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[INFO]
[INFO] --- maven-clean-plugin:2.5:clean (default-clean) @ components-ext ---
[INFO]
[INFO] --- maven-resources-plugin:3.1.0:resources (default-resources) @ components-ext ---
[INFO] Using 'UTF-8' encoding to copy filtered resources.
[INFO] Copying 58 resources
[INFO]
[INFO] --- maven-compiler-plugin:3.8.1:compile (default-compile) @ components-ext ---
[INFO] Changes detected - recompiling the module!
[INFO] Compiling 104 source files to /usr/share/tomcat8/.jenkins/jobs/DL-Learner Merge-M/branches/PR-101/workspace/components-ext/target/classes
[INFO] /usr/share/tomcat8/.jenkins/jobs/DL-Learner Merge-M/branches/PR-101/workspace/components-ext/src/main/java/org/dllearner/algorithms/probabilistic/structure/distributed/unife/leap/LEAPDistributed.java: /usr/share/tomcat8/.jenkins/jobs/DL-Learner Merge-M/branches/PR-101/workspace/components-ext/src/main/java/org/dllearner/algorithms/probabilistic/structure/distributed/unife/leap/LEAPDistributed.java uses or overrides a deprecated API.
[INFO] /usr/share/tomcat8/.jenkins/jobs/DL-Learner Merge-M/branches/PR-101/workspace/components-ext/src/main/java/org/dllearner/algorithms/probabilistic/structure/distributed/unife/leap/LEAPDistributed.java: Recompile with -Xlint:deprecation for details.
[INFO] /usr/share/tomcat8/.jenkins/jobs/DL-Learner Merge-M/branches/PR-101/workspace/components-ext/src/main/java/org/dllearner/algorithms/probabilistic/structure/unife/leap/AbstractLEAP.java: Some input files use unchecked or unsafe operations.
[INFO] /usr/share/tomcat8/.jenkins/jobs/DL-Learner Merge-M/branches/PR-101/workspace/components-ext/src/main/java/org/dllearner/algorithms/probabilistic/structure/unife/leap/AbstractLEAP.java: Recompile with -Xlint:unchecked for details.
[INFO]
[INFO] --- maven-resources-plugin:3.1.0:testResources (default-testResources) @ components-ext ---
[INFO] Using 'UTF-8' encoding to copy filtered resources.
[INFO] Copying 30 resources
[INFO]
[INFO] --- maven-compiler-plugin:3.8.1:testCompile (default-testCompile) @ components-ext ---
[INFO] Changes detected - recompiling the module!
[INFO] Compiling 3 source files to /usr/share/tomcat8/.jenkins/jobs/DL-Learner Merge-M/branches/PR-101/workspace/components-ext/target/test-classes
[INFO]
[INFO] --- maven-surefire-plugin:2.22.1:test (default-test) @ components-ext ---
[INFO] Surefire report directory: /usr/share/tomcat8/.jenkins/jobs/DL-Learner Merge-M/branches/PR-101/workspace/components-ext/target/surefire-reports
[INFO]
[INFO] -------------------------------------------------------
[INFO] T E S T S
[INFO] -------------------------------------------------------
[INFO] Running org.dllearner.algorithms.miles.MILESTest
log4j: Trying to find [log4j.properties] using context classloader jdk.internal.loader.ClassLoaders$AppClassLoader@5ffd2b27.
log4j: Using URL [file:/usr/share/tomcat8/.jenkins/jobs/DL-Learner%20Merge-M/branches/PR-101/workspace/components-ext/target/test-classes/log4j.properties] for automatic log4j configuration.
log4j: Reading configuration from URL file:/usr/share/tomcat8/.jenkins/jobs/DL-Learner%20Merge-M/branches/PR-101/workspace/components-ext/target/test-classes/log4j.properties
log4j: Parsing for [root] with value=[DEBUG, stdout].
log4j: Level token is [DEBUG].
log4j: Category root set to DEBUG
log4j: Parsing appender named "stdout".
log4j: Parsing layout options for "stdout".
log4j: Setting property [conversionPattern] to [%m%n].
log4j: End of parsing for "stdout".
log4j: Setting property [target] to [System.out].
log4j: Parsed "stdout" options.
log4j: Parsing for [org.apache.http.wire] with value=[OFF].
log4j: Level token is [OFF].
log4j: Category org.apache.http.wire set to OFF
log4j: Handling log4j.additivity.org.apache.http.wire=[null]
log4j: Parsing for [org.apache.solr.level] with value=[OFF].
log4j: Level token is [OFF].
log4j: Category org.apache.solr.level set to OFF
log4j: Handling log4j.additivity.org.apache.solr.level=[null]
log4j: Parsing for [org.dllearner.sparqlquerygenerator] with value=[OFF].
log4j: Level token is [OFF].
log4j: Category org.dllearner.sparqlquerygenerator set to OFF
log4j: Handling log4j.additivity.org.dllearner.sparqlquerygenerator=[null]
log4j: Parsing for [org.apache.http] with value=[OFF].
log4j: Level token is [OFF].
log4j: Category org.apache.http set to OFF
log4j: Handling log4j.additivity.org.apache.http=[null]
log4j: Parsing for [org.dllearner.algorithm.tbsl] with value=[INFO].
log4j: Level token is [INFO].
log4j: Category org.dllearner.algorithm.tbsl set to INFO
log4j: Handling log4j.additivity.org.dllearner.algorithm.tbsl=[null]
log4j: Parsing for [org.dllearner.algorithm.tbsl.templator] with value=[WARN].
log4j: Level token is [WARN].
log4j: Category org.dllearner.algorithm.tbsl.templator set to WARN
log4j: Handling log4j.additivity.org.dllearner.algorithm.tbsl.templator=[null]
log4j: Parsing for [org.dllearner.autosparql.server] with value=[OFF].
log4j: Level token is [OFF].
log4j: Category org.dllearner.autosparql.server set to OFF
log4j: Handling log4j.additivity.org.dllearner.autosparql.server=[null]
log4j: Parsing for [org.dllearner.algorithm.tbsl.ltag.parser] with value=[WARN].
log4j: Level token is [WARN].
log4j: Category org.dllearner.algorithm.tbsl.ltag.parser set to WARN
log4j: Handling log4j.additivity.org.dllearner.algorithm.tbsl.ltag.parser=[null]
log4j: Parsing for [org.dllearner.algorithms.probabilistic] with value=[DEBUG, APPENDER_FILE, APPENDER_OUT].
log4j: Level token is [DEBUG].
log4j: Category org.dllearner.algorithms.probabilistic set to DEBUG
log4j: Parsing appender named "APPENDER_FILE".
log4j: Parsing layout options for "APPENDER_FILE".
log4j: Setting property [conversionPattern] to [%d{yyyy-MM-dd HH:mm:ss} %p [%C:%L] - %m%n].
log4j: End of parsing for "APPENDER_FILE".
log4j: Setting property [maxFileSize] to [10MB].
log4j: Setting property [maxBackupIndex] to [1].
log4j: Setting property [file] to [log/leap.log].
log4j: setFile called: log/leap.log, true
log4j: setFile ended
log4j: Parsed "APPENDER_FILE" options.
log4j: Parsing appender named "APPENDER_OUT".
log4j: Parsing layout options for "APPENDER_OUT".
log4j: Setting property [conversionPattern] to [%5p (%F:%L) - %m%n].
log4j: End of parsing for "APPENDER_OUT".
log4j: Parsed "APPENDER_OUT" options.
log4j: Handling log4j.additivity.org.dllearner.algorithms.probabilistic=[null]
log4j: Finished configuring.
SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.
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[====.] 98%@relation rel
@attribute t {0,1}
@data
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Feb 26, 2022 2:17:02 AM com.github.fommil.netlib.ARPACK <clinit>
WARNING: Failed to load implementation from: com.github.fommil.netlib.NativeSystemARPACK
Feb 26, 2022 2:17:02 AM com.github.fommil.netlib.ARPACK <clinit>
WARNING: Failed to load implementation from: com.github.fommil.netlib.NativeRefARPACK
=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,0,1,1,1,0
0,0,0,0,0,0
1,0,1,1,1,0
1,1,0,1,1,0
0,0,0,0,0,0
1,0,1,1,1,0
0,0,1,1,1,0
1,0,1,1,1,0
1,0,1,1,1,0
1,1,1,0,1,0
1,0,1,1,1,0
1,0,1,1,1,0
1,1,0,1,1,0
1,1,1,0,1,0
1,0,1,1,1,0
1,1,1,1,1,0
1,0,1,1,1,0
1,1,1,1,1,0
1,0,1,1,1,0
1,0,1,1,1,0
0,0,1,1,1,0
1,0,1,1,1,0
0,0,0,0,0,0
1,0,1,1,1,0
1,1,1,1,1,0
1,0,1,1,1,0
1,1,1,0,1,0
1,1,1,1,1,0
1,1,1,0,1,0
1,0,1,1,1,0
0,0,0,0,0,0
1,0,1,1,1,0
1,0,1,1,1,0
1,0,1,1,1,0
1,1,1,1,1,0
0,0,0,0,0,0
1,0,1,1,1,0
1,0,1,1,1,0
1,0,1,1,1,0
1,0,1,1,1,0
1,1,1,1,1,0
1,0,1,1,1,0
1,0,1,1,1,0
1,1,1,1,1,0
0,0,0,0,0,0
1,0,1,1,1,0
1,0,1,1,1,0
1,0,1,1,1,0
1,0,1,1,1,0
1,1,0,1,1,0
1,0,1,1,1,0
1,1,1,1,1,0
1,0,1,1,1,0
1,1,1,1,1,0
1,1,1,1,1,0
1,1,1,1,1,0
1,0,1,1,1,0
1,1,1,1,1,0
1,1,1,1,1,0
1,1,1,1,1,0
0,0,0,0,0,0
1,0,1,1,1,0
1,0,1,1,1,0
1,1,0,1,1,0
1,1,1,1,1,0
1,0,1,1,1,0
1,0,1,1,1,0
1,1,1,1,1,0
1,0,1,1,1,0
1,0,1,1,1,0
1,0,1,1,1,0
0,0,0,0,0,0
0,0,1,1,1,0
1,0,1,1,1,0
1,0,1,1,1,0
1,1,1,1,0,0
1,0,1,1,1,0
1,0,1,1,1,0
0,0,1,1,1,0
1,0,1,1,1,0
1,0,1,1,1,0
1,0,1,1,1,0
1,1,1,0,1,0
1,0,1,1,1,0
0,0,1,1,1,0
1,0,1,1,1,0
1,1,0,1,1,0
0,0,0,0,0,0
0,0,1,1,1,0
1,0,1,1,1,0
=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
=== Summary ===
Correctly Classified Instances 94 100 %
Incorrectly Classified Instances 0 0 %
Kappa statistic 1
K&B Relative Info Score 97.4753 %
K&B Information Score 24.164 bits 0.2571 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 0.6259 bits 0.0067 bits/instance
Complexity improvement (Sf) 24.164 bits 0.2571 bits/instance
Mean absolute error 0.0045
Root mean squared error 0.0146
Relative absolute error 4.9294 %
Root relative squared error 7.1554 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
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0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
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=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
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0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
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0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
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0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
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=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
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0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
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=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
@relation rel
@attribute C_0 numeric
@attribute C_1 numeric
@attribute C_2 numeric
@attribute C_3 numeric
@attribute C_4 numeric
@attribute t {0,1}
@data
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1,1,1,1,1,1
1,1,1,1,1,1
1,1,1,1,1,1
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0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
0,0,0,0,0,0
=== Summary ===
Correctly Classified Instances 90 95.7447 %
Incorrectly Classified Instances 4 4.2553 %
Kappa statistic 0
K&B Relative Info Score 5.5498 %
K&B Information Score 1.3758 bits 0.0146 bits/instance
Class complexity | order 0 24.7899 bits 0.2637 bits/instance
Class complexity | scheme 24.8735 bits 0.2646 bits/instance
Complexity improvement (Sf) -0.0836 bits -0.0009 bits/instance
Mean absolute error 0.0817
Root mean squared error 0.2033
Relative absolute error 89.4343 %
Root relative squared error 99.8984 %
Total Number of Instances 94
[INFO] Tests run: 1, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 22.604 s - in org.dllearner.algorithms.miles.MILESTest
[INFO] Running org.dllearner.algorithms.probabilistic.structure.unife.leap.LEAPTest
Current dir: /usr/share/tomcat8/.jenkins/jobs/DL-Learner Merge-M/branches/PR-101/workspace/components-ext
Test case 1 - Equivalent axioms
[.....] 0%
[=....] 20%
[==...] 40%
[===..] 60%
[====.] 80%DEBUG (EDGE.java:63) - Initializing EDGE
Initializing EDGE
Debug logger: false
Creation of the learned ontology...
Successful creation of the learned ontology
Ontology created in 1.0 (ms)
# examples: 3
# positive examples: 3
# examples: 4
# negative examples: 1
Initializing...
Preparing Probability map...
Preparing Probability Map...
Random Seed set to: 0
Created 9 probabilistic axiom
Probability Map computed. Size: 9
Initializing...
Preparing Probability map...
Preparing Probability Map...
Random Seed set to: 0
Created 9 probabilistic axiom
Probability Map computed. Size: 9
Initialization completed
Start finding explanations for every example (Computing BDDs)...
Query 1 of 4 (25%)
Positive Example: stefan Type learnedClass
Axiom: stefan type learnedClass
Explanation(s):
1) learnedClass equivalentTo hasChild some person
markus hasChild anna
hasChild domain person
stefan hasChild markus
Query 2 of 4 (50%)
Positive Example: martin Type learnedClass
Axiom: martin type learnedClass
Explanation(s):
1) learnedClass equivalentTo hasChild some person
martin hasChild heinz
hasChild range person
Query 3 of 4 (75%)
Positive Example: markus Type learnedClass
Axiom: markus type learnedClass
Explanation(s):
1) learnedClass equivalentTo hasChild some person
markus hasChild anna
hasChild range person
Query 4 of 4 (100%)
Negative Example: heinz Type not (learnedClass)
Axiom: heinz type not learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Trying the second method...
Axiom: heinz type learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Explanations founding completed (BDDs computed)
Start EM Algorithm
- n. of probabilistic axioms: 9
- n. of examples: 4
stefan Type learnedClass - prob: 0.71989 - tag: 1 - #vars: 2
martin Type learnedClass - prob: 0.64265 - tag: 2 - #vars: 2
markus Type learnedClass - prob: 0.64265 - tag: 3 - #vars: 2
heinz Type learnedClass - prob: 1.00000 - tag: 4 - #vars: 0
EM cycle: 0
EM cycle: 1
Log-likelihood: -1.21298 cycle: 1
EM cycle: 2
Log-likelihood: 0.00000 cycle: 2
EM cycle: 3
Log-likelihood: 0.00000 cycle: 3
EM completed.
Final Log-Likelihood: 0.00000
Name | Total (ms)
===========================
main | 112
init | 32
Bundle | 63
Bundle.init | 0
Bundle.explain | 53
Bundle.BDDCalc | 1
EM | 6
Creation of the learned ontology...
Successful creation of the learned ontology
Ontology created in 3.0 (ms)
Initializing...
Preparing Probability map...
Preparing Probability Map...
Random Seed set to: 0
Created 10 probabilistic axiom
Probability Map computed. Size: 10
Initialization completed
Start finding explanations for every example (Computing BDDs)...
