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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 Exponential(BasicStructureRandomVariable): | |
| """ | |
| Describes a normal variable | |
| """ | |
| def __init__(self, loc=1, scale=1): | |
| """ | |
| Constructor | |
| Parameters | |
| ----------- | |
| loc | |
| Loc of the distribution (see docs.scipy.org/doc/scipy/reference/generated/scipy.stats.expon.html) | |
| scale | |
| Scale of the distribution | |
| """ | |
| self.loc = loc | |
| self.scale = scale | |
| 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.loc = 0 | |
| self.scale = 1.0 / float(distribution_parameters) | |
| def get_distribution_type(self): | |
| """ | |
| Get current distribution type | |
| Returns | |
| ----------- | |
| distribution_type | |
| String representing the distribution type | |
| """ | |
| return "EXPONENTIAL" | |
| def get_distribution_parameters(self): | |
| """ | |
| Get a string representing distribution parameters | |
| Returns | |
| ----------- | |
| distribution_parameters | |
| String representing distribution parameters | |
| """ | |
| if self.scale > 0: | |
| return str(1.0 / float(self.scale)) | |
| return "UNDEFINED" | |
| 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 expon | |
| if len(values) > 1: | |
| somma = 0 | |
| for value in values: | |
| somma = somma + np.log(expon.pdf(value, 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 expon | |
| if len(values) > 1: | |
| self.loc, self.scale = expon.fit(values, floc=0) | |
| def get_value(self): | |
| """ | |
| Get a random value following the distribution | |
| Returns | |
| ----------- | |
| value | |
| Value obtained following the distribution | |
| """ | |
| from scipy.stats import expon | |
| return expon.rvs(self.loc, self.scale) | |