type TClassifier = class(TComponent);
Abstract class for encapsulation of different classification algorithms.
Derive new classes from TClassifier to implement an algirthm of your choice.
Properties
| Name | Type | Description |
|---|---|---|
| AttributeEnabled | boolean | Set the value at index to True/False, to enable/disable the corresponding attribute. |
| OnFirstRecord | TNotifyEvent | Called by LearnData method and K-NN to request the positioning to the first record. |
| OnIndexNextRecord | TIndexNextRecord | Called by LearnData method and K-NN to browse through the learn dataset. |
| OnLastRecord | TNotifyEvent | Called by LearnData method and K-NN when the last record is fetched. |
| OnNameNextRecord | TNameNextRecord | Called by LearnData method and K-NN to browse through the learn data set. |
| OnPopPosition | TPopBookmark | Called by LearnData method and K-NN to restore the current position of the dataset. |
| OnPushPosition | TPushBookmark | Called by LearnData method and K-NN to save the current position of the dataset. |
| RejectProbability | double | If the response of all classes is below RejectProbability the example will not be classified to any of the known classes. |
Methods
| 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. |
| Destroy | Destroy 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. |
| 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. |
| 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. |