[jboss-svn-commits] JBL Code SVN: r20144 - in labs/jbossrules/contrib/machinelearning/4.0.x: drools-compiler/src/main/java/org/drools/compiler and 22 other directories.
jboss-svn-commits at lists.jboss.org
jboss-svn-commits at lists.jboss.org
Fri May 23 13:46:28 EDT 2008
Author: gizil
Date: 2008-05-23 13:46:27 -0400 (Fri, 23 May 2008)
New Revision: 20144
Added:
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Attribute.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/AttributeValueComparator.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/DecisionTree.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Domain.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/DomainComparator.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Instance.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/InstanceComparator.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/InstanceList.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/LeafNode.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Memory.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/QuantitativeDomain.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Schema.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Stats.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/TreeNode.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/C45Learner.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/ID3Learner.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/Learner.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/LearnerFactory.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/Tester.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/Categorizer.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/ClassDistribution.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/CondClassDistribution.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/Entropy.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/InformationContainer.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/InstDistribution.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/SplitPoint.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/learner_uml.cdx
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/FeatureNotSupported.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/FieldAnnotation.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/NumberComparator.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/ObjectFactory.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/RulePrinter.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/Util.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/test/java/org/drools/learner/
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Car.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/CarExample.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Golf.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/GolfC45Example.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/GolfC45ExampleFromDrl.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/GolfExample.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Nursery.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/NurseryExample.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Poker.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/PokerExample.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Restaurant.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/RestaurantExample.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/RestaurantExampleFromDrl.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/VV.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/abalone/
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/abalone/abalone.data.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/abalone/abalone.names.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/advertisement/
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/advertisement/ad-dataset.zip
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/advertisement/ad-dataset/
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/advertisement/ad-dataset/ad.DOCUMENTATION
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/advertisement/ad-dataset/ad.data
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/advertisement/ad-dataset/ad.names
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/advertisement/ad.DOCUMENTATION.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/advertisement/ad.data.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/advertisement/ad.names.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/annealing/
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/annealing/anneal.data.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/annealing/anneal.names.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/annealing/anneal.test.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/breast-cancer-wisconsin/
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/breast-cancer-wisconsin/breast-cancer-wisconsin.data.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/breast-cancer-wisconsin/breast-cancer-wisconsin.names.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/breast-cancer-wisconsin/unformatted-data.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/breast-cancer-wisconsin/wdbc.data.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/breast-cancer-wisconsin/wdbc.names.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/breast-cancer-wisconsin/wpbc.data.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/breast-cancer-wisconsin/wpbc.names.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/car/
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/car/car.c45-names.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/car/car.data.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/car/car.names.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/credit-screening/
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/credit-screening/credit.lisp.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/credit-screening/credit.names.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/credit-screening/crx.data.txt
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/credit-screening/crx.names.txt
Modified:
labs/jbossrules/contrib/machinelearning/4.0.x/drools-compiler/.classpath
labs/jbossrules/contrib/machinelearning/4.0.x/drools-compiler/.project
labs/jbossrules/contrib/machinelearning/4.0.x/drools-compiler/src/main/java/org/drools/compiler/PackageBuilder.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/.classpath
labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/.project
labs/jbossrules/contrib/machinelearning/4.0.x/drools-decisiontables/.classpath
labs/jbossrules/contrib/machinelearning/4.0.x/drools-decisiontables/.project
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/.classpath
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/.project
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/pom.xml
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/HelloWorldExample.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/PricingRuleDTExample.java
labs/jbossrules/contrib/machinelearning/4.0.x/drools-jbrms/.classpath
labs/jbossrules/contrib/machinelearning/4.0.x/drools-jbrms/.project
labs/jbossrules/contrib/machinelearning/4.0.x/drools-jbrms/JBRMS.mac.launch
labs/jbossrules/contrib/machinelearning/4.0.x/drools-jsr94/.classpath
labs/jbossrules/contrib/machinelearning/4.0.x/drools-jsr94/.project
labs/jbossrules/contrib/machinelearning/4.0.x/drools-repository/.classpath
labs/jbossrules/contrib/machinelearning/4.0.x/drools-repository/.project
Log:
first version of learner. implementation :
- id3, c45
- tester (and output of test results)
- rule printer
- categorization of quantitative (only numerical) domain
Modified: labs/jbossrules/contrib/machinelearning/4.0.x/drools-compiler/.classpath
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-compiler/.classpath 2008-05-23 16:37:12 UTC (rev 20143)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-compiler/.classpath 2008-05-23 17:46:27 UTC (rev 20144)
@@ -1,18 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
<classpath>
- <classpathentry kind="src" path="src/main/java"/>
- <classpathentry kind="src" path="src/main/resources" excluding="**/*.java"/>
- <classpathentry kind="src" path="src/test/java" output="target/test-classes"/>
- <classpathentry kind="src" path="src/test/resources" output="target/test-classes" excluding="**/*.java"/>
- <classpathentry kind="output" path="target/classes"/>
- <classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER"/>
- <classpathentry kind="var" path="M2_REPO/com/thoughtworks/xstream/xstream/1.2.2/xstream-1.2.2.jar"/>
- <classpathentry kind="var" path="M2_REPO/xpp3/xpp3_min/1.1.3.4.O/xpp3_min-1.1.3.4.O.jar"/>
- <classpathentry kind="var" path="M2_REPO/janino/janino/2.5.10/janino-2.5.10.jar"/>
- <classpathentry kind="var" path="M2_REPO/junit/junit/3.8.1/junit-3.8.1.jar"/>
- <classpathentry kind="var" path="M2_REPO/org/antlr/antlr-runtime/3.0/antlr-runtime-3.0.jar"/>
- <classpathentry kind="src" path="/drools-core"/>
- <classpathentry kind="var" path="M2_REPO/org/mvel/mvel/1.3.1-java1.4/mvel-1.3.1-java1.4.jar"/>
- <classpathentry kind="var" path="M2_REPO/org/eclipse/jdt/core/3.2.3.v_686_R32x/core-3.2.3.v_686_R32x.jar"/>
- <classpathentry kind="var" path="M2_REPO/xerces/xercesImpl/2.4.0/xercesImpl-2.4.0.jar"/>
- <classpathentry kind="var" path="M2_REPO/xml-apis/xml-apis/1.0.b2/xml-apis-1.0.b2.jar"/>
-</classpath>
\ No newline at end of file
+ <classpathentry kind="src" output="target-eclipse/classes" path="src/main/java"/>
+ <classpathentry kind="src" output="target-eclipse/test-classes" path="src/test/java">
+ <attributes>
+ <attribute name="maven.type" value="test"/>
+ </attributes>
+ </classpathentry>
+ <classpathentry excluding="**" kind="src" output="src/main/resources" path="src/main/resources"/>
+ <classpathentry excluding="**" kind="src" output="src/test/resources" path="src/test/resources"/>
+ <classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER"/>
+ <classpathentry kind="con" path="org.maven.ide.eclipse.MAVEN2_CLASSPATH_CONTAINER"/>
+ <classpathentry kind="output" path="target-eclipse/classes"/>
+</classpath>
Modified: labs/jbossrules/contrib/machinelearning/4.0.x/drools-compiler/.project
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-compiler/.project 2008-05-23 16:37:12 UTC (rev 20143)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-compiler/.project 2008-05-23 17:46:27 UTC (rev 20144)
@@ -1,15 +1,24 @@
+<?xml version="1.0" encoding="UTF-8"?>
<projectDescription>
- <name>drools-compiler</name>
- <comment>A rule production system</comment>
- <projects>
- <project>drools-core</project>
- </projects>
- <buildSpec>
- <buildCommand>
- <name>org.eclipse.jdt.core.javabuilder</name>
- </buildCommand>
- </buildSpec>
- <natures>
- <nature>org.eclipse.jdt.core.javanature</nature>
- </natures>
-</projectDescription>
\ No newline at end of file
+ <name>drools-compiler</name>
+ <comment>A rule production system</comment>
+ <projects>
+ <project>drools-core</project>
+ </projects>
+ <buildSpec>
+ <buildCommand>
+ <name>org.eclipse.jdt.core.javabuilder</name>
+ <arguments>
+ </arguments>
+ </buildCommand>
+ <buildCommand>
+ <name>org.maven.ide.eclipse.maven2Builder</name>
+ <arguments>
+ </arguments>
+ </buildCommand>
+ </buildSpec>
+ <natures>
+ <nature>org.eclipse.jdt.core.javanature</nature>
+ <nature>org.maven.ide.eclipse.maven2Nature</nature>
+ </natures>
+</projectDescription>
Modified: labs/jbossrules/contrib/machinelearning/4.0.x/drools-compiler/src/main/java/org/drools/compiler/PackageBuilder.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-compiler/src/main/java/org/drools/compiler/PackageBuilder.java 2008-05-23 16:37:12 UTC (rev 20143)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-compiler/src/main/java/org/drools/compiler/PackageBuilder.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -42,6 +42,9 @@
import org.drools.lang.descr.PackageDescr;
import org.drools.lang.descr.QueryDescr;
import org.drools.lang.descr.RuleDescr;
+import org.drools.learner.builder.ID3Learner;
+import org.drools.learner.builder.Learner;
+import org.drools.learner.tools.RulePrinter;
import org.drools.rule.Package;
import org.drools.rule.Rule;
import org.drools.rule.builder.RuleBuildContext;
@@ -159,8 +162,7 @@
* @throws DroolsParserException
* @throws IOException
*/
- public void addPackageFromDrl(final Reader reader) throws DroolsParserException,
- IOException {
+ public void addPackageFromDrl(final Reader reader) throws DroolsParserException, IOException {
final DrlParser parser = new DrlParser();
final PackageDescr pkg = parser.parse( reader );
this.results.addAll( parser.getErrors() );
@@ -207,6 +209,24 @@
this.results.addAll( parser.getErrors() );
addPackage( pkg );
}
+
+ /**
+ * Load a learner to load its rule package from DRL source AND/OR AST.
+ *
+ * @param learner
+ * @throws DroolsParserException
+ * @throws IOException
+ */
+ public void addPackageFromLearner(final Learner learner) throws DroolsParserException, IOException {
+ Reader reader = RulePrinter.readRules(learner);
+ /* final DrlParser parser = new DrlParser();
+ final PackageDescr pkg = parser.parse( reader );
+ this.results.addAll( parser.getErrors() );
+ addPackage( pkg );
+ */
+
+ this.addPackageFromDrl(reader);
+ }
/**
* Add a ruleflow (.rt) asset to this package.
Modified: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/.classpath
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/.classpath 2008-05-23 16:37:12 UTC (rev 20143)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/.classpath 2008-05-23 17:46:27 UTC (rev 20144)
@@ -1,12 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
<classpath>
- <classpathentry kind="src" path="src/main/java"/>
- <classpathentry kind="src" path="src/main/resources" excluding="**/*.java"/>
- <classpathentry kind="src" path="src/test/java" output="target/test-classes"/>
- <classpathentry kind="src" path="src/test/resources" output="target/test-classes" excluding="**/*.java"/>
- <classpathentry kind="output" path="target/classes"/>
- <classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER"/>
- <classpathentry kind="var" path="M2_REPO/com/thoughtworks/xstream/xstream/1.2.2/xstream-1.2.2.jar"/>
- <classpathentry kind="var" path="M2_REPO/xpp3/xpp3_min/1.1.3.4.O/xpp3_min-1.1.3.4.O.jar"/>
- <classpathentry kind="var" path="M2_REPO/junit/junit/3.8.1/junit-3.8.1.jar"/>
- <classpathentry kind="var" path="M2_REPO/org/mvel/mvel/1.3.1-java1.4/mvel-1.3.1-java1.4.jar"/>
-</classpath>
\ No newline at end of file
+ <classpathentry kind="src" output="target-eclipse/classes" path="src/main/java"/>
+ <classpathentry kind="src" output="target-eclipse/test-classes" path="src/test/java">
+ <attributes>
+ <attribute name="maven.type" value="test"/>
+ </attributes>
+ </classpathentry>
+ <classpathentry excluding="**" kind="src" output="src/main/resources" path="src/main/resources"/>
+ <classpathentry excluding="**" kind="src" output="src/test/resources" path="src/test/resources"/>
+ <classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER"/>
+ <classpathentry kind="con" path="org.maven.ide.eclipse.MAVEN2_CLASSPATH_CONTAINER"/>
+ <classpathentry kind="output" path="target-eclipse/classes"/>
+</classpath>
Modified: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/.project
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/.project 2008-05-23 16:37:12 UTC (rev 20143)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/.project 2008-05-23 17:46:27 UTC (rev 20144)
@@ -1,16 +1,28 @@
+<?xml version="1.0" encoding="UTF-8"?>
<projectDescription>
- <name>drools-core</name>
- <comment>A rule production system</comment>
- <projects/>
- <buildSpec>
- <buildCommand>
- <name>org.eclipse.jdt.core.javabuilder</name>
- </buildCommand>
- <buildCommand>
- <name>org.drools.eclipse.droolsbuilder</name>
- </buildCommand>
- </buildSpec>
- <natures>
- <nature>org.eclipse.jdt.core.javanature</nature>
- </natures>
-</projectDescription>
\ No newline at end of file
+ <name>drools-core</name>
+ <comment>A rule production system</comment>
+ <projects>
+ </projects>
+ <buildSpec>
+ <buildCommand>
+ <name>org.eclipse.jdt.core.javabuilder</name>
+ <arguments>
+ </arguments>
+ </buildCommand>
+ <buildCommand>
+ <name>org.drools.eclipse.droolsbuilder</name>
+ <arguments>
+ </arguments>
+ </buildCommand>
+ <buildCommand>
+ <name>org.maven.ide.eclipse.maven2Builder</name>
+ <arguments>
+ </arguments>
+ </buildCommand>
+ </buildSpec>
+ <natures>
+ <nature>org.eclipse.jdt.core.javanature</nature>
+ <nature>org.maven.ide.eclipse.maven2Nature</nature>
+ </natures>
+</projectDescription>
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Attribute.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Attribute.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Attribute.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,41 @@
+package org.drools.learner;
+
+public class Attribute {
+
+ private String name;
+ private Object value;
+
+ //final ClassFieldExtractor extractor = cache.getExtractor( class_obj, f_name, getClass().getClassLoader() );
+
+ public String getName() {
+ return name;
+ }
+
+ public void setName(String _name) {
+ this.name = _name;
+ }
+
+ public Object getValue() {
+ return value;
+ }
+
+ public void setValue(Object _value) {
+ this.value = _value;
+ }
+
+ /* we assume that "another" points to the same attribute type as this */
+ public int compareValue(Attribute another) {
+ /* Boolean, Double, Integer, String...*/
+ return AttributeValueComparator.instance.compare(getValue(), another.getValue());
+ }
+
+ public int hashCode() {
+ return this.value.hashCode();
+ }
+
+ public String toString() {
+ return value.toString();
+ }
+
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Attribute.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/AttributeValueComparator.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/AttributeValueComparator.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/AttributeValueComparator.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,16 @@
+package org.drools.learner;
+
+import java.util.Comparator;
+
+public class AttributeValueComparator implements Comparator<Object> {
+
+ /* we assume that "another" points to the same attribute type as this */
+ public int compare(Object value1, Object value2) {
+ if (value1 instanceof Comparable)
+ return ((Comparable)value1).compareTo(value2); /* Boolean, Double, Integer, String...*/
+ return value1.hashCode() - value2.hashCode();
+ }
+
+ // singleton wannabe
+ public static AttributeValueComparator instance = new AttributeValueComparator();
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/AttributeValueComparator.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/DecisionTree.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/DecisionTree.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/DecisionTree.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,85 @@
+package org.drools.learner;
+
+import java.util.ArrayList;
+import java.util.Collections;
+
+
+public class DecisionTree {
+
+ private Class<?> obj_clazz;
+
+ /* the target attribute */
+ private Domain target;
+
+ ///* all attributes that will be used during classification */
+ private ArrayList<Domain> attrsToClassify;
+
+ private TreeNode root;
+
+ public long FACTS_READ = 0;
+
+ public DecisionTree(Schema inst_schema, String _target) {
+ this.obj_clazz = inst_schema.getObjectClass();
+
+ System.out.println("The target attribute: "+ _target);
+ this.target = inst_schema.getAttrDomain(_target);
+ this.attrsToClassify = new ArrayList<Domain>(inst_schema.getAttrNames().size()-1);
+ for (String attr_name : inst_schema.getAttrNames()) {
+ if (!attr_name.equals(_target)) {
+ System.out.println("Adding the attribute: "+ attr_name);
+ this.attrsToClassify.add(inst_schema.getAttrDomain(attr_name));
+ }
+ }
+ Collections.sort(this.attrsToClassify, new DomainComparator()); // compare the domains by the name
+
+ }
+
+ public DecisionTree(DecisionTree parentTree, Domain exceptDomain) {
+ //this.domainSet = new Hashtable<String, Domain<?>>();
+
+ this.target = parentTree.getTargetDomain();
+ this.attrsToClassify = new ArrayList<Domain>(parentTree.getAttrDomains().size()-1);
+ for (Domain attr_domain : parentTree.getAttrDomains()) {
+ if (!attr_domain.getFName().equals(exceptDomain.getFName()))
+ this.attrsToClassify.add(attr_domain);
+ }
+ //Collections.sort(this.attrsToClassify, new Comparator<Domain>()); // compare the domains by the name
+
+ }
+
+ public Class<?> getObjClass() {
+ return obj_clazz;
+ }
+
+// public void setTarget(String _target) {
+// this.target = _target;
+// }
+ public Domain getTargetDomain() {
+ return target;
+ }
+
+ public ArrayList<Domain> getAttrDomains() {
+ return attrsToClassify;
+ }
+
+ public TreeNode getRoot() {
+ return (root);
+ }
+
+ public void setRoot(TreeNode root) {
+ this.root = root;
+ }
+
+ public Integer test(Instance i) {
+ return this.getRoot().evaluate(i);
+ }
+
+ @Override
+ public String toString() {
+ String out = "Facts scanned " + FACTS_READ + "\n";
+ return out + root.toString();
+ }
+
+
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/DecisionTree.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Domain.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Domain.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Domain.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,144 @@
+package org.drools.learner;
+
+import java.util.ArrayList;
+import java.util.Collections;
+
+public class Domain {
+ private boolean categorical, fixed;
+ private String fName;
+ private Class<?> fType;// not sure if necessary
+ protected ArrayList<Object> fCategories;
+
+ public Domain(String _name, Class<?> _type) {
+ this.fName = _name;
+ this.fType = _type;
+
+ this.categorical = true; // BY DEFAULT, it is categorical
+
+ this.fCategories = new ArrayList<Object>(2);
+
+ }
+ public Domain cheapClone() {
+ Domain dom = new Domain(this.fName, this.fType);
+ //dom.fixed = this.fixed;
+ dom.categorical = this.categorical;
+ //dom.readingSeq = readingSeq;
+ dom.fCategories = this.fCategories;
+
+ return dom;
+ }
+
+
+ public Class<?> getFType() {
+ return this.fType;
+ }
+
+ public String getFName() {
+ return this.fName;
+ }
+ public void setFixed(boolean _fixed) {
+ this.fixed = _fixed;
+ }
+
+ /** Indicates that this domain cannot be changed after creation */
+ boolean isFixed() {
+ return this.fixed;
+ }
+
+ public void setCategorical(boolean _cate) {
+ this.categorical = _cate;
+ }
+
+ /** Indicates that this domain has discrete set of values */
+ public boolean isCategorical() {
+ return this.categorical;
+ }
+
+ public void addCategory(Object value) {
+ if (fixed) return;
+ if (categorical) {
+ if (!fCategories.contains(value)) fCategories.add(value);
+ } else {
+ return;
+ }
+
+ }
+
+ public Object getCategory(int idx) {
+ return fCategories.get(idx);
+ }
+ public int getCategoryCount() {
+ return fCategories.size();
+ }
+
+ public boolean isPossible(Object _value) throws Exception {
+ //System.out.println("Domain.isPossible() start "+ value+ " ?");
+// since the value is coming from the extractor i dont check the type
+// if (_value.getClass()!= this.fType)
+// return false;
+ //System.exit(0);
+ if (fixed) {// if it is boolean type actually you do not need to check for the contains function {TRUE, FALSE}
+ return this.containsValue(_value);
+ } else {
+ return true;
+ }
+
+ }
+
+ public boolean containsValue(Object value) throws Exception {
+ if (categorical) {
+ return this.fCategories.contains(value);
+ } else {
+ if (fCategories.isEmpty() || fCategories.size()==1)
+ throw new Exception(" Domain "+fName+" is constant and not discrete but bounds are not set: possible values size: "+ fCategories.size());
+
+ // they must be sorted
+ return (AttributeValueComparator.instance.compare(value, fCategories.get(0)) >= 0 && AttributeValueComparator.instance.compare(value, fCategories.get(getCategoryCount()-1)) <= 0);
+ //return (nComparator.compare((Number)value, fValues.get(0)) >= 0 && nComparator.compare((Number)value, fValues.get(fValues.size()-1)) <= 0);
+
+ /* should i check if the value is in one of the intervals
+ * this is necessary only if the intervals are unbroken
+ */
+ }
+ }
+
+ public Object getCategoryOf(Object value) {
+ if (categorical) {
+ return value;
+ } else {
+
+ int insertion_point = Collections.binarySearch(this.fCategories, value , AttributeValueComparator.instance);
+ /*
+ * index of the search key, if it is contained in the list; otherwise, (-(insertion point) - 1).
+ * The insertion point is defined as the point at which the key would be inserted into the list:
+ * the index of the first element greater than the key, or list.size(), if all elements in the
+ * list are less than the specified key. Note that this guarantees that the return value will be >= 0
+ * if and only if the key is found.
+ */
+ if (insertion_point >= 0) {
+ return this.fCategories.get(insertion_point);
+ } else {
+ int unfound_insertion_point = -(insertion_point) -1;
+ if (unfound_insertion_point >= this.fCategories.size()) {
+ //System.out.println("insestion point is the size domain "+this);
+ unfound_insertion_point = this.fCategories.size() -1;
+ }
+ return this.fCategories.get(unfound_insertion_point);
+ }
+ }
+
+ }
+
+ public int hashCode() {
+ return fName.hashCode() ^ fCategories.hashCode(); // TODO
+ }
+
+ public String toString() {
+ String out = fName + "";
+// for (Object v: fValues) {
+// out += "-" + v;
+// }
+ return out;
+ }
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Domain.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/DomainComparator.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/DomainComparator.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/DomainComparator.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,19 @@
+package org.drools.learner;
+
+import java.util.Comparator;
+
+
+public class DomainComparator implements Comparator<Domain> {
+
+ public int compare(Domain d0, Domain d1) {
+
+ return d0.getFName().compareTo(d1.getFName());
+// int fNameComp = d0.getFName().compareTo(d1.getFName());
+// if (fNameComp!=0)
+// return fNameComp;
+// else if () // do other stuff
+// return 1;
+// else
+// return 0;
+ }
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/DomainComparator.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Instance.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Instance.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Instance.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,43 @@
+package org.drools.learner;
+
+import java.util.HashMap;
+
+
+public class Instance {
+ private HashMap<String, Attribute> attributes;
+
+ public Instance() {
+ this.attributes = new HashMap<String, Attribute>(); // TODO should i set a size, HOW?
+ }
+
+
+
+ public void setAttr(String _name, Object _value) {
+ Attribute f_attr = new Attribute();
+ f_attr.setName(_name);
+ f_attr.setValue(_value);
+ this.attributes.put(_name, f_attr);
+ }
+
+ public Attribute getAttr(String field_name) {
+ return attributes.get(field_name);
+ }
+ public Object getAttrValue(String field_name) {
+ return attributes.get(field_name).getValue();
+ }
+
+ public int hashCode() {
+ return attributes.hashCode();
+ }
+
+ public String toString() {
+ String out = this.hashCode() + " ";
+ for (String key: attributes.keySet())
+ {
+ out += key +"="+attributes.get(key)+", ";
+ }
+ return out;
+ }
+
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Instance.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/InstanceComparator.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/InstanceComparator.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/InstanceComparator.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,17 @@
+package org.drools.learner;
+
+import java.util.Comparator;
+
+
+public class InstanceComparator implements Comparator<Instance> {
+
+ private String attr_name;
+
+ public InstanceComparator(String _attr_name) {
+ this.attr_name = _attr_name;
+ }
+ public int compare(Instance i0, Instance i1) {
+ return i0.getAttr(this.attr_name).compareValue(i1.getAttr(this.attr_name));
+ }
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/InstanceComparator.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/InstanceList.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/InstanceList.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/InstanceList.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,128 @@
+package org.drools.learner;
+
+import java.util.ArrayList;
+import java.util.Collection;
+import java.util.List;
+
+import org.drools.WorkingMemory;
+import org.drools.base.ClassFieldExtractor;
+import org.drools.common.InternalWorkingMemory;
+
+
+public class InstanceList {
+
+ private Schema schema;
+ private ArrayList<Instance> instances;
+
+ public InstanceList(Schema _schema) {
+ this.schema = _schema;
+ this.instances = new ArrayList<Instance>(); // TODO should put a size?? HOW?
+ }
+
+ public void addFromWorkingMemory(WorkingMemory _session, Object _obj) {
+ // create instance and all attributes according to the schema
+ Instance inst = new Instance();
+
+ for (String f_name : schema.getAttrNames()) {
+ Domain f_domain = schema.getAttrDomain(f_name);
+ ClassFieldExtractor f_extractor = schema.getAttrExtractor(f_name);
+
+ /* from WorkingMemoryLogger, private String extractDeclarations(final Activation activation, final WorkingMemory workingMemory) {
+ * you can cast the WorkingMemory
+ * final Object value = declaration.getValue( (InternalWorkingMemory) workingMemory, handleImpl.getObject() );
+ */
+ Object f_value = f_extractor.getValue( (InternalWorkingMemory) _session, _obj);
+ /* TODO instanity checks
+ * 1- is this value possible by type?
+ * 2- does this value exist in the possible set of values (if the domain type is categorical)?
