| Modifier and Type | Method and Description |
|---|---|
Transducer.TransitionIterator |
CRF4.State.transitionIterator(FeatureVector fv,
String output) |
| Constructor and Description |
|---|
TransitionIterator(CRF_PL.State source,
FeatureVector fv,
String output,
CRF4 memm) |
TransitionIterator(CRF4.State source,
FeatureVector fv,
String output,
CRF4 crf) |
TransitionIterator(MEMM.State source,
FeatureVector fv,
String output,
CRF4 memm) |
| Modifier and Type | Class and Description |
|---|---|
class |
AugmentableFeatureVector |
class |
ExpGain |
class |
FeatureCounts |
class |
GainRatio
List of features along with their thresholds sorted in descending order of
the ratio of (1) information gained by splitting instances on the
feature at its associated threshold value, to (2) the split information.
|
class |
GradientGain |
class |
InfoGain |
class |
KLGain |
class |
LabelVector |
class |
Multinomial
A probability distribution over a set of features represented as a
FeatureVector. |
static class |
Multinomial.Logged
A Multinomial in which the values associated with each feature index fi is
Math.log(probability[fi]) instead of probability[fi].
|
class |
PartiallyRankedFeatureVector |
class |
RankedFeatureVector |
| Modifier and Type | Method and Description |
|---|---|
FeatureVector |
FeatureVectorSequence.getFeatureVector(int i) |
FeatureVector |
Multinomial.randomFeatureVector(Random r,
int size) |
FeatureVector |
Dirichlet.randomFeatureVector(Random r,
int size) |
FeatureVector |
AugmentableFeatureVector.toFeatureVector() |
FeatureVector |
TokenSequence.toFeatureVector(Alphabet dict) |
FeatureVector |
Token.toFeatureVector(Alphabet dict,
boolean binary) |
FeatureVector |
PropertyHolder.toFeatureVector(Alphabet dict,
boolean binary) |
| Modifier and Type | Method and Description |
|---|---|
void |
AugmentableFeatureVector.add(FeatureVector fv)
Adds all indices that are present in some other feature vector
with value 1.0.
|
void |
AugmentableFeatureVector.add(FeatureVector fv,
String prefix)
Adds all features from some other feature vector with weight 1.0.
|
void |
AugmentableFeatureVector.add(FeatureVector fv,
String prefix,
boolean binary)
Adds all features from some other feature vector with weight 1.0.
|
void |
Multinomial.Estimator.increment(FeatureVector fv) |
void |
Multinomial.Estimator.increment(FeatureVector fv,
double scale) |
boolean |
FeatureConjunction.satisfiedBy(FeatureVector fv) |
| Constructor and Description |
|---|
AugmentableFeatureVector(FeatureVector fv) |
DenseFeatureVector(FeatureVector sfv,
int numColumns) |
FeatureVector(FeatureVector fv,
Alphabet newVocab,
FeatureSelection fsNarrow,
FeatureSelection fsWide) |
FeatureVector(FeatureVector fv,
Alphabet newVocab,
int[] conjunctions)
New feature vector containing all the features of "fv", plus new
features created by making conjunctions between the features in
"conjunctions" and all the other features.
|
FeatureVectorSequence(FeatureVector[] featureVectors) |
StringEditFeatureVectorSequence(FeatureVector[] featureVectors,
String s1,
String s2) |
StringEditFeatureVectorSequence(FeatureVector[] featureVectors,
String s1,
String s2,
char delimiter) |
StringEditFeatureVectorSequence(FeatureVector[] featureVectors,
String s1,
String s2,
char delimiter,
HashMap lexic) |
StringEditFeatureVectorSequence(FeatureVector[] featureVectors,
String s1,
String s2,
HashMap lexic) |
| Modifier and Type | Method and Description |
|---|---|
void |
ClusterLearner.getUnNormalizedScores(Matrix2 lambdas,
FeatureVector fv,
double[] scores) |
| Modifier and Type | Method and Description |
|---|---|
void |
CorefClusterAdv.getUnNormalizedScores(Matrix2 lambdas,
FeatureVector fv,
double[] scores) |
String |
CorefClusterAdv.printParamDetails(FeatureVector vec,
Classification classification,
MaxEnt classifier) |
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