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RCR

Razor Cluster Razor Code These compaction algorithms are based on the provided XCS and UCS. Readers can explain the Read_UCS_population.py or Read_XCS_Population to make the compact algorithms to support their own version.

Compaction:

Step 1: import Compaction Step 2: Instantiating the Compaction class

Sample: Compaction(A folder storing the populations for compaction,

                        System type: 0 :UCS 1:XCS,
                             
                        Problem type: 0: MUX 1:Carry 2:Even parity 3: Majority-On
                             
                        Number of features,
                              
                        A list for identifying the compaction algorithms are going to run e.g. [0,1,3]  0: CRA 1: FU1 2: FU3 3: CRA2 4: k1 5: QRC 6: PDRC 7: RCR 8: RCR2 9:RCR3,
                                           Address of file for testing (only for real value domains) default:none ,
              
                        Whether activate 10 folders cross-validation only for real value domains) default: False)

Results from the compaction algorithms will be stored in the Result folder.

Visualization: Step 1: import Visualize_value_pattern Step 2: Instantiating the Visualize_value_pattern class

In Value_knowledge class when self.attribute_List=self.Calculate_Attribute_importance_distribution_Value() the visualization is AFVM when self.attribute_List=self.Calculate_Attribute_importance_distribution_PureImportance() the visualization is AFIM

sample: Visualize_value_pattern(the address for the compacted population, a list of plausible actions e.g. in Boolean domains this will be [0,1], the address for storing the output visualizations)

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