+ *
+ */
+ try {
+ if (f_domain.isPossible(f_value)) {
+ f_domain.addCategory(f_value);
+ inst.setAttr(f_name, f_value);
+ }
+ } catch (Exception e) {
+ System.out.println("Domain: "+f_domain+ " could not add the value "+ f_value);
+ e.printStackTrace();
+ }
+
+ }
+ instances.add(inst);
+ }
+
+ public void addAsInstance(Instance inst) {
+ instances.add(inst);
+ }
+
+ public int getSize() {
+ return instances.size();
+ }
+
+ public List<Instance> getInstances() {
+ return instances;
+ }
+
+ public Schema getSchema() {
+ return schema;
+ }
+ public Collection<String> getTargets() {
+ return schema.getTargets();
+ }
+
+ public Collection<String> getAttributes() {
+ return schema.getAttrNames();
+ }
+
+/* String getClassName();
+
+ void assignTo(Collection<Instance> c);
+
+ Domain getDomain(String attr);
+
+ public Collection<Domain> getDomains();
+
+ public int getSize();
+
+ public String toString();*/
+}
+
+/*
+ Data set: A schema and a set of instances matching the schema.
+ Generally, no ordering on instances is assumed.
+ Most machine learning work uses a single fixed-format table.
+
+
+
+ Instance: A single object of the world from which a model will be learned,
+ or on which a model will be used (e.g., for prediction).
+ In most machine learning work, instances are described by feature vectors;
+ some work uses more complex representations (e.g., containing relations between instances or between parts of instances).
+ = (example, case, record)
+
+ Schema: A description of a data set's attributes and their properties.
+
+ Attribute: A quantity describing an instance.
+ An attribute has a domain defined by the attribute type, which denotes the values that can be taken by an attribute.
+ = (field, variable, feature)
+
+ The following domain types are common:
+ - Categorical: A finite number of discrete values.
+ The type nominal denotes that there is no ordering between the values, such as last names and colors.
+ The type ordinal denotes that there is an ordering,
+ such as in an attribute taking on the values low, medium, or high.
+
+ - Continuous (quantitative): Commonly, subset of real numbers,
+ where there is a measurable difference between the possible values.
+ Integers are usually treated as continuous in practical problems.
+
+ Feature: A feature is the specification of an attribute and its value.
+ For example, color is an attribute. ``Color is blue'' is a feature of an example.
+ Many transformations to the attribute set leave the feature set unchanged
+ (for example, regrouping attribute values or transforming multi-valued attributes to binary attributes).
+ Some authors use feature as a synonym for attribute (e.g., in feature-subset selection).
+
+ Feature vector: A list of features describing an instance.
+ = (record, tuple)
+
+*/
\ No newline at end of file
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/InstanceList.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/LeafNode.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/LeafNode.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/LeafNode.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,61 @@
+package org.drools.learner;
+
+import org.drools.learner.tools.Util;
+
+public class LeafNode extends TreeNode {
+
+ private Object targetCategory;
+ private int num_intances_classified;
+
+ public LeafNode(Domain targetDomain, Object value) {
+ super(targetDomain);
+ this.targetCategory = value;
+ num_intances_classified = 0;
+ }
+
+ public void putNode(Object attributeCategory, TreeNode node) {
+ throw new RuntimeException("cannot add Node to a leaf node");
+ }
+
+ public Object getCategory() {
+ return targetCategory;
+ }
+
+ public void setNumClassification(int size) {
+ this.num_intances_classified= size;
+ }
+
+ public int getNumClassification() {
+ return this.num_intances_classified;
+ }
+
+ public Integer evaluate(Instance i) {
+ String targetFName = super.getDomain().getFName();
+
+ Object attr_value = i.getAttrValue(targetFName);
+ Object i_category = super.getDomain().getCategoryOf(attr_value);
+ if (AttributeValueComparator.instance.compare(i_category, this.targetCategory) == 0) {
+ return Integer.valueOf(1); //correct
+ } else {
+ return Integer.valueOf(0); // mistake
+ }
+
+ }
+
+ public int hashCode() {
+ return super.hashCode() ^ targetCategory.hashCode(); // ^ id;//should i add the children
+ }
+
+ public String toString(){
+ return "DECISION -> " + targetCategory.toString();
+ }
+
+ public String toString(int tab, int depth, StringBuffer buf) {
+ buf.append(Util.ntimes("\t", tab + 1));
+
+ buf.append( super.getDomain().getFName() + ":DECISION->" +targetCategory.toString()+"\n");
+ return buf.toString();
+ }
+
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/LeafNode.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Memory.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Memory.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Memory.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,65 @@
+package org.drools.learner;
+
+import java.util.HashMap;
+import java.util.Iterator;
+
+import org.drools.WorkingMemory;
+import org.drools.learner.tools.FeatureNotSupported;
+
+public class Memory {
+
+ public static Memory createFromWorkingMemory(WorkingMemory _session, Class<?> clazz, int TREE_TYPE) throws FeatureNotSupported {
+ // if mem == null
+ Memory mem = new Memory();
+
+ mem.session = _session;
+
+ mem.setClassToClassify(clazz);
+ // create schema from clazz
+ Schema inst_schema = Schema.createFromClass(clazz, TREE_TYPE);
+
+ // create a instance list that can hold objects from our schema
+ mem.instances.put(clazz, new InstanceList(inst_schema));
+
+ /*
+ * do they create an ObjectTypeNode for each new inserted object type?
+ * even if there is no rule exists.
+ * No probably they do not
+ */
+ Iterator<Object> it_object = _session.iterateObjects(); // how can i get the object type nodes
+ while (it_object.hasNext()) {
+ Object obj = it_object.next();
+ if (clazz.isAssignableFrom(obj.getClass()))
+ mem.instances.get(clazz).addFromWorkingMemory(_session, obj);
+ }
+ //dt.FACTS_READ += facts.size();
+
+ return mem;
+ }
+
+
+ // Drools memory
+ private WorkingMemory session;
+ //// class specification
+ //private Schema schema;
+ Class<?> clazzToClassify;
+
+ // instance list
+ private HashMap<Class<?>,InstanceList> instances;
+
+
+ private Memory() {
+ this.instances = new HashMap<Class<?>, InstanceList>();
+ }
+
+ private void setClassToClassify(Class<?> clazz) {
+ this.clazzToClassify = clazz;
+ }
+
+ public InstanceList getClassInstances() {
+ return instances.get(this.clazzToClassify);
+ }
+
+
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Memory.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/QuantitativeDomain.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/QuantitativeDomain.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/QuantitativeDomain.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,103 @@
+package org.drools.learner;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Comparator;
+
+import org.drools.learner.eval.SplitPoint;
+
+public class QuantitativeDomain extends Domain{
+
+ public static QuantitativeDomain createFromDomain(Domain father) {
+ QuantitativeDomain quantitative = new QuantitativeDomain(father.getFName(), father.getFType());
+ //quantitative.setCategories(father.getCategories());
+
+ return quantitative;
+ }
+
+ public QuantitativeDomain cheapClone() {
+ QuantitativeDomain qdom = new QuantitativeDomain(super.getFName(), super.getFType());
+ if (super.isCategorical()) {
+ System.out.println("QuantitativeDomain.cheapClone() fuck categorical how come QuantitativeDomain cloned " );
+ System.exit(0);
+ }
+ qdom.setCategorical(super.isCategorical());
+
+ //for (int i = 0; i<super.getCategoryCount(); i++)
+ qdom.fCategories = this.fCategories;
+ qdom.splits = this.splits;
+
+ return qdom;
+ }
+
+
+ private ArrayList<SplitPoint> splits;
+
+ private QuantitativeDomain(String _fname, Class<?> _ftype) {
+ super(_fname, _ftype);
+ super.setCategorical(false); // by deafault it is true, must set it to false
+ this.splits = new ArrayList<SplitPoint>();
+
+
+ }
+
+
+ public boolean isCategorical() {
+ return false;
+ }
+
+// public int getIndex(int index) {
+// return splits.get(index).getIndex();
+// }
+ public SplitPoint getSplit(int index) {
+ return splits.get(index);
+ }
+
+ public int getNumIndices() {
+ return splits.size();
+ }
+
+ public boolean addSplitPoint(SplitPoint pair) { //int index, Number value) {
+
+ int insertion_point = Collections.binarySearch(this.splits, pair, getSplitComparator());
+ if (insertion_point >= 0) {
+ return false; /* the pair exists in the list and the return is index of the search key*/
+ } else { /* (-(insertion point) - 1)
+ * the point at which the key would be inserted into the list
+ * the index of the first element greater than the key, or list.size()
+ */
+ int unfound_insertion_point = -(insertion_point) -1;
+ this.splits.add(unfound_insertion_point, pair);
+ super.fCategories.add(unfound_insertion_point, pair.getValue());
+ return true;
+ }
+ /* otherwise *
+ if (!this.containsIndex(index)) {
+ super.splits.add(pair);
+ }
+ Collections.sort(super.fCategories, nComparator); */
+ }
+
+ public boolean containsIndex(int value) {
+ for (Object op: super.fCategories) {
+ SplitPoint sp = (SplitPoint) op;
+ if (sp.getIndex() == value)
+ return true;
+
+ }
+ return false;
+
+ }
+
+ public static Comparator<SplitPoint> getSplitComparator() {
+ return new SplitComparator();
+ }
+
+ private static class SplitComparator implements Comparator<SplitPoint>{
+ public int compare(SplitPoint sp1, SplitPoint sp2) {
+ return sp1.getIndex() - sp2.getIndex();
+
+ }
+ }
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/QuantitativeDomain.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Schema.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Schema.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Schema.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,144 @@
+package org.drools.learner;
+
+import java.lang.annotation.Annotation;
+import java.lang.reflect.Field;
+import java.util.ArrayList;
+import java.util.Collection;
+import java.util.HashSet;
+import java.util.Hashtable;
+import java.util.Set;
+
+import org.drools.base.ClassFieldExtractor;
+import org.drools.base.ClassFieldExtractorCache;
+import org.drools.learner.tools.FeatureNotSupported;
+import org.drools.learner.tools.FieldAnnotation;
+import org.drools.learner.tools.Util;
+
+/*
+ * A description of a data set's attributes and their properties.
+ */
+public class Schema {
+
+ public static Schema createFromClass(Class<?> clazz, int TREE_TYPE) throws FeatureNotSupported {
+ Schema schema = new Schema(clazz);
+ ClassFieldExtractorCache cache = ClassFieldExtractorCache.getInstance();
+
+ ArrayList<Field> element_fields = new ArrayList<Field>();
+ Util.getAllFields(clazz, element_fields);
+ //clazz.getDeclaredFields(); //clazz.getFields();
+ for (Field f: element_fields) {
+ String f_name = f.getName();
+ ClassFieldExtractor f_extractor = cache.getExtractor( clazz, f_name, clazz.getClassLoader() );
+ schema.extractorMap.put(f_name, f_extractor);
+ //f_extractor.
+
+ int f_type = 0;
+ if (f.getType().isPrimitive() || f.getType() == String.class) {
+
+ f_type = 1;
+ } else {
+ f_type = 2;
+ }
+
+ Annotation[] annotations = f.getAnnotations();
+ FieldAnnotation spec = null;
+ for (Annotation a : annotations) {
+ if (a instanceof FieldAnnotation) {
+ spec = (FieldAnnotation)a; // here it is !!!
+ break;
+ }
+ }
+
+ if (spec != null) {
+ if (!spec.ignore() && f.getType() == String.class && !spec.discrete()) {
+
+ throw new FeatureNotSupported("String categorization not supported");
+ }
+ boolean skip = false;
+ switch (TREE_TYPE) {
+ case Util.ID3: //ID3
+ if (spec.ignore() || !spec.discrete())
+ skip = true;
+ break;
+ case Util.C45:
+ if (spec.ignore())
+ skip = true;
+ break;
+ default:
+ if (spec.ignore())
+ skip = true;
+ }
+ // only if the annotations are given and the flag to ignore is set true
+ // then continue to next field
+ if (skip) continue;
+ }
+ Domain fieldDomain = new Domain(f_name, f.getType());
+ if (spec != null) { // if the annotations are given Read them to update domain's properties
+ fieldDomain.setCategorical(spec.discrete());
+ if (spec.target()) { schema.addTarget(f_name); }
+ }// else just set every thing categorical
+
+ if (f.getType() == Boolean.TYPE || f.getType() == Boolean.class) { /* set discrete*/
+// fieldDomain.setCategorical(true); // BY DEFAULT it is categorical
+ fieldDomain.addCategory(Boolean.TRUE);
+ fieldDomain.addCategory(Boolean.FALSE);
+ fieldDomain.setFixed(true);
+ }
+ else if (f.getType() == String.class) { /* BY DEFAULT it is categorical*/}
+
+ /* TODO insanity checks:
+ * 1- the annotations are not given
+ * 2- there is no target specified
+ * 3- more than one target is specified
+ */
+ schema.domainMap.put(f_name, fieldDomain);
+ }
+
+ return schema;
+ }
+
+ // the owner class of the schema
+ private Class<?> klass;
+
+ // key: field name
+ private Hashtable<String, ClassFieldExtractor> extractorMap;
+
+ // key: field name
+ private Hashtable<String, Domain> domainMap;
+
+ private HashSet<String> targets;
+
+ public Schema(Class<?> _klass) {
+ this.klass = _klass;
+ this.extractorMap = new Hashtable<String, ClassFieldExtractor>();
+ this.domainMap = new Hashtable<String, Domain>();
+ this.targets = new HashSet<String>();
+ }
+
+ public Class<?> getObjectClass() {
+ return klass;
+ }
+
+ public boolean addTarget(String _target) {
+ return targets.add(_target);
+ }
+
+ public Set<String> getAttrNames() {
+ return this.domainMap.keySet();
+ }
+
+ public Domain getAttrDomain(String attr_name) {
+ return this.domainMap.get(attr_name);
+ }
+
+ public ClassFieldExtractor getAttrExtractor(String attr_name) {
+ return this.extractorMap.get(attr_name);
+ }
+
+ public Collection<String> getTargets() {
+ return targets;
+ }
+
+
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Schema.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Stats.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Stats.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Stats.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,84 @@
+package org.drools.learner;
+
+import java.io.BufferedWriter;
+import java.io.DataOutputStream;
+import java.io.FileNotFoundException;
+import java.io.FileOutputStream;
+import java.io.FileWriter;
+import java.io.OutputStream;
+import java.io.OutputStreamWriter;
+import java.io.PrintWriter;
+import java.util.ArrayList;
+
+public class Stats {
+
+ public static int INCORRECT = 0, CORRECT = 1, UNKNOWN = 2;
+
+ private Class<?> stat_class;
+ private ArrayList<Integer> histogram;
+
+ private int total_data;
+
+ public Stats( Class<?> _stat_class) {
+ stat_class = _stat_class;
+
+ /*
+ * INCORRECT | CORRECT | UNKNOWN
+ * | | |
+ * 0 1 2
+ */
+ histogram = new ArrayList<Integer>(3);
+ for (int i=0; i < 3; i ++) {
+ histogram.add(new Integer(0));
+ }
+ total_data = 0;
+ }
+ public int getResult(int classification) {
+ return histogram.get(classification);
+ }
+
+ public void change(Integer result, int i) {
+ histogram.set(result, Integer.valueOf(histogram.get(result) + i));
+ total_data += i;
+ }
+
+ public int getTotal() {
+ return total_data;
+ }
+
+ public void print2file(String dataFile) {
+
+ String packageFolders = this.stat_class.getPackage().getName();
+
+ String _packageNames = packageFolders.replace('.', '/');
+
+ String fileName = (dataFile == null || dataFile == "") ? this.stat_class.getSimpleName().toLowerCase()+ ".stats": dataFile;
+
+ String dataFileName = "src/main/rules/"+_packageNames+"/"+ fileName;
+ try {
+ StatsPrinter.print(this, new FileOutputStream(dataFileName));
+ } catch (FileNotFoundException e) {
+ // TODO Auto-generated catch block
+ e.printStackTrace();
+ }
+ }
+
+ public void print2out() {
+ StatsPrinter.print(this, System.out);
+
+ }
+}
+
+class StatsPrinter {
+ public static void print(Stats mystats, OutputStream os) {
+ PrintWriter pwr = new PrintWriter(os);
+
+ // print the statistics of the results to a file
+ pwr.println("TESTING results: incorrect "+ mystats.getResult(Stats.INCORRECT));
+ pwr.println("TESTING results: correct "+ mystats.getResult(Stats.CORRECT));
+ pwr.println("TESTING results: unknown "+ mystats.getResult(Stats.UNKNOWN));
+ pwr.println("TESTING results: Total Number "+ mystats.getTotal());
+
+ pwr.flush();
+ }
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/Stats.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/TreeNode.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/TreeNode.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/TreeNode.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,113 @@
+package org.drools.learner;
+
+import java.util.Collection;
+import java.util.Hashtable;
+
+import org.drools.learner.tools.Util;
+
+public class TreeNode {
+
+ private Domain domain;
+ private Hashtable<Object, TreeNode> children;
+ /* TODO explain
+ * rank:
+ * gain:
+ * gainRatio
+ */
+ private double rank, gain, gainRatio;
+
+ // Number of all instances matching at that node
+ private int num_matching_instances;
+
+ public TreeNode(Domain domain) {
+ this.domain = domain;
+ this.children = new Hashtable<Object, TreeNode>();
+
+ }
+
+ public double getRank() {
+ return rank;
+ }
+
+ public void setRank(double _rank) {
+ this.rank = _rank;
+ }
+
+ public void setNumMatch(int size) {
+ this.num_matching_instances= size;
+ }
+
+ public int getNumMatch() {
+ return this.num_matching_instances;
+ }
+
+ public Domain getDomain() {
+ return domain;
+ }
+
+ public void putNode(Object attributeCategory, TreeNode node) {
+ children.put(attributeCategory, node);
+ }
+
+ public Collection<Object> getChildrenKeys() {
+ return children.keySet();
+ }
+
+ public TreeNode getChild(Object attr_key) {
+ return children.get(attr_key);
+ }
+
+ public Integer evaluate(Instance i) {
+ Object attr_value = i.getAttrValue(this.domain.getFName());
+ Object category = domain.getCategoryOf(attr_value);
+
+ TreeNode my_node = this.getChild(category);
+
+ if (Util.DEBUG_TEST) {
+ String out = "\nDomain:"+this.domain.getFName()+"->";
+ for (int idx = 0; idx < this.domain.getCategoryCount(); idx++) {
+ Object value = this.domain.getCategory(idx);
+ out += value+"-";
+ }
+ out = Util.ntimes("$", 5) + out + " SEARCHING for = "+ attr_value + " in "+ this.domain.getFName();
+
+ out += "\n KEYS:";
+ for (Object key: this.getChildrenKeys()) {
+
+ out += " "+key +"% "+this.getChild(key).getDomain() + " :";
+ }
+ System.out.print(out);
+ System.out.print(" @myclass:"+category+ "\n");
+ System.out.print(" @mynode:"+my_node+ "\n");
+
+ }
+ return my_node.evaluate(i);
+ }
+
+ public int hashCode() {
+ return domain.hashCode(); // ^ id;//should i add the children
+ }
+
+ public String toString() {
+ return toString(1, 5, new StringBuffer());
+ }
+
+ public String toString(int tab, int depth, StringBuffer buf) {
+ //if (depth > 0 && domain != null) {
+ if (domain != null) {
+ buf.append(Util.ntimes("\t", tab));
+ buf.append(Util.ntimes("***",1));
+ buf.append( domain.getFName() + " n.hash:"+this.hashCode()+ " \n");
+ for (Object attributeValue : children.keySet()) {
+ buf.append(Util.ntimes("\t", tab + 1));
+ buf.append("+" + attributeValue );
+ buf.append("\n");
+ TreeNode child = children.get(attributeValue);
+ buf.append(child.toString(tab + 1, --depth, new StringBuffer()));
+ }
+ }
+ return buf.toString();
+ }
+
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/TreeNode.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/C45Learner.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/C45Learner.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/C45Learner.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,181 @@
+package org.drools.learner.builder;
+
+import java.io.Reader;
+import java.io.StringReader;
+import java.util.ArrayList;
+import java.util.Hashtable;
+import java.util.List;
+
+import org.drools.learner.DecisionTree;
+import org.drools.learner.Domain;
+import org.drools.learner.Instance;
+import org.drools.learner.InstanceList;
+import org.drools.learner.LeafNode;
+import org.drools.learner.Memory;
+import org.drools.learner.Stats;
+import org.drools.learner.TreeNode;
+import org.drools.learner.eval.Entropy;
+import org.drools.learner.eval.InformationContainer;
+import org.drools.learner.eval.InstDistribution;
+import org.drools.learner.tools.FeatureNotSupported;
+import org.drools.learner.tools.RulePrinter;
+import org.drools.learner.tools.Util;
+
+public class C45Learner implements Learner{
+
+
+ private int data_size;
+ private DecisionTree c45;
+ private InstanceList input_data;
+ protected ArrayList<Instance> missclassified_data;
+ public C45Learner() {
+ this.data_size = 0;
+ }
+
+ public void setDataSize(int num) {
+ this.data_size = num;
+
+ missclassified_data = new ArrayList<Instance>();
+ }
+
+ public int getDataSize() {
+ return this.data_size;
+ }
+
+ public void build(Memory wm) {
+
+ InstanceList class_instances = wm.getClassInstances();
+ input_data = class_instances;
+ if (class_instances.getTargets().size()>1) {
+ //throw new FeatureNotSupported("There is more than 1 target candidates");
+ System.out.println("There is more than 1 target candidates");
+ System.exit(0);
+ // TODO put the feature not supported exception || implement it
+ }
+
+ if (Util.DEBUG_LEARNER)
+ for (Instance inst: class_instances.getInstances()) {
+ System.out.println("Inst: "+ inst);
+ }
+
+ this.setDataSize(class_instances.getSize());
+ DecisionTree dt = null;
+ for (String target: class_instances.getTargets()) {
+ dt = new DecisionTree(class_instances.getSchema(), target);;
+ if (Util.DEBUG_LEARNER) {
+ System.out.println("Num of attributes: "+ dt.getAttrDomains().size());
+ }
+ //System.exit(0);
+ InstDistribution stats_by_class = new InstDistribution(dt.getTargetDomain());
+ stats_by_class.calculateDistribution(class_instances.getInstances());
+ dt.FACTS_READ += class_instances.getSize();
+
+ TreeNode root = train(dt, stats_by_class);
+ dt.setRoot(root);
+ if (Util.DEBUG_LEARNER) {
+ System.out.println("Result tree\n" + dt);
+ }
+ }
+ this.c45 = dt;
+ }
+
+ public TreeNode train(DecisionTree dt, InstDistribution data_stats) {//List<Instance> data) {
+
+ if (data_stats.getSum() == 0) {
+ throw new RuntimeException("Nothing to classify, factlist is empty");
+ }
+
+ /* let's get the statistics of the results */
+ //InstDistribution stats_by_class = new InstDistribution(dt.getTargetDomain());///* the target domain*/);
+ data_stats.evaluateMajority();
+
+
+ /* if all elements are classified to the same value */
+ if (data_stats.get_num_ideas(/* number of supported target categories*/) == 1) {
+
+ LeafNode classifiedNode = new LeafNode(dt.getTargetDomain() /* target domain*/,
+ data_stats.get_winner_class() /*winner target category*/);
+ classifiedNode.setRank( (double)data_stats.getSum()/
+ (double)this.getDataSize()/* total size of data fed to dt*/);
+ classifiedNode.setNumMatch(data_stats.getSum()); //num of matching instances to the leaf node
+ classifiedNode.setNumClassification(data_stats.getSum()); //num of classified instances at the leaf node
+ return classifiedNode;
+ }
+
+ List<Domain> attribute_domains = dt.getAttrDomains();
+ /* if there is no attribute left in order to continue */
+ if (attribute_domains.size() == 0) {
+ /* an heuristic of the leaf classification*/
+ Object winner = data_stats.get_winner_class(); /*winner target category*/
+ LeafNode noAttributeLeftNode = new LeafNode(dt.getTargetDomain() /* target domain*/,
+ winner);
+ noAttributeLeftNode.setRank((double)data_stats.getVoteFor(winner)/
+ (double)this.getDataSize() /* total size of data fed to dt*/);
+ noAttributeLeftNode.setNumMatch(data_stats.getSum()); //num of matching instances to the leaf node
+ noAttributeLeftNode.setNumClassification(data_stats.getVoteFor(winner)); //num of classified instances at the leaf node
+
+ /* we need to know how many guys cannot be classified and who these guys are */
+ data_stats.missClassifiedInstances(missclassified_data);
+
+ return noAttributeLeftNode;
+ }
+
+
+ InformationContainer best_attr_eval = new InformationContainer();
+ /* choosing the best attribute in order to branch at the current node*/
+ Entropy.chooseAttribute(best_attr_eval, data_stats, attribute_domains);
+ Domain node_domain = best_attr_eval.domain;
+
+ if (Util.DEBUG_LEARNER) {
+ System.out.println(Util.ntimes("*", 20)+" 1st best attr: "+ node_domain);
+ }
+
+ TreeNode currentNode = new TreeNode(node_domain);
+ currentNode.setNumMatch(data_stats.getSum()); //num of matching instances to the leaf node
+ currentNode.setRank((double)data_stats.getSum()/
+ (double)this.getDataSize() /* total size of data fed to dt*/);
+
+
+ Hashtable<Object, InstDistribution> filtered_stats = null;
+ try {
+ filtered_stats = data_stats.split(best_attr_eval);
+ } catch (FeatureNotSupported e) {
+ // TODO Auto-generated catch block
+ e.printStackTrace();
+ }// node_domain);
+
+ dt.FACTS_READ += data_stats.getSum();
+
+ for (int c = 0; c<node_domain.getCategoryCount(); c++) {
+ /* split the last two class at the same time */
+ Object category = node_domain.getCategory(c);
+
+ /* list of domains except the choosen one (&target domain)*/
+ DecisionTree child_dt = new DecisionTree(dt, node_domain);
+
+ if (filtered_stats == null || filtered_stats.get(category) == null || filtered_stats.get(category).getSum() ==0) {
+ /* majority !!!! */