Query 1 of 4 (25%)
Positive Example: stefan Type learnedClass
Axiom: stefan type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild domain person
stefan hasChild markus
learnedClass equivalentTo ( female
or hasChild some person
)
2) stefan hasChild markus
learnedClass equivalentTo ( female
or hasChild some person
)
hasChild range person
3) learnedClass equivalentTo hasChild some person
stefan hasChild markus
hasChild range person
4) learnedClass equivalentTo hasChild some person
male subClassOf person
stefan hasChild markus
markus type male
5) learnedClass equivalentTo hasChild some person
markus hasChild anna
hasChild domain person
stefan hasChild markus
Query 2 of 4 (50%)
Positive Example: martin Type learnedClass
Axiom: martin type learnedClass
Explanation(s):
1) martin hasChild heinz
learnedClass equivalentTo ( female
or hasChild some person
)
hasChild range person
2) learnedClass equivalentTo hasChild some person
martin hasChild heinz
hasChild range person
3) learnedClass equivalentTo hasChild some person
heinz type male
male subClassOf person
martin hasChild heinz
Query 3 of 4 (75%)
Positive Example: markus Type learnedClass
Axiom: markus type learnedClass
Explanation(s):
1) markus hasChild anna
learnedClass equivalentTo ( female
or hasChild some person
)
hasChild range person
2) learnedClass equivalentTo hasChild some person
markus hasChild anna
hasChild range person
3) learnedClass equivalentTo hasChild some person
markus hasChild anna
hasChild domain person
anna hasChild heinz
Query 4 of 4 (100%)
Negative Example: heinz Type not (learnedClass)
Axiom: heinz type not learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Trying the second method...
Axiom: heinz type learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Explanations founding completed (BDDs computed)
Start EM Algorithm
- n. of probabilistic axioms: 10
- n. of examples: 4
stefan Type learnedClass - prob: 0.79105 - tag: 1 - #vars: 5
martin Type learnedClass - prob: 0.75738 - tag: 2 - #vars: 4
markus Type learnedClass - prob: 0.76136 - tag: 3 - #vars: 4
heinz Type learnedClass - prob: 1.00000 - tag: 4 - #vars: 0
EM cycle: 0
EM cycle: 1
Log-likelihood: -0.78494 cycle: 1
EM cycle: 2
Log-likelihood: -0.22112 cycle: 2
EM cycle: 3
Log-likelihood: -0.14687 cycle: 3
EM cycle: 4
Log-likelihood: -0.11459 cycle: 4
EM cycle: 5
Log-likelihood: -0.09587 cycle: 5
EM cycle: 6
Log-likelihood: -0.08346 cycle: 6
EM cycle: 7
Log-likelihood: -0.07450 cycle: 7
EM cycle: 8
Log-likelihood: -0.06768 cycle: 8
EM cycle: 9
Log-likelihood: -0.06227 cycle: 9
EM cycle: 10
Log-likelihood: -0.05786 cycle: 10
EM cycle: 11
Log-likelihood: -0.05416 cycle: 11
EM cycle: 12
Log-likelihood: -0.05102 cycle: 12
EM cycle: 13
Log-likelihood: -0.04833 cycle: 13
EM cycle: 14
Log-likelihood: -0.04598 cycle: 14
EM cycle: 15
Log-likelihood: -0.04391 cycle: 15
EM cycle: 16
Log-likelihood: -0.04205 cycle: 16
EM cycle: 17
Log-likelihood: -0.04039 cycle: 17
EM cycle: 18
Log-likelihood: -0.03890 cycle: 18
EM cycle: 19
Log-likelihood: -0.03753 cycle: 19
EM cycle: 20
Log-likelihood: -0.03628 cycle: 20
EM cycle: 21
Log-likelihood: -0.03515 cycle: 21
EM cycle: 22
Log-likelihood: -0.03408 cycle: 22
EM cycle: 23
Log-likelihood: -0.03310 cycle: 23
EM cycle: 24
Log-likelihood: -0.03223 cycle: 24
EM cycle: 25
Log-likelihood: -0.03138 cycle: 25
EM cycle: 26
Log-likelihood: -0.03059 cycle: 26
EM cycle: 27
Log-likelihood: -0.02985 cycle: 27
EM cycle: 28
Log-likelihood: -0.02916 cycle: 28
EM cycle: 29
Log-likelihood: -0.02849 cycle: 29
EM cycle: 30
Log-likelihood: -0.02787 cycle: 30
EM cycle: 31
Log-likelihood: -0.02729 cycle: 31
EM cycle: 32
Log-likelihood: -0.02677 cycle: 32
EM cycle: 33
Log-likelihood: -0.02621 cycle: 33
EM cycle: 34
Log-likelihood: -0.02574 cycle: 34
EM cycle: 35
Log-likelihood: -0.02526 cycle: 35
EM cycle: 36
Log-likelihood: -0.02483 cycle: 36
EM cycle: 37
Log-likelihood: -0.02438 cycle: 37
EM cycle: 38
Log-likelihood: -0.02395 cycle: 38
EM cycle: 39
Log-likelihood: -0.02358 cycle: 39
EM cycle: 40
Log-likelihood: -0.02322 cycle: 40
EM cycle: 41
Log-likelihood: -0.02284 cycle: 41
EM cycle: 42
Log-likelihood: -0.02250 cycle: 42
EM cycle: 43
Log-likelihood: -0.02216 cycle: 43
EM cycle: 44
Log-likelihood: -0.02183 cycle: 44
EM cycle: 45
Log-likelihood: -0.02151 cycle: 45
EM cycle: 46
Log-likelihood: -0.02121 cycle: 46
EM cycle: 47
Log-likelihood: -0.02095 cycle: 47
EM completed.
Final Log-Likelihood: -0.02095
Name | Total (ms)
===========================
main | 124
init | 1
Bundle | 85
Bundle.init | 1
Bundle.explain | 81
Bundle.BDDCalc | 0
EM | 36
Initializing...
Preparing Probability map...
Preparing Probability Map...
Random Seed set to: 0
Created 10 probabilistic axiom
Probability Map computed. Size: 10
Initialization completed
Start finding explanations for every example (Computing BDDs)...
Query 1 of 4 (25%)
Positive Example: stefan Type learnedClass
Axiom: stefan type learnedClass
Explanation(s):
1) learnedClass equivalentTo ( not male
or hasChild some person
)
markus hasChild anna
hasChild domain person
stefan hasChild markus
2) learnedClass equivalentTo hasChild some person
markus hasChild anna
hasChild domain person
stefan hasChild markus
3) learnedClass equivalentTo hasChild some person
stefan hasChild markus
hasChild range person
Query 2 of 4 (50%)
Positive Example: martin Type learnedClass
Axiom: martin type learnedClass
Explanation(s):
1) learnedClass equivalentTo ( not male
or hasChild some person
)
martin hasChild heinz
hasChild range person
2) learnedClass equivalentTo hasChild some person
martin hasChild heinz
hasChild range person
3) learnedClass equivalentTo hasChild some person
heinz type male
male subClassOf person
martin hasChild heinz
Query 3 of 4 (75%)
Positive Example: markus Type learnedClass
Axiom: markus type learnedClass
Explanation(s):
1) learnedClass equivalentTo ( not male
or hasChild some person
)
markus hasChild anna
hasChild range person
2) learnedClass equivalentTo hasChild some person
markus hasChild anna
hasChild range person
3) learnedClass equivalentTo hasChild some person
markus hasChild anna
hasChild domain person
anna hasChild heinz
Query 4 of 4 (100%)
Negative Example: heinz Type not (learnedClass)
Axiom: heinz type not learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Trying the second method...
Axiom: heinz type learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Explanations founding completed (BDDs computed)
Start EM Algorithm
- n. of probabilistic axioms: 10
- n. of examples: 4
stefan Type learnedClass - prob: 0.72690 - tag: 1 - #vars: 4
martin Type learnedClass - prob: 0.75738 - tag: 2 - #vars: 4
markus Type learnedClass - prob: 0.76136 - tag: 3 - #vars: 4
heinz Type learnedClass - prob: 1.00000 - tag: 4 - #vars: 0
EM cycle: 0
EM cycle: 1
Log-likelihood: -0.86953 cycle: 1
EM cycle: 2
Log-likelihood: -0.27628 cycle: 2
EM cycle: 3
Log-likelihood: -0.19277 cycle: 3
EM cycle: 4
Log-likelihood: -0.15547 cycle: 4
EM cycle: 5
Log-likelihood: -0.13352 cycle: 5
EM cycle: 6
Log-likelihood: -0.11868 cycle: 6
EM cycle: 7
Log-likelihood: -0.10786 cycle: 7
EM cycle: 8
Log-likelihood: -0.09954 cycle: 8
EM cycle: 9
Log-likelihood: -0.09284 cycle: 9
EM cycle: 10
Log-likelihood: -0.08736 cycle: 10
EM cycle: 11
Log-likelihood: -0.08275 cycle: 11
EM cycle: 12
Log-likelihood: -0.07881 cycle: 12
EM cycle: 13
Log-likelihood: -0.07536 cycle: 13
EM cycle: 14
Log-likelihood: -0.07235 cycle: 14
EM cycle: 15
Log-likelihood: -0.06966 cycle: 15
EM cycle: 16
Log-likelihood: -0.06729 cycle: 16
EM cycle: 17
Log-likelihood: -0.06513 cycle: 17
EM cycle: 18
Log-likelihood: -0.06319 cycle: 18
EM cycle: 19
Log-likelihood: -0.06141 cycle: 19
EM cycle: 20
Log-likelihood: -0.05975 cycle: 20
EM cycle: 21
Log-likelihood: -0.05826 cycle: 21
EM cycle: 22
Log-likelihood: -0.05685 cycle: 22
EM cycle: 23
Log-likelihood: -0.05553 cycle: 23
EM cycle: 24
Log-likelihood: -0.05434 cycle: 24
EM cycle: 25
Log-likelihood: -0.05321 cycle: 25
EM cycle: 26
Log-likelihood: -0.05211 cycle: 26
EM cycle: 27
Log-likelihood: -0.05116 cycle: 27
EM cycle: 28
Log-likelihood: -0.05018 cycle: 28
EM cycle: 29
Log-likelihood: -0.04931 cycle: 29
EM cycle: 30
Log-likelihood: -0.04849 cycle: 30
EM cycle: 31
Log-likelihood: -0.04769 cycle: 31
EM cycle: 32
Log-likelihood: -0.04692 cycle: 32
EM cycle: 33
Log-likelihood: -0.04616 cycle: 33
EM cycle: 34
Log-likelihood: -0.04551 cycle: 34
EM cycle: 35
Log-likelihood: -0.04484 cycle: 35
EM cycle: 36
Log-likelihood: -0.04421 cycle: 36
EM cycle: 37
Log-likelihood: -0.04361 cycle: 37
EM cycle: 38
Log-likelihood: -0.04306 cycle: 38
EM cycle: 39
Log-likelihood: -0.04248 cycle: 39
EM cycle: 40
Log-likelihood: -0.04195 cycle: 40
EM cycle: 41
Log-likelihood: -0.04145 cycle: 41
EM cycle: 42
Log-likelihood: -0.04096 cycle: 42
EM cycle: 43
Log-likelihood: -0.04050 cycle: 43
EM cycle: 44
Log-likelihood: -0.04004 cycle: 44
EM cycle: 45
Log-likelihood: -0.03961 cycle: 45
EM cycle: 46
Log-likelihood: -0.03919 cycle: 46
EM cycle: 47
Log-likelihood: -0.03875 cycle: 47
EM cycle: 48
Log-likelihood: -0.03835 cycle: 48
EM cycle: 49
Log-likelihood: -0.03799 cycle: 49
EM cycle: 50
Log-likelihood: -0.03763 cycle: 50
EM cycle: 51
Log-likelihood: -0.03725 cycle: 51
EM cycle: 52
Log-likelihood: -0.03691 cycle: 52
EM cycle: 53
Log-likelihood: -0.03655 cycle: 53
EM cycle: 54
Log-likelihood: -0.03622 cycle: 54
EM cycle: 55
Log-likelihood: -0.03592 cycle: 55
EM cycle: 56
Log-likelihood: -0.03559 cycle: 56
EM cycle: 57
Log-likelihood: -0.03529 cycle: 57
EM cycle: 58
Log-likelihood: -0.03502 cycle: 58
EM completed.
Final Log-Likelihood: -0.03502
Name | Total (ms)
===========================
main | 83
init | 1
Bundle | 62
Bundle.init | 1
Bundle.explain | 55
Bundle.BDDCalc | 2
EM | 20
Test case 2 - SubClassOf axioms
[.....] 0%
[=....] 20%
[==...] 40%
[===..] 60%
[====.] 80%DEBUG (EDGE.java:63) - Initializing EDGE
Initializing EDGE
Debug logger: false
Creation of the learned ontology...
Successful creation of the learned ontology
Ontology created in 0.0 (ms)
# examples: 3
# positive examples: 3
# examples: 4
# negative examples: 1
Initializing...
Preparing Probability map...
Preparing Probability Map...
Random Seed set to: 0
Created 9 probabilistic axiom
Probability Map computed. Size: 9
Initializing...
Preparing Probability map...
Preparing Probability Map...
Random Seed set to: 0
Created 9 probabilistic axiom
Probability Map computed. Size: 9
Initialization completed
Start finding explanations for every example (Computing BDDs)...
Query 1 of 4 (25%)
Positive Example: stefan Type learnedClass
Axiom: stefan type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild domain person
stefan hasChild markus
hasChild some person subClassOf learnedClass
2) stefan hasChild markus
hasChild range person
hasChild some person subClassOf learnedClass
Query 2 of 4 (50%)
Positive Example: martin Type learnedClass
Axiom: martin type learnedClass
Explanation(s):
1) martin hasChild heinz
hasChild range person
hasChild some person subClassOf learnedClass
2) heinz type male
male subClassOf person
martin hasChild heinz
hasChild some person subClassOf learnedClass
Query 3 of 4 (75%)
Positive Example: markus Type learnedClass
Axiom: markus type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild range person
hasChild some person subClassOf learnedClass
2) markus hasChild anna
hasChild domain person
anna hasChild heinz
hasChild some person subClassOf learnedClass
Query 4 of 4 (100%)
Negative Example: heinz Type not (learnedClass)
Axiom: heinz type not learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Trying the second method...
Axiom: heinz type learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Explanations founding completed (BDDs computed)
Start EM Algorithm
- n. of probabilistic axioms: 9
- n. of examples: 4
stefan Type learnedClass - prob: 0.33261 - tag: 1 - #vars: 3
martin Type learnedClass - prob: 0.31862 - tag: 2 - #vars: 3
markus Type learnedClass - prob: 0.33261 - tag: 3 - #vars: 3
heinz Type learnedClass - prob: 1.00000 - tag: 4 - #vars: 0
EM cycle: 0
EM cycle: 1
Log-likelihood: -3.34532 cycle: 1
EM cycle: 2
Log-likelihood: -0.03892 cycle: 2
EM cycle: 3
Log-likelihood: -0.03532 cycle: 3
EM cycle: 4
Log-likelihood: -0.03320 cycle: 4
EM cycle: 5
Log-likelihood: -0.03176 cycle: 5
EM cycle: 6
Log-likelihood: -0.03064 cycle: 6
EM cycle: 7
Log-likelihood: -0.02978 cycle: 7
EM cycle: 8
Log-likelihood: -0.02906 cycle: 8
EM cycle: 9
Log-likelihood: -0.02847 cycle: 9
EM cycle: 10
Log-likelihood: -0.02791 cycle: 10
EM cycle: 11
Log-likelihood: -0.02747 cycle: 11
EM cycle: 12
Log-likelihood: -0.02705 cycle: 12
EM cycle: 13
Log-likelihood: -0.02670 cycle: 13
EM cycle: 14
Log-likelihood: -0.02636 cycle: 14
EM cycle: 15
Log-likelihood: -0.02605 cycle: 15
EM cycle: 16
Log-likelihood: -0.02578 cycle: 16
EM completed.
