type TClassesListItem = class(TPersistent);
Abstract class for items of a list of clasification classes.
This is an abstract class defined to manage data for a classification class. Each classification class holds a list of attributes (parameters) and the related statistics. The statistics is obtained during the learn run. This statistics is then used when comparing each class to the new (to be classified) example and computing the response of each class. The classification class with the strongest response wins.
Methods
| Name | Description |
|---|---|
| Classify | Classify record with mixed real and discrete attributes. |
| ClassifyDiscrete | Classify record with discrete attributes. |
| ClassifyFloat | Classify record with real valued attributes. |
| Create | Create the component and pass a TClassesList object as an owner. |
| Learn | Learn a new record containing both discrete and real attributes. |
| LearnDiscrete | Learn the values of discrete attributes of the new example. |
| LearnFloat | Learn the values of continuous (real valued) attributes of the new example. |
| LoadFromStream | Load the object from the stream. |
| PriorProbability | Compute prior probability after the learning has been completed. |
| Reset | Reset learned data. |
| ResetWeights | Reset the Weight field of every attribute back to one. |
| SaveToStream | Save the object to stream. |
| SoftReset | Resets learned data without reseting weights and excluded attributes. |
Constants
| Name | Type | Description |
|---|---|---|
| DiscreteA | TDiscreteRecordArray | Stores the statistics about the discrete attributes. |
| FloatA | TFloatRecordArray | Stores the statistics about the continuous (real valued) attributes. |