+ LeafNode majorityNode = new LeafNode(dt.getTargetDomain(), data_stats.get_winner_class());
+ majorityNode.setRank(-1.0); //it does not classify any instance
+ majorityNode.setNumMatch(0);
+ majorityNode.setNumClassification(0);
+ currentNode.putNode(category, majorityNode);
+ } else {
+ TreeNode newNode = train(child_dt, filtered_stats.get(category));//, attributeNames_copy
+ currentNode.putNode(category, newNode);
+ }
+ }
+ return currentNode;
+
+ }
+
+ public DecisionTree getTree() {
+ return c45;
+ }
+
+ public int getTreeType() {
+ return Util.C45;
+ }
+
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/C45Learner.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/ID3Learner.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/ID3Learner.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/ID3Learner.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,195 @@
+package org.drools.learner.builder;
+
+import java.io.Reader;
+import java.io.StringReader;
+import java.util.ArrayList;
+import java.util.Hashtable;
+import java.util.List;
+
+import org.drools.learner.DecisionTree;
+import org.drools.learner.Domain;
+import org.drools.learner.Instance;
+import org.drools.learner.InstanceList;
+import org.drools.learner.LeafNode;
+import org.drools.learner.Memory;
+import org.drools.learner.TreeNode;
+import org.drools.learner.eval.Entropy;
+import org.drools.learner.eval.InstDistribution;
+
+
+import org.drools.learner.tools.RulePrinter;
+import org.drools.learner.tools.Util;
+
+public class ID3Learner implements Learner {
+
+
+ private int data_size;
+ private DecisionTree id3;
+ private InstanceList input_data;
+
+ public ID3Learner() {
+ this.data_size = 0;
+ }
+
+ /* TODO updating the working memory
+ * (non-Javadoc)
+ * @see org.drools.learner.Learner#build(org.drools.WorkingMemory)
+ *
+
+ ObjectSet fs = wm.getSet(klass, all_discrete);
+ Collection<Domain<?>> domains = fs.getDomains();
+ Object element = ObjectReader.read(klass, domains, line, separator);
+ fs.insert(element);
+ */
+
+ public void setDataSize(int num) {
+ this.data_size = num;
+ }
+
+ public int getDataSize() {
+ return this.data_size;
+ }
+
+ /*
+ * the memory has the information
+ * the instances: the objects which the decision tree will work on
+ * the schema: the definition of the object instance
+ * (Class<?>) klass, String targetField, List<String> workingAttributes
+ */
+ public void build(Memory wm) {
+
+ //ArrayList<String> attrs = new ArrayList<String>(wm.getAttributes());
+ //Collections.sort(attrs);
+ // foreach instanceList
+ this.input_data = wm.getClassInstances();
+ for (Instance inst: input_data.getInstances()) {
+ System.out.println("Inst: "+ inst);
+ }
+
+ this.setDataSize(input_data.getSize());
+ DecisionTree dt = null;
+ for (String target: input_data.getTargets()) {
+ dt = new DecisionTree(input_data.getSchema(), target);
+ //dt.setTarget(target);
+
+ if (Util.DEBUG_LEARNER) {
+ System.out.println("Num of attributes: "+ dt.getAttrDomains().size());
+ }
+ //System.exit(0);
+ InstDistribution stats_by_class = new InstDistribution(dt.getTargetDomain());
+ stats_by_class.calculateDistribution(input_data.getInstances());
+ dt.FACTS_READ += input_data.getSize();
+
+ TreeNode root = train(dt, stats_by_class);
+ dt.setRoot(root);
+ if (Util.DEBUG_LEARNER) {
+ System.out.println("Result tree\n" + dt);
+ }
+ }
+ id3 = dt;
+ }
+
+ public Reader readRules() {
+ if (this.id3 == null) {
+ System.out.println("There is tree/rule to process");
+ return null;
+ }
+
+ RulePrinter my_printer = new RulePrinter(); //bocuk.getNum_fact_trained()
+ boolean sort_via_rank = true;
+ boolean print = true;
+ my_printer.printer(this.id3, sort_via_rank);
+
+ String all_rules = my_printer.write2string();
+ if (print) {
+ //my_printer.write2file("examples", "src/rules/examples/" + file);
+ if (Util.DEBUG_LEARNER) {
+ System.out.println(all_rules);
+ }
+ my_printer.write2File(all_rules, false, "", Util.ID3 );
+ }
+
+ return new StringReader(all_rules);
+ }
+
+ public TreeNode train(DecisionTree dt, InstDistribution data_stats) {//List<Instance> data) {
+
+ if (data_stats.getSum() == 0) {
+ throw new RuntimeException("Nothing to classify, factlist is empty");
+ }
+
+ /* let's get the statistics of the results */
+ //InstDistribution stats_by_class = new InstDistribution(dt.getTargetDomain());///* the target domain*/);
+ data_stats.evaluateMajority();
+
+
+ /* if all elements are classified to the same value */
+ if (data_stats.get_num_ideas(/* number of supported target categories*/) == 1) {
+
+ LeafNode classifiedNode = new LeafNode(dt.getTargetDomain() /* target domain*/,
+ data_stats.get_winner_class() /*winner target category*/);
+ classifiedNode.setRank( (double)data_stats.getSum()/
+ (double)this.getDataSize()/* total size of data fed to dt*/);
+ classifiedNode.setNumMatch(data_stats.getSum()); //num of matching instances to the leaf node
+ classifiedNode.setNumClassification(data_stats.getSum()); //num of classified instances at the leaf node
+ return classifiedNode;
+ }
+ List<Domain> attribute_domains = dt.getAttrDomains();
+
+ /* if there is no attribute left in order to continue */
+ if (attribute_domains.size() == 0) {
+ /* an heuristic of the leaf classification*/
+ Object winner = data_stats.get_winner_class(); /*winner target category*/
+ LeafNode noAttributeLeftNode = new LeafNode(dt.getTargetDomain() /* target domain*/,
+ winner);
+ noAttributeLeftNode.setRank((double)data_stats.getVoteFor(winner)/
+ (double)this.getDataSize() /* total size of data fed to dt*/);
+ noAttributeLeftNode.setNumMatch(data_stats.getSum()); //num of matching instances to the leaf node
+ noAttributeLeftNode.setNumClassification(data_stats.getVoteFor(winner)); //num of classified instances at the leaf node
+ return noAttributeLeftNode;
+ }
+
+
+ /* id3 starts: choose the attribute according to the entropy function
+ * entrophy function: data, info of the data wrt target domain, info for each attribute
+ * */
+ Domain node_domain = Entropy.chooseAttributeAsCategorical(data_stats, attribute_domains);
+
+ //System.out.println(Util.ntimes("*", 20)+" 1st best attr: "+ node_domain);
+
+ TreeNode currentNode = new TreeNode(node_domain);
+ currentNode.setNumMatch(data_stats.getSum()); //num of matching instances to the leaf node
+ Hashtable<Object, InstDistribution> filtered_stats = data_stats.splitFromCategorical(node_domain, null);
+ dt.FACTS_READ += data_stats.getSum();
+
+ for (int c=0; c<node_domain.getCategoryCount(); c++) {
+ /* split the last two class at the same time */
+ Object category = node_domain.getCategory(c);
+
+ /* list of domains except the choosen one (&target domain)*/
+ DecisionTree child_dt = new DecisionTree(dt, node_domain);
+
+ if (filtered_stats.get(category) == null || filtered_stats.get(category).getSum() ==0) {
+ /* majority !!!! */
+ LeafNode majorityNode = new LeafNode(dt.getTargetDomain(), data_stats.get_winner_class());
+ majorityNode.setRank(-1.0); //it does not classify any instance
+ majorityNode.setNumMatch(0);
+ majorityNode.setNumClassification(0);
+ currentNode.putNode(category, majorityNode);
+ } else {
+ TreeNode newNode = train(child_dt, filtered_stats.get(category));//, attributeNames_copy
+ currentNode.putNode(category, newNode);
+ }
+ }
+ return currentNode;
+
+ }
+
+ public DecisionTree getTree() {
+ return id3;
+ }
+
+ public int getTreeType() {
+ return Util.ID3;
+ }
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/ID3Learner.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/Learner.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/Learner.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/Learner.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,21 @@
+package org.drools.learner.builder;
+
+import java.io.Reader;
+
+import org.drools.learner.DecisionTree;
+import org.drools.learner.Memory;
+
+public interface Learner {
+ /*
+ * the memory has the information
+ * the instances: the objects which the decision tree will work on
+ * the schema: the definition of the object instance
+ * (Class<?>) klass, String targetField, List<String> workingAttributes
+ */
+ void build(Memory wm);
+
+ DecisionTree getTree();
+
+ int getTreeType();
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/Learner.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/LearnerFactory.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/LearnerFactory.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/LearnerFactory.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,61 @@
+package org.drools.learner.builder;
+
+import org.drools.WorkingMemory;
+import org.drools.learner.Memory;
+import org.drools.learner.tools.FeatureNotSupported;
+import org.drools.learner.tools.Util;
+
+public class LearnerFactory {
+
+ //static int TREE_TYPE = ID3;
+
+ public static Learner createID3(WorkingMemory wm, Class<? extends Object> obj_class) throws FeatureNotSupported {
+
+ /*
+ * Quesitons:
+ * 1- which class to work with? : obj_class
+ * 2- what is its target attribute?
+ * 3- what are the objects
+ */
+
+ /* create the memory */
+ // process the annotations => create the
+
+ Memory mem = Memory.createFromWorkingMemory(wm, obj_class, Util.ID3);
+ // set to memory type only the discrete domains
+
+ ID3Learner id3 = new ID3Learner();
+ id3.build(mem);//obj_class, target_attr, working_attr
+
+ Tester.test(id3.getTree(), mem.getClassInstances());
+ return id3;
+ }
+
+
+ public static Learner createC45(WorkingMemory wm, Class<? extends Object> obj_class) throws FeatureNotSupported {
+
+ /* create the memory */
+ Memory mem = Memory.createFromWorkingMemory(wm, obj_class, Util.C45);
+ // set to memory type only the discrete domains
+
+ C45Learner c45 = new C45Learner();
+ c45.build(mem);//obj_class, target_attr, working_attr
+ Tester.test(c45.getTree(), mem.getClassInstances());
+ return c45;
+ }
+
+ public static Learner createID3(WorkingMemory wm) {
+
+ /*
+ * Quesitons:
+ * 1- which class to work with?
+ * 2- what is its target attribute?
+ * 3- what are the objects
+ */
+ //String target_attr = ObjectReader.getTargetAnnotation(arest.getClass());
+
+ ID3Learner id3 = new ID3Learner();
+ //id3.build(wm);
+ return id3;
+ }
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/LearnerFactory.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/Tester.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/Tester.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/Tester.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,45 @@
+package org.drools.learner.builder;
+
+import org.drools.learner.DecisionTree;
+import org.drools.learner.Instance;
+import org.drools.learner.InstanceList;
+import org.drools.learner.Stats;
+import org.drools.learner.tools.Util;
+
+public class Tester {
+
+ /* test the entire set*/
+ public static void test(DecisionTree dt, InstanceList data) {
+ if (dt == null) {
+ System.out.println("The tree is not created");
+ System.exit(0);
+ }
+
+ if (Util.DEBUG_TEST) {
+ System.out.println(Util.ntimes("\n", 2)+Util.ntimes("$", 5)+" TESTING "+Util.ntimes("\n", 2));
+ }
+
+ Stats evaluation = new Stats(dt.getObjClass());
+ int i = 0;
+ for (Instance instant : data.getInstances()) {
+
+ Integer result = dt.test(instant);
+ if (Util.DEBUG_TEST) {
+ System.out.println(Util.ntimes("#\n", 1)+i+ " <START> TEST: instant="+ instant + " = target "+ result);
+ } else {
+ if (i%1000 ==0) System.out.print(".");
+ }
+ evaluation.change(result, 1);
+ i ++;
+ }
+
+ if (Util.PRINT_STATS) {
+ if (Util.DEBUG_TEST) {
+ evaluation.print2out();
+ }
+ evaluation.print2file("");
+ }
+ return;
+ }
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/builder/Tester.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/Categorizer.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/Categorizer.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/Categorizer.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,211 @@
+package org.drools.learner.eval;
+
+import java.util.ArrayList;
+import java.util.Collections;
+
+
+
+import org.drools.learner.Domain;
+import org.drools.learner.Instance;
+import org.drools.learner.InstanceComparator;
+import org.drools.learner.QuantitativeDomain;
+
+import org.drools.learner.tools.Util;
+
+
+public class Categorizer {
+
+ private ArrayList<Instance> data;
+ private QuantitativeDomain splitDomain;
+ private Domain targetDomain;
+
+ private InstanceComparator targetComp, attrComp;
+
+ private ClassDistribution distribution;
+
+
+ /* TODO make it singleton */
+ private static final Object key0 = Integer.valueOf(0);
+ private static final Object key1 = Integer.valueOf(1);
+ private static final Domain binaryDomain = init_binary_domain();
+
+
+
+ /* TODO put this into globals file */
+ private int maxDepth = 1;
+
+ public Categorizer(InstDistribution _data_in_class) {
+ //List<Instance> _instancese
+ this.data = new ArrayList<Instance>(_data_in_class.getSum());
+ this.distribution = _data_in_class;
+ this.targetDomain = _data_in_class.getClassDomain();
+
+ for (int category = 0; category<targetDomain.getCategoryCount(); category++) {
+ Object targetCategory = targetDomain.getCategory(category);
+ data.addAll(_data_in_class.getSupportersFor(targetCategory));
+ }
+
+ this.targetComp = new InstanceComparator(targetDomain.getFName());
+ }
+
+ public void findSplits(QuantitativeDomain attrDomain) {
+ this.splitDomain = attrDomain;
+ this.attrComp = new InstanceComparator(attrDomain.getFName());
+
+ init_binary_domain();
+
+ // CATEGORIZATION 1. sort the values
+ Collections.sort(this.data, this.attrComp);
+
+ int last_index = this.data.size()-1;
+ Object last_value = data.get(last_index).getAttrValue(this.splitDomain.getFName());
+ this.splitDomain.addSplitPoint(new SplitPoint(last_index, last_value));
+
+ find_a_split(0, this.data.size(), this.maxDepth, this.distribution);
+ return;
+ }
+
+ public static Domain init_binary_domain() {
+ //if (binaryDomain == null) {
+ Domain bD = new Domain("binary", Integer.class); //splitDomain.cheapClone();
+ bD.addCategory(key0); //addSplitPoint(new SplitPoint(0, key0));
+ bD.addCategory(key1); //addSplitPoint(new SplitPoint(1, key1));
+ //}
+ return bD;
+ }
+
+ public ArrayList<Instance> getInstances() {
+ return data;
+ }
+
+ private double find_a_split(int begin_index, int size, int depth,
+ ClassDistribution facts_in_class) {
+ if (data.size() <= 1 || (size-begin_index)<2) {
+ if (Util.DEBUG_CATEGORIZER) {
+ System.out.println("fact.size <=1 returning 0.0....");
+ }
+ return 0.0;
+ }
+// if (facts_in_class.getSum() == 0) {
+// return 0.0; // there is no one in it
+// }
+ facts_in_class.evaluateMajority();
+ if (facts_in_class.get_num_ideas()==1) {
+ if (Util.DEBUG_CATEGORIZER) {
+ System.out.println("getNum_supported_target_classes=1 returning 0.0....");
+ }
+ return 0.0; //?
+ }
+
+ if (depth == 0) {
+ if (Util.DEBUG_CATEGORIZER) {
+ System.out.println("depth == 0 returning 0.0....");
+ }
+ return 0.0;
+ }
+
+ /* initialize the distribution */
+ CondClassDistribution instances_by_attr = new CondClassDistribution(binaryDomain, this.targetDomain);
+ instances_by_attr.setTotal(data.size());
+ instances_by_attr.setDistForAttrValue(key1, facts_in_class);
+ instances_by_attr.setTotal(data.size());
+
+ double best_sum = 100000.0;
+
+ int num_split_points = 0, split_index = begin_index+1;
+ int last_index = size-1;
+ Object last_value = data.get(last_index).getAttrValue(this.splitDomain.getFName());
+ SplitPoint bestPoint = new SplitPoint(last_index, last_value);
+ //split_points.get(end_index-1);
+ //SplitPoint bestPoint = split_points.get(split_points.size()-1);
+ CondClassDistribution best_distribution = null;//instances_by_attr;
+ Instance i1 = data.get(begin_index), i2;
+
+ if (Util.DEBUG_CATEGORIZER) {
+ System.out.println("\nentropy.info_cont() SEARCHING: "+begin_index+ " until "+size+" attr "+this.splitDomain.getFName()+ " "+ i1 );
+ }
+ for(int index =begin_index+1; index < size; index ++) {
+ i2= data.get(index);
+
+ /* every time i read a new instance and change the place in the distribution */
+ instances_by_attr.change(key0, i1.getAttrValue(this.targetDomain.getFName()), +1);
+ instances_by_attr.change(key1, i1.getAttrValue(this.targetDomain.getFName()), -1);
+ if (Util.DEBUG_CATEGORIZER) {
+ System.out.println("BOK " +i1+" vs "+i2);
+ }
+ /*
+ * CATEGORIZATION 2.1. Cut points are points in the sorted list above where the class labels change.
+ * Eg. if I had five instances with values for the attribute of interest and labels
+ * (1.0,A), (1.4,A), (1.7, A), (2.0,B), (3.0, B), (7.0, A), then there are only
+ * two cutpoints of interest: 1.85 and 5 (mid-way between the points
+ * where the classes change from A to B or vice versa).
+ */
+ if ( targetComp.compare(i1, i2)!=0 && attrComp.compare(i1, i2)!=0) {
+ num_split_points++;
+ if (Util.DEBUG_CATEGORIZER) {
+ System.out.println("entropy.info_cont() SEARCHING: "+(index)+" attr "+this.splitDomain.getFName()+ " "+ i2 );
+ }
+ // the cut point
+ Object cp_i = i1.getAttrValue(this.splitDomain.getFName());
+ Object cp_i_next = i2.getAttrValue(this.splitDomain.getFName());
+
+ Class<?> fClass = this.splitDomain.getFType();
+
+ Object cut_point = Util.calculateMidPoint(fClass, cp_i, cp_i_next);
+
+ /*
+ * CATEGORIZATION 3. Evaluate your favourite disparity measure
+ * (info gain, gain ratio, gini coefficient, chi-squared test) on the cut point
+ * and calculate its gain
+ */
+ double sum = Entropy.calc_info_attr(instances_by_attr);
+ if (sum < best_sum) {
+ best_sum = sum;
+ split_index = index;
+
+ if (Util.DEBUG_CATEGORIZER) {
+ System.out.println(Util.ntimes("?", 10)+"** FOUND: @"+(index)+" target ("+ i1.getAttrValue(this.targetDomain.getFName())
+ +"-|T|-"+ i2.getAttrValue(this.targetDomain.getFName())+")");
+ }
+ bestPoint = new SplitPoint(index-1, cut_point);
+ bestPoint.setInformationValue(best_sum);
+
+// if (best_distribution != null)
+// best_distribution.clear();
+ best_distribution = new CondClassDistribution(instances_by_attr);
+ }
+ }
+ i1 = i2;
+
+ }
+ if (best_distribution != null) {
+ if (Util.DEBUG_CATEGORIZER) {
+ System.out.println("bp:"+ bestPoint);
+ }
+
+ this.splitDomain.addSplitPoint(bestPoint);
+
+ /*
+ * TODO : can we put the conditional class distribution to its correct place instead of the split
+ */
+ if (Util.DEBUG_CATEGORIZER) {
+ System.out.println("bd:"+ best_distribution.getNumCondClasses());
+ }
+ double sum1 = find_a_split(begin_index, split_index, depth-1, best_distribution.getDistributionOf(key0));
+
+ double sum2 = find_a_split(split_index, size, depth-1, best_distribution.getDistributionOf(key1));
+
+ return sum1 + sum2;
+ } else
+ return 0.0;
+
+ /* return best_sum;
+ * TODO find the correct formula of the sumi's for the return
+ */
+
+
+
+ }
+
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/Categorizer.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/ClassDistribution.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/ClassDistribution.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/ClassDistribution.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,129 @@
+package org.drools.learner.eval;
+
+import java.util.Hashtable;
+
+import org.drools.learner.Domain;
+import org.drools.learner.tools.Util;
+
+
+/* simple histogram keeps the number of instances in each class
+ * class: categories of the target attribute*/
+public class ClassDistribution {
+
+ protected Domain target_attr;
+ private Hashtable<Object, Integer> quantity_by_class;
+
+ private String sum_key = Util.sum();
+ private int num_category_ideas;
+ private Object winner_category;
+
+ public ClassDistribution(Domain targetDomain) {
+ this.target_attr = targetDomain;
+
+ this.quantity_by_class = new Hashtable<Object, Integer>(this.target_attr.getCategoryCount() + 1);
+ for (int c=0; c<this.target_attr.getCategoryCount(); c++) {
+ Object category = this.target_attr.getCategory(c);
+ quantity_by_class.put(category, 0);
+ }
+
+ quantity_by_class.put(sum_key, 0);
+
+ num_category_ideas = 0;
+ }
+
+ public ClassDistribution(ClassDistribution copy_dist) {
+ this.target_attr = copy_dist.getClassDomain();
+ this.quantity_by_class = new Hashtable<Object, Integer>(this.target_attr.getCategoryCount() + 1);
+ this.setDistribution(copy_dist);
+
+ this.num_category_ideas = copy_dist.get_num_ideas();
+ this.winner_category = copy_dist.get_winner_class();
+
+ }
+
+ public void setSum(int sum) {
+ quantity_by_class.put(sum_key, sum);
+ }
+
+ public int getSum() {
+ return quantity_by_class.get(sum_key).intValue();
+ }
+
+ public Domain getClassDomain() {
+ return target_attr;
+ }
+
+ public void change(Object target_category, int i) {
+ /* TODO ????
+ * if (target_category == sum_key) return;
+ */
+ int num_1 = quantity_by_class.get(target_category).intValue();
+ num_1 += i;
+ quantity_by_class.put(target_category, num_1);
+
+ //quantity_by_class.put(target_category, quantity_by_class.get(target_category)+i);
+ }
+
+ public int getVoteFor(Object targetCategory) {
+ return quantity_by_class.get(targetCategory).intValue();
+ }
+
+ public void evaluateMajority() {
+ int winner_vote = 0;
+ int num_ideas = 0; // the number of target categories that the instances belong to
+
+ Object winner = null;
+
+ for (int c=0; c<this.target_attr.getCategoryCount(); c++) {
+ Object category = this.target_attr.getCategory(c);
+
+ int num_in_class = this.getVoteFor(category);
+ if (num_in_class > 0) {
+ num_ideas++;
+ if (num_in_class > winner_vote) {
+ winner_vote = num_in_class;
+ winner = category;
+ }
+ }
+ }
+
+ this.set_num_ideas(num_ideas);
+ this.set_winner_class(winner);
+ }
+
+ public void set_num_ideas(int num_supperted_target_classes) {
+ this.num_category_ideas = num_supperted_target_classes;
+ }
+
+ public void set_winner_class(Object the_winner_target_class) {
+ this.winner_category = the_winner_target_class;
+ }
+
+ public int get_num_ideas() {
+ return this.num_category_ideas;
+ }
+
+ public Object get_winner_class() {
+ return this.winner_category;
+ }
+
+ public String toString() {
+ String out = "ClassDist: target:"+ this.target_attr.getFName()+ " total: "+ this.getSum() + " & categories:";
+ for (int c=0; c<this.target_attr.getCategoryCount(); c++) {
+ Object category = this.target_attr.getCategory(c);
+ out += this.getVoteFor(category) +" @"+category+ ", ";
+ }
+// out +="\n";
+ return out;
+ }
+
+ public void setDistribution(ClassDistribution targetDist) {
+ for (int c=0; c<this.target_attr.getCategoryCount(); c++) {
+ Object category = this.target_attr.getCategory(c);
+ this.quantity_by_class.put(category, targetDist.getVoteFor(category));
+ }
+ this.quantity_by_class.put(sum_key, targetDist.getSum());
+ }
+
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/ClassDistribution.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/CondClassDistribution.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/CondClassDistribution.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/CondClassDistribution.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,101 @@
+package org.drools.learner.eval;
+
+import java.util.Hashtable;
+
+import org.drools.learner.Domain;
+import org.drools.learner.tools.Util;
+
+public class CondClassDistribution {
+
+ private String sum_key = Util.sum();
+
+ private Domain cond_attr; // domain of the attr we distribute the instances conditionally
+ private Hashtable<Object, ClassDistribution> cond_quantity_by_class;
+
+ private int total_num;
+
+ public CondClassDistribution(Domain attributeDomain, Domain targetDomain) {
+ this.cond_attr = attributeDomain;
+ //List<?> attributeValues = this.domain.getValues();
+ cond_quantity_by_class = new Hashtable<Object, ClassDistribution>(attributeDomain.getCategoryCount());
+
+ for (int c_idx = 0; c_idx<attributeDomain.getCategoryCount(); c_idx++) {
+ Object attr_category = attributeDomain.getCategory(c_idx);
+ cond_quantity_by_class.put(attr_category, new ClassDistribution(targetDomain));
+ }
+
+ }
+
+ public CondClassDistribution(CondClassDistribution copy) {
+ this.cond_attr = copy.getAttrDomain();
+ this.cond_quantity_by_class = new Hashtable<Object, ClassDistribution>(copy.getNumCondClasses());
+
+ for (int c_idx = 0; c_idx< this.cond_attr.getCategoryCount(); c_idx++) {
+ Object attr_category = this.cond_attr.getCategory(c_idx);
+ this.cond_quantity_by_class.put(attr_category, new ClassDistribution(copy.getDistributionOf(attr_category)));
+ }
+ this.total_num = copy.getTotal();
+
+ }
+
+ public int getTotal() {
+ return this.total_num;
+ }
+
+ public void setTotal(int size) {
+ this.total_num = size;
+ }
+
+ public Domain getAttrDomain() {
+ return cond_attr;
+ }
+// public String getTargetName() {
+// return target_attr.getFName();
+// }
+
+ public Object getCondClass(int idx) {
+ return cond_attr.getCategory(idx);
+ }
+
+ public int getNumCondClasses() {
+ return cond_attr.getCategoryCount();
+ }
+
+ public int getTotal_AttrCategory(Object attr_value) {
+ return cond_quantity_by_class.get(attr_value).getSum();
+ }
+
+ public void change(Object attr_category, Object target_class, int i) {
+// System.out.print("The cond_dist: a_cat:"+ attr_category+" t_cat:"+ target_class);
+// System.out.println(" the nums: "+cond_quantity_by_class.get(attr_category));
+
+ cond_quantity_by_class.get(attr_category).change(target_class, i);
+ cond_quantity_by_class.get(attr_category).change(sum_key, i);
+
+ }
+
+ public ClassDistribution getDistributionOf(Object attr_value) {
+ return cond_quantity_by_class.get(attr_value);
+ }
+ public void clear() {
+ this.cond_quantity_by_class.clear();
+ // all for each?
