Name 

Description 

 

OnIndexNextRecord 

Called by LearnData method and K-NN to browse through the learn dataset.  

 

OnNameNextRecord 

Called by LearnData method and K-NN to browse through the learn data set.  

 

OnPopPosition 

Called by LearnData method and K-NN to restore the current position of the dataset.  

 

OnPushPosition 

Called by LearnData method and K-NN to save the current position of the dataset.  

 

Name 

Description 

 

AssignAttributeState 

Copies the attribute Enabled fields from Source.  

 

AttributeQuality 

Returns an array of attribute indexes sorted by quality.  

 

ClassesName 

Get the name of the class at Index.  

 

Classify 

Determine the most probable class to which the example with values of discrete attributes in DiscreteRecord and values of real valued attributes in FloatRecord belongs to.  

 

ClassifyResponse 

Determine the response of all classe for the example with values of discrete attributes in DiscreteRecord and values of real valued attributes in FloatRecord.  

 

ClassifyTest 

Performs the classification on the test data, by calling the OnClassifyTest event.  

 

ClassIndex 

Get the index of the class with Name.  

 

Clear 

Clear all learned data and all class descriptions.  

 

Create 

Create the component.  

 

DisableAttributes 

Disable real valued and discrete attributes for all classes.  

 

DisableDiscreteAttributes 

Disable discrete attributes for all classes.  

 

DisableFloatAttributes 

Disable real valued attributes for all classes.  

 

EnableAttributes 

Enable real valued and discrete attributes for all classes.  

 

EnableDiscreteAttributes 

Enable discrete attributes for all classes.  

 

EnableFloatAttributes 

Enable real valued attributes for all classes.  

 

Entropy 

Returns the average information entropy.  

 

Learn 

Learn a new record belonging to class with ClassName.  

 

LearnData 

Call this method to perform the learn operation on the learn data.  

 

LearnIndex 

Learn a new record belonging to class with ClassIndex.  

 

LoadFromFile 

Load the component from file named FileName.  

 

LoadFromStream 

Load the component from stream.  

 

MaxDiscreteEntropy 

Returns the maximum entropy of the discrete attribute at AttributeIndex.  

 

MaxEntropy 

Returns the maximum entropy found to be of the attribute at Index.  

 

MaxFloatEntropy 

Returns the maximum entropy of the real valued attribute at AttributeIndex.  

 

MaxPriorProbability 

Returns the index of the class, which has the highest count of examples and thus the highest prior probability, with ClassIndex.  

 

MissingFloatNumber 

Returns numerical representation of MissingFloatValue.  

 

PostPrune 

Pruning disables some attributes, to improve classification accuracy.  

 

PrePrune 

Pruning disables some attributes, to improve classificaiton accuracy.  

 

Prune 

Performs pre-pruning and post-pruning and enables only those attributes giving best classification accuracy towards the test dataset.  

 

Reset 

Reset all learned data including attribute weights and attribute Enabled fields, but keep the class descriptions.  

 

SaveToFile 

Save the component to file named FileName.  

 

SaveToStream 

Save the component to stream.  

 

SoftReset 

Reset all learned data except attribute weights and attribute Enabled fields and keep the class descriptions.  

 

SortIndexRecord 

Sort an array of TIndexRecords.  

 

Name 

Description 

 

AttributeEnabled 

Set the value at index to True/False, to enable/disable the corresponding attribute.  

 

Classes 

Pointer to list of classes with attribute descriptions and stored learned data.  

 

OnFirstRecord 

Called by LearnData method and K-NN to request the positioning to the first record.  

 

OnLastRecord 

Called by LearnData method and K-NN when the last record is fetched.  

 

RejectProbability 

If the response of all classes is below RejectProbability the example will not be classified to any of the known classes.  

 

Name 

Description 

 

OnClassifyTest 

Called by PrePrune, PostPrune and Prune methods and ClassifyTest methods.  

 

Name 

Description 

 

AttributeCount 

Total number of attributes.  

 

DiscreteAttributeCount 

Total number of discrete valued attributes or fields holding discreting values in the dataset.  

 

FloatAttributeCount 

Total number of real valued attributes or fields holding real values in the dataset.  

 

IndexesZeroBased 

Set to True, if Class indexes are zero based.  

 

MissingDiscreteIndex 

Specifies the value indicating a "missing value" (no entry) for discrete attributes in the dataset.  

 

MissingFloatValue 

Specifies the value indicating a "missing value" (no entry) for real valued attributes in the dataset.  

 

NormalizeAttributeCount 

If true, the number of attributes compared will be normalized between comparisons.  

 

UsePriorProbability 

Set this property to True, if you want the linear classifier to rely also on prior probability of individual classes when performing classification.