribution_parameters.split(";")[1])
        self.scale = float(distribution_parameters.split(";")[2])




    def get_distribution_type(self):
        """
        Get current distribution type

        Returns
        -----------
        distribution_type
            String representing the distribution type
        """
        return "LOGNORMAL"




    def get_distribution_parameters(self):
        """
        Get a string representing distribution parameters

        Returns
        -----------
        distribution_parameters
            String representing distribution parameters
        """
        return str(self.s) + ";" + str(self.loc) + ";" + str(self.scale)




    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 lognorm

        if len(values) > 1:
            somma = 0
            for value in values:
                somma = somma + np.log(
                    lognorm.pdf(value, self.s, self.loc, self.scale)
                )
            return somma
        return -sys.float_info.max




    def calculate_parameters(self, values):
        """
        Calculate parameters of the current distribution

        Parameters
        -----------
        values
            Empirical values to work on
        """
        from scipy.stats import lognorm

        if len(values) > 1:
            self.s, self.loc, self.scale = lognorm.fit(values)




    def get_value(self):
        """
        Get a random value following the distribution

        Returns
        -----------
        value
            Value obtained following the distribution
        """
        from scipy.stats import lognorm

        return lognorm.rvs(self.s, self.loc, self.scale)