+ }
+
+ public void setDistForAttrValue(Object attr_value, ClassDistribution facts_in_class) {
+ //cond_quantity_by_class.put(attr_value, targetDist);
+ /* TODO should i make a clone */
+ cond_quantity_by_class.get(attr_value).setDistribution(facts_in_class);
+
+ }
+ public String toString() {
+ String out = "\nCondClassDist: attr: "+cond_attr.getFName()+" total num: "+ this.getTotal() + "\n" ;
+ for (int c_idx = 0; c_idx<cond_attr.getCategoryCount(); c_idx++) {
+ Object attr_category = cond_attr.getCategory(c_idx);
+ ClassDistribution td = cond_quantity_by_class.get(attr_category);
+ out += "(ATTR:"+attr_category+ "=> "+ td +")";
+ }
+ return out;
+ }
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/CondClassDistribution.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/Entropy.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/Entropy.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/Entropy.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,254 @@
+package org.drools.learner.eval;
+
+
+import java.util.ArrayList;
+import java.util.List;
+
+import org.drools.learner.Domain;
+import org.drools.learner.Instance;
+import org.drools.learner.QuantitativeDomain;
+import org.drools.learner.tools.Util;
+
+public class Entropy {
+
+ //public Entropy
+ /*
+ * - chooses the best attribute,
+ * - return the choosen attributes evaluation results (information gain and/or gain ratio)
+ * can process categorical, and quantitative attribute domains
+ *
+ * used by:
+ * c45Alternator, c45Learner, c45Iterator
+ */
+ public static Domain chooseAttribute(InformationContainer eval, InstDistribution insts_by_target, List<Domain> attr_domains) {
+ // List<Instance> instances
+
+ double dt_info = calc_info(insts_by_target);
+
+ double greatestGain = -1000;
+ ArrayList<Instance> sorted_instances = null, best_sorted_instances = null;
+ Domain best_attr = attr_domains.get(0);
+ for (Domain attr_domain : attr_domains) {
+ /* No need to clone the domain as soon as no need to change the domain
+ * All domains are categorical so i will use them the way they are
+ */
+ double gain = 0.0;
+ if (attr_domain.isCategorical()) {
+ gain = dt_info - info_attr(insts_by_target, attr_domain);
+ } else {
+ // the continuous domain
+
+ QuantitativeDomain trialDomain = QuantitativeDomain.createFromDomain(attr_domain);
+
+ Categorizer visitor = new Categorizer(insts_by_target);
+ visitor.findSplits(trialDomain);
+
+ // trial domain modified
+ if (trialDomain.getNumIndices()==1) {
+ gain = 0.0;
+ } else {
+ gain = dt_info - info_contattr(visitor.getInstances(), insts_by_target.getClassDomain(), trialDomain);
+ }
+ attr_domain = trialDomain;
+ sorted_instances = visitor.getInstances();
+
+ /*
+ if (Util.DEBUG) {
+ int index = 0;
+ for (Integer i: split_indices) {
+ System.out.print("Split indices:"+ i);
+ System.out.print(" domain "+attrDomain.getValues().get(index));
+ System.out.print(","+attrDomain.getIndices().get(index));
+ System.out.println(" fact "+visitor.getSortedFact(i));
+ index++;
+ }
+ }
+ /* */
+ }
+ if (Util.DEBUG_ENTROPY) System.out.println("Attribute: " + attr_domain + " the gain: " + gain);
+ if (gain > greatestGain) {
+ greatestGain = gain;
+ best_attr = attr_domain;
+ best_sorted_instances = sorted_instances;
+ }
+ }
+
+ // Clone the best attribute domain cause it is going to be the domain of the treenode
+ eval.domain = best_attr.cheapClone();
+ eval.sorted_data = best_sorted_instances;
+ eval.gain = greatestGain;
+ //eval.gain_ratio = greatestGain/greatestSplitGain;
+ return eval.domain;
+ }
+
+// /*
+// * to choose the best attribute
+// * can process categorical, and quantitative attribute domains
+// * used by c45Learner, c45Iterator
+// */
+// public static Domain chooseAttribute(InstDistribution insts_by_target, List<Domain> attr_domains) {
+// InformationContainer evals = new InformationContainer();
+// chooseAttribute(evals, insts_by_target, attr_domains);
+// return evals.domain;
+// }
+
+ /*
+ * to choose the best attribute
+ * can process only the categorical attribute domains
+ * used by id3Learner
+ */
+ public static Domain chooseAttributeAsCategorical(InstDistribution insts_by_target, List<Domain> attr_domains) {
+
+ double dt_info = calc_info(insts_by_target);
+ double greatestGain = -1000;
+ Domain best_attr = attr_domains.get(0);
+ for (Domain attr_domain : attr_domains) {
+ /* No need to clone the domain as soon as no need to change the domain
+ * All domains are categorical so i will use them the way they are
+ */
+ double gain = dt_info - info_attr(insts_by_target, attr_domain);
+ //if (Util.DEBUG) System.out.println("Attribute: " + attr + " the gain: " + gain);
+ if (gain > greatestGain) {
+ greatestGain = gain;
+ best_attr = attr_domain;
+ }
+ }
+ // Clone the best attribute domain cause it is going to be the domain of the treenode
+ return best_attr.cheapClone();
+ }
+
+
+ public static double info_attr(InstDistribution insts_by_target, Domain attr_domain) {
+
+ Domain target_domain = insts_by_target.getClassDomain();
+
+ if (Util.DEBUG_ENTROPY) {
+ System.out.println("What is the attributeToSplit? " + attr_domain);
+ }
+
+ /* initialize the hashtable */
+ CondClassDistribution insts_by_attr = new CondClassDistribution(attr_domain, target_domain);
+ insts_by_attr.setTotal(insts_by_target.getSum());
+
+ if (Util.DEBUG_ENTROPY) {
+ System.out.println("Cond distribution for "+ attr_domain + " \n"+ insts_by_attr);
+ }
+
+ for (int category = 0; category<target_domain.getCategoryCount(); category++) {
+ Object targetCategory = target_domain.getCategory(category);
+
+ for (Instance inst: insts_by_target.getSupportersFor(targetCategory)) {
+ Object inst_attr_category = inst.getAttrValue(attr_domain.getFName());
+
+ Object inst_class = inst.getAttrValue(target_domain.getFName());
+
+ if (!targetCategory.equals(inst_class)) {
+ System.out.println("How the fuck they are not the same ? "+ targetCategory + " " + inst_class);
+ System.exit(0);
+ }
+ insts_by_attr.change(inst_attr_category, targetCategory, +1);
+
+ }
+ }
+ double sum = calc_info_attr(insts_by_attr);
+ return sum;
+ }
+
+
+ /* calculates the information of a quantitative domain given the split indexes of instances
+ * a wrapper for the quantitative domain to be able to calculate the stats
+ * */
+ public static double info_contattr(List<Instance> data, Domain targetDomain, QuantitativeDomain splitDomain) {
+
+ String targetAttr = targetDomain.getFName();
+
+ CondClassDistribution instances_by_attr = new CondClassDistribution(splitDomain, targetDomain);
+ instances_by_attr.setTotal(data.size());
+
+ int index = 0;
+ int split_index = 0;
+ Object attr_key = splitDomain.getCategory(split_index);
+ for (Instance i : data) {
+
+ if (index == splitDomain.getSplit(split_index).getIndex()+1 ) {
+ attr_key = splitDomain.getCategory(split_index+1);
+ split_index++;
+ }
+ Object targetKey = i.getAttrValue(targetAttr);
+ instances_by_attr.change(attr_key, targetKey, +1);
+
+
+ index++;
+ }
+ double sum = calc_info_attr(instances_by_attr);
+ return sum;
+
+ }
+
+ /*
+ * for both
+ */
+ public static double calc_info_attr( CondClassDistribution instances_by_attr) {
+ //Collection<Object> attributeValues = instances_by_attr.getAttributes();
+ int data_size = instances_by_attr.getTotal();
+ double sum = 0.0;
+ if (data_size>0)
+ for (int attr_idx=0; attr_idx<instances_by_attr.getNumCondClasses(); attr_idx++) {
+ Object attr_category = instances_by_attr.getCondClass(attr_idx);
+ int total_num_attr = instances_by_attr.getTotal_AttrCategory(attr_category);
+
+ if (total_num_attr > 0) {
+ double prob = (double) total_num_attr / (double) data_size;
+ if (Util.DEBUG_ENTROPY) {
+ System.out.print("{("+total_num_attr +"/"+data_size +":"+prob +")* [");
+ }
+ double info = calc_info(instances_by_attr.getDistributionOf(attr_category));
+
+ sum += prob * info;
+ if (Util.DEBUG_ENTROPY) {
+ System.out.print("]} ");
+ }
+ }
+ }
+ if (Util.DEBUG_ENTROPY) {
+ System.out.println("\n == "+sum);
+ }
+ return sum;
+ }
+
+ /* you can calculate this before */
+ /**
+ * it returns the information value of facts entropy that characterizes the
+ * (im)purity of an arbitrary collection of examples
+ *
+ * @param quantity_by_class the distribution of the instances by the class attribute (target)
+ */
+ public static double calc_info(ClassDistribution quantity_by_class) {
+
+ int data_size = quantity_by_class.getSum();
+
+ double prob, sum = 0;
+ String out =" ";
+ Domain target_domain = quantity_by_class.getClassDomain();
+ for (int category = 0; category<target_domain.getCategoryCount(); category++) {
+
+ Object targetCategory = target_domain.getCategory(category);
+ int num_in_class = quantity_by_class.getVoteFor(targetCategory);
+
+ if (num_in_class > 0) {
+ prob = (double) num_in_class / (double) data_size;
+ /* TODO what if it is a sooo small number ???? */
+ if (Util.DEBUG_ENTROPY) {
+ out += "("+num_in_class+ "/"+data_size+":"+prob+")" +"*"+ Util.log2(prob) + " + ";
+ }
+ sum -= prob * Util.log2(prob);
+ }
+ }
+ if (Util.DEBUG_ENTROPY) {
+ System.out.print(out +"= " +sum);
+ }
+ return sum;
+ }
+
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/Entropy.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/InformationContainer.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/InformationContainer.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/InformationContainer.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,24 @@
+package org.drools.learner.eval;
+
+import java.util.ArrayList;
+
+import org.drools.learner.Domain;
+import org.drools.learner.Instance;
+
+public class InformationContainer {
+
+ public Domain domain;
+ public double gain;
+ public double gain_ratio;
+ public ArrayList<Instance> sorted_data;
+
+ public InformationContainer() {
+
+ }
+ public InformationContainer(Domain _domain, double _gain, double _gain_ratio) {
+ this.domain = _domain;
+ this.gain = _gain;
+ this.gain_ratio = _gain_ratio;
+ }
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/InformationContainer.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/InstDistribution.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/InstDistribution.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/InstDistribution.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,203 @@
+package org.drools.learner.eval;
+
+import java.util.ArrayList;
+import java.util.Hashtable;
+import java.util.List;
+
+import org.drools.learner.Domain;
+import org.drools.learner.Instance;
+import org.drools.learner.QuantitativeDomain;
+import org.drools.learner.tools.FeatureNotSupported;
+import org.drools.learner.tools.Util;
+
+/* addition to the ClassDistribution
+ * it keeps the instances themselves by their target value in an hashmap */
+public class InstDistribution extends ClassDistribution{
+
+ private String attr_sum = Util.sum();
+ private Hashtable<Object, List<Instance>> instance_by_class;
+
+ //private Hashtable<Object, List<Instance>>
+
+ public InstDistribution(Domain targetDomain) {
+ super(targetDomain);
+
+ instance_by_class = new Hashtable<Object, List<Instance>>(targetDomain.getCategoryCount());
+ for (int t=0; t<targetDomain.getCategoryCount(); t++) {
+ Object obj_t = targetDomain.getCategory(t);
+ instance_by_class.put(obj_t, new ArrayList<Instance>());
+ }
+ }
+
+ public void calculateDistribution(List<Instance> instances){
+ int data_size = 0;
+ String tName = super.getClassDomain().getFName();
+ for (Instance inst : instances) {
+ data_size++;
+ Object target_key = inst.getAttrValue(tName);
+ super.change(target_key, +1); // add one for vote for the target value : target_key
+ this.addSupporter(target_key, inst);
+
+ }
+ //super.change(attr_sum, data_size); // TODO should i write special function for changing the sum
+ super.setSum(data_size);
+ }
+
+ public List<Instance> getSupportersFor(Object category) {
+ return instance_by_class.get(category);
+ }
+
+ private void addSupporter(Object target_category, Instance inst) {
+ this.instance_by_class.get(target_category).add(inst);
+ }
+
+
+ private Hashtable<Object, InstDistribution> instantiateLists(Domain splitDomain) {
+ Domain target_domain = super.getClassDomain();
+ Hashtable<Object, InstDistribution> instLists = new Hashtable<Object, InstDistribution>(splitDomain.getCategoryCount());
+ for (int c=0; c<splitDomain.getCategoryCount(); c++) {
+ Object category = splitDomain.getCategory(c);
+ instLists.put(category, new InstDistribution(target_domain));
+ }
+ return instLists;
+ }
+
+ /* spliting during the training for C45TreeIterator */
+ public Hashtable<Object, InstDistribution> split(InformationContainer splitDomainEval) throws FeatureNotSupported {
+ Domain splitDomain = splitDomainEval.domain;
+ Hashtable<Object, InstDistribution> instLists = this.instantiateLists(splitDomain);
+
+ if (splitDomain.isCategorical()) {
+ this.splitFromCategorical(splitDomain, instLists);
+ } else {
+ if (splitDomain instanceof QuantitativeDomain && splitDomainEval.sorted_data != null) {
+ QuantitativeDomain q_splitDomain = (QuantitativeDomain) splitDomain;
+
+
+ //Collections.sort(facts, choosenDomain.factComparator()); /* hack*/
+ this.splitFromQuantitative(splitDomainEval.sorted_data, q_splitDomain, instLists);
+ }
+ else {
+ throw new FeatureNotSupported("Can not split a quatitative domain if it's object type is not QuantitativeDomain "+splitDomain);
+ }
+
+ }
+
+ return instLists;
+ }
+
+
+ public Hashtable<Object, InstDistribution> splitFromCategorical(Domain splitDomain, Hashtable<Object, InstDistribution> instLists) {
+ if (instLists == null)
+ instLists = this.instantiateLists(splitDomain);
+
+ Domain target_domain = super.getClassDomain();
+ String attrName = splitDomain.getFName();
+ for (int category = 0; category<target_domain.getCategoryCount() ; category++) {
+ Object targetCategory = target_domain.getCategory(category);
+ //this.calculateDistribution(this.getSupportersFor(targetCategory));
+
+ for (Instance inst: this.getSupportersFor(targetCategory)) {
+ Object inst_attr_category = inst.getAttrValue(attrName);
+
+ instLists.get(inst_attr_category).change(targetCategory, +1); // add one for vote for the target value : target_key
+ instLists.get(inst_attr_category).change(attr_sum, +1);
+ instLists.get(inst_attr_category).addSupporter(targetCategory, inst);
+
+ }
+ }
+ return instLists;
+ }
+
+ private void splitFromQuantitative(ArrayList<Instance> data,
+ QuantitativeDomain attributeDomain, Hashtable<Object, InstDistribution> instLists) {
+
+ String attributeName = attributeDomain.getFName();
+ String targetName = super.getClassDomain().getFName();
+
+ if (Util.DEBUG_DIST) {
+ System.out.println("FactProcessor.splitFacts_cont() attr_split "+ attributeName);
+ }
+
+// if (Util.DEBUG_DISTRIBUTION) {
+// System.out.println("FactProcessor.splitFacts_cont() haniymis benim repsentativelerim: "+ splitValues.size() + " and the split points "+ splitIndices.size());
+//
+// System.out.println("FactProcessor.splitFacts_cont() before splitting "+ facts.size());
+//
+// int index = 0;
+// int split_index = 0;
+// Object attr_key = splitValues.get(split_index);
+// for (Fact f : facts) {
+//
+// if (index == splitIndices.get(split_index).intValue()+1 ) {
+// System.out.print("PRINT* (");
+// attr_key = splitValues.get(split_index+1);
+// split_index++;
+// } else {
+// System.out.print("PRINT (");
+// }
+// System.out.println(split_index+"): fact "+f);
+// index++;
+// }
+//
+// }
+
+
+ int start_point = 0;
+ for (int index = 0; index < attributeDomain.getNumIndices(); index ++) {
+ int integer_index = attributeDomain.getSplit(index).getIndex(); //splits_it.next().intValue();
+
+ Object inst_attr_category = attributeDomain.getSplit(index).getValue(); //splitValues.get(index);
+ //System.out.println("FactProcessor.splitFacts_cont() new category: "+ category);
+
+ try {
+
+ if (Util.DEBUG_DIST) {
+ System.out.println("FactProcessor.splitFacts_cont() new category: "+ inst_attr_category );
+ System.out.println(" ("+start_point+","+integer_index+")");
+ }
+
+ List<Instance> data_at_category = data.subList(start_point, integer_index+1);
+ for (Instance i: data_at_category) {
+
+ Object targetCategory = i.getAttrValue(targetName);
+
+ instLists.get(inst_attr_category).change(targetCategory, +1); // add one for vote for the target value : target_key
+ instLists.get(inst_attr_category).change(attr_sum, +1);
+ instLists.get(inst_attr_category).addSupporter(targetCategory, i);
+ }
+ start_point = integer_index+1;
+
+ } catch (Exception e) {
+ // TODO Auto-generated catch block
+ e.printStackTrace();
+ }
+ }
+ }
+
+ public void missClassifiedInstances(ArrayList<Instance> missclassification) {
+ Object winner = super.get_winner_class();
+
+ for (int idx = 0; idx < super.target_attr.getCategoryCount(); idx++) {
+ Object looser = super.target_attr.getCategory(idx);
+ int num_supp = this.getVoteFor(looser);
+
+ if ((num_supp > 0) && !winner.equals(looser)) {
+
+ //System.out.println(Util.ntimes("DANIEL", 2)+ " one looser ? "+looser + " num of sup="+num_supp);
+ //System.out.println(" the num of supporters = "+ stats.getVoteFor(looser));
+ //System.out.println(" but the guys "+ stats.getSupportersFor(looser));
+ //System.out.println("How many bok: "+stats.getSupportersFor(looser).size());
+ missclassification.addAll(getSupportersFor(looser));
+ } else {
+ //System.out.println(Util.ntimes("DANIEL", 5)+ "how many times matching?? not a looser "+ looser );
+ }
+ }
+
+ }
+
+
+
+}
+
+
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/InstDistribution.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/SplitPoint.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/SplitPoint.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/SplitPoint.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,26 @@
+package org.drools.learner.eval;
+
+public class SplitPoint {
+
+ private int index;
+ private Object cut_point;
+ private double info_value;
+
+ public SplitPoint(int _index, Object _point) {
+ this.index = _index;
+ this.cut_point = _point;
+ }
+
+ public Object getValue() {
+ return cut_point;
+ }
+
+ public int getIndex() {
+ return index;
+ }
+
+ public void setInformationValue(double info_sum) {
+ this.info_value = info_sum;
+ }
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/eval/SplitPoint.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/learner_uml.cdx
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/learner_uml.cdx (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/learner_uml.cdx 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,46 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<slime:DocumentModel xmi:version="2.0" xmlns:xmi="http://www.omg.org/XMI" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:slime="http://de.mvmsoft.slimeuml.model.slimemodel">
+ <children xsi:type="slime:ClassModel" x="1873" y="37" width="63" height="84" incomingConnections="//@children.9/@outgoingConnections.0 //@children.15/@outgoingConnections.0" name="Learner" classText="Learner" methodText=" build()
 readRules()" qualifiedClassName="org.drools.learner.builder.Learner" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner.builder{Learner.java[Learner"/>
+ <children xsi:type="slime:ClassModel" x="41" y="37" width="106" height="84" name="InstanceComparator" classText="InstanceComparator" methodText="+ InstanceComparator()
+ compare()" qualifiedClassName="org.drools.learner.InstanceComparator" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner{InstanceComparator.java[InstanceComparator"/>
+ <children xsi:type="slime:ClassModel" x="43" y="353" width="120" height="216" name="CondClassDistribution" classText="CondClassDistribution" methodText="+ CondClassDistribution()
+ CondClassDistribution()
+ getTotal()
+ setTotal()
+ getAttrDomain()
+ getCondClass()
+ getNumCondClasses()
+ getTotal_AttrCategory()
+ change()
+ getDistributionOf()
+ clear()
+ setDistForAttrValue()
+ toString()" qualifiedClassName="org.drools.learner.eval.CondClassDistribution" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner.eval{CondClassDistribution.java[CondClassDistribution"/>
+ <children xsi:type="slime:ClassModel" x="37" y="134" width="190" height="132" name="Instance" classText="Instance" methodText="+ void Instance()
+ void setAttr(String _name, Object _value)
+ Attribute getAttr(String field_name)
+ Object getAttrValue(String field_name)
+ int hashCode()
+ String toString()" attributeText="- HashMap<String,Attribute> attributes" qualifiedClassName="org.drools.learner.Instance" javaProjectName="drools-core" style="2023" type="=drools-core/src\/main\/java<org.drools.learner{Instance.java[Instance"/>
+ <children xsi:type="slime:ClassModel" x="921" y="500" width="100" height="240" incomingConnections="//@children.22/@outgoingConnections.0" name="Domain" classText="Domain" methodText="+ Domain()
+ cheapClone()
+ getType()
+ getFName()
+ setFixed()
 isFixed()
+ setCategorical()
+ isCategorical()
+ addCategory()
+ getCategory()
+ getCategoryCount()
+ isPossible()
+ containsValue()
+ compareValues()
+ hashCode()" qualifiedClassName="org.drools.learner.Domain" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner{Domain.java[Domain"/>
+ <children xsi:type="slime:ClassModel" x="725" y="168" width="106" height="108" name="SplitPoint" classText="SplitPoint" methodText="+ SplitPoint()
+ getValue()
+ getIndex()
+ setInformationValue()" qualifiedClassName="org.drools.learner.eval.SplitPoint" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner.eval{SplitPoint.java[SplitPoint"/>
+ <children xsi:type="slime:ClassModel" x="1704" y="217" width="94" height="204" incomingConnections="//@children.17/@outgoingConnections.0" name="TreeNode" classText="TreeNode" methodText="+ TreeNode()
+ getRank()
+ setRank()
+ setNumMatch()
+ getNumMatch()
+ getDomain()
+ putNode()
+ getChildrenKeys()
+ getChild()
+ hashCode()
+ toString()
+ toString()" qualifiedClassName="org.drools.learner.TreeNode" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner{TreeNode.java[TreeNode"/>
+ <children xsi:type="slime:ClassModel" x="610" y="37" width="83" height="120" name="FieldAnnotation" classText="FieldAnnotation" methodText=" readingSeq()
 ignore()
 target()
 discrete()
 values()" qualifiedClassName="org.drools.learner.tools.FieldAnnotation" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner.tools{FieldAnnotation.java[FieldAnnotation"/>
+ <children xsi:type="slime:ClassModel" x="868" y="343" width="197" height="132" name="Memory" classText="Memory" methodText="+ createFromWorkingMemory()
- Memory()
- setClassToClassify()
+ getClassInstances()" attributeText="- WorkingMemory session
 Class<?> clazzToClassify
- HashMap<Class<?>,InstanceList> instances" qualifiedClassName="org.drools.learner.Memory" javaProjectName="drools-core" style="2019" type="=drools-core/src\/main\/java<org.drools.learner{Memory.java[Memory"/>
+ <children xsi:type="slime:ClassModel" x="1810" y="317" width="74" height="132" name="ID3Learner" classText="ID3Learner" methodText="+ ID3Learner()
+ setDataSize()
+ getDataSize()
+ build()
+ readRules()
+ train()" qualifiedClassName="org.drools.learner.builder.ID3Learner" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner.builder{ID3Learner.java[ID3Learner">
+ <outgoingConnections xsi:type="slime:ClassConnectionModel" target="//@children.0" type="REALIZATION" toNavigable="true"/>
+ </children>
+ <children xsi:type="slime:ClassModel" x="873" y="37" width="121" height="192" name="RulePrinter" classText="RulePrinter" methodText="+ RulePrinter()
+ getRuleClass()
+ getBoundOnNumRules()
+ setBoundOnNumRules()
+ getNumInstances()
+ setNumInstances()
+ printer()
- dfs()
- spitRule()
+ write2File()
+ write2string()" qualifiedClassName="org.drools.learner.tools.RulePrinter" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner.tools{RulePrinter.java[RulePrinter"/>
+ <children xsi:type="slime:ClassModel" x="1034" y="37" width="134" height="204" name="Rule" classText="Rule" methodText=" Rule()
+ addCondition()
+ addAction()
+ setObjectClass()
+ getObjectClassName()
- getId()
+ setId()
- setNumClassifiedInstances()
+ getNumClassifiedInstances()
+ setRank()
+ getRank()
+ toString()" qualifiedClassName="org.drools.learner.tools.Rule" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner.tools{RulePrinter.java[Rule"/>
+ <children xsi:type="slime:ClassModel" x="1201" y="45" width="82" height="132" name="NodeValue" classText="NodeValue" methodText="+ NodeValue()
+ getFName()
+ getNodeValue()
+ setNodeValue()
+ getNode()
+ toString()" qualifiedClassName="org.drools.learner.tools.NodeValue" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner.tools{RulePrinter.java[NodeValue"/>
+ <children xsi:type="slime:ClassModel" x="772" y="28" width="84" height="72" name="RuleComparator" classText="RuleComparator" methodText="+ compare()" qualifiedClassName="org.drools.learner.tools.Rule$RuleComparator" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner.tools{RulePrinter.java[Rule[RuleComparator"/>
+ <children xsi:type="slime:ClassModel" x="1348" y="140" width="112" height="72" name="FeatureNotSupported" classText="FeatureNotSupported" methodText="+ FeatureNotSupported()" qualifiedClassName="org.drools.learner.tools.FeatureNotSupported" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner.tools{FeatureNotSupported.java[FeatureNotSupported"/>
+ <children xsi:type="slime:ClassModel" x="1924" y="317" width="74" height="132" name="C45Learner" classText="C45Learner" methodText="+ C45Learner()
+ setDataSize()
+ getDataSize()
+ build()
+ train()
+ readRules()" qualifiedClassName="org.drools.learner.builder.C45Learner" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner.builder{C45Learner.java[C45Learner">
+ <outgoingConnections xsi:type="slime:ClassConnectionModel" target="//@children.0" type="REALIZATION" toNavigable="true"/>
+ </children>
+ <children xsi:type="slime:ClassModel" x="619" y="287" width="75" height="108" name="Categorizer" classText="Categorizer" methodText="+ Categorizer()
+ findSplits()
+ getInstances()
- find_a_split()" qualifiedClassName="org.drools.learner.eval.Categorizer" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner.eval{Categorizer.java[Categorizer"/>
+ <children xsi:type="slime:ClassModel" x="1551" y="20" width="113" height="156" name="LeafNode" classText="LeafNode" methodText="+ LeafNode()
+ putNode()
+ getCategory()
+ setNumClassification()
+ getNumClassification()
+ hashCode()
+ toString()
+ toString()" qualifiedClassName="org.drools.learner.LeafNode" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner{LeafNode.java[LeafNode">
+ <outgoingConnections xsi:type="slime:ClassConnectionModel" target="//@children.6" type="GENERALIZATION" toNavigable="true"/>
+ </children>
+ <children xsi:type="slime:ClassModel" x="728" y="296" width="150" height="120" name="InformationContainer" classText="InformationContainer" methodText="+ InformationContainer()
+ InformationContainer()" attributeText="+ Domain domain
+ double gain
+ double gain_ratio
+ ArrayList<Instance> sorted_data" qualifiedClassName="org.drools.learner.eval.InformationContainer" javaProjectName="drools-core" style="2019" type="=drools-core/src\/main\/java<org.drools.learner.eval{InformationContainer.java[InformationContainer"/>
+ <children xsi:type="slime:ClassModel" x="1368" y="38" width="98" height="96" name="ObjectFactory" classText="ObjectFactory" methodText="- ObjectFactory()
