pm4py.objects.random_variables.exponential package#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.
This program 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions Contact: info@processintelligence.solutions
Submodules#
pm4py.objects.random_variables.exponential.random_variable module#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.
This program 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions Contact: info@processintelligence.solutions
- class pm4py.objects.random_variables.exponential.random_variable.Exponential(loc=1, scale=1)[source]#
Bases:
BasicStructureRandomVariable
Describes a normal variable
- read_from_string(distribution_parameters)[source]#
Initialize distribution parameters from string
Parameters#
- distribution_parameters
Current distribution parameters as exported on the Petri net
- get_distribution_type()[source]#
Get current distribution type
Returns#
- distribution_type
String representing the distribution type
- get_distribution_parameters()[source]#
Get a string representing distribution parameters
Returns#
- distribution_parameters
String representing distribution parameters
- calculate_loglikelihood(values)[source]#
Calculate log likelihood
Parameters#
- values
Empirical values to work on
Returns#
- likelihood
Log likelihood that the values follows the distribution