Final Log-Likelihood: -0.02578
Name | Total (ms)
===========================
main | 46
init | 1
Bundle | 38
Bundle.init | 0
Bundle.explain | 35
Bundle.BDDCalc | 0
EM | 5
Creation of the learned ontology...
Successful creation of the learned ontology
Ontology created in 1.0 (ms)
Initializing...
Preparing Probability map...
Preparing Probability Map...
Random Seed set to: 0
Created 10 probabilistic axiom
Probability Map computed. Size: 10
Initialization completed
Start finding explanations for every example (Computing BDDs)...
Query 1 of 4 (25%)
Positive Example: stefan Type learnedClass
Axiom: stefan type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild domain person
stefan hasChild markus
hasChild some person subClassOf learnedClass
2) stefan hasChild markus
hasChild range person
hasChild some person subClassOf learnedClass
3) ( female
or hasChild some person
) subClassOf learnedClass
stefan hasChild markus
hasChild range person
4) ( female
or hasChild some person
) subClassOf learnedClass
male subClassOf person
stefan hasChild markus
markus type male
5) markus hasChild anna
( female
or hasChild some person
) subClassOf learnedClass
hasChild domain person
stefan hasChild markus
Query 2 of 4 (50%)
Positive Example: martin Type learnedClass
Axiom: martin type learnedClass
Explanation(s):
1) martin hasChild heinz
hasChild range person
hasChild some person subClassOf learnedClass
2) heinz type male
male subClassOf person
martin hasChild heinz
hasChild some person subClassOf learnedClass
3) ( female
or hasChild some person
) subClassOf learnedClass
heinz type male
male subClassOf person
martin hasChild heinz
Query 3 of 4 (75%)
Positive Example: markus Type learnedClass
Axiom: markus type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild range person
hasChild some person subClassOf learnedClass
2) markus hasChild anna
hasChild domain person
anna hasChild heinz
hasChild some person subClassOf learnedClass
3) markus hasChild anna
( female
or hasChild some person
) subClassOf learnedClass
hasChild domain person
anna hasChild heinz
4) markus hasChild anna
female subClassOf person
( female
or hasChild some person
) subClassOf learnedClass
anna type female
Query 4 of 4 (100%)
Negative Example: heinz Type not (learnedClass)
Axiom: heinz type not learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Trying the second method...
Axiom: heinz type learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Explanations founding completed (BDDs computed)
Start EM Algorithm
- n. of probabilistic axioms: 10
- n. of examples: 4
stefan Type learnedClass - prob: 0.58738 - tag: 1 - #vars: 5
martin Type learnedClass - prob: 0.50757 - tag: 2 - #vars: 4
markus Type learnedClass - prob: 0.56874 - tag: 3 - #vars: 5
heinz Type learnedClass - prob: 1.00000 - tag: 4 - #vars: 0
EM cycle: 0
EM cycle: 1
Log-likelihood: -1.77449 cycle: 1
EM cycle: 2
Log-likelihood: -0.56019 cycle: 2
EM cycle: 3
Log-likelihood: -0.40000 cycle: 3
EM cycle: 4
Log-likelihood: -0.32903 cycle: 4
EM cycle: 5
Log-likelihood: -0.28721 cycle: 5
EM cycle: 6
Log-likelihood: -0.25892 cycle: 6
EM cycle: 7
Log-likelihood: -0.23819 cycle: 7
EM cycle: 8
Log-likelihood: -0.22218 cycle: 8
EM cycle: 9
Log-likelihood: -0.20929 cycle: 9
EM cycle: 10
Log-likelihood: -0.19868 cycle: 10
EM cycle: 11
Log-likelihood: -0.18970 cycle: 11
EM cycle: 12
Log-likelihood: -0.18203 cycle: 12
EM cycle: 13
Log-likelihood: -0.17531 cycle: 13
EM cycle: 14
Log-likelihood: -0.16940 cycle: 14
EM cycle: 15
Log-likelihood: -0.16414 cycle: 15
EM cycle: 16
Log-likelihood: -0.15941 cycle: 16
EM cycle: 17
Log-likelihood: -0.15515 cycle: 17
EM cycle: 18
Log-likelihood: -0.15125 cycle: 18
EM cycle: 19
Log-likelihood: -0.14771 cycle: 19
EM cycle: 20
Log-likelihood: -0.14444 cycle: 20
EM cycle: 21
Log-likelihood: -0.14138 cycle: 21
EM cycle: 22
Log-likelihood: -0.13858 cycle: 22
EM cycle: 23
Log-likelihood: -0.13597 cycle: 23
EM cycle: 24
Log-likelihood: -0.13352 cycle: 24
EM cycle: 25
Log-likelihood: -0.13123 cycle: 25
EM cycle: 26
Log-likelihood: -0.12908 cycle: 26
EM cycle: 27
Log-likelihood: -0.12704 cycle: 27
EM cycle: 28
Log-likelihood: -0.12514 cycle: 28
EM cycle: 29
Log-likelihood: -0.12334 cycle: 29
EM cycle: 30
Log-likelihood: -0.12159 cycle: 30
EM cycle: 31
Log-likelihood: -0.11994 cycle: 31
EM cycle: 32
Log-likelihood: -0.11840 cycle: 32
EM cycle: 33
Log-likelihood: -0.11691 cycle: 33
EM cycle: 34
Log-likelihood: -0.11547 cycle: 34
EM cycle: 35
Log-likelihood: -0.11414 cycle: 35
EM cycle: 36
Log-likelihood: -0.11284 cycle: 36
EM cycle: 37
Log-likelihood: -0.11159 cycle: 37
EM cycle: 38
Log-likelihood: -0.11040 cycle: 38
EM cycle: 39
Log-likelihood: -0.10923 cycle: 39
EM cycle: 40
Log-likelihood: -0.10814 cycle: 40
EM cycle: 41
Log-likelihood: -0.10707 cycle: 41
EM cycle: 42
Log-likelihood: -0.10605 cycle: 42
EM cycle: 43
Log-likelihood: -0.10505 cycle: 43
EM cycle: 44
Log-likelihood: -0.10409 cycle: 44
EM cycle: 45
Log-likelihood: -0.10317 cycle: 45
EM cycle: 46
Log-likelihood: -0.10227 cycle: 46
EM cycle: 47
Log-likelihood: -0.10143 cycle: 47
EM cycle: 48
Log-likelihood: -0.10057 cycle: 48
EM cycle: 49
Log-likelihood: -0.09976 cycle: 49
EM cycle: 50
Log-likelihood: -0.09898 cycle: 50
EM cycle: 51
Log-likelihood: -0.09823 cycle: 51
EM cycle: 52
Log-likelihood: -0.09747 cycle: 52
EM cycle: 53
Log-likelihood: -0.09674 cycle: 53
EM cycle: 54
Log-likelihood: -0.09603 cycle: 54
EM cycle: 55
Log-likelihood: -0.09536 cycle: 55
EM cycle: 56
Log-likelihood: -0.09469 cycle: 56
EM cycle: 57
Log-likelihood: -0.09404 cycle: 57
EM cycle: 58
Log-likelihood: -0.09341 cycle: 58
EM cycle: 59
Log-likelihood: -0.09280 cycle: 59
EM cycle: 60
Log-likelihood: -0.09219 cycle: 60
EM cycle: 61
Log-likelihood: -0.09160 cycle: 61
EM cycle: 62
Log-likelihood: -0.09103 cycle: 62
EM cycle: 63
Log-likelihood: -0.09049 cycle: 63
EM cycle: 64
Log-likelihood: -0.08993 cycle: 64
EM cycle: 65
Log-likelihood: -0.08940 cycle: 65
EM cycle: 66
Log-likelihood: -0.08889 cycle: 66
EM cycle: 67
Log-likelihood: -0.08838 cycle: 67
EM cycle: 68
Log-likelihood: -0.08789 cycle: 68
EM cycle: 69
Log-likelihood: -0.08738 cycle: 69
EM cycle: 70
Log-likelihood: -0.08691 cycle: 70
EM cycle: 71
Log-likelihood: -0.08644 cycle: 71
EM cycle: 72
Log-likelihood: -0.08598 cycle: 72
EM cycle: 73
Log-likelihood: -0.08555 cycle: 73
EM cycle: 74
Log-likelihood: -0.08509 cycle: 74
EM cycle: 75
Log-likelihood: -0.08465 cycle: 75
EM cycle: 76
Log-likelihood: -0.08424 cycle: 76
EM cycle: 77
Log-likelihood: -0.08383 cycle: 77
EM cycle: 78
Log-likelihood: -0.08341 cycle: 78
EM cycle: 79
Log-likelihood: -0.08302 cycle: 79
EM cycle: 80
Log-likelihood: -0.08262 cycle: 80
EM cycle: 81
Log-likelihood: -0.08224 cycle: 81
EM cycle: 82
Log-likelihood: -0.08187 cycle: 82
EM cycle: 83
Log-likelihood: -0.08149 cycle: 83
EM cycle: 84
Log-likelihood: -0.08113 cycle: 84
EM cycle: 85
Log-likelihood: -0.08076 cycle: 85
EM cycle: 86
Log-likelihood: -0.08042 cycle: 86
EM cycle: 87
Log-likelihood: -0.08007 cycle: 87
EM cycle: 88
Log-likelihood: -0.07972 cycle: 88
EM cycle: 89
Log-likelihood: -0.07940 cycle: 89
EM cycle: 90
Log-likelihood: -0.07904 cycle: 90
EM cycle: 91
Log-likelihood: -0.07875 cycle: 91
EM cycle: 92
Log-likelihood: -0.07841 cycle: 92
EM cycle: 93
Log-likelihood: -0.07809 cycle: 93
EM cycle: 94
Log-likelihood: -0.07779 cycle: 94
EM cycle: 95
Log-likelihood: -0.07750 cycle: 95
EM cycle: 96
Log-likelihood: -0.07719 cycle: 96
EM cycle: 97
Log-likelihood: -0.07691 cycle: 97
EM completed.
Final Log-Likelihood: -0.07691
Name | Total (ms)
===========================
main | 96
init | 0
Bundle | 71
Bundle.init | 0
Bundle.explain | 66
Bundle.BDDCalc | 2
EM | 24
Initializing...
Preparing Probability map...
Preparing Probability Map...
Random Seed set to: 0
Created 10 probabilistic axiom
Probability Map computed. Size: 10
Initialization completed
Start finding explanations for every example (Computing BDDs)...
Query 1 of 4 (25%)
Positive Example: stefan Type learnedClass
Axiom: stefan type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild domain person
stefan hasChild markus
hasChild some person subClassOf learnedClass
2) stefan hasChild markus
hasChild range person
hasChild some person subClassOf learnedClass
3) stefan type male
( male
and hasChild some person
) subClassOf learnedClass
stefan hasChild markus
hasChild range person
4) stefan type male
( male
and hasChild some person
) subClassOf learnedClass
male subClassOf person
stefan hasChild markus
markus type male
5) father subClassOf male
( male
and hasChild some person
) subClassOf learnedClass
stefan type father
male subClassOf person
stefan hasChild markus
markus type male
6) stefan type male
markus hasChild anna
( male
and hasChild some person
) subClassOf learnedClass
hasChild domain person
stefan hasChild markus
Query 2 of 4 (50%)
Positive Example: martin Type learnedClass
Axiom: martin type learnedClass
Explanation(s):
1) martin hasChild heinz
hasChild range person
hasChild some person subClassOf learnedClass
2) heinz type male
male subClassOf person
martin hasChild heinz
hasChild some person subClassOf learnedClass
3) martin type male
( male
and hasChild some person
) subClassOf learnedClass
heinz type male
male subClassOf person
martin hasChild heinz
Query 3 of 4 (75%)
Positive Example: markus Type learnedClass
Axiom: markus type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild range person
hasChild some person subClassOf learnedClass
2) markus hasChild anna
hasChild domain person
anna hasChild heinz
hasChild some person subClassOf learnedClass
3) markus hasChild anna
( male
and hasChild some person
) subClassOf learnedClass
hasChild domain person
markus type male
anna hasChild heinz
4) markus hasChild anna
female subClassOf person
( male
and hasChild some person
) subClassOf learnedClass
markus type male
anna type female
Query 4 of 4 (100%)
Negative Example: heinz Type not (learnedClass)
Axiom: heinz type not learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Trying the second method...
