CRFParameters (Tribuo 4.0.2 API)

java.lang.Object
- org.tribuo.classification.sgd.crf.CRFParameters

All Implemented Interfaces:

Serializable, Parameters
```
public class CRFParameters
extends Object
implements Parameters, Serializable
```
A Parameters for training a CRF using SGD.

See Also:

Serialized Form

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`Tensor[]`	`get()` Get a reference to the underlying `Tensor` array.
`double`	`getBias(int id)`
`ChainHelper.ChainCliqueValues`	`getCliqueValues(SparseVector[] features)` Generates the local scores and tuples them with the label - label transition weights.
`Tensor[]`	`getEmptyCopy()` Returns a 3 element `Tensor` array.
`DenseVector`	`getFeatureWeights(int id)`
`DenseVector[]`	`getLocalScores(SparseVector[] features)` Generate the local scores (i.e., the linear classifier for each token).
`double`	`getWeight(int labelID, int featureID)`
`Tensor[]`	`merge(Tensor[][] gradients, int size)` Merge together an array of gradient arrays.
`int[]`	`predict(SparseVector[] features)` Generate a prediction using Viterbi.
`List<Double>`	`predictConfidenceUsingCBP(SparseVector[] features, List<Chunk> chunks)` This predicts per chunk confidence using the constrained forward backward algorithm from Culotta and McCallum 2004.
`DenseVector[]`	`predictMarginals(SparseVector[] features)` Generate a prediction using Belief Propagation.
`void`	`set(Tensor[] newWeights)` Set the underlying `Tensor` array to newWeights.
`void`	`update(Tensor[] gradients)` Apply gradients to the parameters.
`com.oracle.labs.mlrg.olcut.util.Pair<Double,Tensor[]>`	`valueAndGradient(SparseVector[] features, int[] labels)` Generates predictions based on the input features and labels, then scores those predictions to produce a loss for the example and a gradient update.

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

- Method Detail
  - getFeatureWeights
```
public DenseVector getFeatureWeights(int id)
```
  - getBias
```
public double getBias(int id)
```
  - getWeight
```
public double getWeight(int labelID,
                        int featureID)
```
  - getLocalScores
```
public DenseVector[] getLocalScores(SparseVector[] features)
```
    Generate the local scores (i.e., the linear classifier for each token).
    
    Parameters:
    
    features - An array of SparseVectors, one per token.
    
    Returns:
    
    An array of DenseVectors representing the scores per label for each token.
  - getCliqueValues
```
public ChainHelper.ChainCliqueValues getCliqueValues(SparseVector[] features)
```
    Generates the local scores and tuples them with the label - label transition weights.
    
    Parameters:
    
    features - The per token SparseVector of features.
    
    Returns:
    
    A tuple containing the array of DenseVector scores and the label - label transition weights.
  - predict
```
public int[] predict(SparseVector[] features)
```
    Generate a prediction using Viterbi.
    
    Parameters:
    
    features - The per token SparseVector of features.
    
    Returns:
    
    An int array giving the predicted label per token.
  - predictMarginals
```
public DenseVector[] predictMarginals(SparseVector[] features)
```
    Generate a prediction using Belief Propagation.
    
    Parameters:
    
    features - The per token SparseVector of features.
    
    Returns:
    
    A DenseVector per token containing the marginal distribution over labels.
  - predictConfidenceUsingCBP
```
public List<Double> predictConfidenceUsingCBP(SparseVector[] features,
                                              List<Chunk> chunks)
```
    This predicts per chunk confidence using the constrained forward backward algorithm from Culotta and McCallum 2004.
    Runs one pass of BP to get the normalizing constant, and then a further chunks.size() passes of constrained forward backward.
    
    Parameters:
    
    features - The per token SparseVector of features.
    
    chunks - A list of extracted chunks to pin constrain the labels to.
    
    Returns:
    
    A list containing the confidence value for each chunk.
  - valueAndGradient
```
public com.oracle.labs.mlrg.olcut.util.Pair<Double,Tensor[]> valueAndGradient(SparseVector[] features,
                                                                              int[] labels)
```
    Generates predictions based on the input features and labels, then scores those predictions to produce a loss for the example and a gradient update.
    
    Parameters:
    
    features - The per token SparseVector of features.
    
    labels - The per token ground truth labels.
    
    Returns:
    
    A Pair containing the loss for this example and the associated gradient.
  - getEmptyCopy
```
public Tensor[] getEmptyCopy()
```
    Returns a 3 element Tensor array. The first element is a DenseVector of label biases. The second element is a DenseMatrix of feature-label weights. The third element is a DenseMatrix of label-label transition weights.
    
    Specified by:
    
    getEmptyCopy in interface Parameters
    
    Returns:
    
    A Tensor array.
  - get
```
public Tensor[] get()
```
    Description copied from interface: Parameters
    
    Get a reference to the underlying Tensor array.
    
    Specified by:
    
    get in interface Parameters
    
    Returns:
    
    The parameters.
  - set
```
public void set(Tensor[] newWeights)
```
    Description copied from interface: Parameters
    
    Set the underlying Tensor array to newWeights.
    
    Specified by:
    
    set in interface Parameters
    
    Parameters:
    
    newWeights - New parameters to store in this object.
  - update
```
public void update(Tensor[] gradients)
```
    Description copied from interface: Parameters
    
    Apply gradients to the parameters. Assumes that gradients is the same length as the parameters, and each Tensor is the same size as the corresponding one from the parameters.
    The gradients are added to the parameters.
    
    Specified by:
    
    update in interface Parameters
    
    Parameters:
    
    gradients - A Tensor array of updates, with the length equal to Parameters.get().length.
  - merge
```
public Tensor[] merge(Tensor[][] gradients,
                      int size)
```
    Description copied from interface: Parameters
    
    Merge together an array of gradient arrays. Assumes the first dimension is the number of gradient arrays and the second dimension is the number of parameter Tensors.
    
    Specified by:
    
    merge in interface Parameters
    
    Parameters:
    
    gradients - An array of gradient update arrays.
    
    size - The number of elements of gradients to merge. Allows gradients to have unused elements.
    
    Returns:
    
    A single Tensor array of the summed gradients.

Class CRFParameters

Method Summary

Methods inherited from class java.lang.Object

Method Detail

getFeatureWeights

getBias

getWeight

getLocalScores

getCliqueValues

predict

predictMarginals

predictConfidenceUsingCBP

valueAndGradient

getEmptyCopy

get

set

update

merge