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Provides common types and algorithm implementations for the fast prototyping integrative AI systems with the AIDDL framework.

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;; This example problem was taken from:
;; Machine Learning by T. Mitchell., McGraw-Hill, New York, (1997)
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(#mod self org.aiddl.test.learning.classification.problem-01)

(#req EVAL org.aiddl.eval.namespace)
(#nms nms-types type-ops@EVAL)

(#req T org.aiddl.common.domain)

(#req M  org.aiddl.common.learning.supervised)
(#req DT org.aiddl.common.learning.supervised.decision-tree)

(#type OutlookDomain (enum { Sunny Overcast Rain }))
(#type TemperatureDomain (enum { Hot Mild Cool }))
(#type HumidityDomain (enum { High Normal }))
(#type WindDomain (enum { Weak Strong }))
(#type PlayTennisDomain (enum { Yes No }))


( (ClassificationProblem@M [^$OutlookDomain ^$TemperatureDomain ^$HumidityDomain
                             ^$WindDomain    ^$PlayTennisDomain])
  problem 
  (
    attributes : [Outlook Temperature Humidity Wind PlayTennis]
    types : [^$OutlookDomain ^$TemperatureDomain ^$HumidityDomain ^$WindDomain ^$PlayTennisDomain]
    label :       PlayTennis
    data : [
			[Sunny       Hot    High    Weak    No ]
			[Sunny       Hot    High    Strong  No ]
			[Overcast    Hot    High    Weak    Yes]
			[Rain        Mild   High    Weak    Yes]
			[Rain        Cool   Normal  Weak    Yes]
			[Rain        Cool   Normal  Strong  No ]
			[Overcast    Cool   Normal  Strong  Yes]
			[Sunny       Mild   High    Weak    No ]
			[Sunny       Cool   Normal  Weak    Yes]
			[Rain        Mild   Normal  Weak    Yes]
			[Sunny       Mild   Normal  Strong  Yes]
			[Overcast    Mild   High    Strong  Yes]
			[Overcast    Hot    Normal  Weak    Yes]
			[Rain        Mild   High    Strong  No ]
    ]))