Axiom: heinz type learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Explanations founding completed (BDDs computed)
Start EM Algorithm
- n. of probabilistic axioms: 10
- n. of examples: 4
stefan Type learnedClass - prob: 0.58738 - tag: 1 - #vars: 6
martin Type learnedClass - prob: 0.50757 - tag: 2 - #vars: 4
markus Type learnedClass - prob: 0.56874 - tag: 3 - #vars: 5
heinz Type learnedClass - prob: 1.00000 - tag: 4 - #vars: 0
EM cycle: 0
EM cycle: 1
Log-likelihood: -1.77449 cycle: 1
EM cycle: 2
Log-likelihood: -0.56019 cycle: 2
EM cycle: 3
Log-likelihood: -0.40000 cycle: 3
EM cycle: 4
Log-likelihood: -0.32903 cycle: 4
EM cycle: 5
Log-likelihood: -0.28721 cycle: 5
EM cycle: 6
Log-likelihood: -0.25892 cycle: 6
EM cycle: 7
Log-likelihood: -0.23819 cycle: 7
EM cycle: 8
Log-likelihood: -0.22218 cycle: 8
EM cycle: 9
Log-likelihood: -0.20929 cycle: 9
EM cycle: 10
Log-likelihood: -0.19868 cycle: 10
EM cycle: 11
Log-likelihood: -0.18970 cycle: 11
EM cycle: 12
Log-likelihood: -0.18203 cycle: 12
EM cycle: 13
Log-likelihood: -0.17531 cycle: 13
EM cycle: 14
Log-likelihood: -0.16940 cycle: 14
EM cycle: 15
Log-likelihood: -0.16414 cycle: 15
EM cycle: 16
Log-likelihood: -0.15941 cycle: 16
EM cycle: 17
Log-likelihood: -0.15515 cycle: 17
EM cycle: 18
Log-likelihood: -0.15125 cycle: 18
EM cycle: 19
Log-likelihood: -0.14771 cycle: 19
EM cycle: 20
Log-likelihood: -0.14444 cycle: 20
EM cycle: 21
Log-likelihood: -0.14138 cycle: 21
EM cycle: 22
Log-likelihood: -0.13858 cycle: 22
EM cycle: 23
Log-likelihood: -0.13597 cycle: 23
EM cycle: 24
Log-likelihood: -0.13352 cycle: 24
EM cycle: 25
Log-likelihood: -0.13123 cycle: 25
EM cycle: 26
Log-likelihood: -0.12908 cycle: 26
EM cycle: 27
Log-likelihood: -0.12704 cycle: 27
EM cycle: 28
Log-likelihood: -0.12514 cycle: 28
EM cycle: 29
Log-likelihood: -0.12334 cycle: 29
EM cycle: 30
Log-likelihood: -0.12159 cycle: 30
EM cycle: 31
Log-likelihood: -0.11994 cycle: 31
EM cycle: 32
Log-likelihood: -0.11840 cycle: 32
EM cycle: 33
Log-likelihood: -0.11691 cycle: 33
EM cycle: 34
Log-likelihood: -0.11547 cycle: 34
EM cycle: 35
Log-likelihood: -0.11414 cycle: 35
EM cycle: 36
Log-likelihood: -0.11284 cycle: 36
EM cycle: 37
Log-likelihood: -0.11159 cycle: 37
EM cycle: 38
Log-likelihood: -0.11040 cycle: 38
EM cycle: 39
Log-likelihood: -0.10923 cycle: 39
EM cycle: 40
Log-likelihood: -0.10814 cycle: 40
EM cycle: 41
Log-likelihood: -0.10707 cycle: 41
EM cycle: 42
Log-likelihood: -0.10605 cycle: 42
EM cycle: 43
Log-likelihood: -0.10505 cycle: 43
EM cycle: 44
Log-likelihood: -0.10409 cycle: 44
EM cycle: 45
Log-likelihood: -0.10317 cycle: 45
EM cycle: 46
Log-likelihood: -0.10227 cycle: 46
EM cycle: 47
Log-likelihood: -0.10143 cycle: 47
EM cycle: 48
Log-likelihood: -0.10057 cycle: 48
EM cycle: 49
Log-likelihood: -0.09976 cycle: 49
EM cycle: 50
Log-likelihood: -0.09898 cycle: 50
EM cycle: 51
Log-likelihood: -0.09823 cycle: 51
EM cycle: 52
Log-likelihood: -0.09747 cycle: 52
EM cycle: 53
Log-likelihood: -0.09674 cycle: 53
EM cycle: 54
Log-likelihood: -0.09603 cycle: 54
EM cycle: 55
Log-likelihood: -0.09536 cycle: 55
EM cycle: 56
Log-likelihood: -0.09469 cycle: 56
EM cycle: 57
Log-likelihood: -0.09404 cycle: 57
EM cycle: 58
Log-likelihood: -0.09341 cycle: 58
EM cycle: 59
Log-likelihood: -0.09280 cycle: 59
EM cycle: 60
Log-likelihood: -0.09219 cycle: 60
EM cycle: 61
Log-likelihood: -0.09160 cycle: 61
EM cycle: 62
Log-likelihood: -0.09103 cycle: 62
EM cycle: 63
Log-likelihood: -0.09049 cycle: 63
EM cycle: 64
Log-likelihood: -0.08993 cycle: 64
EM cycle: 65
Log-likelihood: -0.08940 cycle: 65
EM cycle: 66
Log-likelihood: -0.08889 cycle: 66
EM cycle: 67
Log-likelihood: -0.08838 cycle: 67
EM cycle: 68
Log-likelihood: -0.08789 cycle: 68
EM cycle: 69
Log-likelihood: -0.08738 cycle: 69
EM cycle: 70
Log-likelihood: -0.08691 cycle: 70
EM cycle: 71
Log-likelihood: -0.08644 cycle: 71
EM cycle: 72
Log-likelihood: -0.08598 cycle: 72
EM cycle: 73
Log-likelihood: -0.08555 cycle: 73
EM cycle: 74
Log-likelihood: -0.08509 cycle: 74
EM cycle: 75
Log-likelihood: -0.08465 cycle: 75
EM cycle: 76
Log-likelihood: -0.08424 cycle: 76
EM cycle: 77
Log-likelihood: -0.08383 cycle: 77
EM cycle: 78
Log-likelihood: -0.08341 cycle: 78
EM cycle: 79
Log-likelihood: -0.08302 cycle: 79
EM cycle: 80
Log-likelihood: -0.08262 cycle: 80
EM cycle: 81
Log-likelihood: -0.08224 cycle: 81
EM cycle: 82
Log-likelihood: -0.08187 cycle: 82
EM cycle: 83
Log-likelihood: -0.08149 cycle: 83
EM cycle: 84
Log-likelihood: -0.08113 cycle: 84
EM cycle: 85
Log-likelihood: -0.08076 cycle: 85
EM cycle: 86
Log-likelihood: -0.08042 cycle: 86
EM cycle: 87
Log-likelihood: -0.08007 cycle: 87
EM cycle: 88
Log-likelihood: -0.07972 cycle: 88
EM cycle: 89
Log-likelihood: -0.07940 cycle: 89
EM cycle: 90
Log-likelihood: -0.07904 cycle: 90
EM cycle: 91
Log-likelihood: -0.07875 cycle: 91
EM cycle: 92
Log-likelihood: -0.07841 cycle: 92
EM cycle: 93
Log-likelihood: -0.07809 cycle: 93
EM cycle: 94
Log-likelihood: -0.07779 cycle: 94
EM cycle: 95
Log-likelihood: -0.07750 cycle: 95
EM cycle: 96
Log-likelihood: -0.07719 cycle: 96
EM cycle: 97
Log-likelihood: -0.07691 cycle: 97
EM completed.
Final Log-Likelihood: -0.07691
Name | Total (ms)
===========================
main | 84
init | 1
Bundle | 67
Bundle.init | 0
Bundle.explain | 61
Bundle.BDDCalc | 1
EM | 15
Initializing...
Preparing Probability map...
Preparing Probability Map...
Random Seed set to: 0
Created 10 probabilistic axiom
Probability Map computed. Size: 10
Initialization completed
Start finding explanations for every example (Computing BDDs)...
Query 1 of 4 (25%)
Positive Example: stefan Type learnedClass
Axiom: stefan type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild domain person
stefan hasChild markus
hasChild some person subClassOf learnedClass
2) stefan hasChild markus
hasChild range person
hasChild some person subClassOf learnedClass
3) ( not male
or hasChild some person
) subClassOf learnedClass
stefan hasChild markus
hasChild range person
4) ( not male
or hasChild some person
) subClassOf learnedClass
male subClassOf person
stefan hasChild markus
markus type male
5) markus hasChild anna
hasChild domain person
( not male
or hasChild some person
) subClassOf learnedClass
stefan hasChild markus
Query 2 of 4 (50%)
Positive Example: martin Type learnedClass
Axiom: martin type learnedClass
Explanation(s):
1) martin hasChild heinz
hasChild range person
hasChild some person subClassOf learnedClass
2) heinz type male
male subClassOf person
martin hasChild heinz
hasChild some person subClassOf learnedClass
3) heinz type male
( not male
or hasChild some person
) subClassOf learnedClass
male subClassOf person
martin hasChild heinz
Query 3 of 4 (75%)
Positive Example: markus Type learnedClass
Axiom: markus type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild range person
hasChild some person subClassOf learnedClass
2) markus hasChild anna
hasChild domain person
anna hasChild heinz
hasChild some person subClassOf learnedClass
3) markus hasChild anna
hasChild domain person
( not male
or hasChild some person
) subClassOf learnedClass
anna hasChild heinz
4) markus hasChild anna
female subClassOf person
( not male
or hasChild some person
) subClassOf learnedClass
anna type female
Query 4 of 4 (100%)
Negative Example: heinz Type not (learnedClass)
Axiom: heinz type not learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Trying the second method...
Axiom: heinz type learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Explanations founding completed (BDDs computed)
Start EM Algorithm
- n. of probabilistic axioms: 10
- n. of examples: 4
stefan Type learnedClass - prob: 0.58738 - tag: 1 - #vars: 5
martin Type learnedClass - prob: 0.50757 - tag: 2 - #vars: 4
markus Type learnedClass - prob: 0.56874 - tag: 3 - #vars: 5
heinz Type learnedClass - prob: 1.00000 - tag: 4 - #vars: 0
EM cycle: 0
EM cycle: 1
Log-likelihood: -1.77449 cycle: 1
EM cycle: 2
Log-likelihood: -0.56019 cycle: 2
EM cycle: 3
Log-likelihood: -0.40000 cycle: 3
EM cycle: 4
Log-likelihood: -0.32903 cycle: 4
EM cycle: 5
Log-likelihood: -0.28721 cycle: 5
EM cycle: 6
Log-likelihood: -0.25892 cycle: 6
EM cycle: 7
Log-likelihood: -0.23819 cycle: 7
EM cycle: 8
Log-likelihood: -0.22218 cycle: 8
EM cycle: 9
Log-likelihood: -0.20929 cycle: 9
EM cycle: 10
Log-likelihood: -0.19868 cycle: 10
EM cycle: 11
Log-likelihood: -0.18970 cycle: 11
EM cycle: 12
Log-likelihood: -0.18203 cycle: 12
EM cycle: 13
Log-likelihood: -0.17531 cycle: 13
EM cycle: 14
Log-likelihood: -0.16940 cycle: 14
EM cycle: 15
Log-likelihood: -0.16414 cycle: 15
EM cycle: 16
Log-likelihood: -0.15941 cycle: 16
EM cycle: 17
Log-likelihood: -0.15515 cycle: 17
EM cycle: 18
Log-likelihood: -0.15125 cycle: 18
EM cycle: 19
Log-likelihood: -0.14771 cycle: 19
EM cycle: 20
Log-likelihood: -0.14444 cycle: 20
EM cycle: 21
Log-likelihood: -0.14138 cycle: 21
EM cycle: 22
Log-likelihood: -0.13858 cycle: 22
EM cycle: 23
Log-likelihood: -0.13597 cycle: 23
EM cycle: 24
Log-likelihood: -0.13352 cycle: 24
EM cycle: 25
Log-likelihood: -0.13123 cycle: 25
EM cycle: 26
Log-likelihood: -0.12908 cycle: 26
EM cycle: 27
Log-likelihood: -0.12704 cycle: 27
EM cycle: 28
Log-likelihood: -0.12514 cycle: 28
EM cycle: 29
Log-likelihood: -0.12334 cycle: 29
EM cycle: 30
Log-likelihood: -0.12159 cycle: 30
EM cycle: 31
Log-likelihood: -0.11994 cycle: 31
EM cycle: 32
Log-likelihood: -0.11840 cycle: 32
EM cycle: 33
Log-likelihood: -0.11691 cycle: 33
EM cycle: 34
Log-likelihood: -0.11547 cycle: 34
EM cycle: 35
Log-likelihood: -0.11414 cycle: 35
EM cycle: 36
Log-likelihood: -0.11284 cycle: 36
EM cycle: 37
Log-likelihood: -0.11159 cycle: 37
EM cycle: 38
Log-likelihood: -0.11040 cycle: 38
EM cycle: 39
Log-likelihood: -0.10923 cycle: 39
EM cycle: 40
Log-likelihood: -0.10814 cycle: 40
EM cycle: 41
Log-likelihood: -0.10707 cycle: 41
EM cycle: 42
Log-likelihood: -0.10605 cycle: 42
EM cycle: 43
Log-likelihood: -0.10505 cycle: 43
EM cycle: 44
Log-likelihood: -0.10409 cycle: 44
EM cycle: 45
Log-likelihood: -0.10317 cycle: 45
EM cycle: 46
Log-likelihood: -0.10227 cycle: 46
EM cycle: 47
Log-likelihood: -0.10143 cycle: 47
EM cycle: 48
Log-likelihood: -0.10057 cycle: 48
EM cycle: 49
Log-likelihood: -0.09976 cycle: 49
EM cycle: 50
Log-likelihood: -0.09898 cycle: 50
EM cycle: 51
Log-likelihood: -0.09823 cycle: 51
EM cycle: 52
Log-likelihood: -0.09747 cycle: 52
EM cycle: 53
Log-likelihood: -0.09674 cycle: 53
EM cycle: 54
Log-likelihood: -0.09603 cycle: 54
EM cycle: 55
Log-likelihood: -0.09536 cycle: 55
EM cycle: 56
Log-likelihood: -0.09469 cycle: 56
EM cycle: 57
Log-likelihood: -0.09404 cycle: 57
EM cycle: 58
Log-likelihood: -0.09341 cycle: 58
EM cycle: 59
Log-likelihood: -0.09280 cycle: 59
EM cycle: 60
Log-likelihood: -0.09219 cycle: 60
EM cycle: 61
Log-likelihood: -0.09160 cycle: 61
EM cycle: 62
Log-likelihood: -0.09103 cycle: 62
EM cycle: 63
Log-likelihood: -0.09049 cycle: 63
EM cycle: 64
Log-likelihood: -0.08993 cycle: 64
EM cycle: 65
Log-likelihood: -0.08940 cycle: 65
EM cycle: 66
Log-likelihood: -0.08889 cycle: 66
EM cycle: 67
Log-likelihood: -0.08838 cycle: 67
EM cycle: 68
Log-likelihood: -0.08789 cycle: 68
EM cycle: 69
Log-likelihood: -0.08738 cycle: 69
EM cycle: 70
Log-likelihood: -0.08691 cycle: 70
EM cycle: 71
Log-likelihood: -0.08644 cycle: 71
EM cycle: 72
Log-likelihood: -0.08598 cycle: 72
EM cycle: 73
Log-likelihood: -0.08555 cycle: 73
EM cycle: 74
Log-likelihood: -0.08509 cycle: 74
EM cycle: 75
Log-likelihood: -0.08465 cycle: 75
EM cycle: 76
Log-likelihood: -0.08424 cycle: 76
EM cycle: 77
Log-likelihood: -0.08383 cycle: 77
EM cycle: 78
Log-likelihood: -0.08341 cycle: 78
EM cycle: 79
Log-likelihood: -0.08302 cycle: 79
EM cycle: 80
Log-likelihood: -0.08262 cycle: 80
EM cycle: 81
Log-likelihood: -0.08224 cycle: 81
EM cycle: 82
Log-likelihood: -0.08187 cycle: 82
EM cycle: 83
Log-likelihood: -0.08149 cycle: 83
EM cycle: 84
Log-likelihood: -0.08113 cycle: 84
EM cycle: 85
Log-likelihood: -0.08076 cycle: 85
EM cycle: 86
Log-likelihood: -0.08042 cycle: 86
EM cycle: 87
Log-likelihood: -0.08007 cycle: 87
EM cycle: 88
Log-likelihood: -0.07972 cycle: 88
EM cycle: 89
Log-likelihood: -0.07940 cycle: 89
EM cycle: 90
Log-likelihood: -0.07904 cycle: 90
EM cycle: 91
Log-likelihood: -0.07875 cycle: 91
EM cycle: 92
Log-likelihood: -0.07841 cycle: 92
EM cycle: 93
Log-likelihood: -0.07809 cycle: 93
EM cycle: 94
Log-likelihood: -0.07779 cycle: 94
EM cycle: 95
Log-likelihood: -0.07750 cycle: 95
EM cycle: 96
Log-likelihood: -0.07719 cycle: 96
EM cycle: 97
Log-likelihood: -0.07691 cycle: 97
EM completed.
Final Log-Likelihood: -0.07691
Name | Total (ms)
===========================
main | 72
init | 1
Bundle | 54
Bundle.init | 0
Bundle.explain | 50
Bundle.BDDCalc | 2
EM | 17
Initializing...
Preparing Probability map...
Preparing Probability Map...
Random Seed set to: 0
Created 10 probabilistic axiom
Probability Map computed. Size: 10
Initialization completed
Start finding explanations for every example (Computing BDDs)...
