gml_utils.md

gml toolkit

gml_utils [source]

gml commonly used tools, currently mainly provides the following methods:

1. load_easy_instance_from_file(filename) [source]

   Function: load easy from csv file
   Parameter:
   · filename - the path of csv file
   Return: simple instance list
   Return type: list

2. separate_variables(variables) [source]

   Function: Divide variables into evidential variables and latent variables
   Parameter:
   · variables - variables
   Return: evidential variables list, latent variables list
   Return type: list

3. init_evidence_interval(evidence_interval_count) [source]

   Function: Initialize the evidence interval
   Parameter:
   · evidence_interval_count - the number of intervals
   Return: a list containing evidence_interval_count intervals
   Return type: list

4. init_evidence(features,evidence_interval,observed_variables_set) [source]

   Function: Add the evidence_interval attribute and the evidence_count attribute for each feature
   Parameter:
   · features - features
   · evidence_interval - evidential interval
   · observed_variables_set - the set of evidential varibles
   Return: none
   Return type: none

5. update_evidence(variables,features,var_id_list,evidence_interval)[source]

   Function: update evidence_interval and evidence_count after label variables
   Parameter: · variables - variables
   · features - features
   · var_id_list - Variable collection
   · evidence_interval - evidence_interval
   Return: none
   Return type: none

6. init_bound(variables,features)[source]

   Function: init para bound
   Parameter: · variables - variables
   · features - features
   Return: none
   Return type: none

7. update_bound(variables,features,var_id_list))[source]

   Function: update tau and alpha bound after label variables
   Parameter: · variables - variables
   · features - features
   · var_id_list - Variable collection
   Return: none
   Return type: none

8. entropy(probability) [source]

   Function: calculate entropy after given probability
   Parameter:
   · probability - One probability or list of probabilities
   Return: One entropy or a list of entropies
   Return type: floating or list

9. open_p(weight) [source]

   Function: Calculate approximate probability Parameter:
   · weight - Weight
   Return: One entropy or a list of entropies
   Return type: floating

10. combine_evidences_with_ds(mass_functions, normalization) [source]

    Function: Summarize and calculate the evidential value of different sources Parameter:
    · mass_functions - mass function
    · normalization - Whether to normalize
    Return: Summarized the calculated evidential value
    Return type: list

class Logger(object) [source]
The log class is used to output the results to the file and the console at the same time. Currently, the following methods are mainly provided

Parameter:

• object – File object

1. write(message) [source]

   Function: Write to file and console at the same time
   Parameter:
   · message - What's written
   Return: none
   Return type: none