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'''
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
class Normal(BasicStructureRandomVariable):
"""
Describes a normal variable
"""
def __init__(self, mu=0, sigma=1):
"""
Constructor
Parameters
-----------
mu
Average of the normal distribution
sigma
Standard deviation of the normal distribution
"""
self.mu = mu
self.sigma = sigma
self.priority = 0
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.mu = distribution_parameters.split(";")[0]
self.sigma = distribution_parameters.split(";")[1]
def get_distribution_type(self):
"""
Get current distribution type
Returns
-----------
distribution_type
String representing the distribution type
"""
return "NORMAL"
def get_distribution_parameters(self):
"""
Get a string representing distribution parameters
Returns
-----------
distribution_parameters
String representing distribution parameters
"""
return str(self.mu) + ";" + str(self.sigma)
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 norm
if len(values) > 1:
somma = 0
for value in values:
somma = somma + np.log(norm.pdf(value, self.mu, self.sigma))
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 norm
if len(values) > 1:
self.mu, self.sigma = norm.fit(values)
def get_value(self):
"""
Get a random value following the distribution
Returns
-----------
value
Value obtained following the distribution
"""
from scipy.stats import norm
return norm.rvs(self.mu, self.sigma)
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