Query 1 of 4 (25%)
Positive Example: stefan Type learnedClass
Axiom: stefan type learnedClass
Explanation(s):
1) stefan type male
markus hasChild anna
hasChild domain person
female disjointWith male
stefan hasChild markus
( not female
and hasChild some person
) subClassOf learnedClass
2) markus hasChild anna
father subClassOf male
hasChild domain person
stefan type father
female disjointWith male
stefan hasChild markus
( not female
and hasChild some person
) subClassOf learnedClass
3) father subClassOf male
stefan type father
female disjointWith male
stefan hasChild markus
( not female
and hasChild some person
) subClassOf learnedClass
hasChild range person
4) stefan hasChild markus
hasChild range person
hasChild some person subClassOf learnedClass
5) male subClassOf person
stefan hasChild markus
markus type male
hasChild some person subClassOf learnedClass
6) markus hasChild anna
hasChild domain person
stefan hasChild markus
hasChild some person subClassOf learnedClass
7) stefan type male
female disjointWith male
stefan hasChild markus
( not female
and hasChild some person
) subClassOf learnedClass
hasChild range person
Query 2 of 4 (50%)
Positive Example: martin Type learnedClass
Axiom: martin type learnedClass
Explanation(s):
1) martin type male
female disjointWith male
martin hasChild heinz
( not female
and hasChild some person
) subClassOf learnedClass
hasChild range person
2) father subClassOf male
female disjointWith male
martin hasChild heinz
( not female
and hasChild some person
) subClassOf learnedClass
hasChild range person
martin type father
3) martin hasChild heinz
hasChild range person
hasChild some person subClassOf learnedClass
4) heinz type male
male subClassOf person
martin hasChild heinz
hasChild some person subClassOf learnedClass
5) martin type male
heinz type male
female disjointWith male
male subClassOf person
martin hasChild heinz
( not female
and hasChild some person
) subClassOf learnedClass
Query 3 of 4 (75%)
Positive Example: markus Type learnedClass
Axiom: markus type learnedClass
Explanation(s):
1) markus hasChild anna
female disjointWith male
( not female
and hasChild some person
) subClassOf learnedClass
markus type male
hasChild range person
2) markus hasChild anna
hasChild range person
hasChild some person subClassOf learnedClass
3) markus hasChild anna
hasChild domain person
anna hasChild heinz
hasChild some person subClassOf learnedClass
Query 4 of 4 (100%)
Negative Example: heinz Type not (learnedClass)
Axiom: heinz type not learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Trying the second method...
Axiom: heinz type learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Explanations founding completed (BDDs computed)
Start EM Algorithm
- n. of probabilistic axioms: 10
- n. of examples: 4
stefan Type learnedClass - prob: 0.58453 - tag: 1 - #vars: 7
martin Type learnedClass - prob: 0.57445 - tag: 2 - #vars: 6
markus Type learnedClass - prob: 0.57237 - tag: 3 - #vars: 5
heinz Type learnedClass - prob: 1.00000 - tag: 4 - #vars: 0
EM cycle: 0
EM cycle: 1
Log-likelihood: -1.64927 cycle: 1
EM cycle: 2
Log-likelihood: -0.52366 cycle: 2
EM cycle: 3
Log-likelihood: -0.37929 cycle: 3
EM cycle: 4
Log-likelihood: -0.31548 cycle: 4
EM cycle: 5
Log-likelihood: -0.27787 cycle: 5
EM cycle: 6
Log-likelihood: -0.25243 cycle: 6
EM cycle: 7
Log-likelihood: -0.23381 cycle: 7
EM cycle: 8
Log-likelihood: -0.21937 cycle: 8
EM cycle: 9
Log-likelihood: -0.20779 cycle: 9
EM cycle: 10
Log-likelihood: -0.19819 cycle: 10
EM cycle: 11
Log-likelihood: -0.19014 cycle: 11
EM cycle: 12
Log-likelihood: -0.18319 cycle: 12
EM cycle: 13
Log-likelihood: -0.17713 cycle: 13
EM cycle: 14
Log-likelihood: -0.17179 cycle: 14
EM cycle: 15
Log-likelihood: -0.16704 cycle: 15
EM cycle: 16
Log-likelihood: -0.16276 cycle: 16
EM cycle: 17
Log-likelihood: -0.15889 cycle: 17
EM cycle: 18
Log-likelihood: -0.15537 cycle: 18
EM cycle: 19
Log-likelihood: -0.15217 cycle: 19
EM cycle: 20
Log-likelihood: -0.14917 cycle: 20
EM cycle: 21
Log-likelihood: -0.14641 cycle: 21
EM cycle: 22
Log-likelihood: -0.14387 cycle: 22
EM cycle: 23
Log-likelihood: -0.14148 cycle: 23
EM cycle: 24
Log-likelihood: -0.13925 cycle: 24
EM cycle: 25
Log-likelihood: -0.13716 cycle: 25
EM cycle: 26
Log-likelihood: -0.13519 cycle: 26
EM cycle: 27
Log-likelihood: -0.13337 cycle: 27
EM cycle: 28
Log-likelihood: -0.13160 cycle: 28
EM cycle: 29
Log-likelihood: -0.12997 cycle: 29
EM cycle: 30
Log-likelihood: -0.12838 cycle: 30
EM cycle: 31
Log-likelihood: -0.12685 cycle: 31
EM cycle: 32
Log-likelihood: -0.12545 cycle: 32
EM cycle: 33
Log-likelihood: -0.12409 cycle: 33
EM cycle: 34
Log-likelihood: -0.12281 cycle: 34
EM cycle: 35
Log-likelihood: -0.12152 cycle: 35
EM cycle: 36
Log-likelihood: -0.12034 cycle: 36
EM cycle: 37
Log-likelihood: -0.11919 cycle: 37
EM cycle: 38
Log-likelihood: -0.11810 cycle: 38
EM cycle: 39
Log-likelihood: -0.11706 cycle: 39
EM cycle: 40
Log-likelihood: -0.11602 cycle: 40
EM cycle: 41
Log-likelihood: -0.11506 cycle: 41
EM cycle: 42
Log-likelihood: -0.11412 cycle: 42
EM cycle: 43
Log-likelihood: -0.11318 cycle: 43
EM cycle: 44
Log-likelihood: -0.11229 cycle: 44
EM cycle: 45
Log-likelihood: -0.11145 cycle: 45
EM cycle: 46
Log-likelihood: -0.11065 cycle: 46
EM cycle: 47
Log-likelihood: -0.10984 cycle: 47
EM cycle: 48
Log-likelihood: -0.10906 cycle: 48
EM cycle: 49
Log-likelihood: -0.10830 cycle: 49
EM cycle: 50
Log-likelihood: -0.10756 cycle: 50
EM cycle: 51
Log-likelihood: -0.10688 cycle: 51
EM cycle: 52
Log-likelihood: -0.10620 cycle: 52
EM cycle: 53
Log-likelihood: -0.10551 cycle: 53
EM cycle: 54
Log-likelihood: -0.10486 cycle: 54
EM cycle: 55
Log-likelihood: -0.10422 cycle: 55
EM cycle: 56
Log-likelihood: -0.10362 cycle: 56
EM cycle: 57
Log-likelihood: -0.10303 cycle: 57
EM cycle: 58
Log-likelihood: -0.10242 cycle: 58
EM cycle: 59
Log-likelihood: -0.10187 cycle: 59
EM cycle: 60
Log-likelihood: -0.10131 cycle: 60
EM cycle: 61
Log-likelihood: -0.10076 cycle: 61
EM cycle: 62
Log-likelihood: -0.10022 cycle: 62
EM cycle: 63
Log-likelihood: -0.09968 cycle: 63
EM cycle: 64
Log-likelihood: -0.09919 cycle: 64
EM cycle: 65
Log-likelihood: -0.09870 cycle: 65
EM cycle: 66
Log-likelihood: -0.09821 cycle: 66
EM cycle: 67
Log-likelihood: -0.09773 cycle: 67
EM cycle: 68
Log-likelihood: -0.09728 cycle: 68
EM cycle: 69
Log-likelihood: -0.09680 cycle: 69
EM cycle: 70
Log-likelihood: -0.09639 cycle: 70
EM cycle: 71
Log-likelihood: -0.09592 cycle: 71
EM cycle: 72
Log-likelihood: -0.09550 cycle: 72
EM cycle: 73
Log-likelihood: -0.09507 cycle: 73
EM cycle: 74
Log-likelihood: -0.09469 cycle: 74
EM cycle: 75
Log-likelihood: -0.09427 cycle: 75
EM cycle: 76
Log-likelihood: -0.09386 cycle: 76
EM cycle: 77
Log-likelihood: -0.09351 cycle: 77
EM cycle: 78
Log-likelihood: -0.09312 cycle: 78
EM cycle: 79
Log-likelihood: -0.09273 cycle: 79
EM cycle: 80
Log-likelihood: -0.09235 cycle: 80
EM cycle: 81
Log-likelihood: -0.09202 cycle: 81
EM cycle: 82
Log-likelihood: -0.09165 cycle: 82
EM cycle: 83
Log-likelihood: -0.09131 cycle: 83
EM cycle: 84
Log-likelihood: -0.09094 cycle: 84
EM cycle: 85
Log-likelihood: -0.09062 cycle: 85
EM cycle: 86
Log-likelihood: -0.09030 cycle: 86
EM cycle: 87
Log-likelihood: -0.08999 cycle: 87
EM cycle: 88
Log-likelihood: -0.08965 cycle: 88
EM cycle: 89
Log-likelihood: -0.08931 cycle: 89
EM cycle: 90
Log-likelihood: -0.08902 cycle: 90
EM cycle: 91
Log-likelihood: -0.08874 cycle: 91
EM completed.
Final Log-Likelihood: -0.08874
Name | Total (ms)
===========================
main | 121
init | 0
Bundle | 101
Bundle.init | 0
Bundle.explain | 100
Bundle.BDDCalc | 1
EM | 17
Initializing...
Preparing Probability map...
Preparing Probability Map...
Random Seed set to: 0
Created 10 probabilistic axiom
Probability Map computed. Size: 10
Initialization completed
Start finding explanations for every example (Computing BDDs)...
Query 1 of 4 (25%)
Positive Example: stefan Type learnedClass
Axiom: stefan type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild domain person
stefan hasChild markus
hasChild some person subClassOf learnedClass
2) stefan hasChild markus
hasChild range person
hasChild some person subClassOf learnedClass
3) ( male
and ( female
or hasChild some person
)
) subClassOf learnedClass
stefan type male
stefan hasChild markus
hasChild range person
4) ( male
and ( female
or hasChild some person
)
) subClassOf learnedClass
stefan type male
male subClassOf person
stefan hasChild markus
markus type male
5) ( male
and ( female
or hasChild some person
)
) subClassOf learnedClass
stefan type male
markus hasChild anna
hasChild domain person
stefan hasChild markus
Query 2 of 4 (50%)
Positive Example: martin Type learnedClass
Axiom: martin type learnedClass
Explanation(s):
1) martin hasChild heinz
hasChild range person
hasChild some person subClassOf learnedClass
2) heinz type male
male subClassOf person
martin hasChild heinz
hasChild some person subClassOf learnedClass
3) ( male
and ( female
or hasChild some person
)
) subClassOf learnedClass
martin type male
heinz type male
male subClassOf person
martin hasChild heinz
Query 3 of 4 (75%)
Positive Example: markus Type learnedClass
Axiom: markus type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild range person
hasChild some person subClassOf learnedClass
2) markus hasChild anna
hasChild domain person
anna hasChild heinz
hasChild some person subClassOf learnedClass
3) ( male
and ( female
or hasChild some person
)
) subClassOf learnedClass
markus hasChild anna
hasChild domain person
markus type male
anna hasChild heinz
4) ( male
and ( female
or hasChild some person
)
) subClassOf learnedClass
markus hasChild anna
female subClassOf person
markus type male
anna type female
Query 4 of 4 (100%)
Negative Example: heinz Type not (learnedClass)
Axiom: heinz type not learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Trying the second method...