+ fromFileAsObject()
- read()" qualifiedClassName="org.drools.learner.tools.ObjectFactory" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner.tools{ObjectFactory.java[ObjectFactory"/>
+ <children xsi:type="slime:ClassModel" x="1688" y="22" width="163" height="204" name="DecisionTree" classText="DecisionTree" methodText="+ DecisionTree()
+ DecisionTree()
+ getObjClass()
+ getTargetDomain()
+ getAttrDomains()
+ getRoot()
+ setRoot()
+ toString()" attributeText="- Class<?> obj_clazz
- Domain target
- ArrayList<Domain> attrsToClassify
- TreeNode root
+ long FACTS_READ" qualifiedClassName="org.drools.learner.DecisionTree" javaProjectName="drools-core" style="2019" type="=drools-core/src\/main\/java<org.drools.learner{DecisionTree.java[DecisionTree"/>
+ <children xsi:type="slime:ClassModel" x="1800" y="478" width="170" height="252" name="Util" classText="Util" methodText="+ ntimes()
+ sum()
+ log2()
+ getAllFields()
+ getType()
+ convertPrimitiveType()
+ main()" attributeText="+ int ID3
+ int C45
+ boolean DEBUG
+ int MAX_NUM_RULES
+ boolean ONLY_ACTIVE_RULES
+ boolean SORT_RULES_BY_RANK
+ boolean PRINT_RULES
+ boolean bok
+ Boolean bokkafa
+ String sbok" qualifiedClassName="org.drools.learner.tools.Util" javaProjectName="drools-core" style="2019" type="=drools-core/src\/main\/java<org.drools.learner.tools{Util.java[Util"/>
+ <children xsi:type="slime:ClassModel" x="747" y="515" width="105" height="144" name="QuantitativeDomain" classText="QuantitativeDomain" methodText="+ cheapClone()
- QuantitativeDomain()
+ isCategorical()
+ getSplit()
+ getNumIndices()
+ addSplitPoint()
+ containsIndex()" qualifiedClassName="org.drools.learner.QuantitativeDomain" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner{QuantitativeDomain.java[QuantitativeDomain">
+ <outgoingConnections xsi:type="slime:ClassConnectionModel" target="//@children.4" type="GENERALIZATION" toNavigable="true"/>
+ </children>
+ <children xsi:type="slime:ClassModel" x="744" y="98" width="85" height="72" name="SplitComparator" classText="SplitComparator" methodText="+ compare()" qualifiedClassName="org.drools.learner.QuantitativeDomain$SplitComparator" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner{QuantitativeDomain.java[QuantitativeDomain[SplitComparator"/>
+ <children xsi:type="slime:ClassModel" x="1547" y="264" width="98" height="72" name="DomainComparator" classText="DomainComparator" methodText="+ compare()" qualifiedClassName="org.drools.learner.DomainComparator" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner{DomainComparator.java[DomainComparator"/>
+ <children xsi:type="slime:ClassModel" x="229" y="339" width="98" height="228" incomingConnections="//@children.31/@outgoingConnections.0" name="ClassDistribution" classText="ClassDistribution" methodText="+ ClassDistribution()
+ ClassDistribution()
+ setSum()
+ getSum()
+ getClassDomain()
+ change()
+ getVoteFor()
+ evaluateMajority()
+ set_num_ideas()
+ set_winner_class()
+ get_num_ideas()
+ get_winner_class()
+ toString()
+ setDistribution()" qualifiedClassName="org.drools.learner.eval.ClassDistribution" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner.eval{ClassDistribution.java[ClassDistribution"/>
+ <children xsi:type="slime:ClassModel" x="1099" y="567" width="82" height="144" name="Attribute" classText="Attribute" methodText="+ getName()
+ setName()
+ getValue()
+ setValue()
+ compareValue()
+ hashCode()
+ toString()" qualifiedClassName="org.drools.learner.Attribute" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner{Attribute.java[Attribute"/>
+ <children xsi:type="slime:ClassModel" x="1221" y="571" width="91" height="144" name="Schema" classText="Schema" methodText="+ Schema()
+ getObjectClass()
+ addTarget()
+ getAttrNames()
+ getAttrDomain()
+ getAttrExtractor()
+ getTargets()" qualifiedClassName="org.drools.learner.Schema" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner{Schema.java[Schema"/>
+ <children xsi:type="slime:ClassModel" x="1855" y="168" width="78" height="96" name="LearnerFactory" classText="LearnerFactory" methodText="+ createID3()
+ createC45()
+ createID3()" qualifiedClassName="org.drools.learner.builder.LearnerFactory" javaProjectName="drools-core" style="2019" type="=drools-core/src\/main\/java<org.drools.learner.builder{LearnerFactory.java[LearnerFactory"/>
+ <children xsi:type="slime:ClassModel" x="1324" y="578" width="132" height="156" name="InstanceList" classText="InstanceList" methodText="+ InstanceList()
+ addFromWorkingMemory()
+ addAsInstance()
+ getSize()
+ getInstances()
+ getSchema()
+ getTargets()
+ getAttributes()" qualifiedClassName="org.drools.learner.InstanceList" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner{InstanceList.java[InstanceList"/>
+ <children xsi:type="slime:ClassModel" x="1373" y="302" width="106" height="84" name="NumberComparator" classText="NumberComparator" methodText="+ NumberComparator()
+ compare()" qualifiedClassName="org.drools.learner.tools.NumberComparator" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner.tools{NumberComparator.java[NumberComparator"/>
+ <children xsi:type="slime:ClassModel" x="399" y="403" width="113" height="156" name="InstDistribution" classText="InstDistribution" methodText="+ InstDistribution()
+ calculateDistribution()
+ getSupportersFor()
- addSupporter()
- instantiateLists()
+ split()
+ splitFromCategorical()
- splitFromQuantitative()" qualifiedClassName="org.drools.learner.eval.InstDistribution" javaProjectName="drools-core" type="=drools-core/src\/main\/java<org.drools.learner.eval{InstDistribution.java[InstDistribution">
+ <outgoingConnections xsi:type="slime:ClassConnectionModel" target="//@children.25" type="GENERALIZATION" toNavigable="true"/>
+ </children>
+ <children xsi:type="slime:ClassModel" x="663" y="346" width="146" height="132" name="Entropy" classText="Entropy" methodText="+ chooseAttribute()
+ chooseAttributeAsCategorical()
+ info_attr()
+ info_contattr()
+ calc_info_attr()
+ calc_info()" qualifiedClassName="org.drools.learner.eval.Entropy" javaProjectName="drools-core" style="2019" type="=drools-core/src\/main\/java<org.drools.learner.eval{Entropy.java[Entropy"/>
+</slime:DocumentModel>
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/FeatureNotSupported.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/FeatureNotSupported.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/FeatureNotSupported.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,9 @@
+package org.drools.learner.tools;
+
+public class FeatureNotSupported extends Exception {
+
+ public FeatureNotSupported(String string) {
+ super(string);
+ }
+
+}
\ No newline at end of file
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/FeatureNotSupported.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/FieldAnnotation.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/FieldAnnotation.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/FieldAnnotation.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,13 @@
+package org.drools.learner.tools;
+
+import java.lang.annotation.*;
+
+ at Retention(RetentionPolicy.RUNTIME)
+ at Target({ElementType.METHOD, ElementType.FIELD})
+public @interface FieldAnnotation {
+ int readingSeq();
+ boolean ignore() default false;
+ boolean target() default false;
+ boolean discrete() default true;
+ String[] values() default {"bok"};
+}
\ No newline at end of file
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/FieldAnnotation.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/NumberComparator.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/NumberComparator.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/NumberComparator.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,18 @@
+package org.drools.learner.tools;
+
+import java.io.Serializable;
+import java.util.Comparator;
+
+public class NumberComparator implements Comparator<Number>, Serializable {
+ public NumberComparator() {
+ }
+
+ public int compare(Number n0, Number n1) {
+ if (n0.doubleValue() < n1.doubleValue())
+ return -1;
+ else if (n0.doubleValue() > n1.doubleValue())
+ return 1;
+ else
+ return 0;
+ }
+}
\ No newline at end of file
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/NumberComparator.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/ObjectFactory.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/ObjectFactory.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/ObjectFactory.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,291 @@
+package org.drools.learner.tools;
+
+import java.io.BufferedReader;
+import java.io.File;
+import java.io.FileReader;
+import java.lang.annotation.Annotation;
+import java.lang.reflect.Field;
+import java.lang.reflect.InvocationTargetException;
+import java.lang.reflect.Method;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.List;
+
+
+public class ObjectFactory {
+
+// public static List<Object> getObjectsFromFile(Class<?> clazz, String filename, String separator) {
+//
+// ObjectFactory class_factory = new ObjectFactory(clazz);
+// List<Object> new_objects = fromFileAsObject(filename, separator);
+//
+// }
+ public static List<Object> getObjects(Class<?> obj_class, String filename) {
+ ObjectFactory class_factory = new ObjectFactory(obj_class);
+ try {
+ //return class_factory.fromFileAsObject("src/main/java/org/drools"+filename, ",");
+ return class_factory.fromFileAsObject(filename, ",");
+ } catch (Exception e) {
+ // TODO Auto-generated catch block
+ e.printStackTrace();
+ }
+ return null;
+ }
+
+ private Class<?> obj_clazz;
+ private ArrayList<Field> clazz_fields;
+ private HashMap<String,Integer> field_sequences;
+
+ private ObjectFactory (Class<?> clazz) {
+ this.obj_clazz = clazz;
+ this.clazz_fields = new ArrayList<Field>();
+ Util.getAllFields(clazz, clazz_fields);
+
+ this.field_sequences = getFieldReadingSeqs(clazz, clazz_fields);
+ }
+
+ public List<Object> fromFileAsObject(String filename, String separator)
+ throws Exception
+ {
+
+ List<Object> obj_read = new ArrayList<Object>();
+
+ /**/
+ File file =new File(filename);
+ if(!file.exists()){
+ System.out.println("where is the file ? "+ filename);
+ //System.exit(0);
+
+ file =new File("src/main/java/org/drools/examples/learner/"+filename);
+ if(!file.exists()){
+ System.out.println("where is still the file ? "+ file);
+ System.exit(0);
+ }
+ }
+
+ BufferedReader reader = new BufferedReader(new FileReader(file));
+ /**/
+
+// BufferedReader reader = new BufferedReader(new InputStreamReader(
+// this.obj_clazz.getResourceAsStream(filename)));// "../data/"
+ String line;
+ while ((line = reader.readLine()) != null) {
+ line = line.trim();
+ if (line.length() == 0)
+ break;
+ Object element = this.read(line, separator);
+ //System.out.println("New object "+ element);
+ obj_read.add(element);
+
+ }
+ return obj_read;
+ }
+
+ private Object read(String data, String separator) throws Exception {
+ Object element = null;
+
+ try {
+ element = this.obj_clazz.newInstance();
+
+
+ if (data.endsWith("."))
+ data = data.substring(0, data.length() - 1);
+ List<String> fieldValues = Arrays.asList(data.split(separator));
+
+
+ Method[] element_methods = this.obj_clazz.getDeclaredMethods();
+
+ for (Method m : element_methods) {
+ String m_name = m.getName();
+ Class<?>[] param_type_name = m.getParameterTypes();
+ if (ObjectFactory.isSetter(m_name) & ObjectFactory.isSimpleType(param_type_name)) {
+ String basicFieldName = ObjectFactory.getFieldName(m_name);
+
+ /* TODO dont use the basic field name as the real field name, there can be a capital/miniscul
+ * letter problem
+ */
+ Field f = null;
+ for (Field _f: this.clazz_fields) {
+ if (_f.getName().equalsIgnoreCase(basicFieldName)) {
+ f = _f;
+ break;
+ }
+
+ }
+
+ if (f == null) {
+ throw new Exception("Field("+basicFieldName+") could not be found for the class("+this.obj_clazz+")");
+ }
+ else {
+ String fieldString = fieldValues.get(this.field_sequences.get(f.getName()));
+ Object fieldValue = readString(f, fieldString.trim());
+
+ try {
+ m.invoke(element, fieldValue);
+ } catch (IllegalArgumentException e) {
+ // TODO Auto-generated catch block
+ e.printStackTrace();
+ } catch (InvocationTargetException e) {
+ System.out.println("invoking the method of the element e:"+element + " v:"+ fieldValue);
+
+ e.printStackTrace();
+ }
+
+
+ }
+ }
+ }
+
+ /*
+ for (Field f: element_fields) {
+ String f_name = f.getName();
+
+ String fieldString = fieldValues.get(fieldSequences.get(f_name));
+ Object fieldValue = readString(f, fieldString);
+
+ ClassFieldExtractor f_extractor = cache.getExtractor( clazz, f_name, clazz.getClassLoader() );
+// f_extractor.
+
+ }*/
+ } catch (InstantiationException e) {
+ // TODO Auto-generated catch block
+ e.printStackTrace();
+ } catch (IllegalAccessException e) {
+ // TODO Auto-generated catch block
+ e.printStackTrace();
+ }
+ return element;
+ }
+
+
+ private static HashMap<String,Integer> getFieldReadingSeqs(Class<? extends Object> classObj, ArrayList<Field> element_fields) {
+
+ HashMap<String,Integer> readingSeqs = new HashMap<String,Integer>(element_fields.size()) ;
+ for( Field f: element_fields) {
+ String f_name = f.getName();
+
+ Annotation[] annotations = f.getAnnotations();
+ FieldAnnotation spec = null;
+ for (Annotation a : annotations) {
+ if (a instanceof FieldAnnotation) {
+ spec = (FieldAnnotation)a; // here it is !!!
+ readingSeqs.put(f_name, spec.readingSeq());
+ break;
+ }
+ }
+ }
+ return readingSeqs;
+ }
+
+ private static Object readString(Field f, String text) {
+
+ Class<?> type_name = f.getType();
+ if (type_name.isPrimitive()) { //boolean, byte, char, short, int, long, float, and double.
+ if (type_name == Boolean.TYPE) {
+ return readBooleanFromString(text);
+ } else if (type_name == Integer.TYPE) {
+ return readIntegerFromString(text);
+ } else if (type_name == Short.TYPE) {
+ return readShortFromString(text);
+ } else if (type_name == Long.TYPE) {
+ return readLongFromString(text);
+ } else if (type_name == Double.TYPE) {
+ return readDoubleFromString(text);
+ } else if (type_name == Float.TYPE) {
+ return readFloatFromString(text);
+ } else {
+ System.out.print("ObjectFactory.readString(field, text) primitive type "+type_name);
+ return null;
+ }
+ } else if (type_name.isAssignableFrom(String.class)) {
+ return text.trim();
+ } else if (type_name.isAssignableFrom(Boolean.class)) {
+ return readBooleanFromString(text);
+ } else if (type_name.isAssignableFrom(Integer.class)) {
+ return readIntegerFromString(text);
+ } else if (type_name.isAssignableFrom(Short.class)) {
+ return readShortFromString(text);
+ } else if (type_name.isAssignableFrom(Long.class)) {
+ return readLongFromString(text);
+ } else if (type_name.isAssignableFrom(Double.class)) {
+ return readDoubleFromString(text);
+ } else if (type_name.isAssignableFrom(Float.class)) {
+ return readFloatFromString(text);
+ } else {
+ System.out.print("ObjectFactory.readString(field, text) object type "+type_name);
+ return null;
+ }
+
+ }
+
+
+ private static Object readBooleanFromString(String data) {
+ //System.out.print("What is the data : "+ data);
+ if (data.trim().equalsIgnoreCase("true"))
+ return Boolean.TRUE;
+ else if ((data.trim().equalsIgnoreCase("false")))
+ return Boolean.FALSE;
+ else if (data.trim().equalsIgnoreCase("1"))
+ return Boolean.TRUE;
+ else if (data.trim().equalsIgnoreCase("0"))
+ return Boolean.FALSE;
+ else
+ return Boolean.parseBoolean(data);
+ }
+
+ private static Object readIntegerFromString(String data) {
+ return Integer.parseInt(data);
+ }
+ private static Object readShortFromString(String data) {
+ return Short.parseShort(data);
+ }
+ private static Object readLongFromString(String data) {
+ return Long.parseLong(data);
+ }
+ private static Object readFloatFromString(String data) {
+ return Float.parseFloat(data);
+ }
+
+ private static Object readDoubleFromString(String data) {
+ return Double.parseDouble(data);
+ }
+
+ private static boolean isSetter(String m_name) {
+ if (m_name.startsWith("set") || m_name.startsWith("is") )
+ return true;
+ else
+ return false;
+ }
+
+ private static String getFieldName(String method_name) {
+ if (method_name.startsWith("get") || method_name.startsWith("set"))
+ return method_name.substring(3, method_name.length()).toLowerCase();
+ else if (method_name.startsWith("is"))
+ return method_name.substring(2, method_name.length()).toLowerCase();
+ else
+ return null;
+ }
+
+ private static boolean isSimpleType(Class<?>[] type_name) {
+ if (type_name.length==1) {
+ if (type_name[0].isPrimitive() || type_name[0] == String.class)
+ return true;
+ if (type_name[0] == Boolean.class || type_name[0] == Boolean.TYPE ||
+ type_name[0] == Integer.class || type_name[0] == Integer.TYPE ||
+ type_name[0] == Long.class || type_name[0] == Long.TYPE ||
+ type_name[0] == Double.class || type_name[0] == Double.TYPE ||
+ type_name[0] == Float.class || type_name[0] == Float.TYPE ||
+ type_name[0] == String.class)
+ return true;
+ else
+ return false;
+ }
+ else
+ return false;
+ }
+
+
+
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/ObjectFactory.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/RulePrinter.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/RulePrinter.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/RulePrinter.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,451 @@
+package org.drools.learner.tools;
+
+import java.io.BufferedWriter;
+import java.io.File;
+import java.io.FileWriter;
+import java.io.IOException;
+import java.io.Reader;
+import java.io.StringReader;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Comparator;
+import java.util.Iterator;
+import java.util.Stack;
+
+import org.drools.learner.AttributeValueComparator;
+import org.drools.learner.DecisionTree;
+import org.drools.learner.LeafNode;
+import org.drools.learner.TreeNode;
+import org.drools.learner.builder.Learner;
+
+public class RulePrinter {
+
+ public static Reader readRules(Learner learner) {
+ if (learner.getTree() == null) {
+ System.out.println("There is tree/rule to process");
+ return null;
+ }
+
+ RulePrinter my_printer = new RulePrinter(); //bocuk.getNum_fact_trained()
+ my_printer.setBoundOnNumRules(Util.MAX_NUM_RULES);
+ my_printer.printer(learner.getTree(), Util.SORT_RULES_BY_RANK);
+
+ String all_rules = my_printer.write2string();
+ if (Util.PRINT_RULES) {
+ //my_printer.write2file("examples", "src/rules/examples/" + file);
+ if (Util.DEBUG_RULE_PRINTER) {
+ System.out.println(all_rules);
+ }
+ my_printer.write2File(all_rules, false, "", learner.getTreeType());
+ }
+
+ return new StringReader(all_rules);
+ }
+
+ private Class<?> rule_clazz;
+
+ private Stack<NodeValue> nodes;
+
+ private ArrayList<Rule> rules;
+ //private ArrayList<String> ruleText;
+
+
+ private int bound_on_num_rules, num_instances;
+
+ //private NumberComparator nComparator;
+
+ public RulePrinter() {
+ /* most important */
+ this.nodes = new Stack<NodeValue>();
+
+ this.rules = new ArrayList<Rule>();
+ //ruleText = new ArrayList<String>();
+
+ this.bound_on_num_rules = -1;
+ this.num_instances = -1;
+
+ //this.nComparator = new NumberComparator();
+ }
+
+ public Class<?> getRuleClass() {
+ return rule_clazz;
+ }
+
+ public int getBoundOnNumRules() {
+ return bound_on_num_rules;
+ }
+
+ public void setBoundOnNumRules(int max_num_rules) {
+ this.bound_on_num_rules = max_num_rules;
+ }
+
+ public int getNumInstances() {
+ return this.num_instances;
+ }
+
+ public void setNumInstances(int num) {
+ this.num_instances = num;
+ }
+
+ public void printer(DecisionTree dt, boolean sort) {//, PrintStream object
+ this.rule_clazz = dt.getObjClass();
+ this.num_instances = dt.getRoot().getNumMatch();
+ dfs(dt.getRoot());
+
+ if (sort)
+ Collections.sort(rules, Rule.getRankComparator());
+ }
+
+ private void dfs(TreeNode my_node) {
+ //System.out.println("How many guys there of "+my_node.getDomain().getName() +" : "+my_node.getDomain().getValues().size());
+
+ NodeValue node_value = new NodeValue(my_node);
+ nodes.push(node_value);
+
+ if (my_node instanceof LeafNode) {
+ node_value.setNodeValue(((LeafNode) my_node).getCategory()); //getValue(null));
+ //ruleText.add(print(nodes));
+ //rule_list.add(spit(nodes));
+ // what if more than one condition (more than one leafNode)
+
+ Rule newRule = spitRule(nodes);
+ newRule.setId(rules.size());
+ rules.add(newRule);
+ return;
+ }
+
+ for (Object attributeValue : my_node.getChildrenKeys()) {
+ //System.out.println("Domain: "+ my_node.getDomain().getName() + " the value:"+ attributeValue);
+ node_value.setNodeValue(attributeValue);
+ TreeNode child = my_node.getChild(attributeValue);
+ dfs(child);
+ nodes.pop();
+ }
+ return;
+ }
+
+ private Rule spitRule(Stack<NodeValue> nodes) {
+ //, Stack<NodeValue> leaves // if more than one leaf
+ Rule newRule = new Rule(nodes.size());// (nodes, leaves) //if more than one leaf
+ newRule.setObjectClass(this.getRuleClass());
+ Iterator<NodeValue> it = nodes.iterator();
+ while (it.hasNext()) {
+
+ NodeValue current = it.next();
+ if (it.hasNext()) {
+ newRule.addCondition(current);
+ } else {
+ newRule.addAction(current);
+ }
+ }
+ return newRule;
+ }
+
+ public void write2File(String toWrite, boolean append, String dataFile, int TREE_TYPE) {
+
+ String packageFolders = this.getRuleClass().getPackage().getName();
+
+ String _packageNames = packageFolders.replace('.', '/');
+
+ String fileName = (dataFile == null || dataFile == "") ? this.getRuleClass().getSimpleName().toLowerCase(): dataFile;
+
+ switch (TREE_TYPE) {
+ case Util.ID3:
+ fileName += "_id3" + ".drl";
+ break;
+ case Util.C45:
+ fileName += "_c45" + ".drl";
+ break;
+ default:
+ fileName += "_?" + ".drl";
+ }
+
+ String dataFileName = "src/main/rules/"+_packageNames+"/"+ fileName;
+
+ System.out.println("file:"+ dataFileName);
+ File file =new File(dataFileName);
+ if (append)
+ {
+ if(!file.exists())
+ System.out.println("File doesnot exit, creating...");
+ try {
+ BufferedWriter out = new BufferedWriter(new FileWriter(dataFileName, true));
+ out.write(toWrite);
+ out.close();
+ //System.out.println("I wrote "+ toWrite);
+ } catch (IOException e) {
+ System.out.println("No I cannot write to the file (appending) e:"+ e);
+ /* TODO */
+ }
+
+ } else {
+ if(file.exists()&& (file.length()>0))
+ file.delete();
+ try {
+ BufferedWriter out = new BufferedWriter(new FileWriter(dataFileName));
+ out.write(toWrite);
+ out.close();
+ if (Util.DEBUG_RULE_PRINTER) {
+ System.out.println("I wrote "+ toWrite);
+ }
+ } catch (IOException e) {
+ System.out.println("No I cannot write to the file (creating new file) e:"+ e);
+ /* TODO */
+ }
+ }
+ }
+
+ public String write2string(){//String packageName) {
+ StringBuffer outputBuffer = new StringBuffer();
+
+ String packageName = this.getRuleClass().getPackage().getName();
+
+ if (Util.DEBUG_RULE_PRINTER) {
+ System.out.println("Package name: "+ packageName);
+ }
+ if (packageName != null)
+ outputBuffer.append("package " + packageName +";\n\n");
+
+ else {
+ //TODO throw exception
+ }
+
+ if (Util.DEBUG_RULE_PRINTER) {
+ System.out.println("//Num of rules " +rules.size()+"\n");
+ }
+ int total_num_facts=0;
+ int i = 0, active_i = 0;
+ for( Rule rule: rules) {
+ i++;
+ if (Util.ONLY_ACTIVE_RULES) {
+ if (rule.getRank() >= 0) {
+ active_i++;
+ if (Util.DEBUG_RULE_PRINTER) {
+ System.out.println("//Active rules " +i + " write to drl \n"+ rule +"\n");
+ }
+ outputBuffer.append(rule.toString());
+ outputBuffer.append("\n");
+ }
+
+ } else {
+ if (rule.getRank() >= 0) {
+ active_i++;
+ }
+ if (Util.DEBUG_RULE_PRINTER) {
+ System.out.println("//rule " +i + " write to drl \n"+ rule +"\n");
+ }
+ outputBuffer.append(rule.toString());
+ outputBuffer.append("\n");
+ }
+ total_num_facts += rule.getNumClassifiedInstances();
+ if (this.getBoundOnNumRules()>0 && i >= this.getBoundOnNumRules())
+ break;
+ }
+ outputBuffer.append("//THE END: Total number of facts correctly classified= "+ total_num_facts + " over "+ this.getNumInstances());
+ outputBuffer.append("\n//with " + active_i + " number of rules over "+i+" total number of rules ");
+ outputBuffer.append("\n"); // EOF
+
+ return outputBuffer.toString();
+ }
+}
+
+class Rule {
+
+ private Class<?> attr_obj; // object class name
+ private ArrayList<NodeValue> conditions;
+ private ArrayList<NodeValue> actions;
+
+ private double rank; // matching ratio
+ private int num_classified_instances;// number of instances matching that rule
+
+ private int id; // unique id, need a unique name in the drl file
+
+
+ Rule(int numCond) {
+ conditions = new ArrayList<NodeValue>(numCond);
+ actions = new ArrayList<NodeValue>(1);
+ }
+
+ public void addCondition(NodeValue current) {
+ NodeValue nv = new NodeValue(current.getNode());
+ nv.setNodeValue(current.getNodeValue());
+ conditions.add(nv);
+ }
+ public void addAction(NodeValue current) {
+ NodeValue nv = new NodeValue(current.getNode());
+ nv.setNodeValue(current.getNodeValue());
+ actions.add(nv);
+ this.setRank(((LeafNode)current.getNode()).getRank());
+ this.setNumClassifiedInstances(((LeafNode)current.getNode()).getNumClassification());
+ }
+ public void setObjectClass(Class<?> obj) {
+ attr_obj= obj;
+ }
+ public String getObjectClassName() {
+ return attr_obj.getSimpleName();
+ }
+
+ private int getId() {
+ return id;
+ }
+
+ public void setId(int id) {
+ this.id= id;
+ }
+
+ private void setNumClassifiedInstances(int dataSize) {
+ this.num_classified_instances = dataSize;
+ }
+ public int getNumClassifiedInstances() {
+ return this.num_classified_instances;
+ }
+
+ public void setRank(double r) {
+ this.rank = r;
+ }
+ public double getRank() {
+ return this.rank;
+ }
+
+ public String toString() {
+ /*
+ rule "Good Bye"
+ dialect "java"
+ when
+ Message( status == Message.GOODBYE, message : message )
+ then
+ System.out.println( "Goodbye: " + message );
+ end
+ */
+
+ String out = ""; //"rule \"#"+getId()+" "+decision+" rank:"+rank+"\" \n";
+
+ out += "\t when";
+ out += "\n\t\t "+this.getObjectClassName() +"("+ "";
+ for (NodeValue cond: conditions) {
+ out += cond + ", ";
+ }
+
+ String action = "";
+ String decision = "";
+ for (NodeValue act: actions) {
+ out += act.getFName() + " : "+act.getFName()+" , ";
+ action += act.getNodeValue() + " , ";
+ decision += act.getFName() + " ";
+ }
+ action = action.substring(0, action.length()-3);
+ out = out.substring(0, out.length()-3) + ")\n";
+
+ out += "\t then ";
+ out += "\n\t\t System.out.println(\"Decision on "+decision+"= \"+" + decision + "+\": ("+action+")\");\n";
+ if (getRank() <0)
+ out += "\n\t\t System.out.println(\"But no matching fact found = DOES not fire on\");\n";
+ out = "rule \"#"+getId()+" "+decision+ "= "+action+" classifying "+this.getNumClassifiedInstances()+" num of facts with rank:"+getRank() +"\" \n" + out;
+
+ out += "end\n";
+
+ return out;
+ }
+
+ public static Comparator<Rule> getRankComparator() {
+ return new RuleComparator();
+ }
+
+ private static class RuleComparator implements Comparator<Rule>{
+ // this will sort from best rank to least rank
+ public int compare(Rule r1, Rule r2) {
+ if (r1.getRank() < r2.getRank())
+ return 1; // normally -1
+ else if (r1.getRank() > r2.getRank())
+ return -1; // normally 1
+ else
+ return 0;
+ }
+ }
+
+}
+
+
+class NodeValue {
+ private TreeNode node;
+ private Object nodeValue; // should it be Attribute???
