IDyadFeatureTransform (jaicore-ml 0.1.6 API)

All Known Implementing Classes:

BiliniearFeatureTransform
```
public interface IDyadFeatureTransform
```
Feature transformation interface for the FeatureTransformPLDyadRanker.

Method Summary

All Methods Instance Methods Abstract Methods Default Methods
Modifier and Type	Method	Description
`default java.util.Map<IDyadRankingInstance,java.util.Map<Dyad,ai.libs.jaicore.math.linearalgebra.Vector>>`	`getPreComputedFeatureTransforms(DyadRankingDataset dataset)`	Precomputed the feature transforms for the dataset, this can speed up the runtime as the feature transform will be reduced to O(1) at the cost of O(n).
`int`	`getTransformedVectorLength(int alternativeLength, int instanceLength)`	Get the length of the vector returned by the transform method.
`ai.libs.jaicore.math.linearalgebra.Vector`	`transform(Dyad dyad)`	Transform the instance of the given dyad (models the skill).

- Method Detail
  - transform
```
ai.libs.jaicore.math.linearalgebra.Vector transform(Dyad dyad)
```
    Transform the instance of the given dyad (models the skill).
    
    Parameters:
    
    dyad - the dyad to transform
    
    Returns:
    
    the transformed instance values for the dyad
  - getTransformedVectorLength
```
int getTransformedVectorLength(int alternativeLength,
                               int instanceLength)
```
    Get the length of the vector returned by the transform method.
    
    Parameters:
    
    alternativeLength - the length of the alternative vector of the transformed dyad
    
    instanceLength - the length of the instance vector of the transformed dyad
    
    Returns:
    
    the length of the transformed feature vector
  - getPreComputedFeatureTransforms
```
default java.util.Map<IDyadRankingInstance,java.util.Map<Dyad,ai.libs.jaicore.math.linearalgebra.Vector>> getPreComputedFeatureTransforms(DyadRankingDataset dataset)
```
    Precomputed the feature transforms for the dataset, this can speed up the runtime as the feature transform will be reduced to O(1) at the cost of O(n).
    
    Parameters:
    
    dataset -
    
    Returns:
    
    the feature transform