Axiom: heinz type learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Explanations founding completed (BDDs computed)
Start EM Algorithm
- n. of probabilistic axioms: 10
- n. of examples: 4
stefan Type learnedClass - prob: 0.58738 - tag: 1 - #vars: 5
martin Type learnedClass - prob: 0.50757 - tag: 2 - #vars: 4
markus Type learnedClass - prob: 0.56874 - tag: 3 - #vars: 5
heinz Type learnedClass - prob: 1.00000 - tag: 4 - #vars: 0
EM cycle: 0
EM cycle: 1
Log-likelihood: -1.77449 cycle: 1
EM cycle: 2
Log-likelihood: -0.56019 cycle: 2
EM cycle: 3
Log-likelihood: -0.40000 cycle: 3
EM cycle: 4
Log-likelihood: -0.32903 cycle: 4
EM cycle: 5
Log-likelihood: -0.28721 cycle: 5
EM cycle: 6
Log-likelihood: -0.25892 cycle: 6
EM cycle: 7
Log-likelihood: -0.23819 cycle: 7
EM cycle: 8
Log-likelihood: -0.22218 cycle: 8
EM cycle: 9
Log-likelihood: -0.20929 cycle: 9
EM cycle: 10
Log-likelihood: -0.19868 cycle: 10
EM cycle: 11
Log-likelihood: -0.18970 cycle: 11
EM cycle: 12
Log-likelihood: -0.18203 cycle: 12
EM cycle: 13
Log-likelihood: -0.17531 cycle: 13
EM cycle: 14
Log-likelihood: -0.16940 cycle: 14
EM cycle: 15
Log-likelihood: -0.16414 cycle: 15
EM cycle: 16
Log-likelihood: -0.15941 cycle: 16
EM cycle: 17
Log-likelihood: -0.15515 cycle: 17
EM cycle: 18
Log-likelihood: -0.15125 cycle: 18
EM cycle: 19
Log-likelihood: -0.14771 cycle: 19
EM cycle: 20
Log-likelihood: -0.14444 cycle: 20
EM cycle: 21
Log-likelihood: -0.14138 cycle: 21
EM cycle: 22
Log-likelihood: -0.13858 cycle: 22
EM cycle: 23
Log-likelihood: -0.13597 cycle: 23
EM cycle: 24
Log-likelihood: -0.13352 cycle: 24
EM cycle: 25
Log-likelihood: -0.13123 cycle: 25
EM cycle: 26
Log-likelihood: -0.12908 cycle: 26
EM cycle: 27
Log-likelihood: -0.12704 cycle: 27
EM cycle: 28
Log-likelihood: -0.12514 cycle: 28
EM cycle: 29
Log-likelihood: -0.12334 cycle: 29
EM cycle: 30
Log-likelihood: -0.12159 cycle: 30
EM cycle: 31
Log-likelihood: -0.11994 cycle: 31
EM cycle: 32
Log-likelihood: -0.11840 cycle: 32
EM cycle: 33
Log-likelihood: -0.11691 cycle: 33
EM cycle: 34
Log-likelihood: -0.11547 cycle: 34
EM cycle: 35
Log-likelihood: -0.11414 cycle: 35
EM cycle: 36
Log-likelihood: -0.11284 cycle: 36
EM cycle: 37
Log-likelihood: -0.11159 cycle: 37
EM cycle: 38
Log-likelihood: -0.11040 cycle: 38
EM cycle: 39
Log-likelihood: -0.10923 cycle: 39
EM cycle: 40
Log-likelihood: -0.10814 cycle: 40
EM cycle: 41
Log-likelihood: -0.10707 cycle: 41
EM cycle: 42
Log-likelihood: -0.10605 cycle: 42
EM cycle: 43
Log-likelihood: -0.10505 cycle: 43
EM cycle: 44
Log-likelihood: -0.10409 cycle: 44
EM cycle: 45
Log-likelihood: -0.10317 cycle: 45
EM cycle: 46
Log-likelihood: -0.10227 cycle: 46
EM cycle: 47
Log-likelihood: -0.10143 cycle: 47
EM cycle: 48
Log-likelihood: -0.10057 cycle: 48
EM cycle: 49
Log-likelihood: -0.09976 cycle: 49
EM cycle: 50
Log-likelihood: -0.09898 cycle: 50
EM cycle: 51
Log-likelihood: -0.09823 cycle: 51
EM cycle: 52
Log-likelihood: -0.09747 cycle: 52
EM cycle: 53
Log-likelihood: -0.09674 cycle: 53
EM cycle: 54
Log-likelihood: -0.09603 cycle: 54
EM cycle: 55
Log-likelihood: -0.09536 cycle: 55
EM cycle: 56
Log-likelihood: -0.09469 cycle: 56
EM cycle: 57
Log-likelihood: -0.09404 cycle: 57
EM cycle: 58
Log-likelihood: -0.09341 cycle: 58
EM cycle: 59
Log-likelihood: -0.09280 cycle: 59
EM cycle: 60
Log-likelihood: -0.09219 cycle: 60
EM cycle: 61
Log-likelihood: -0.09160 cycle: 61
EM cycle: 62
Log-likelihood: -0.09103 cycle: 62
EM cycle: 63
Log-likelihood: -0.09049 cycle: 63
EM cycle: 64
Log-likelihood: -0.08993 cycle: 64
EM cycle: 65
Log-likelihood: -0.08940 cycle: 65
EM cycle: 66
Log-likelihood: -0.08889 cycle: 66
EM cycle: 67
Log-likelihood: -0.08838 cycle: 67
EM cycle: 68
Log-likelihood: -0.08789 cycle: 68
EM cycle: 69
Log-likelihood: -0.08738 cycle: 69
EM cycle: 70
Log-likelihood: -0.08691 cycle: 70
EM cycle: 71
Log-likelihood: -0.08644 cycle: 71
EM cycle: 72
Log-likelihood: -0.08598 cycle: 72
EM cycle: 73
Log-likelihood: -0.08555 cycle: 73
EM cycle: 74
Log-likelihood: -0.08509 cycle: 74
EM cycle: 75
Log-likelihood: -0.08465 cycle: 75
EM cycle: 76
Log-likelihood: -0.08424 cycle: 76
EM cycle: 77
Log-likelihood: -0.08383 cycle: 77
EM cycle: 78
Log-likelihood: -0.08341 cycle: 78
EM cycle: 79
Log-likelihood: -0.08302 cycle: 79
EM cycle: 80
Log-likelihood: -0.08262 cycle: 80
EM cycle: 81
Log-likelihood: -0.08224 cycle: 81
EM cycle: 82
Log-likelihood: -0.08187 cycle: 82
EM cycle: 83
Log-likelihood: -0.08149 cycle: 83
EM cycle: 84
Log-likelihood: -0.08113 cycle: 84
EM cycle: 85
Log-likelihood: -0.08076 cycle: 85
EM cycle: 86
Log-likelihood: -0.08042 cycle: 86
EM cycle: 87
Log-likelihood: -0.08007 cycle: 87
EM cycle: 88
Log-likelihood: -0.07972 cycle: 88
EM cycle: 89
Log-likelihood: -0.07940 cycle: 89
EM cycle: 90
Log-likelihood: -0.07904 cycle: 90
EM cycle: 91
Log-likelihood: -0.07875 cycle: 91
EM cycle: 92
Log-likelihood: -0.07841 cycle: 92
EM cycle: 93
Log-likelihood: -0.07809 cycle: 93
EM cycle: 94
Log-likelihood: -0.07779 cycle: 94
EM cycle: 95
Log-likelihood: -0.07750 cycle: 95
EM cycle: 96
Log-likelihood: -0.07719 cycle: 96
EM cycle: 97
Log-likelihood: -0.07691 cycle: 97
EM completed.
Final Log-Likelihood: -0.07691
Name | Total (ms)
===========================
main | 83
init | 1
Bundle | 64
Bundle.init | 0
Bundle.explain | 59
Bundle.BDDCalc | 0
EM | 18
Initializing...
Preparing Probability map...
Preparing Probability Map...
Random Seed set to: 0
Created 10 probabilistic axiom
Probability Map computed. Size: 10
Initialization completed
Start finding explanations for every example (Computing BDDs)...
Query 1 of 4 (25%)
Positive Example: stefan Type learnedClass
Axiom: stefan type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild domain person
stefan hasChild markus
hasChild some person subClassOf learnedClass
2) stefan hasChild markus
hasChild range person
hasChild some person subClassOf learnedClass
3) stefan type male
stefan type father
( male
and ( father
or hasChild some person
)
) subClassOf learnedClass
4) father subClassOf male
stefan type father
( male
and ( father
or hasChild some person
)
) subClassOf learnedClass
5) stefan type male
stefan hasChild markus
hasChild range person
( male
and ( father
or hasChild some person
)
) subClassOf learnedClass
Query 2 of 4 (50%)
Positive Example: martin Type learnedClass
Axiom: martin type learnedClass
Explanation(s):
1) martin hasChild heinz
hasChild range person
hasChild some person subClassOf learnedClass
2) martin type male
( male
and ( father
or hasChild some person
)
) subClassOf learnedClass
martin type father
3) father subClassOf male
( male
and ( father
or hasChild some person
)
) subClassOf learnedClass
martin type father
4) heinz type male
male subClassOf person
martin hasChild heinz
hasChild some person subClassOf learnedClass
Query 3 of 4 (75%)
Positive Example: markus Type learnedClass
Axiom: markus type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild range person
hasChild some person subClassOf learnedClass
2) markus type father
markus type male
( male
and ( father
or hasChild some person
)
) subClassOf learnedClass
3) father subClassOf male
markus type father
( male
and ( father
or hasChild some person
)
) subClassOf learnedClass
4) markus hasChild anna
hasChild domain person
anna hasChild heinz
hasChild some person subClassOf learnedClass
5) markus hasChild anna
hasChild domain person
markus type male
( male
and ( father
or hasChild some person
)
) subClassOf learnedClass
anna hasChild heinz
Query 4 of 4 (100%)
Negative Example: heinz Type not (learnedClass)
Axiom: heinz type not learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Trying the second method...
Axiom: heinz type learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Explanations founding completed (BDDs computed)
Start EM Algorithm
- n. of probabilistic axioms: 10
- n. of examples: 4
stefan Type learnedClass - prob: 0.58824 - tag: 1 - #vars: 5
martin Type learnedClass - prob: 0.58459 - tag: 2 - #vars: 5
markus Type learnedClass - prob: 0.58824 - tag: 3 - #vars: 5
heinz Type learnedClass - prob: 1.00000 - tag: 4 - #vars: 0
EM cycle: 0
EM cycle: 1
Log-likelihood: -1.59810 cycle: 1
EM cycle: 2
Log-likelihood: -0.50132 cycle: 2
EM cycle: 3
Log-likelihood: -0.36236 cycle: 3
EM cycle: 4
Log-likelihood: -0.30119 cycle: 4
EM cycle: 5
Log-likelihood: -0.26528 cycle: 5
EM cycle: 6
Log-likelihood: -0.24101 cycle: 6
EM cycle: 7
Log-likelihood: -0.22327 cycle: 7
EM cycle: 8
Log-likelihood: -0.20956 cycle: 8
EM cycle: 9
Log-likelihood: -0.19857 cycle: 9
EM cycle: 10
Log-likelihood: -0.18949 cycle: 10
EM cycle: 11
Log-likelihood: -0.18182 cycle: 11
EM cycle: 12
Log-likelihood: -0.17527 cycle: 12
EM cycle: 13
Log-likelihood: -0.16952 cycle: 13
EM cycle: 14
Log-likelihood: -0.16447 cycle: 14
EM cycle: 15
Log-likelihood: -0.15996 cycle: 15
EM cycle: 16
Log-likelihood: -0.15594 cycle: 16
EM cycle: 17
Log-likelihood: -0.15229 cycle: 17
EM cycle: 18
Log-likelihood: -0.14894 cycle: 18
EM cycle: 19
Log-likelihood: -0.14589 cycle: 19
EM cycle: 20
Log-likelihood: -0.14308 cycle: 20
EM cycle: 21
Log-likelihood: -0.14049 cycle: 21
EM cycle: 22
Log-likelihood: -0.13810 cycle: 22
EM cycle: 23
Log-likelihood: -0.13587 cycle: 23
EM cycle: 24
Log-likelihood: -0.13373 cycle: 24
EM cycle: 25
Log-likelihood: -0.13179 cycle: 25
EM cycle: 26
Log-likelihood: -0.12997 cycle: 26
EM cycle: 27
Log-likelihood: -0.12819 cycle: 27
EM cycle: 28
Log-likelihood: -0.12653 cycle: 28
EM cycle: 29
Log-likelihood: -0.12498 cycle: 29
EM cycle: 30
Log-likelihood: -0.12350 cycle: 30
EM cycle: 31
Log-likelihood: -0.12210 cycle: 31
EM cycle: 32
Log-likelihood: -0.12076 cycle: 32
EM cycle: 33
Log-likelihood: -0.11949 cycle: 33
EM cycle: 34
Log-likelihood: -0.11828 cycle: 34
EM cycle: 35
Log-likelihood: -0.11706 cycle: 35
EM cycle: 36
Log-likelihood: -0.11596 cycle: 36
EM cycle: 37
Log-likelihood: -0.11493 cycle: 37
EM cycle: 38
Log-likelihood: -0.11388 cycle: 38
EM cycle: 39
Log-likelihood: -0.11288 cycle: 39
EM cycle: 40
Log-likelihood: -0.11194 cycle: 40
EM cycle: 41
Log-likelihood: -0.11100 cycle: 41
EM cycle: 42
Log-likelihood: -0.11012 cycle: 42
EM cycle: 43
Log-likelihood: -0.10926 cycle: 43
EM cycle: 44
Log-likelihood: -0.10844 cycle: 44
EM cycle: 45
Log-likelihood: -0.10765 cycle: 45
EM cycle: 46
Log-likelihood: -0.10688 cycle: 46
EM cycle: 47
Log-likelihood: -0.10611 cycle: 47
EM cycle: 48
Log-likelihood: -0.10538 cycle: 48
EM cycle: 49
Log-likelihood: -0.10469 cycle: 49
EM cycle: 50
Log-likelihood: -0.10402 cycle: 50
EM cycle: 51
Log-likelihood: -0.10336 cycle: 51
EM cycle: 52
Log-likelihood: -0.10271 cycle: 52
EM cycle: 53
Log-likelihood: -0.10210 cycle: 53
EM cycle: 54
Log-likelihood: -0.10147 cycle: 54
EM cycle: 55
Log-likelihood: -0.10086 cycle: 55
EM cycle: 56
Log-likelihood: -0.10031 cycle: 56
EM cycle: 57
Log-likelihood: -0.09974 cycle: 57
EM cycle: 58
Log-likelihood: -0.09921 cycle: 58
EM cycle: 59
Log-likelihood: -0.09865 cycle: 59
EM cycle: 60
Log-likelihood: -0.09814 cycle: 60
EM cycle: 61
Log-likelihood: -0.09762 cycle: 61
EM cycle: 62
Log-likelihood: -0.09712 cycle: 62
EM cycle: 63
Log-likelihood: -0.09663 cycle: 63
EM cycle: 64
Log-likelihood: -0.09618 cycle: 64
EM cycle: 65
Log-likelihood: -0.09573 cycle: 65
EM cycle: 66
Log-likelihood: -0.09524 cycle: 66
EM cycle: 67
Log-likelihood: -0.09480 cycle: 67
EM cycle: 68
Log-likelihood: -0.09437 cycle: 68
EM cycle: 69
Log-likelihood: -0.09395 cycle: 69
EM cycle: 70
Log-likelihood: -0.09353 cycle: 70
EM cycle: 71
Log-likelihood: -0.09313 cycle: 71
EM cycle: 72
Log-likelihood: -0.09274 cycle: 72
EM cycle: 73
Log-likelihood: -0.09234 cycle: 73
EM cycle: 74
Log-likelihood: -0.09196 cycle: 74
EM cycle: 75
Log-likelihood: -0.09156 cycle: 75
EM cycle: 76
Log-likelihood: -0.09121 cycle: 76
EM cycle: 77
Log-likelihood: -0.09084 cycle: 77
EM cycle: 78
Log-likelihood: -0.09049 cycle: 78
EM cycle: 79
Log-likelihood: -0.09016 cycle: 79
EM cycle: 80
Log-likelihood: -0.08980 cycle: 80
EM cycle: 81
Log-likelihood: -0.08945 cycle: 81
EM cycle: 82
Log-likelihood: -0.08914 cycle: 82
EM cycle: 83
Log-likelihood: -0.08881 cycle: 83
EM cycle: 84
Log-likelihood: -0.08847 cycle: 84
EM cycle: 85
Log-likelihood: -0.08817 cycle: 85
EM cycle: 86
Log-likelihood: -0.08787 cycle: 86
EM cycle: 87
Log-likelihood: -0.08756 cycle: 87
EM cycle: 88
Log-likelihood: -0.08726 cycle: 88
EM cycle: 89
Log-likelihood: -0.08697 cycle: 89
EM cycle: 90
Log-likelihood: -0.08665 cycle: 90
EM cycle: 91
Log-likelihood: -0.08637 cycle: 91
EM completed.
Final Log-Likelihood: -0.08637
Name | Total (ms)
===========================
main | 85
init | 0
Bundle | 69
Bundle.init | 0
Bundle.explain | 67
Bundle.BDDCalc | 1
EM | 15
Initializing...
Preparing Probability map...
Preparing Probability Map...
Random Seed set to: 0
Created 10 probabilistic axiom
Probability Map computed. Size: 10
Initialization completed
Start finding explanations for every example (Computing BDDs)...
Query 1 of 4 (25%)
Positive Example: stefan Type learnedClass
Axiom: stefan type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild domain person
stefan hasChild markus
hasChild some person subClassOf learnedClass
2) stefan hasChild markus
hasChild range person
hasChild some person subClassOf learnedClass
3) stefan type male
( male
and ( father
or hasChild some male
)
) subClassOf learnedClass
stefan type father
4) father subClassOf male
( male
and ( father
or hasChild some male
)
) subClassOf learnedClass
stefan type father
Query 2 of 4 (50%)
Positive Example: martin Type learnedClass
Axiom: martin type learnedClass
Explanation(s):
1) martin hasChild heinz
hasChild range person
hasChild some person subClassOf learnedClass
2) martin type male
( male
and ( father
or hasChild some male
)
) subClassOf learnedClass
martin type father
3) father subClassOf male
( male
and ( father
or hasChild some male
)
) subClassOf learnedClass
martin type father
4) heinz type male
male subClassOf person
martin hasChild heinz
hasChild some person subClassOf learnedClass
Query 3 of 4 (75%)
Positive Example: markus Type learnedClass
Axiom: markus type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild range person
hasChild some person subClassOf learnedClass
2) ( male
and ( father
or hasChild some male
)
) subClassOf learnedClass
markus type father
markus type male
3) markus hasChild anna
hasChild domain person
anna hasChild heinz
hasChild some person subClassOf learnedClass
Query 4 of 4 (100%)
Negative Example: heinz Type not (learnedClass)
Axiom: heinz type not learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Trying the second method...