+
+ public NodeValue(TreeNode n) {
+ this.node = n;
+ }
+
+ public String getFName() {
+ return node.getDomain().getFName();
+ }
+
+ public Object getNodeValue() {
+ return nodeValue;
+ }
+ public void setNodeValue(Object category) {
+ this.nodeValue = category;
+
+ }
+
+ public TreeNode getNode() {
+ return node;
+ }
+
+ @SuppressWarnings("unchecked")
+ public String toString() {
+ String fName = node.getDomain().getFName();
+ String value;
+ if (node.getDomain().getFType() == String.class)
+ value = "\""+nodeValue+ "\"";
+ else
+ value = nodeValue + "";
+
+ if (node.getDomain().isCategorical())
+ return fName + " == "+ value;
+ else {
+
+ int size = node.getDomain().getCategoryCount()-1;
+ //System.out.println("How many guys there of "+node.getDomain().getName() +" and the value "+nodeValue+" : "+size);
+
+ int idx = size;
+ if (nodeValue instanceof Number) {
+ for (; idx>=0; idx--) {
+ Object categoryValue = node.getDomain().getCategory(idx);
+ if (nodeValue instanceof Comparable && categoryValue instanceof Comparable) {
+ // TODO ask this to daniel???
+ if (Util.DEBUG_RULE_PRINTER) {
+ System.out.println("NodeValue:"+ nodeValue+ " c-"+nodeValue.getClass() +" & category:"+ categoryValue+ " c-"+categoryValue.getClass());
+ }
+ if ( AttributeValueComparator.instance.compare(nodeValue, categoryValue) == 0 ) {
+ break;
+ }
+ } else {
+ System.out.println("Fuck not comparable NodeValue:"+ nodeValue+ " c-"+nodeValue.getClass() +" & category:"+ categoryValue+ " c-"+categoryValue.getClass());
+ System.exit(0);
+ }
+
+ }
+ } else {
+ /* TODO implement the String setting */
+ System.out.println("Fuck not number:"+ nodeValue+ " c-"+nodeValue.getClass());
+ System.exit(0);
+
+ }
+
+ if (idx == 0)
+ return fName + " <= "+ value;
+ else if (idx == size)
+ // if the category is the last one that the rule is domain.name > category(last-1)
+ return fName+ " > "+ node.getDomain().getCategory(size-1);
+ else {
+ //return node.getDomain().getCategory(idx) + " < " + fName+ " <= "+ node.getDomain().getCategory(idx+1);
+ // Why drools does not support category(idx) < domain.name <= category(idx+1)
+ if (Util.DEBUG_RULE_PRINTER) {
+ System.out.println("value "+ value + "=====?????"+ node.getDomain().getCategory(idx+1));
+ }
+
+ return fName+ " <= "+ value; // node.getDomain().getCategory(idx+1);
+ }
+ }
+ }
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/RulePrinter.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/Util.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/Util.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/Util.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,157 @@
+package org.drools.learner.tools;
+
+import java.lang.reflect.Field;
+import java.util.ArrayList;
+import java.util.Collections;
+
+public class Util {
+
+ public static final int ID3 = 1, C45 = 2;
+
+ //public static final boolean DEBUG = false;
+ public static final boolean DEBUG_TEST = false;
+
+ public static final boolean PRINT_STATS = true;
+
+ public static final boolean DEBUG_RULE_PRINTER = false;
+
+ public static final boolean DEBUG_LEARNER = false;
+
+ public static final boolean DEBUG_CATEGORIZER = false;
+
+ public static final boolean DEBUG_ENTROPY = false;
+
+ public static final boolean DEBUG_DIST = false;
+
+ public static int MAX_NUM_RULES = 1000;
+ public static boolean ONLY_ACTIVE_RULES = true; /* TODO into global settings */
+
+ public static boolean SORT_RULES_BY_RANK = true;
+ public static boolean PRINT_RULES = true;
+
+
+ public static String ntimes(String s,int n){
+ StringBuffer buf = new StringBuffer();
+ for (int i = 0; i < n; i++) {
+ buf.append(s);
+ }
+ return buf.toString();
+ }
+
+ public static String sum() {
+ return "sum";
+ }
+
+ public static double log2(double prob) {
+ return Math.log(prob) / Math.log(2);
+ }
+
+ /* TODO make this all_fields arraylist as hashmap */
+ public static void getAllFields(Class<?> clazz, ArrayList<Field> all_fields) {
+ if (clazz == Object.class)
+ return;
+
+ //Field [] element_fields_ = clazz.getFields();
+ Field [] element_fields = clazz.getDeclaredFields(); //clazz.getFields();
+ for (Field f: element_fields) {
+ all_fields.add(f);
+ }
+ getAllFields(clazz.getSuperclass(), all_fields);
+
+ return;
+ }
+
+ public static Object calculateMidPoint(Class<?> fClass, Object cp_i, Object cp_i_next) {
+ if (fClass.isAssignableFrom(Integer.class) || fClass == Integer.TYPE) {
+ return ((Integer)cp_i + (Integer) cp_i_next) / 2;
+ } else if (fClass.isAssignableFrom(Short.class) || fClass == Short.TYPE) {
+ return ((Short)cp_i + (Short) cp_i_next) / 2;
+ } else if (fClass.isAssignableFrom(Long.class) || fClass == Long.TYPE) {
+ return ((Long)cp_i + (Long) cp_i_next) / 2;
+ } else if (fClass.isAssignableFrom(Double.class) || fClass == Double.TYPE) {
+ return ((Double)cp_i + (Double) cp_i_next) / 2;
+ } else if (fClass.isAssignableFrom(Float.class) || fClass == Float.TYPE) {
+ return ((Float)cp_i + (Float) cp_i_next) / 2;
+ } else {
+ System.out.print("What should i do then Categorizer.find_a_split() for "+fClass);
+ System.exit(0);
+ }
+ return 0.0;
+ }
+
+ public static int getType(Class<?>[] type_name) {
+ /*
+ * simpletype.contains(type_name)
+ * how do you get the simple type
+ */
+ if (type_name.length==1) {
+ if (type_name[0].getName().equalsIgnoreCase("boolean") ||
+ type_name[0].getName().equalsIgnoreCase("int") ||
+ type_name[0].getName().equalsIgnoreCase("double") ||
+ type_name[0].getName().equalsIgnoreCase("float"))
+ return 1;
+ else if (type_name[0].getName().equalsIgnoreCase("java.lang.String"))
+ return 2;
+ else
+ return -1;
+ } else
+ return 0;
+ }
+
+ public static Object convertPrimitiveType (Object primitiveValue) {
+ Class klass = primitiveValue.getClass();
+ if (klass.equals(Boolean.TYPE)) {
+ return (Boolean)primitiveValue;
+
+ }
+ return null;
+
+ }
+
+ public boolean bok = false;
+
+ public Boolean bokkafa = Boolean.FALSE;
+ public String sbok = "x";
+
+ public static final void main(final String[] args) throws Exception {
+ System.out.println(Util.convertPrimitiveType(false));
+
+ Util u = new Util();
+ Field f = u.getClass().getField("bok");
+ System.out.println(Util.convertPrimitiveType(f.get(u)));
+ if (f.getType().isPrimitive())
+ System.out.println("Primitive" + f.get(u));
+ else
+ System.out.println("Not Primitive" + f.get(u));
+ Field fkafa = u.getClass().getField("bokkafa");
+ if (fkafa.getType().isPrimitive())
+ System.out.println("Primitive" + fkafa.get(u));
+ else
+ System.out.println("Not Primitive kafa" + fkafa.get(u));
+ Field f_s = u.getClass().getField("sbok");
+ if (f_s.getType() == String.class)
+ System.out.println(f_s.get(u));
+
+ String abd = "abd";
+ String abcm = "abcm";
+ ArrayList<String> x = new ArrayList<String>(2);
+ x.add(abd);
+ x.add(abcm);
+ x.add("q");
+ x.add("abdm");
+ x.add("abc");
+ System.out.println("the result: "+abd.compareTo(abcm));
+
+ Collections.sort(x);
+
+ for (String s:x){
+ System.out.println(s);
+ }
+
+
+ }
+
+
+
+}
+
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-core/src/main/java/org/drools/learner/tools/Util.java
___________________________________________________________________
Name: svn:eol-style
+ native
Modified: labs/jbossrules/contrib/machinelearning/4.0.x/drools-decisiontables/.classpath
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-decisiontables/.classpath 2008-05-23 16:37:12 UTC (rev 20143)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-decisiontables/.classpath 2008-05-23 17:46:27 UTC (rev 20144)
@@ -1,18 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
<classpath>
- <classpathentry kind="src" path="src/main/java"/>
- <classpathentry kind="src" path="src/main/resources" excluding="**/*.java"/>
- <classpathentry kind="src" path="src/test/java" output="target/test-classes"/>
- <classpathentry kind="src" path="src/test/resources" output="target/test-classes" excluding="**/*.java"/>
- <classpathentry kind="output" path="target/classes"/>
- <classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER"/>
- <classpathentry kind="var" path="M2_REPO/jexcelapi/jxl/2.4.2/jxl-2.4.2.jar"/>
- <classpathentry kind="var" path="M2_REPO/junit/junit/3.8.1/junit-3.8.1.jar"/>
- <classpathentry kind="src" path="/drools-compiler"/>
- <classpathentry kind="src" path="/drools-core"/>
- <classpathentry kind="var" path="M2_REPO/org/mvel/mvel/1.3.1-java1.4/mvel-1.3.1-java1.4.jar"/>
- <classpathentry kind="var" path="M2_REPO/org/antlr/antlr-runtime/3.0/antlr-runtime-3.0.jar"/>
- <classpathentry kind="var" path="M2_REPO/org/eclipse/jdt/core/3.2.3.v_686_R32x/core-3.2.3.v_686_R32x.jar"/>
- <classpathentry kind="var" path="M2_REPO/janino/janino/2.5.10/janino-2.5.10.jar"/>
- <classpathentry kind="var" path="M2_REPO/xml-apis/xml-apis/1.0.b2/xml-apis-1.0.b2.jar"/>
- <classpathentry kind="var" path="M2_REPO/xerces/xercesImpl/2.4.0/xercesImpl-2.4.0.jar"/>
-</classpath>
\ No newline at end of file
+ <classpathentry kind="src" output="target-eclipse/classes" path="src/main/java"/>
+ <classpathentry kind="src" output="target-eclipse/test-classes" path="src/test/java">
+ <attributes>
+ <attribute name="maven.type" value="test"/>
+ </attributes>
+ </classpathentry>
+ <classpathentry excluding="**" kind="src" output="src/main/resources" path="src/main/resources"/>
+ <classpathentry excluding="**" kind="src" output="src/test/resources" path="src/test/resources"/>
+ <classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER"/>
+ <classpathentry kind="con" path="org.maven.ide.eclipse.MAVEN2_CLASSPATH_CONTAINER"/>
+ <classpathentry kind="output" path="target-eclipse/classes"/>
+</classpath>
Modified: labs/jbossrules/contrib/machinelearning/4.0.x/drools-decisiontables/.project
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-decisiontables/.project 2008-05-23 16:37:12 UTC (rev 20143)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-decisiontables/.project 2008-05-23 17:46:27 UTC (rev 20144)
@@ -1,16 +1,25 @@
+<?xml version="1.0" encoding="UTF-8"?>
<projectDescription>
- <name>drools-decisiontables</name>
- <comment>A rule production system</comment>
- <projects>
- <project>drools-compiler</project>
- <project>drools-core</project>
- </projects>
- <buildSpec>
- <buildCommand>
- <name>org.eclipse.jdt.core.javabuilder</name>
- </buildCommand>
- </buildSpec>
- <natures>
- <nature>org.eclipse.jdt.core.javanature</nature>
- </natures>
-</projectDescription>
\ No newline at end of file
+ <name>drools-decisiontables</name>
+ <comment>A rule production system</comment>
+ <projects>
+ <project>drools-compiler</project>
+ <project>drools-core</project>
+ </projects>
+ <buildSpec>
+ <buildCommand>
+ <name>org.eclipse.jdt.core.javabuilder</name>
+ <arguments>
+ </arguments>
+ </buildCommand>
+ <buildCommand>
+ <name>org.maven.ide.eclipse.maven2Builder</name>
+ <arguments>
+ </arguments>
+ </buildCommand>
+ </buildSpec>
+ <natures>
+ <nature>org.eclipse.jdt.core.javanature</nature>
+ <nature>org.maven.ide.eclipse.maven2Nature</nature>
+ </natures>
+</projectDescription>
Modified: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/.classpath
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/.classpath 2008-05-23 16:37:12 UTC (rev 20143)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/.classpath 2008-05-23 17:46:27 UTC (rev 20144)
@@ -1,14 +1,15 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<classpath>
- <classpathentry kind="src" path="src/main/java"/>
- <classpathentry excluding="**/*.java" kind="src" path="src/main/resources"/>
- <classpathentry excluding="**/*.java" kind="src" path="src/main/rules"/>
- <classpathentry kind="src" output="target/test-classes" path="src/test/java"/>
- <classpathentry excluding="**/*.java" kind="src" output="target/test-classes" path="src/test/resources"/>
- <classpathentry kind="lib" path="lib/foxtrot-2.0.jar"/>
- <classpathentry kind="lib" path="lib/jgoodies-forms-1.0.4.jar"/>
- <classpathentry combineaccessrules="false" kind="src" path="/drools-compiler"/>
- <classpathentry combineaccessrules="false" kind="src" path="/drools-core"/>
- <classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER"/>
- <classpathentry kind="output" path="target/classes"/>
-</classpath>
+<?xml version="1.0" encoding="UTF-8"?>
+<classpath>
+ <classpathentry kind="src" output="target-eclipse/classes" path="src/main/java"/>
+ <classpathentry kind="src" output="target-eclipse/test-classes" path="src/test/java">
+ <attributes>
+ <attribute name="maven.type" value="test"/>
+ </attributes>
+ </classpathentry>
+ <classpathentry excluding="**" kind="src" output="src/main/resources" path="src/main/resources"/>
+ <classpathentry excluding="**" kind="src" output="src/main/rules" path="src/main/rules"/>
+ <classpathentry excluding="**" kind="src" output="src/test/resources" path="src/test/resources"/>
+ <classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER"/>
+ <classpathentry kind="con" path="org.maven.ide.eclipse.MAVEN2_CLASSPATH_CONTAINER"/>
+ <classpathentry kind="output" path="target-eclipse/classes"/>
+</classpath>
Modified: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/.project
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/.project 2008-05-23 16:37:12 UTC (rev 20143)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/.project 2008-05-23 17:46:27 UTC (rev 20144)
@@ -15,8 +15,14 @@
<arguments>
</arguments>
</buildCommand>
+ <buildCommand>
+ <name>org.maven.ide.eclipse.maven2Builder</name>
+ <arguments>
+ </arguments>
+ </buildCommand>
</buildSpec>
<natures>
<nature>org.eclipse.jdt.core.javanature</nature>
+ <nature>org.maven.ide.eclipse.maven2Nature</nature>
</natures>
</projectDescription>
Modified: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/pom.xml
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/pom.xml 2008-05-23 16:37:12 UTC (rev 20143)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/pom.xml 2008-05-23 17:46:27 UTC (rev 20144)
@@ -1,107 +1,112 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
-
- <modelVersion>4.0.0</modelVersion>
- <parent>
- <artifactId>drools</artifactId>
- <groupId>org.drools</groupId>
- <version>4.0.7.SNAPSHOT</version>
- </parent>
-
- <!--
- TODO make this packing pom and make 2 submodules: example-waltz and example-manners
- (and possibly example-common too)
- -->
- <artifactId>drools-examples</artifactId>
- <packaging>jar</packaging>
- <name>Drools :: examples</name>
-
- <repositories>
- <repository>
- <id>basedir</id>
- <url>file://${basedir}/../m2_repo</url>
- </repository>
- </repositories>
-
- <dependencies>
- <!-- Internal dependencies -->
- <dependency>
- <groupId>org.drools</groupId>
- <artifactId>drools-core</artifactId>
- </dependency>
- <dependency>
- <groupId>org.drools</groupId>
- <artifactId>drools-compiler</artifactId>
- </dependency>
- <dependency>
- <groupId>org.drools</groupId>
- <artifactId>drools-decisiontables</artifactId>
- </dependency>
-
- <!-- External dependencies -->
- <dependency>
- <groupId>foxtrot</groupId>
- <artifactId>foxtrot</artifactId>
- </dependency>
- <dependency>
- <groupId>com.jgoodies</groupId>
- <artifactId>forms</artifactId>
- </dependency>
- </dependencies>
-
- <properties>
- <project.mainClass>org.drools.benchmark.waltz.WaltzBenchmark</project.mainClass>
- </properties>
- <build>
- <resources>
- <resource>
- <directory>src/main/resources</directory>
- </resource>
- <resource>
- <directory>src/main/rules</directory>
- </resource>
- </resources>
- <plugins>
- <plugin>
- <groupId>org.apache.maven.plugins</groupId>
- <artifactId>maven-eclipse-plugin</artifactId>
- <configuration>
- <additionalBuildcommands>
- <buildcommand>org.drools.eclipse.droolsbuilder</buildcommand>
- </additionalBuildcommands>
- </configuration>
- </plugin>
- <plugin>
- <groupId>org.apache.maven.plugins</groupId>
- <artifactId>maven-jar-plugin</artifactId>
- <configuration>
- <archive>
- <manifest>
- <mainClass>${project.mainClass}</mainClass>
- <addClasspath>true</addClasspath>
- </manifest>
- </archive>
- </configuration>
- </plugin>
-
- <plugin>
- <groupId>org.codehaus.mojo</groupId>
- <artifactId>exec-maven-plugin</artifactId>
- <version>1.0.2</version>
- <configuration>
- <executable>java</executable>
- <!-- only waltz12 is configured at the moment -->
- <!-- TODO waltz50, manners64 and manners128 -->
- <arguments>
- <argument>-Xms1024M</argument>
- <argument>-Xmx1024M</argument>
- <argument>-cp</argument>
- <classpath/>
- <argument>${project.mainClass}</argument>
- <argument>waltz12.dat</argument>
- </arguments>
- </configuration>
- </plugin>
- </plugins>
- </build>
-</project>
+<?xml version="1.0" encoding="UTF-8"?>
+<project xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
+
+ <modelVersion>4.0.0</modelVersion>
+ <parent>
+ <artifactId>drools</artifactId>
+ <groupId>org.drools</groupId>
+ <version>4.0.8.SNAPSHOT</version>
+ </parent>
+
+ <!--
+ TODO make this packing pom and make 2 submodules: example-waltz and example-manners
+ (and possibly example-common too)
+ -->
+ <artifactId>drools-examples</artifactId>
+ <packaging>jar</packaging>
+ <name>Drools :: examples</name>
+
+ <repositories>
+ <repository>
+ <id>basedir</id>
+ <url>file://${basedir}/../m2_repo</url>
+ </repository>
+ </repositories>
+
+ <dependencies>
+ <!-- Internal dependencies -->
+ <dependency>
+ <groupId>org.drools</groupId>
+ <artifactId>drools-core</artifactId>
+ </dependency>
+ <dependency>
+ <groupId>org.drools</groupId>
+ <artifactId>drools-compiler</artifactId>
+ </dependency>
+ <dependency>
+ <groupId>org.drools</groupId>
+ <artifactId>drools-decisiontables</artifactId>
+ </dependency>
+
+ <!-- External dependencies -->
+ <dependency>
+ <groupId>foxtrot</groupId>
+ <artifactId>foxtrot</artifactId>
+ </dependency>
+ <dependency>
+ <groupId>com.jgoodies</groupId>
+ <artifactId>forms</artifactId>
+ </dependency>
+ <dependency>
+ <groupId>com.thoughtworks.xstream</groupId>
+ <artifactId>xstream</artifactId>
+ <version>1.3</version>
+ </dependency>
+ </dependencies>
+
+ <properties>
+ <project.mainClass>org.drools.benchmark.waltz.WaltzBenchmark</project.mainClass>
+ </properties>
+ <build>
+ <resources>
+ <resource>
+ <directory>src/main/resources</directory>
+ </resource>
+ <resource>
+ <directory>src/main/rules</directory>
+ </resource>
+ </resources>
+ <plugins>
+ <plugin>
+ <groupId>org.apache.maven.plugins</groupId>
+ <artifactId>maven-eclipse-plugin</artifactId>
+ <configuration>
+ <additionalBuildcommands>
+ <buildcommand>org.drools.eclipse.droolsbuilder</buildcommand>
+ </additionalBuildcommands>
+ </configuration>
+ </plugin>
+ <plugin>
+ <groupId>org.apache.maven.plugins</groupId>
+ <artifactId>maven-jar-plugin</artifactId>
+ <configuration>
+ <archive>
+ <manifest>
+ <mainClass>${project.mainClass}</mainClass>
+ <addClasspath>true</addClasspath>
+ </manifest>
+ </archive>
+ </configuration>
+ </plugin>
+
+ <plugin>
+ <groupId>org.codehaus.mojo</groupId>
+ <artifactId>exec-maven-plugin</artifactId>
+ <version>1.0.2</version>
+ <configuration>
+ <executable>java</executable>
+ <!-- only waltz12 is configured at the moment -->
+ <!-- TODO waltz50, manners64 and manners128 -->
+ <arguments>
+ <argument>-Xms1024M</argument>
+ <argument>-Xmx1024M</argument>
+ <argument>-cp</argument>
+ <classpath/>
+ <argument>${project.mainClass}</argument>
+ <argument>waltz12.dat</argument>
+ </arguments>
+ </configuration>
+ </plugin>
+ </plugins>
+ </build>
+</project>
\ No newline at end of file
Modified: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/HelloWorldExample.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/HelloWorldExample.java 2008-05-23 16:37:12 UTC (rev 20143)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/HelloWorldExample.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -22,7 +22,8 @@
public static final void main(final String[] args) throws Exception {
//read in the source
- final Reader source = new InputStreamReader( HelloWorldExample.class.getResourceAsStream( "HelloWorld.drl" ) );
+ //final Reader source = new InputStreamReader( HelloWorldExample.class.getResourceAsStream( "HelloWorld.drl" ) );
+ final Reader source = new InputStreamReader( HelloWorldExample.class.getResourceAsStream( "Bogus.drl" ) );
final PackageBuilder builder = new PackageBuilder();
@@ -57,7 +58,7 @@
session.fireAllRules();
- logger.writeToDisk();
+ logger.writeToDisk();
session.dispose();
}
Modified: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/PricingRuleDTExample.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/PricingRuleDTExample.java 2008-05-23 16:37:12 UTC (rev 20143)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/PricingRuleDTExample.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -50,7 +50,6 @@
System.out.println("DISCOUNT IS: " + policy.getDiscountPercent());
return policy.getBasePrice();
-
}
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Car.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Car.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Car.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,95 @@
+package org.drools.examples.learner;
+
+import org.drools.learner.tools.FieldAnnotation;
+
+
+public class Car {
+ @FieldAnnotation(readingSeq = 0)
+ private String buying; //"vhigh", "high", "med", "low"
+ @FieldAnnotation(readingSeq = 1)
+ private String maint; //"vhigh", "high", "med", "low"
+ @FieldAnnotation(readingSeq = 2)
+ private String doors; //"2", "3", "4", "5more"
+ @FieldAnnotation(readingSeq = 3)
+ private String persons; //"2", "4", "more"
+ @FieldAnnotation(readingSeq = 4)
+ private String lug_boot; //"small", "med", "big"
+ @FieldAnnotation(readingSeq = 5)
+ private String safety; //"low", "med", "high"
+ @FieldAnnotation(readingSeq = 6, target = true)
+ private String target; //"unacc", "acc", "good", "vgood"
+
+
+ public Car() {
+
+ }
+ public String getBuying() {
+ return buying;
+ }
+
+ public void setBuying(String buying) {
+ this.buying = buying;
+ }
+
+ public String getMaint() {
+ return maint;
+ }
+
+ public void setMaint(String maint) {
+ this.maint = maint;
+ }
+
+ public String getDoors() {
+ return doors;
+ }
+
+ public void setDoors(String doors) {
+ this.doors = doors;
+ }
+
+ public String getPersons() {
+ return persons;
+ }
+
+ public void setPersons(String persons) {
+ this.persons = persons;
+ }
+
+ public String getLug_boot() {
+ return lug_boot;
+ }
+
+ public void setLug_boot(String lug_boot) {
+ this.lug_boot = lug_boot;
+ }
+
+ public String getSafety() {
+ return safety;
+ }
+
+ public void setSafety(String safety) {
+ this.safety = safety;
+ }
+
+ public String getTarget() {
+ return target;
+ }
+
+ public void setTarget(String carClass) {
+ this.target = carClass;
+ }
+
+
+ public String toString() {
+ String out = "Car(buy:" +getBuying() +
+ " doors:"+getDoors()+
+ " lug_boot:"+getLug_boot()+
+ " maint:"+getMaint()+
+ " persons:"+getPersons()+
+ " safety:"+getSafety()+
+ " target:"+getTarget();
+ return out;
+ }
+
+
+}
\ No newline at end of file
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Car.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/CarExample.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/CarExample.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/CarExample.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,61 @@
+package org.drools.examples.learner;
+
+import java.util.List;
+
+import org.drools.RuleBase;
+import org.drools.RuleBaseFactory;
+import org.drools.StatefulSession;
+import org.drools.audit.WorkingMemoryFileLogger;
+import org.drools.compiler.PackageBuilder;
+import org.drools.event.DebugAgendaEventListener;
+import org.drools.event.DebugWorkingMemoryEventListener;
+import org.drools.learner.builder.Learner;
+import org.drools.learner.builder.LearnerFactory;
+import org.drools.learner.tools.ObjectFactory;
+
+public class CarExample {
+
+
+ public static final void main(final String[] args) throws Exception {
+ // my rule base
+ final RuleBase ruleBase = RuleBaseFactory.newRuleBase();
+
+ final StatefulSession session = ruleBase.newStatefulSession(); // LearningSession
+
+ // what are these listeners???
