pm4py.objects.random_variables.constant0 package#

PM4Py – A Process Mining Library for Python

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Submodules#

pm4py.objects.random_variables.constant0.random_variable module#

PM4Py – A Process Mining Library for Python

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.constant0.random_variable.Constant0[source]#

Bases: Uniform

Describes a constant0-equal-to-0 random 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_transition_type()[source]#

Get the type of transition associated to the current distribution

Returns#

transition_type: String representing the type of the transition

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

get_value()[source]#

Get a random value following the distribution

Returns#

value: Value obtained following the distribution

get_values(no_values=400)[source]#

Get some random values following the distribution

Parameters#

no_values: Number of values to return

Returns#

values: Values extracted according to the probability distribution

calculate_loglikelihood(values, tol=0.0001)[source]#

Calculate log likelihood

Parameters#

values: Empirical values to work on
tol: Tolerance about float values (consider them 0?)

Returns#

likelihood: Log likelihood that the values follows the distribution