Axiom: heinz type learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Explanations founding completed (BDDs computed)
Start EM Algorithm
- n. of probabilistic axioms: 10
- n. of examples: 4
stefan Type learnedClass - prob: 0.58824 - tag: 1 - #vars: 5
martin Type learnedClass - prob: 0.58459 - tag: 2 - #vars: 5
markus Type learnedClass - prob: 0.58824 - tag: 3 - #vars: 4
heinz Type learnedClass - prob: 1.00000 - tag: 4 - #vars: 0
EM cycle: 0
EM cycle: 1
Log-likelihood: -1.59810 cycle: 1
EM cycle: 2
Log-likelihood: -0.50132 cycle: 2
EM cycle: 3
Log-likelihood: -0.36236 cycle: 3
EM cycle: 4
Log-likelihood: -0.30119 cycle: 4
EM cycle: 5
Log-likelihood: -0.26528 cycle: 5
EM cycle: 6
Log-likelihood: -0.24101 cycle: 6
EM cycle: 7
Log-likelihood: -0.22327 cycle: 7
EM cycle: 8
Log-likelihood: -0.20956 cycle: 8
EM cycle: 9
Log-likelihood: -0.19857 cycle: 9
EM cycle: 10
Log-likelihood: -0.18949 cycle: 10
EM cycle: 11
Log-likelihood: -0.18182 cycle: 11
EM cycle: 12
Log-likelihood: -0.17527 cycle: 12
EM cycle: 13
Log-likelihood: -0.16952 cycle: 13
EM cycle: 14
Log-likelihood: -0.16447 cycle: 14
EM cycle: 15
Log-likelihood: -0.15996 cycle: 15
EM cycle: 16
Log-likelihood: -0.15594 cycle: 16
EM cycle: 17
Log-likelihood: -0.15229 cycle: 17
EM cycle: 18
Log-likelihood: -0.14894 cycle: 18
EM cycle: 19
Log-likelihood: -0.14589 cycle: 19
EM cycle: 20
Log-likelihood: -0.14308 cycle: 20
EM cycle: 21
Log-likelihood: -0.14049 cycle: 21
EM cycle: 22
Log-likelihood: -0.13810 cycle: 22
EM cycle: 23
Log-likelihood: -0.13587 cycle: 23
EM cycle: 24
Log-likelihood: -0.13373 cycle: 24
EM cycle: 25
Log-likelihood: -0.13179 cycle: 25
EM cycle: 26
Log-likelihood: -0.12997 cycle: 26
EM cycle: 27
Log-likelihood: -0.12819 cycle: 27
EM cycle: 28
Log-likelihood: -0.12653 cycle: 28
EM cycle: 29
Log-likelihood: -0.12498 cycle: 29
EM cycle: 30
Log-likelihood: -0.12350 cycle: 30
EM cycle: 31
Log-likelihood: -0.12210 cycle: 31
EM cycle: 32
Log-likelihood: -0.12076 cycle: 32
EM cycle: 33
Log-likelihood: -0.11949 cycle: 33
EM cycle: 34
Log-likelihood: -0.11828 cycle: 34
EM cycle: 35
Log-likelihood: -0.11706 cycle: 35
EM cycle: 36
Log-likelihood: -0.11596 cycle: 36
EM cycle: 37
Log-likelihood: -0.11493 cycle: 37
EM cycle: 38
Log-likelihood: -0.11388 cycle: 38
EM cycle: 39
Log-likelihood: -0.11288 cycle: 39
EM cycle: 40
Log-likelihood: -0.11194 cycle: 40
EM cycle: 41
Log-likelihood: -0.11100 cycle: 41
EM cycle: 42
Log-likelihood: -0.11012 cycle: 42
EM cycle: 43
Log-likelihood: -0.10926 cycle: 43
EM cycle: 44
Log-likelihood: -0.10844 cycle: 44
EM cycle: 45
Log-likelihood: -0.10765 cycle: 45
EM cycle: 46
Log-likelihood: -0.10688 cycle: 46
EM cycle: 47
Log-likelihood: -0.10611 cycle: 47
EM cycle: 48
Log-likelihood: -0.10538 cycle: 48
EM cycle: 49
Log-likelihood: -0.10469 cycle: 49
EM cycle: 50
Log-likelihood: -0.10402 cycle: 50
EM cycle: 51
Log-likelihood: -0.10336 cycle: 51
EM cycle: 52
Log-likelihood: -0.10271 cycle: 52
EM cycle: 53
Log-likelihood: -0.10210 cycle: 53
EM cycle: 54
Log-likelihood: -0.10147 cycle: 54
EM cycle: 55
Log-likelihood: -0.10086 cycle: 55
EM cycle: 56
Log-likelihood: -0.10031 cycle: 56
EM cycle: 57
Log-likelihood: -0.09974 cycle: 57
EM cycle: 58
Log-likelihood: -0.09921 cycle: 58
EM cycle: 59
Log-likelihood: -0.09865 cycle: 59
EM cycle: 60
Log-likelihood: -0.09814 cycle: 60
EM cycle: 61
Log-likelihood: -0.09762 cycle: 61
EM cycle: 62
Log-likelihood: -0.09712 cycle: 62
EM cycle: 63
Log-likelihood: -0.09663 cycle: 63
EM cycle: 64
Log-likelihood: -0.09618 cycle: 64
EM cycle: 65
Log-likelihood: -0.09573 cycle: 65
EM cycle: 66
Log-likelihood: -0.09524 cycle: 66
EM cycle: 67
Log-likelihood: -0.09480 cycle: 67
EM cycle: 68
Log-likelihood: -0.09437 cycle: 68
EM cycle: 69
Log-likelihood: -0.09395 cycle: 69
EM cycle: 70
Log-likelihood: -0.09353 cycle: 70
EM cycle: 71
Log-likelihood: -0.09313 cycle: 71
EM cycle: 72
Log-likelihood: -0.09274 cycle: 72
EM cycle: 73
Log-likelihood: -0.09234 cycle: 73
EM cycle: 74
Log-likelihood: -0.09196 cycle: 74
EM cycle: 75
Log-likelihood: -0.09156 cycle: 75
EM cycle: 76
Log-likelihood: -0.09121 cycle: 76
EM cycle: 77
Log-likelihood: -0.09084 cycle: 77
EM cycle: 78
Log-likelihood: -0.09049 cycle: 78
EM cycle: 79
Log-likelihood: -0.09016 cycle: 79
EM cycle: 80
Log-likelihood: -0.08980 cycle: 80
EM cycle: 81
Log-likelihood: -0.08945 cycle: 81
EM cycle: 82
Log-likelihood: -0.08914 cycle: 82
EM cycle: 83
Log-likelihood: -0.08881 cycle: 83
EM cycle: 84
Log-likelihood: -0.08847 cycle: 84
EM cycle: 85
Log-likelihood: -0.08817 cycle: 85
EM cycle: 86
Log-likelihood: -0.08787 cycle: 86
EM cycle: 87
Log-likelihood: -0.08756 cycle: 87
EM cycle: 88
Log-likelihood: -0.08726 cycle: 88
EM cycle: 89
Log-likelihood: -0.08697 cycle: 89
EM cycle: 90
Log-likelihood: -0.08665 cycle: 90
EM cycle: 91
Log-likelihood: -0.08637 cycle: 91
EM completed.
Final Log-Likelihood: -0.08637
Name | Total (ms)
===========================
main | 67
init | 1
Bundle | 55
Bundle.init | 0
Bundle.explain | 53
Bundle.BDDCalc | 1
EM | 11
Initializing...
Preparing Probability map...
Preparing Probability Map...
Random Seed set to: 0
Created 10 probabilistic axiom
Probability Map computed. Size: 10
Initialization completed
Start finding explanations for every example (Computing BDDs)...
Query 1 of 4 (25%)
Positive Example: stefan Type learnedClass
Axiom: stefan type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild domain person
stefan hasChild markus
hasChild some person subClassOf learnedClass
2) stefan hasChild markus
hasChild range person
hasChild some person subClassOf learnedClass
3) stefan type male
stefan type father
( male
and ( father
or hasChild some female
)
) subClassOf learnedClass
4) father subClassOf male
stefan type father
( male
and ( father
or hasChild some female
)
) subClassOf learnedClass
Query 2 of 4 (50%)
Positive Example: martin Type learnedClass
Axiom: martin type learnedClass
Explanation(s):
1) martin hasChild heinz
hasChild range person
hasChild some person subClassOf learnedClass
2) martin type male
martin type father
( male
and ( father
or hasChild some female
)
) subClassOf learnedClass
3) father subClassOf male
martin type father
( male
and ( father
or hasChild some female
)
) subClassOf learnedClass
4) heinz type male
male subClassOf person
martin hasChild heinz
hasChild some person subClassOf learnedClass
Query 3 of 4 (75%)
Positive Example: markus Type learnedClass
Axiom: markus type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild range person
hasChild some person subClassOf learnedClass
2) markus type father
markus type male
( male
and ( father
or hasChild some female
)
) subClassOf learnedClass
3) father subClassOf male
markus type father
( male
and ( father
or hasChild some female
)
) subClassOf learnedClass
4) markus hasChild anna
hasChild domain person
anna hasChild heinz
hasChild some person subClassOf learnedClass
5) markus hasChild anna
markus type male
anna type female
( male
and ( father
or hasChild some female
)
) subClassOf learnedClass
Query 4 of 4 (100%)
Negative Example: heinz Type not (learnedClass)
Axiom: heinz type not learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Trying the second method...
Axiom: heinz type learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Explanations founding completed (BDDs computed)
Start EM Algorithm
- n. of probabilistic axioms: 10
- n. of examples: 4
stefan Type learnedClass - prob: 0.58824 - tag: 1 - #vars: 5
martin Type learnedClass - prob: 0.58459 - tag: 2 - #vars: 5
markus Type learnedClass - prob: 0.58824 - tag: 3 - #vars: 5
heinz Type learnedClass - prob: 1.00000 - tag: 4 - #vars: 0
EM cycle: 0
EM cycle: 1
Log-likelihood: -1.59810 cycle: 1
EM cycle: 2
Log-likelihood: -0.50132 cycle: 2
EM cycle: 3
Log-likelihood: -0.36236 cycle: 3
EM cycle: 4
Log-likelihood: -0.30119 cycle: 4
EM cycle: 5
Log-likelihood: -0.26528 cycle: 5
EM cycle: 6
Log-likelihood: -0.24101 cycle: 6
EM cycle: 7
Log-likelihood: -0.22327 cycle: 7
EM cycle: 8
Log-likelihood: -0.20956 cycle: 8
EM cycle: 9
Log-likelihood: -0.19857 cycle: 9
EM cycle: 10
Log-likelihood: -0.18949 cycle: 10
EM cycle: 11
Log-likelihood: -0.18182 cycle: 11
EM cycle: 12
Log-likelihood: -0.17527 cycle: 12
EM cycle: 13
Log-likelihood: -0.16952 cycle: 13
EM cycle: 14
Log-likelihood: -0.16447 cycle: 14
EM cycle: 15
Log-likelihood: -0.15996 cycle: 15
EM cycle: 16
Log-likelihood: -0.15594 cycle: 16
EM cycle: 17
Log-likelihood: -0.15229 cycle: 17
EM cycle: 18
Log-likelihood: -0.14894 cycle: 18
EM cycle: 19
Log-likelihood: -0.14589 cycle: 19
EM cycle: 20
Log-likelihood: -0.14308 cycle: 20
EM cycle: 21
Log-likelihood: -0.14049 cycle: 21
EM cycle: 22
Log-likelihood: -0.13810 cycle: 22
EM cycle: 23
Log-likelihood: -0.13587 cycle: 23
EM cycle: 24
Log-likelihood: -0.13373 cycle: 24
EM cycle: 25
Log-likelihood: -0.13179 cycle: 25
EM cycle: 26
Log-likelihood: -0.12997 cycle: 26
EM cycle: 27
Log-likelihood: -0.12819 cycle: 27
EM cycle: 28
Log-likelihood: -0.12653 cycle: 28
EM cycle: 29
Log-likelihood: -0.12498 cycle: 29
EM cycle: 30
Log-likelihood: -0.12350 cycle: 30
EM cycle: 31
Log-likelihood: -0.12210 cycle: 31
EM cycle: 32
Log-likelihood: -0.12076 cycle: 32
EM cycle: 33
Log-likelihood: -0.11949 cycle: 33
EM cycle: 34
Log-likelihood: -0.11828 cycle: 34
EM cycle: 35
Log-likelihood: -0.11706 cycle: 35
EM cycle: 36
Log-likelihood: -0.11596 cycle: 36
EM cycle: 37
Log-likelihood: -0.11493 cycle: 37
EM cycle: 38
Log-likelihood: -0.11388 cycle: 38
EM cycle: 39
Log-likelihood: -0.11288 cycle: 39
EM cycle: 40
Log-likelihood: -0.11194 cycle: 40
EM cycle: 41
Log-likelihood: -0.11100 cycle: 41
EM cycle: 42
Log-likelihood: -0.11012 cycle: 42
EM cycle: 43
Log-likelihood: -0.10926 cycle: 43
EM cycle: 44
Log-likelihood: -0.10844 cycle: 44
EM cycle: 45
Log-likelihood: -0.10765 cycle: 45
EM cycle: 46
Log-likelihood: -0.10688 cycle: 46
EM cycle: 47
Log-likelihood: -0.10611 cycle: 47
EM cycle: 48
Log-likelihood: -0.10538 cycle: 48
EM cycle: 49
Log-likelihood: -0.10469 cycle: 49
EM cycle: 50
Log-likelihood: -0.10402 cycle: 50
EM cycle: 51
Log-likelihood: -0.10336 cycle: 51
EM cycle: 52
Log-likelihood: -0.10271 cycle: 52
EM cycle: 53
Log-likelihood: -0.10210 cycle: 53
EM cycle: 54
Log-likelihood: -0.10147 cycle: 54
EM cycle: 55
Log-likelihood: -0.10086 cycle: 55
EM cycle: 56
Log-likelihood: -0.10031 cycle: 56
EM cycle: 57
Log-likelihood: -0.09974 cycle: 57
EM cycle: 58
Log-likelihood: -0.09921 cycle: 58
EM cycle: 59
Log-likelihood: -0.09865 cycle: 59
EM cycle: 60
Log-likelihood: -0.09814 cycle: 60
EM cycle: 61
Log-likelihood: -0.09762 cycle: 61
EM cycle: 62
Log-likelihood: -0.09712 cycle: 62
EM cycle: 63
Log-likelihood: -0.09663 cycle: 63
EM cycle: 64
Log-likelihood: -0.09618 cycle: 64
EM cycle: 65
Log-likelihood: -0.09573 cycle: 65
EM cycle: 66
Log-likelihood: -0.09524 cycle: 66
EM cycle: 67
Log-likelihood: -0.09480 cycle: 67
EM cycle: 68
Log-likelihood: -0.09437 cycle: 68
EM cycle: 69
Log-likelihood: -0.09395 cycle: 69
EM cycle: 70
Log-likelihood: -0.09353 cycle: 70
EM cycle: 71
Log-likelihood: -0.09313 cycle: 71
EM cycle: 72
Log-likelihood: -0.09274 cycle: 72
EM cycle: 73
Log-likelihood: -0.09234 cycle: 73
EM cycle: 74
Log-likelihood: -0.09196 cycle: 74
EM cycle: 75
Log-likelihood: -0.09156 cycle: 75
EM cycle: 76
Log-likelihood: -0.09121 cycle: 76
EM cycle: 77
Log-likelihood: -0.09084 cycle: 77
EM cycle: 78
Log-likelihood: -0.09049 cycle: 78
EM cycle: 79
Log-likelihood: -0.09016 cycle: 79
EM cycle: 80
Log-likelihood: -0.08980 cycle: 80
EM cycle: 81
Log-likelihood: -0.08945 cycle: 81
EM cycle: 82
Log-likelihood: -0.08914 cycle: 82
EM cycle: 83
Log-likelihood: -0.08881 cycle: 83
EM cycle: 84
Log-likelihood: -0.08847 cycle: 84
EM cycle: 85
Log-likelihood: -0.08817 cycle: 85
EM cycle: 86
Log-likelihood: -0.08787 cycle: 86
EM cycle: 87
Log-likelihood: -0.08756 cycle: 87
EM cycle: 88
Log-likelihood: -0.08726 cycle: 88
EM cycle: 89
Log-likelihood: -0.08697 cycle: 89
EM cycle: 90
Log-likelihood: -0.08665 cycle: 90
EM cycle: 91
Log-likelihood: -0.08637 cycle: 91
EM completed.