+ session.addEventListener( new DebugAgendaEventListener() );
+ session.addEventListener( new DebugWorkingMemoryEventListener() );
+
+ final WorkingMemoryFileLogger logger = new WorkingMemoryFileLogger( session );
+ logger.setFileName( "log/car" );
+
+ String inputFile = new String("data/car/car.data.txt");
+ Class<?> obj_class = Car.class;
+ List<Object> facts = ObjectFactory.getObjects(obj_class, inputFile);
+ for (Object r : facts) {
+ session.insert(r);
+ }
+
+ // instantiate a learner for a specific object class and pass session to train
+ Learner learner = LearnerFactory.createID3(session, obj_class);
+ //Learner learner = LearnerFactory.createC45(session, obj_class);
+
+ final PackageBuilder builder = new PackageBuilder();
+ //this wil generate the rules, then parse and compile in one step
+ builder.addPackageFromLearner( learner );
+ ruleBase.addPackage( builder.getPackage() );
+ /*
+ final Reader source = new InputStreamReader( HelloWorldExample.class.getResourceAsStream( "HelloWorld.drl" ) );
+ //get the compiled package (which is serializable)
+ final Package pkg = builder.getPackage();
+ //add the package to a rulebase (deploy the rule package).
+ ruleBase.addPackage( pkg );
+ */
+
+ session.fireAllRules();
+
+ logger.writeToDisk();
+
+ session.dispose();
+ }
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/CarExample.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Golf.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Golf.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Golf.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,63 @@
+package org.drools.examples.learner;
+
+import org.drools.learner.tools.FieldAnnotation;
+
+public class Golf {
+
+ @FieldAnnotation(readingSeq=0, values= {"sunny", "overcast", "rain"})
+ private String outlook; //: sunny, overcast, rain.
+ @FieldAnnotation(readingSeq=1, discrete=false)
+ private int temperature; //: continuous.
+ @FieldAnnotation(readingSeq=2, discrete=false)
+ private int humidity; //: continuous.
+ @FieldAnnotation(readingSeq=3)
+ private boolean windy;
+
+ @FieldAnnotation(target =true, readingSeq = 4)
+ private String decision; //Play, Don't Play.
+
+ public Golf() {
+
+ }
+
+ public String getOutlook() {
+ return outlook;
+ }
+
+ public void setOutlook(String outlook) {
+ this.outlook = outlook;
+ }
+
+ public int getTemperature() {
+ return temperature;
+ }
+
+ public void setTemperature(int temperature) {
+ this.temperature = temperature;
+ }
+
+ public int getHumidity() {
+ return humidity;
+ }
+
+ public void setHumidity(int humidity) {
+ this.humidity = humidity;
+ }
+
+ public boolean isWindy() {
+ return windy;
+ }
+
+ public void setWindy(boolean windy) {
+ this.windy = windy;
+ }
+
+ public String getDecision() {
+ return decision;
+ }
+
+ public void setDecision(String decision) {
+ this.decision = decision;
+ }
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Golf.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/GolfC45Example.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/GolfC45Example.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/GolfC45Example.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,59 @@
+package org.drools.examples.learner;
+
+import java.util.List;
+
+import org.drools.RuleBase;
+import org.drools.RuleBaseFactory;
+import org.drools.StatefulSession;
+import org.drools.audit.WorkingMemoryFileLogger;
+import org.drools.compiler.PackageBuilder;
+import org.drools.event.DebugAgendaEventListener;
+import org.drools.event.DebugWorkingMemoryEventListener;
+import org.drools.learner.builder.Learner;
+import org.drools.learner.builder.LearnerFactory;
+import org.drools.learner.tools.ObjectFactory;
+
+public class GolfC45Example {
+
+ public static final void main(final String[] args) throws Exception {
+ // my rule base
+ final RuleBase ruleBase = RuleBaseFactory.newRuleBase();
+ //ruleBase.addPackage( pkg );
+
+ final StatefulSession session = ruleBase.newStatefulSession();
+ // LearningSession
+
+ // what are these listeners???
+ session.addEventListener( new DebugAgendaEventListener() );
+ session.addEventListener( new DebugWorkingMemoryEventListener() );
+
+ final WorkingMemoryFileLogger logger = new WorkingMemoryFileLogger( session );
+ logger.setFileName( "log/golf_c45" );
+
+ String inputFile = new String("data/golf/golf.data.txt");
+ Class<?> obj_class = Golf.class;
+ List<Object> facts = ObjectFactory.getObjects(obj_class, inputFile);
+ for (Object r : facts) {
+ session.insert(r);
+ }
+
+ // instantiate a learner for a specific object class and pass session to train
+ Learner learner = LearnerFactory.createC45(session, obj_class);
+
+ final PackageBuilder builder = new PackageBuilder();
+ //this wil generate the rules, then parse and compile in one step
+ builder.addPackageFromLearner( learner );
+ /*
+ * get the compiled package (which is serializable) from the builder
+ * add the package to a rulebase (deploy the rule package).
+ */
+ ruleBase.addPackage( builder.getPackage() );
+
+ session.fireAllRules();
+
+ logger.writeToDisk();
+
+ session.dispose();
+ }
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/GolfC45Example.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/GolfC45ExampleFromDrl.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/GolfC45ExampleFromDrl.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/GolfC45ExampleFromDrl.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,64 @@
+package org.drools.examples.learner;
+
+import java.io.InputStreamReader;
+import java.io.Reader;
+import java.util.List;
+
+import org.drools.RuleBase;
+import org.drools.RuleBaseFactory;
+import org.drools.StatefulSession;
+import org.drools.audit.WorkingMemoryFileLogger;
+import org.drools.compiler.PackageBuilder;
+import org.drools.event.DebugAgendaEventListener;
+import org.drools.event.DebugWorkingMemoryEventListener;
+import org.drools.learner.tools.ObjectFactory;
+import org.drools.rule.Package;
+
+public class GolfC45ExampleFromDrl {
+
+ public static final void main(final String[] args) throws Exception {
+ //read in the source
+ //final Reader source = new InputStreamReader( HelloWorldExample.class.getResourceAsStream( "HelloWorld.drl" ) );
+ final Reader source = new InputStreamReader( Restaurant.class.getResourceAsStream( "golf2.drl" ) );
+
+ final PackageBuilder builder = new PackageBuilder();
+
+ //this wil parse and compile in one step
+ builder.addPackageFromDrl( source );
+
+ // Check the builder for errors
+ if ( builder.hasErrors() ) {
+ System.out.println( builder.getErrors().toString() );
+ throw new RuntimeException( "Unable to compile \"golf2.drl\".");
+ }
+
+ //get the compiled package (which is serializable)
+ final Package pkg = builder.getPackage();
+
+ //add the package to a rulebase (deploy the rule package).
+ final RuleBase ruleBase = RuleBaseFactory.newRuleBase();
+ ruleBase.addPackage( pkg );
+
+ final StatefulSession session = ruleBase.newStatefulSession();
+
+ session.addEventListener( new DebugAgendaEventListener() );
+ session.addEventListener( new DebugWorkingMemoryEventListener() );
+
+ final WorkingMemoryFileLogger logger = new WorkingMemoryFileLogger( session );
+ logger.setFileName( "log/golfc45_fromdrl" );
+
+ String inputFile = new String("data/golf/golf.data.txt");
+ Class<?> obj_class = Golf.class;
+ List<Object> facts = ObjectFactory.getObjects(obj_class, inputFile);
+ for (Object r : facts) {
+ session.insert(r);
+ }
+
+ session.fireAllRules();
+
+ logger.writeToDisk();
+
+ session.dispose();
+ }
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/GolfC45ExampleFromDrl.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/GolfExample.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/GolfExample.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/GolfExample.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,62 @@
+package org.drools.examples.learner;
+
+import java.util.List;
+
+import org.drools.RuleBase;
+import org.drools.RuleBaseFactory;
+import org.drools.StatefulSession;
+import org.drools.audit.WorkingMemoryFileLogger;
+import org.drools.compiler.PackageBuilder;
+import org.drools.event.DebugAgendaEventListener;
+import org.drools.event.DebugWorkingMemoryEventListener;
+import org.drools.learner.builder.Learner;
+import org.drools.learner.builder.LearnerFactory;
+import org.drools.learner.tools.ObjectFactory;
+
+public class GolfExample {
+
+ public static final void main(final String[] args) throws Exception {
+ // my rule base
+ final RuleBase ruleBase = RuleBaseFactory.newRuleBase();
+ //ruleBase.addPackage( pkg );
+
+ final StatefulSession session = ruleBase.newStatefulSession();
+ // LearningSession
+
+ // what are these listeners???
+ session.addEventListener( new DebugAgendaEventListener() );
+ session.addEventListener( new DebugWorkingMemoryEventListener() );
+
+ final WorkingMemoryFileLogger logger = new WorkingMemoryFileLogger( session );
+ logger.setFileName( "log/golf" );
+
+ String inputFile = new String("data/golf/golf.data.txt");
+ Class<?> obj_class = Golf.class;
+ List<Object> facts = ObjectFactory.getObjects(obj_class, inputFile);
+ for (Object r : facts) {
+ session.insert(r);
+ }
+
+ // instantiate a learner for a specific object class and pass session to train
+ Learner learner = LearnerFactory.createID3(session, obj_class);
+
+ final PackageBuilder builder = new PackageBuilder();
+ //this wil generate the rules, then parse and compile in one step
+ builder.addPackageFromLearner( learner );
+ ruleBase.addPackage( builder.getPackage() );
+ /*
+ final Reader source = new InputStreamReader( HelloWorldExample.class.getResourceAsStream( "HelloWorld.drl" ) );
+ //get the compiled package (which is serializable)
+ final Package pkg = builder.getPackage();
+ //add the package to a rulebase (deploy the rule package).
+ ruleBase.addPackage( pkg );
+ */
+
+ session.fireAllRules();
+
+ logger.writeToDisk();
+
+ session.dispose();
+ }
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/GolfExample.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Nursery.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Nursery.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Nursery.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,94 @@
+package org.drools.examples.learner;
+
+import org.drools.learner.tools.FieldAnnotation;
+
+public class Nursery {
+ @FieldAnnotation(readingSeq =0)
+ private String parents; //"usual","pretentious", "great_pret"
+ @FieldAnnotation(readingSeq =1)
+ private String has_nurs; //"proper","less_proper", "improper", "critical", "very_crit"
+ @FieldAnnotation(readingSeq =2)
+ private String children; //"1", "2", "3","more"
+ @FieldAnnotation(readingSeq =3)
+ private String form; //"complete","completed", "incomplete", "foster"
+ @FieldAnnotation(readingSeq =4)
+ private String housing; //"convenient","less_conv", "critical"
+ @FieldAnnotation(readingSeq =5)
+ private String finance; //"convenient","inconv"
+ @FieldAnnotation(readingSeq =6)
+ private String social; //"nonprob","slightly_prob", "problematic"
+ @FieldAnnotation(readingSeq =7)
+ private String health; //"recommended","priority", "not_recom"
+ @FieldAnnotation(readingSeq =8, target = true)
+ private String classnursery; //"not_recom", "recommend", "very_recom", "priority","spec_prior"
+
+
+ public String getParents() {
+ return parents;
+ }
+
+ public void setParents(String parents) {
+ this.parents = parents;
+ }
+ public String getHas_nurs() {
+ return has_nurs;
+ }
+
+ public void setHas_nurs(String has_nurs) {
+ this.has_nurs = has_nurs;
+ }
+ public String getForm() {
+ return form;
+ }
+
+ public void setForm(String form) {
+ this.form = form;
+ }
+ public String getChildren() {
+ return children;
+ }
+
+ public void setChildren(String children) {
+ this.children = children;
+ }
+ public String getHousing() {
+ return housing;
+ }
+
+ public void setHousing(String housing) {
+ this.housing = housing;
+ }
+
+ public String getFinance() {
+ return finance;
+ }
+
+ public void setFinance(String finance) {
+ this.finance = finance;
+ }
+ public String getSocial() {
+ return social;
+ }
+
+ public void setSocial(String social) {
+ this.social = social;
+ }
+ public String getHealth() {
+ return health;
+ }
+
+ public void setHealth(String health) {
+ this.health = health;
+ }
+ public String getClassnursery() {
+ return classnursery;
+ }
+
+ public void setClassnursery(String classnursery) {
+ this.classnursery = classnursery;
+ }
+
+
+
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Nursery.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/NurseryExample.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/NurseryExample.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/NurseryExample.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,61 @@
+package org.drools.examples.learner;
+
+import java.util.List;
+
+import org.drools.RuleBase;
+import org.drools.RuleBaseFactory;
+import org.drools.StatefulSession;
+import org.drools.audit.WorkingMemoryFileLogger;
+import org.drools.compiler.PackageBuilder;
+import org.drools.event.DebugAgendaEventListener;
+import org.drools.event.DebugWorkingMemoryEventListener;
+import org.drools.learner.builder.Learner;
+import org.drools.learner.builder.LearnerFactory;
+import org.drools.learner.tools.ObjectFactory;
+
+public class NurseryExample {
+
+
+ public static final void main(final String[] args) throws Exception {
+ // my rule base
+ final RuleBase ruleBase = RuleBaseFactory.newRuleBase();
+
+ final StatefulSession session = ruleBase.newStatefulSession(); // LearningSession
+
+ // what are these listeners???
+ session.addEventListener( new DebugAgendaEventListener() );
+ session.addEventListener( new DebugWorkingMemoryEventListener() );
+
+ final WorkingMemoryFileLogger logger = new WorkingMemoryFileLogger( session );
+ logger.setFileName( "log/nursery" );
+
+ String inputFile = new String("data/nursery/nursery.data.txt");
+ Class<?> obj_class = Nursery.class;
+ List<Object> facts = ObjectFactory.getObjects(obj_class, inputFile);
+ for (Object r : facts) {
+ session.insert(r);
+ }
+
+ // instantiate a learner for a specific object class and pass session to train
+ //Learner learner = LearnerFactory.createID3(session, obj_class);
+ Learner learner = LearnerFactory.createC45(session, obj_class);
+
+ final PackageBuilder builder = new PackageBuilder();
+ //this wil generate the rules, then parse and compile in one step
+ builder.addPackageFromLearner( learner );
+ ruleBase.addPackage( builder.getPackage() );
+ /*
+ final Reader source = new InputStreamReader( HelloWorldExample.class.getResourceAsStream( "HelloWorld.drl" ) );
+ //get the compiled package (which is serializable)
+ final Package pkg = builder.getPackage();
+ //add the package to a rulebase (deploy the rule package).
+ ruleBase.addPackage( pkg );
+ */
+
+ session.fireAllRules();
+
+ logger.writeToDisk();
+
+ session.dispose();
+ }
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/NurseryExample.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Poker.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Poker.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Poker.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,130 @@
+package org.drools.examples.learner;
+
+import org.drools.learner.tools.FieldAnnotation;
+
+
+
+public class Poker {
+ @FieldAnnotation(readingSeq = 0)
+ private int s1; // 'Suit of card #1': Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs}
+ @FieldAnnotation(readingSeq = 1, discrete=false)
+ private int c1; // 'Rank of card #1': Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King)
+
+ @FieldAnnotation(readingSeq = 2)
+ private int s2; // 'Suit of card #2': Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs}
+ @FieldAnnotation(readingSeq = 3, discrete=false)
+ private int c2; // 'Rank of card #2': Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King)
+
+ @FieldAnnotation(readingSeq = 4)
+ private int s3; // 'Suit of card #3': Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs}
+ @FieldAnnotation(readingSeq = 5, discrete=false)
+ private int c3; // 'Rank of card #3': Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King)
+
+ @FieldAnnotation(readingSeq = 6)
+ private int s4; // 'Suit of card #4': Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs}
+ @FieldAnnotation(readingSeq = 7, discrete=false)
+ private int c4; // 'Rank of card #4': Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King)
+
+ @FieldAnnotation(readingSeq = 8)
+ private int s5; // 'Suit of card #5': Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs}
+ @FieldAnnotation(readingSeq = 9, discrete=false)
+ private int c5; // 'Rank of card #5': Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King)
+
+ @FieldAnnotation(readingSeq = 10, target = true)
+ private int poker_hand;
+
+ public Poker() {
+
+ }
+
+ public int getS1() {
+ return s1;
+ }
+
+ public void setS1(int s1) {
+ this.s1 = s1;
+ }
+
+ public int getC1() {
+ return c1;
+ }
+
+ public void setC1(int c1) {
+ this.c1 = c1;
+ }
+
+ public int getS2() {
+ return s2;
+ }
+
+ public void setS2(int s2) {
+ this.s2 = s2;
+ }
+
+ public int getC2() {
+ return c2;
+ }
+
+ public void setC2(int c2) {
+ this.c2 = c2;
+ }
+
+ public int getS3() {
+ return s3;
+ }
+
+ public void setS3(int s3) {
+ this.s3 = s3;
+ }
+
+ public int getC3() {
+ return c3;
+ }
+
+ public void setC3(int c3) {
+ this.c3 = c3;
+ }
+
+ public int getS4() {
+ return s4;
+ }
+
+ public void setS4(int s4) {
+ this.s4 = s4;
+ }
+
+ public int getC4() {
+ return c4;
+ }
+
+ public void setC4(int c4) {
+ this.c4 = c4;
+ }
+
+ public int getS5() {
+ return s5;
+ }
+
+ public void setS5(int s5) {
+ this.s5 = s5;
+ }
+
+ public int getC5() {
+ return c5;
+ }
+
+ public void setC5(int c5) {
+ this.c5 = c5;
+ }
+
+ public int getPoker_hand() {
+ return poker_hand;
+ }
+
+ public void setPoker_hand(int poker_hand) {
+ this.poker_hand = poker_hand;
+ }
+
+
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Poker.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/PokerExample.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/PokerExample.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/PokerExample.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,59 @@
+package org.drools.examples.learner;
+
+import java.util.List;
+
+import org.drools.RuleBase;
+import org.drools.RuleBaseFactory;
+import org.drools.StatefulSession;
+import org.drools.audit.WorkingMemoryFileLogger;
+import org.drools.compiler.PackageBuilder;
+import org.drools.event.DebugAgendaEventListener;
+import org.drools.event.DebugWorkingMemoryEventListener;
+import org.drools.learner.builder.Learner;
+import org.drools.learner.builder.LearnerFactory;
+import org.drools.learner.tools.ObjectFactory;
+
+public class PokerExample {
+
+ public static final void main(final String[] args) throws Exception {
+ // my rule base
+ final RuleBase ruleBase = RuleBaseFactory.newRuleBase();
+ //ruleBase.addPackage( pkg );
+
+ final StatefulSession session = ruleBase.newStatefulSession();
+ // LearningSession
+
+ // what are these listeners???
+ session.addEventListener( new DebugAgendaEventListener() );
+ session.addEventListener( new DebugWorkingMemoryEventListener() );
+
+ final WorkingMemoryFileLogger logger = new WorkingMemoryFileLogger( session );
+ logger.setFileName( "log/poker" );
+
+ String inputFile = new String("data/poker/poker-hand-training-true.data.txt");
+ Class<?> obj_class = Poker.class;
+ List<Object> facts = ObjectFactory.getObjects(obj_class, inputFile);
+ for (Object r : facts) {
+ session.insert(r);
+ }
+
+ // instantiate a learner for a specific object class and pass session to train
+ Learner learner = LearnerFactory.createC45(session, obj_class);
+
+ final PackageBuilder builder = new PackageBuilder();
+ //this wil generate the rules, then parse and compile in one step
+ builder.addPackageFromLearner( learner );
+ /*
+ * get the compiled package (which is serializable) from the builder
+ * add the package to a rulebase (deploy the rule package).
