pm4py.analysis#
Functions
Checks if the input Petri net satisfies the WF-net (Workflow net) conditions: 1. |
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Checks if a given Petri net is a sound Workflow net (WF-net). |
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Applies clustering to the provided event log by extracting profiles for the log's traces and clustering them using a Scikit-Learn clusterer (default is K-Means with two clusters). |
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Computes the Earth Mover Distance (EMD) between two stochastic languages. |
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Constructs the synchronous product net between a trace and a Petri net process model. |
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Generates a marking for a given Petri net based on specified places and token counts. |
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Retrieves the set of transitions that are enabled in a given marking of a Petri net. |
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Inserts artificial start and end activities into an event log or a Pandas DataFrame. |
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Inserts arrival and finish rate information for each case into a Pandas DataFrame. |
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Inserts service time, waiting time, and sojourn time information for each case into a Pandas DataFrame. |
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Calculates the maximal decomposition of an accepting Petri net into its maximal components. |
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Reduces the number of implicit places in the provided Petri net. |
Reduces the number of invisible transitions in the provided Petri net. |
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Computes the simplicity metric for a given Petri net model. |
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Computes a heuristic value (an underestimation of the cost of an alignment) between a trace and a synchronous product net using the extended marking equation with the standard cost function. |
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Solves the marking equation of a Petri net using an Integer Linear Programming (ILP) approach. |