DyadRankingFeatureTransformNegativeLogLikelihood (jaicore-ml 0.1.6 API)

java.lang.Object
- ai.libs.jaicore.ml.dyadranking.optimizing.DyadRankingFeatureTransformNegativeLogLikelihood

All Implemented Interfaces:

IGradientDescendableFunction, IDyadRankingFeatureTransformPLGradientDescendableFunction
```
public class DyadRankingFeatureTransformNegativeLogLikelihood
extends java.lang.Object
implements IDyadRankingFeatureTransformPLGradientDescendableFunction
```
Implements the negative log-likelihood function for the feature transformation Placket-Luce dyad ranker. In particular, this implmentation is the NLL of [1] (we adhere their notation here). This NLL is a convex function, which we can optimize using an IOptimizationAlgorithm, together with the DyadRankingFeatureTransformNegativeLogLikelihoodDerivative. [1] SchÃ¤fer, D. & HÃ¼llermeier, Dyad ranking using Plackettâ€“Luce models based on joint feature representations, https://link.springer.com/article/10.1007%2Fs10994-017-5694-9

Constructor Summary

Constructors
Constructor Description

DyadRankingFeatureTransformNegativeLogLikelihood()

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`double`	`apply(ai.libs.jaicore.math.linearalgebra.Vector w)`	Algorithm (18) of [1].
`void`	`initialize(DyadRankingDataset dataset, java.util.Map<IDyadRankingInstance,java.util.Map<Dyad,ai.libs.jaicore.math.linearalgebra.Vector>> featureTransforms)`	Initializes the function with the given dataset.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - DyadRankingFeatureTransformNegativeLogLikelihood
```
public DyadRankingFeatureTransformNegativeLogLikelihood()
```
- Method Detail
  - initialize
```
public void initialize(DyadRankingDataset dataset,
                       java.util.Map<IDyadRankingInstance,java.util.Map<Dyad,ai.libs.jaicore.math.linearalgebra.Vector>> featureTransforms)
```
    Description copied from interface: IDyadRankingFeatureTransformPLGradientDescendableFunction
    
    Initializes the function with the given dataset.
    
    Specified by:
    
    initialize in interface IDyadRankingFeatureTransformPLGradientDescendableFunction
    
    Parameters:
    
    dataset - the dataset to use
    
    featureTransforms - the feature precomputed feature transforms
  - apply
```
public double apply(ai.libs.jaicore.math.linearalgebra.Vector w)
```
    Algorithm (18) of [1]. We adhere their notations, but, unify the sums.
    
    Specified by:
    
    apply in interface IGradientDescendableFunction
    
    Parameters:
    
    w - the point to which to apply the function
    
    Returns:
    
    the function value at the applied point