+ */
+ ruleBase.addPackage( builder.getPackage() );
+
+ session.fireAllRules();
+
+ logger.writeToDisk();
+
+ session.dispose();
+ }
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/PokerExample.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Restaurant.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Restaurant.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Restaurant.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,151 @@
+package org.drools.examples.learner;
+
+import org.drools.learner.tools.FieldAnnotation;
+
+//import dt.memory.DomainSpec;
+
+public class Restaurant {
+
+
+ private boolean alternate; //yesno
+ private boolean bar; //yesno
+ private boolean fri_sat ; //yesno
+ private boolean hungry; //yesno
+ private String patrons; //String[]{"None","Some","Full"});
+ private int price; //",new String[]{"$","$$","$$$"});
+ private boolean raining; //yesno
+ private boolean reservation; //yesno
+ private String type; //",new String[]{"French","Italian","Thai","Burger"});
+ private String wait_estimate; //",new String[]{"0-10","10-30","30-60",">60"});
+ @FieldAnnotation(target =true, readingSeq = 0)
+ private boolean will_wait; //yesno
+
+
+ public Restaurant (boolean alt, boolean b, boolean f_s, boolean hung, String pat, int pri,
+ boolean rain, boolean reserv, String t, String wait, boolean will) {
+ alternate = alt; //yesno
+ bar = b; //yesno
+ fri_sat = f_s; //yesno
+ hungry = hung; //yesno
+ patrons = pat; //",new String[]{"None","Some","Full"});
+ price = pri; //",new String[]{"$","$$","$$$"});
+ raining = rain; //yesno
+ reservation = reserv; //yesno
+ type = t; //",new String[]{"French","Italian","Thai","Burger"});
+ wait_estimate = wait; //",new String[]{"0-10","10-30","30-60",">60"});
+ will_wait = will; //yesno
+ }
+
+
+ public boolean getAlternate() {
+ return alternate;
+ }
+
+
+ public void setAlternate(boolean alternate) {
+ this.alternate = alternate;
+ }
+
+
+ public boolean getBar() {
+ return bar;
+ }
+
+
+ public void setBar(boolean bar) {
+ this.bar = bar;
+ }
+
+
+ public boolean getFri_sat() {
+ return fri_sat;
+ }
+
+
+ public void setFri_sat(boolean fri_sat) {
+ this.fri_sat = fri_sat;
+ }
+
+
+ public boolean getHungry() {
+ return hungry;
+ }
+
+
+ public void setHungry(boolean hungry) {
+ this.hungry = hungry;
+ }
+
+
+ public String getPatrons() {
+ return patrons;
+ }
+
+
+ public void setPatrons(String patrons) {
+ this.patrons = patrons;
+ }
+
+
+ public int getPrice() {
+ return price;
+ }
+
+
+ public void setPrice(int price) {
+ this.price = price;
+ }
+
+
+ public boolean getRaining() {
+ return raining;
+ }
+
+
+ public void setRaining(boolean raining) {
+ this.raining = raining;
+ }
+
+
+ public boolean getReservation() {
+ return reservation;
+ }
+
+
+ public void setReservation(boolean reservation) {
+ this.reservation = reservation;
+ }
+
+
+ public String getType() {
+ return type;
+ }
+
+
+ public void setType(String type) {
+ this.type = type;
+ }
+
+
+ public String getWait_estimate() {
+ return wait_estimate;
+ }
+
+
+ public void setWait_estimate(String wait_estimate) {
+ this.wait_estimate = wait_estimate;
+ }
+
+
+ public boolean getWill_wait() {
+ return will_wait;
+ }
+
+
+ public void setWill_wait(boolean will_wait) {
+ this.will_wait = will_wait;
+ }
+
+
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/Restaurant.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/RestaurantExample.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/RestaurantExample.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/RestaurantExample.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,118 @@
+package org.drools.examples.learner;
+
+import java.util.ArrayList;
+import java.util.List;
+
+import org.drools.RuleBase;
+import org.drools.RuleBaseFactory;
+import org.drools.StatefulSession;
+import org.drools.audit.WorkingMemoryFileLogger;
+import org.drools.compiler.PackageBuilder;
+import org.drools.event.DebugAgendaEventListener;
+import org.drools.event.DebugWorkingMemoryEventListener;
+import org.drools.learner.builder.Learner;
+import org.drools.learner.builder.LearnerFactory;
+
+public class RestaurantExample {
+
+ public static final void main(final String[] args) throws Exception {
+ // my rule base
+ final RuleBase ruleBase = RuleBaseFactory.newRuleBase();
+ //ruleBase.addPackage( pkg );
+
+ final StatefulSession session = ruleBase.newStatefulSession();
+ // LearningSession
+
+ // what are these listeners???
+ session.addEventListener( new DebugAgendaEventListener() );
+ session.addEventListener( new DebugWorkingMemoryEventListener() );
+
+ final WorkingMemoryFileLogger logger = new WorkingMemoryFileLogger( session );
+ logger.setFileName( "log/restaurants" );
+
+ List<Object> facts = getRestaurants();
+ for (Object r : facts) {
+ session.insert(r);
+ }
+
+ // instantiate a learner for a specific object class and pass session to train
+ Learner learner = LearnerFactory.createID3(session, Restaurant.class);
+
+ final PackageBuilder builder = new PackageBuilder();
+ //this wil generate the rules, then parse and compile in one step
+ builder.addPackageFromLearner( learner );
+ ruleBase.addPackage( builder.getPackage() );
+ /*
+ final Reader source = new InputStreamReader( HelloWorldExample.class.getResourceAsStream( "HelloWorld.drl" ) );
+ //get the compiled package (which is serializable)
+ final Package pkg = builder.getPackage();
+ //add the package to a rulebase (deploy the rule package).
+ ruleBase.addPackage( pkg );
+ */
+
+ session.fireAllRules();
+
+ logger.writeToDisk();
+
+ session.dispose();
+ }
+
+ public static List<Object> getRestaurants() {
+ ArrayList<Object> rests = new ArrayList<Object>();
+ rests.add(new Restaurant(true, false, false, true, "Full", 1, false,
+ false, "Thai", "30-60", false));
+ rests.add(new Restaurant(false, true, false, false, "Some", 1, false,
+ false, "Burger", "0-10", true));
+ rests.add(new Restaurant(true, false, true, true, "Full", 1, true,
+ false, "Thai", "10-30", true));
+ rests.add(new Restaurant(true, false, true, false, "Full", 3, false,
+ true, "French", ">60", false));
+ rests.add(new Restaurant(false, true, false, true, "Some", 2, true,
+ true, "Italian", "0-10", true));
+ rests.add(new Restaurant(false, true, false, false, "None", 1, true,
+ false, "Burger", "0-10", false));
+ rests.add(new Restaurant(false, false, false, true, "Some", 2, true,
+ true, "Thai", "0-10", true));
+ rests.add(new Restaurant(false, true, true, false, "Full", 1, true,
+ false, "Burger", ">60", false));
+ rests.add(new Restaurant(true, true, true, true, "Full", 3, false,
+ true, "Italian", "10-30", false));
+ rests.add(new Restaurant(false, false, false, false, "None", 1, false,
+ false, "Thai", "0-10", false));
+ rests.add(new Restaurant(true, true, true, true, "Full", 1, false,
+ false, "Burger", "30-60", true));
+
+ return rests;
+ }
+
+ public static List<Object> getVVs() {
+ ArrayList<Object> rests = new ArrayList<Object>();
+ rests.add(new VV(true, false, false, true, "Full", 1, false,
+ false, "Thai", "30-60", false));
+// rests.add(new Restaurant(false, true, false, false, "Some", 1, false,
+// false, "Burger", "0-10", true));
+// rests.add(new Restaurant(true, false, true, true, "Full", 1, true,
+// false, "Thai", "10-30", true));
+// rests.add(new Restaurant(true, false, true, false, "Full", 3, false,
+// true, "French", ">60", false));
+// rests.add(new Restaurant(false, true, false, true, "Some", 2, true,
+// true, "Italian", "0-10", true));
+// rests.add(new Restaurant(false, true, false, false, "None", 1, true,
+// false, "Burger", "0-10", false));
+// rests.add(new Restaurant(false, false, false, true, "Some", 2, true,
+// true, "Thai", "0-10", true));
+// rests.add(new Restaurant(false, true, true, false, "Full", 1, true,
+// false, "Burger", ">60", false));
+// rests.add(new Restaurant(true, true, true, true, "Full", 3, false,
+// true, "Italian", "10-30", false));
+// rests.add(new Restaurant(false, false, false, false, "None", 1, false,
+// false, "Thai", "0-10", false));
+// rests.add(new Restaurant(true, true, true, true, "Full", 1, false,
+// false, "Burger", "30-60", true));
+
+ return rests;
+ }
+
+
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/RestaurantExample.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/RestaurantExampleFromDrl.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/RestaurantExampleFromDrl.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/RestaurantExampleFromDrl.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,61 @@
+package org.drools.examples.learner;
+
+import java.io.InputStreamReader;
+import java.io.Reader;
+import java.util.List;
+
+import org.drools.RuleBase;
+import org.drools.RuleBaseFactory;
+import org.drools.StatefulSession;
+import org.drools.audit.WorkingMemoryFileLogger;
+import org.drools.compiler.PackageBuilder;
+import org.drools.event.DebugAgendaEventListener;
+import org.drools.event.DebugWorkingMemoryEventListener;
+import org.drools.rule.Package;
+
+public class RestaurantExampleFromDrl {
+
+ public static final void main(final String[] args) throws Exception {
+ //read in the source
+ //final Reader source = new InputStreamReader( HelloWorldExample.class.getResourceAsStream( "HelloWorld.drl" ) );
+ final Reader source = new InputStreamReader( Restaurant.class.getResourceAsStream( "restaurant.drl" ) );
+
+ final PackageBuilder builder = new PackageBuilder();
+
+ //this wil parse and compile in one step
+ builder.addPackageFromDrl( source );
+
+ // Check the builder for errors
+ if ( builder.hasErrors() ) {
+ System.out.println( builder.getErrors().toString() );
+ throw new RuntimeException( "Unable to compile \"HelloWorld.drl\".");
+ }
+
+ //get the compiled package (which is serializable)
+ final Package pkg = builder.getPackage();
+
+ //add the package to a rulebase (deploy the rule package).
+ final RuleBase ruleBase = RuleBaseFactory.newRuleBase();
+ ruleBase.addPackage( pkg );
+
+ final StatefulSession session = ruleBase.newStatefulSession();
+
+ session.addEventListener( new DebugAgendaEventListener() );
+ session.addEventListener( new DebugWorkingMemoryEventListener() );
+
+ final WorkingMemoryFileLogger logger = new WorkingMemoryFileLogger( session );
+ logger.setFileName( "log/restaurant_fromdrl" );
+
+ List<Object> facts = RestaurantExample.getRestaurants();
+ for (Object r : facts) {
+ session.insert(r);
+ }
+
+ session.fireAllRules();
+
+ logger.writeToDisk();
+
+ session.dispose();
+ }
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/RestaurantExampleFromDrl.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/VV.java
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/VV.java (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/VV.java 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,12 @@
+package org.drools.examples.learner;
+
+public class VV extends Restaurant {
+
+ public VV(boolean alt, boolean b, boolean f_s, boolean hung, String pat,
+ int pri, boolean rain, boolean reserv, String t, String wait,
+ boolean will) {
+ super(alt, b, f_s, hung, pat, pri, rain, reserv, t, wait, will);
+ // TODO Auto-generated constructor stub
+ }
+
+}
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/VV.java
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/abalone/abalone.data.txt
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/abalone/abalone.data.txt (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/abalone/abalone.data.txt 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,4177 @@
+M,0.455,0.365,0.095,0.514,0.2245,0.101,0.15,15
+M,0.35,0.265,0.09,0.2255,0.0995,0.0485,0.07,7
+F,0.53,0.42,0.135,0.677,0.2565,0.1415,0.21,9
+M,0.44,0.365,0.125,0.516,0.2155,0.114,0.155,10
+I,0.33,0.255,0.08,0.205,0.0895,0.0395,0.055,7
+I,0.425,0.3,0.095,0.3515,0.141,0.0775,0.12,8
+F,0.53,0.415,0.15,0.7775,0.237,0.1415,0.33,20
+F,0.545,0.425,0.125,0.768,0.294,0.1495,0.26,16
+M,0.475,0.37,0.125,0.5095,0.2165,0.1125,0.165,9
+F,0.55,0.44,0.15,0.8945,0.3145,0.151,0.32,19
+F,0.525,0.38,0.14,0.6065,0.194,0.1475,0.21,14
+M,0.43,0.35,0.11,0.406,0.1675,0.081,0.135,10
+M,0.49,0.38,0.135,0.5415,0.2175,0.095,0.19,11
+F,0.535,0.405,0.145,0.6845,0.2725,0.171,0.205,10
+F,0.47,0.355,0.1,0.4755,0.1675,0.0805,0.185,10
+M,0.5,0.4,0.13,0.6645,0.258,0.133,0.24,12
+I,0.355,0.28,0.085,0.2905,0.095,0.0395,0.115,7
+F,0.44,0.34,0.1,0.451,0.188,0.087,0.13,10
+M,0.365,0.295,0.08,0.2555,0.097,0.043,0.1,7
+M,0.45,0.32,0.1,0.381,0.1705,0.075,0.115,9
+M,0.355,0.28,0.095,0.2455,0.0955,0.062,0.075,11
+I,0.38,0.275,0.1,0.2255,0.08,0.049,0.085,10
+F,0.565,0.44,0.155,0.9395,0.4275,0.214,0.27,12
+F,0.55,0.415,0.135,0.7635,0.318,0.21,0.2,9
+F,0.615,0.48,0.165,1.1615,0.513,0.301,0.305,10
+F,0.56,0.44,0.14,0.9285,0.3825,0.188,0.3,11
+F,0.58,0.45,0.185,0.9955,0.3945,0.272,0.285,11
+M,0.59,0.445,0.14,0.931,0.356,0.234,0.28,12
+M,0.605,0.475,0.18,0.9365,0.394,0.219,0.295,15
+M,0.575,0.425,0.14,0.8635,0.393,0.227,0.2,11
+M,0.58,0.47,0.165,0.9975,0.3935,0.242,0.33,10
+F,0.68,0.56,0.165,1.639,0.6055,0.2805,0.46,15
+M,0.665,0.525,0.165,1.338,0.5515,0.3575,0.35,18
+F,0.68,0.55,0.175,1.798,0.815,0.3925,0.455,19
+F,0.705,0.55,0.2,1.7095,0.633,0.4115,0.49,13
+M,0.465,0.355,0.105,0.4795,0.227,0.124,0.125,8
+F,0.54,0.475,0.155,1.217,0.5305,0.3075,0.34,16
+F,0.45,0.355,0.105,0.5225,0.237,0.1165,0.145,8
+F,0.575,0.445,0.135,0.883,0.381,0.2035,0.26,11
+M,0.355,0.29,0.09,0.3275,0.134,0.086,0.09,9
+F,0.45,0.335,0.105,0.425,0.1865,0.091,0.115,9
+F,0.55,0.425,0.135,0.8515,0.362,0.196,0.27,14
+I,0.24,0.175,0.045,0.07,0.0315,0.0235,0.02,5
+I,0.205,0.15,0.055,0.042,0.0255,0.015,0.012,5
+I,0.21,0.15,0.05,0.042,0.0175,0.0125,0.015,4
+I,0.39,0.295,0.095,0.203,0.0875,0.045,0.075,7
+M,0.47,0.37,0.12,0.5795,0.293,0.227,0.14,9
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+F,0.595,0.455,0.14,0.914,0.3895,0.2225,0.271,9
+F,0.6,0.5,0.17,1.13,0.4405,0.267,0.335,11
+F,0.615,0.495,0.155,1.0805,0.52,0.19,0.32,9
+M,0.63,0.505,0.155,1.105,0.492,0.226,0.325,11
+M,0.63,0.49,0.155,1.229,0.535,0.29,0.335,11
+F,0.635,0.495,0.175,1.2355,0.5205,0.3085,0.347,10
+F,0.645,0.535,0.19,1.2395,0.468,0.2385,0.424,10
+F,0.65,0.505,0.165,1.357,0.5725,0.281,0.43,11
+M,0.655,0.525,0.18,1.402,0.624,0.2935,0.365,13
+F,0.655,0.5,0.22,1.359,0.642,0.3255,0.405,13
+M,0.67,0.535,0.19,1.669,0.7465,0.2935,0.508,11
+M,0.67,0.525,0.2,1.7405,0.6205,0.297,0.657,11
+M,0.695,0.53,0.21,1.51,0.664,0.4095,0.385,10
+M,0.695,0.55,0.195,1.6645,0.727,0.36,0.445,11
+M,0.77,0.605,0.175,2.0505,0.8005,0.526,0.355,11
+I,0.28,0.215,0.07,0.124,0.063,0.0215,0.03,6
+I,0.33,0.23,0.08,0.14,0.0565,0.0365,0.046,7
+I,0.35,0.25,0.075,0.1695,0.0835,0.0355,0.041,6
+I,0.37,0.28,0.09,0.218,0.0995,0.0545,0.0615,7
+I,0.43,0.315,0.115,0.384,0.1885,0.0715,0.11,8
+I,0.435,0.33,0.095,0.393,0.219,0.075,0.0885,6
+I,0.44,0.35,0.11,0.3805,0.1575,0.0895,0.115,6
+M,0.475,0.37,0.11,0.4895,0.2185,0.107,0.146,8
+M,0.475,0.36,0.14,0.5135,0.241,0.1045,0.155,8
+I,0.48,0.355,0.11,0.4495,0.201,0.089,0.14,8
+F,0.56,0.44,0.135,0.8025,0.35,0.1615,0.259,9
+F,0.585,0.475,0.165,1.053,0.458,0.217,0.3,11
+F,0.585,0.455,0.17,0.9945,0.4255,0.263,0.2845,11
+M,0.385,0.255,0.1,0.3175,0.137,0.068,0.092,8
+I,0.39,0.31,0.085,0.344,0.181,0.0695,0.079,7
+I,0.39,0.29,0.1,0.2845,0.1255,0.0635,0.081,7
+I,0.405,0.3,0.085,0.3035,0.15,0.0505,0.088,7
+I,0.475,0.365,0.115,0.499,0.232,0.0885,0.156,10
+M,0.5,0.38,0.125,0.577,0.269,0.1265,0.1535,9
+F,0.515,0.4,0.125,0.615,0.2865,0.123,0.1765,8
+M,0.52,0.385,0.165,0.791,0.375,0.18,0.1815,10
+M,0.55,0.43,0.13,0.8395,0.3155,0.1955,0.2405,10
+M,0.56,0.43,0.155,0.8675,0.4,0.172,0.229,8
+F,0.565,0.45,0.165,0.887,0.37,0.239,0.249,11
+M,0.59,0.44,0.135,0.966,0.439,0.2145,0.2605,10
+M,0.6,0.475,0.205,1.176,0.5255,0.2875,0.308,9
+F,0.625,0.485,0.15,1.0945,0.531,0.261,0.296,10
+M,0.71,0.555,0.195,1.9485,0.9455,0.3765,0.495,12
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/abalone/abalone.data.txt
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/abalone/abalone.names.txt
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/abalone/abalone.names.txt (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/abalone/abalone.names.txt 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,145 @@
+1. Title of Database: Abalone data
+
+2. Sources:
+
+ (a) Original owners of database:
+ Marine Resources Division
+ Marine Research Laboratories - Taroona
+ Department of Primary Industry and Fisheries, Tasmania
+ GPO Box 619F, Hobart, Tasmania 7001, Australia
+ (contact: Warwick Nash +61 02 277277, wnash at dpi.tas.gov.au)
+
+ (b) Donor of database:
+ Sam Waugh (Sam.Waugh at cs.utas.edu.au)
+ Department of Computer Science, University of Tasmania
+ GPO Box 252C, Hobart, Tasmania 7001, Australia
+
+ (c) Date received: December 1995
+
+
+3. Past Usage:
+
+ Sam Waugh (1995) "Extending and benchmarking Cascade-Correlation", PhD
+ thesis, Computer Science Department, University of Tasmania.
+
+ -- Test set performance (final 1044 examples, first 3133 used for training):
+ 24.86% Cascade-Correlation (no hidden nodes)
+ 26.25% Cascade-Correlation (5 hidden nodes)
+ 21.5% C4.5
+ 0.0% Linear Discriminate Analysis
+ 3.57% k=5 Nearest Neighbour
+ (Problem encoded as a classification task)
+
+ -- Data set samples are highly overlapped. Further information is required
+ to separate completely using affine combinations. Other restrictions
+ to data set examined.
+
+ David Clark, Zoltan Schreter, Anthony Adams "A Quantitative Comparison of
+ Dystal and Backpropagation", submitted to the Australian Conference on
+ Neural Networks (ACNN'96). Data set treated as a 3-category classification
+ problem (grouping ring classes 1-8, 9 and 10, and 11 on).
+
+ -- Test set performance (3133 training, 1044 testing as above):
+ 64% Backprop
+ 55% Dystal
+ -- Previous work (Waugh, 1995) on same data set:
+ 61.40% Cascade-Correlation (no hidden nodes)
+ 65.61% Cascade-Correlation (5 hidden nodes)
+ 59.2% C4.5
+ 32.57% Linear Discriminate Analysis
+ 62.46% k=5 Nearest Neighbour
+
+
+4. Relevant Information Paragraph:
+
+ Predicting the age of abalone from physical measurements. The age of
+ abalone is determined by cutting the shell through the cone, staining it,
+ and counting the number of rings through a microscope -- a boring and
+ time-consuming task. Other measurements, which are easier to obtain, are
+ used to predict the age. Further information, such as weather patterns
+ and location (hence food availability) may be required to solve the problem.
+
+ From the original data examples with missing values were removed (the
+ majority having the predicted value missing), and the ranges of the
+ continuous values have been scaled for use with an ANN (by dividing by 200).
+
+ Data comes from an original (non-machine-learning) study:
+
+ Warwick J Nash, Tracy L Sellers, Simon R Talbot, Andrew J Cawthorn and
+ Wes B Ford (1994) "The Population Biology of Abalone (_Haliotis_
+ species) in Tasmania. I. Blacklip Abalone (_H. rubra_) from the North
+ Coast and Islands of Bass Strait", Sea Fisheries Division, Technical
+ Report No. 48 (ISSN 1034-3288)
+
+
+5. Number of Instances: 4177
+
+
+6. Number of Attributes: 8
+
+
+7. Attribute information:
+
+ Given is the attribute name, attribute type, the measurement unit and a
+ brief description. The number of rings is the value to predict: either
+ as a continuous value or as a classification problem.
+
+ Name Data Type Meas. Description
+ ---- --------- ----- -----------
+ Sex nominal M, F, and I (infant)
+ Length continuous mm Longest shell measurement
+ Diameter continuous mm perpendicular to length
+ Height continuous mm with meat in shell
+ Whole weight continuous grams whole abalone
+ Shucked weight continuous grams weight of meat
+ Viscera weight continuous grams gut weight (after bleeding)
+ Shell weight continuous grams after being dried
+ Rings integer +1.5 gives the age in years
+
+ Statistics for numeric domains:
+
+ Length Diam Height Whole Shucked Viscera Shell Rings
+ Min 0.075 0.055 0.000 0.002 0.001 0.001 0.002 1
+ Max 0.815 0.650 1.130 2.826 1.488 0.760 1.005 29
+ Mean 0.524 0.408 0.140 0.829 0.359 0.181 0.239 9.934
+ SD 0.120 0.099 0.042 0.490 0.222 0.110 0.139 3.224
+ Correl 0.557 0.575 0.557 0.540 0.421 0.504 0.628 1.0
+
+
+8. Missing Attribute Values: None
+
+
+9. Class Distribution:
+
+ Class Examples
+ ----- --------
+ 1 1
+ 2 1
+ 3 15
+ 4 57
+ 5 115
+ 6 259
+ 7 391
+ 8 568
+ 9 689
+ 10 634
+ 11 487
+ 12 267
+ 13 203
+ 14 126
+ 15 103
+ 16 67
+ 17 58
+ 18 42
+ 19 32
+ 20 26
+ 21 14
+ 22 6
+ 23 9
+ 24 2
+ 25 1
+ 26 1
+ 27 2
+ 29 1
+ ----- ----
+ Total 4177
Property changes on: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/abalone/abalone.names.txt
___________________________________________________________________
Name: svn:eol-style
+ native
Added: labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/advertisement/ad-dataset/ad.DOCUMENTATION
===================================================================
--- labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/advertisement/ad-dataset/ad.DOCUMENTATION (rev 0)
+++ labs/jbossrules/contrib/machinelearning/4.0.x/drools-examples/drools-examples-drl/src/main/java/org/drools/examples/learner/data/advertisement/ad-dataset/ad.DOCUMENTATION 2008-05-23 17:46:27 UTC (rev 20144)
@@ -0,0 +1,56 @@
+1. Title of Database: Internet advertisements
+
+2. Sources:
+ (a) Creator & donor: Nicholas Kushmerick <nick at ucd.ie>
+ (c) Generated: April-July 1998
+
+3. Past Usage:
+ N. Kushmerick (1999). "Learning to remove Internet advertisements",
+ 3rd Int Conf Autonomous Agents. Available at
+ www.cs.ucd.ie/staff/nick/research/download/kushmerick-aa99.ps.gz.
+ Accuracy >97% using C4.5rules in predicting whether an image is an
+ advertisement.
+
+4. This dataset represents a set of possible advertisements on
+ Internet pages. The features encode the geometry of the image (if
+ available) as well as phrases occuring in the URL, the image's URL and
+ alt text, the anchor text, and words occuring near the anchor text.
+ The task is to predict whether an image is an advertisement ("ad") or
+ not ("nonad").
+
+5. Number of Instances: 3279 (2821 nonads, 458 ads)
+
+6. Number of Attributes: 1558 (3 continous; others binary; this is the
+ "STANDARD encoding" mentioned in the [Kushmerick, 99].)
+ One or more of the three continous features are missing in 28%
+ of the instances; missing values should be interpreted as "unknown".
+
+7. See [Kushmerick, 99] for details of the attributes; in
+ ".names" format:
+
+ height: continuous. | possibly missing
+ width: continuous. | possibly missing
+ aratio: continuous. | possibly missing
+ local: 0,1.
+ | 457 features from url terms, each of the form "url*term1+term2..."