Final Log-Likelihood: -0.08637
Name | Total (ms)
===========================
main | 84
init | 0
Bundle | 70
Bundle.init | 0
Bundle.explain | 66
Bundle.BDDCalc | 1
EM | 14
Initializing...
Preparing Probability map...
Preparing Probability Map...
Random Seed set to: 0
Created 10 probabilistic axiom
Probability Map computed. Size: 10
Initialization completed
Start finding explanations for every example (Computing BDDs)...
Query 1 of 4 (25%)
Positive Example: stefan Type learnedClass
Axiom: stefan type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild domain person
stefan hasChild markus
hasChild some person subClassOf learnedClass
2) stefan hasChild markus
hasChild range person
hasChild some person subClassOf learnedClass
3) stefan type male
( male
and ( father
or hasChild some father
)
) subClassOf learnedClass
stefan type father
4) father subClassOf male
( male
and ( father
or hasChild some father
)
) subClassOf learnedClass
stefan type father
Query 2 of 4 (50%)
Positive Example: martin Type learnedClass
Axiom: martin type learnedClass
Explanation(s):
1) martin hasChild heinz
hasChild range person
hasChild some person subClassOf learnedClass
2) martin type male
( male
and ( father
or hasChild some father
)
) subClassOf learnedClass
martin type father
3) father subClassOf male
( male
and ( father
or hasChild some father
)
) subClassOf learnedClass
martin type father
4) heinz type male
male subClassOf person
martin hasChild heinz
hasChild some person subClassOf learnedClass
Query 3 of 4 (75%)
Positive Example: markus Type learnedClass
Axiom: markus type learnedClass
Explanation(s):
1) markus hasChild anna
hasChild range person
hasChild some person subClassOf learnedClass
2) ( male
and ( father
or hasChild some father
)
) subClassOf learnedClass
markus type father
markus type male
3) markus hasChild anna
hasChild domain person
anna hasChild heinz
hasChild some person subClassOf learnedClass
Query 4 of 4 (100%)
Negative Example: heinz Type not (learnedClass)
Axiom: heinz type not learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Trying the second method...
Axiom: heinz type learnedClass
Explanation: AXIOM IS NOT ENTAILED!
Explanations founding completed (BDDs computed)
Start EM Algorithm
- n. of probabilistic axioms: 10
- n. of examples: 4
stefan Type learnedClass - prob: 0.58824 - tag: 1 - #vars: 5
martin Type learnedClass - prob: 0.58459 - tag: 2 - #vars: 5
markus Type learnedClass - prob: 0.58824 - tag: 3 - #vars: 4
heinz Type learnedClass - prob: 1.00000 - tag: 4 - #vars: 0
EM cycle: 0
EM cycle: 1
Log-likelihood: -1.59810 cycle: 1
EM cycle: 2
Log-likelihood: -0.50132 cycle: 2
EM cycle: 3
Log-likelihood: -0.36236 cycle: 3
EM cycle: 4
Log-likelihood: -0.30119 cycle: 4
EM cycle: 5
Log-likelihood: -0.26528 cycle: 5
EM cycle: 6
Log-likelihood: -0.24101 cycle: 6
EM cycle: 7
Log-likelihood: -0.22327 cycle: 7
EM cycle: 8
Log-likelihood: -0.20956 cycle: 8
EM cycle: 9
Log-likelihood: -0.19857 cycle: 9
EM cycle: 10
Log-likelihood: -0.18949 cycle: 10
EM cycle: 11
Log-likelihood: -0.18182 cycle: 11
EM cycle: 12
Log-likelihood: -0.17527 cycle: 12
EM cycle: 13
Log-likelihood: -0.16952 cycle: 13
EM cycle: 14
Log-likelihood: -0.16447 cycle: 14
EM cycle: 15
Log-likelihood: -0.15996 cycle: 15
EM cycle: 16
Log-likelihood: -0.15594 cycle: 16
EM cycle: 17
Log-likelihood: -0.15229 cycle: 17
EM cycle: 18
Log-likelihood: -0.14894 cycle: 18
EM cycle: 19
Log-likelihood: -0.14589 cycle: 19
EM cycle: 20
Log-likelihood: -0.14308 cycle: 20
EM cycle: 21
Log-likelihood: -0.14049 cycle: 21
EM cycle: 22
Log-likelihood: -0.13810 cycle: 22
EM cycle: 23
Log-likelihood: -0.13587 cycle: 23
EM cycle: 24
Log-likelihood: -0.13373 cycle: 24
EM cycle: 25
Log-likelihood: -0.13179 cycle: 25
EM cycle: 26
Log-likelihood: -0.12997 cycle: 26
EM cycle: 27
Log-likelihood: -0.12819 cycle: 27
EM cycle: 28
Log-likelihood: -0.12653 cycle: 28
EM cycle: 29
Log-likelihood: -0.12498 cycle: 29
EM cycle: 30
Log-likelihood: -0.12350 cycle: 30
EM cycle: 31
Log-likelihood: -0.12210 cycle: 31
EM cycle: 32
Log-likelihood: -0.12076 cycle: 32
EM cycle: 33
Log-likelihood: -0.11949 cycle: 33
EM cycle: 34
Log-likelihood: -0.11828 cycle: 34
EM cycle: 35
Log-likelihood: -0.11706 cycle: 35
EM cycle: 36
Log-likelihood: -0.11596 cycle: 36
EM cycle: 37
Log-likelihood: -0.11493 cycle: 37
EM cycle: 38
Log-likelihood: -0.11388 cycle: 38
EM cycle: 39
Log-likelihood: -0.11288 cycle: 39
EM cycle: 40
Log-likelihood: -0.11194 cycle: 40
EM cycle: 41
Log-likelihood: -0.11100 cycle: 41
EM cycle: 42
Log-likelihood: -0.11012 cycle: 42
EM cycle: 43
Log-likelihood: -0.10926 cycle: 43
EM cycle: 44
Log-likelihood: -0.10844 cycle: 44
EM cycle: 45
Log-likelihood: -0.10765 cycle: 45
EM cycle: 46
Log-likelihood: -0.10688 cycle: 46
EM cycle: 47
Log-likelihood: -0.10611 cycle: 47
EM cycle: 48
Log-likelihood: -0.10538 cycle: 48
EM cycle: 49
Log-likelihood: -0.10469 cycle: 49
EM cycle: 50
Log-likelihood: -0.10402 cycle: 50
EM cycle: 51
Log-likelihood: -0.10336 cycle: 51
EM cycle: 52
Log-likelihood: -0.10271 cycle: 52
EM cycle: 53
Log-likelihood: -0.10210 cycle: 53
EM cycle: 54
Log-likelihood: -0.10147 cycle: 54
EM cycle: 55
Log-likelihood: -0.10086 cycle: 55
EM cycle: 56
Log-likelihood: -0.10031 cycle: 56
EM cycle: 57
Log-likelihood: -0.09974 cycle: 57
EM cycle: 58
Log-likelihood: -0.09921 cycle: 58
EM cycle: 59
Log-likelihood: -0.09865 cycle: 59
EM cycle: 60
Log-likelihood: -0.09814 cycle: 60
EM cycle: 61
Log-likelihood: -0.09762 cycle: 61
EM cycle: 62
Log-likelihood: -0.09712 cycle: 62
EM cycle: 63
Log-likelihood: -0.09663 cycle: 63
EM cycle: 64
Log-likelihood: -0.09618 cycle: 64
EM cycle: 65
Log-likelihood: -0.09573 cycle: 65
EM cycle: 66
Log-likelihood: -0.09524 cycle: 66
EM cycle: 67
Log-likelihood: -0.09480 cycle: 67
EM cycle: 68
Log-likelihood: -0.09437 cycle: 68
EM cycle: 69
Log-likelihood: -0.09395 cycle: 69
EM cycle: 70
Log-likelihood: -0.09353 cycle: 70
EM cycle: 71
Log-likelihood: -0.09313 cycle: 71
EM cycle: 72
Log-likelihood: -0.09274 cycle: 72
EM cycle: 73
Log-likelihood: -0.09234 cycle: 73
EM cycle: 74
Log-likelihood: -0.09196 cycle: 74
EM cycle: 75
Log-likelihood: -0.09156 cycle: 75
EM cycle: 76
Log-likelihood: -0.09121 cycle: 76
EM cycle: 77
Log-likelihood: -0.09084 cycle: 77
EM cycle: 78
Log-likelihood: -0.09049 cycle: 78
EM cycle: 79
Log-likelihood: -0.09016 cycle: 79
EM cycle: 80
Log-likelihood: -0.08980 cycle: 80
EM cycle: 81
Log-likelihood: -0.08945 cycle: 81
EM cycle: 82
Log-likelihood: -0.08914 cycle: 82
EM cycle: 83
Log-likelihood: -0.08881 cycle: 83
EM cycle: 84
Log-likelihood: -0.08847 cycle: 84
EM cycle: 85
Log-likelihood: -0.08817 cycle: 85
EM cycle: 86
Log-likelihood: -0.08787 cycle: 86
EM cycle: 87
Log-likelihood: -0.08756 cycle: 87
EM cycle: 88
Log-likelihood: -0.08726 cycle: 88
EM cycle: 89
Log-likelihood: -0.08697 cycle: 89
EM cycle: 90
Log-likelihood: -0.08665 cycle: 90
EM cycle: 91
Log-likelihood: -0.08637 cycle: 91
EM completed.
Final Log-Likelihood: -0.08637
Name | Total (ms)
===========================
main | 72
init | 1
Bundle | 57
Bundle.init | 0
Bundle.explain | 54
Bundle.BDDCalc | 1
EM | 14
Test case 3 - Dummy parameter learner
[.....] 0%
[=....] 20%
[==...] 40%
[===..] 60%
[====.] 80%Debug logger: false
INFO (DummyParameterLearner.java:150) - Successful creation of the learned ontology
Successful creation of the learned ontology
INFO (DummyParameterLearner.java:151) - Ontology created in 0.0 (ms)
Ontology created in 0.0 (ms)
INFO (DummyParameterLearner.java:242) - Created 9 probabilistic axiom
Created 9 probabilistic axiom
INFO (DummyParameterLearner.java:247) - Probability Map computed. Size: 9
Probability Map computed. Size: 9
INFO (DummyParameterLearner.java:150) - Successful creation of the learned ontology
Successful creation of the learned ontology
INFO (DummyParameterLearner.java:151) - Ontology created in 1.0 (ms)
Ontology created in 1.0 (ms)
INFO (DummyParameterLearner.java:242) - Created 10 probabilistic axiom
Created 10 probabilistic axiom
INFO (DummyParameterLearner.java:247) - Probability Map computed. Size: 10
Probability Map computed. Size: 10
INFO (DummyParameterLearner.java:242) - Created 10 probabilistic axiom
Created 10 probabilistic axiom
INFO (DummyParameterLearner.java:247) - Probability Map computed. Size: 10
Probability Map computed. Size: 10
INFO (DummyParameterLearner.java:242) - Created 10 probabilistic axiom
Created 10 probabilistic axiom
INFO (DummyParameterLearner.java:247) - Probability Map computed. Size: 10
Probability Map computed. Size: 10
INFO (DummyParameterLearner.java:242) - Created 10 probabilistic axiom
Created 10 probabilistic axiom
INFO (DummyParameterLearner.java:247) - Probability Map computed. Size: 10
Probability Map computed. Size: 10
INFO (DummyParameterLearner.java:242) - Created 10 probabilistic axiom
Created 10 probabilistic axiom
INFO (DummyParameterLearner.java:247) - Probability Map computed. Size: 10
Probability Map computed. Size: 10
INFO (DummyParameterLearner.java:242) - Created 10 probabilistic axiom
Created 10 probabilistic axiom
INFO (DummyParameterLearner.java:247) - Probability Map computed. Size: 10
Probability Map computed. Size: 10
INFO (DummyParameterLearner.java:242) - Created 10 probabilistic axiom
Created 10 probabilistic axiom
INFO (DummyParameterLearner.java:247) - Probability Map computed. Size: 10
Probability Map computed. Size: 10
INFO (DummyParameterLearner.java:242) - Created 10 probabilistic axiom
Created 10 probabilistic axiom
INFO (DummyParameterLearner.java:247) - Probability Map computed. Size: 10
Probability Map computed. Size: 10
INFO (DummyParameterLearner.java:242) - Created 10 probabilistic axiom
Created 10 probabilistic axiom
INFO (DummyParameterLearner.java:247) - Probability Map computed. Size: 10
Probability Map computed. Size: 10
[INFO] Tests run: 3, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 31.365 s - in org.dllearner.algorithms.probabilistic.structure.unife.leap.LEAPTest
[INFO] Running org.dllearner.algorithms.probabilistic.structure.distributed.unife.leap.AppTest
[INFO] Tests run: 1, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 0.004 s - in org.dllearner.algorithms.probabilistic.structure.distributed.unife.leap.AppTest
[INFO]
[INFO] Results:
[INFO]
[INFO] Tests run: 5, Failures: 0, Errors: 0, Skipped: 0
[INFO]
[JENKINS] Recording test results
[INFO]
[INFO] --- maven-jar-plugin:3.2.0:jar (default-jar) @ components-ext ---
[INFO] Building jar: /usr/share/tomcat8/.jenkins/jobs/DL-Learner Merge-M/branches/PR-101/workspace/components-ext/target/components-ext-1.5.1-SNAPSHOT.jar
[INFO]
[INFO] --- maven-install-plugin:2.4:install (default-install) @ components-ext ---
[INFO] Installing /usr/share/tomcat8/.jenkins/jobs/DL-Learner Merge-M/branches/PR-101/workspace/components-ext/target/components-ext-1.5.1-SNAPSHOT.jar to /usr/share/tomcat8/.m2/repository/org/dllearner/components-ext/1.5.1-SNAPSHOT/components-ext-1.5.1-SNAPSHOT.jar
[INFO] Installing /usr/share/tomcat8/.jenkins/jobs/DL-Learner Merge-M/branches/PR-101/workspace/components-ext/pom.xml to /usr/share/tomcat8/.m2/repository/org/dllearner/components-ext/1.5.1-SNAPSHOT/components-ext-1.5.1-SNAPSHOT.pom
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