A B C D E F G H I J K L M N O P Q R S T U V W X

A

absoluteDifference(GenericCounter<E>, GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
Returns |c1 - c2|.
absolutelyDiscountedDistribution(GenericCounter<E>, int, double) - Static method in class edu.stanford.nlp.stats.Distribution
 
AbstractCachingDiffFunction - Class in edu.stanford.nlp.optimization
 
AbstractCachingDiffFunction() - Constructor for class edu.stanford.nlp.optimization.AbstractCachingDiffFunction
 
AbstractLinearClassifierFactory - Class in edu.stanford.nlp.classify
Shared methods for training a LinearClassifier.
AbstractLinearClassifierFactory() - Constructor for class edu.stanford.nlp.classify.AbstractLinearClassifierFactory
 
AbstractMapLabel - Class in edu.stanford.nlp.ling
An abstract class for Label objects which store attributes in a Map.
AbstractMapLabel() - Constructor for class edu.stanford.nlp.ling.AbstractMapLabel
 
AbstractMapLabel(MapFactory) - Constructor for class edu.stanford.nlp.ling.AbstractMapLabel
 
AbstractStochasticCachingDiffFunction - Class in edu.stanford.nlp.optimization
 
AbstractStochasticCachingDiffFunction() - Constructor for class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
AbstractStochasticCachingDiffFunction.SamplingMethod - Enum in edu.stanford.nlp.optimization
 
accept(T) - Method in interface edu.stanford.nlp.util.Filter
Checks if the given object passes the filter.
accuracy(Iterator) - Method in class edu.stanford.nlp.classify.NaiveBayesClassifier
 
accuracy() - Method in class edu.stanford.nlp.classify.PRCurve
 
AccuracyStats - Class in edu.stanford.nlp.stats
Utility class for aggregating counts of true positives, false positives, and false negatives and computing precision/recall/F1 stats.
AccuracyStats(ProbabilisticClassifier, GeneralDataset, Object) - Constructor for class edu.stanford.nlp.stats.AccuracyStats
 
AccuracyStats(Object, String) - Constructor for class edu.stanford.nlp.stats.AccuracyStats
 
AdaptedGaussianPriorObjectiveFunction - Class in edu.stanford.nlp.classify
Adapt the mean of the Gaussian Prior by shifting the mean to the previously trained weights
AdaptedGaussianPriorObjectiveFunction(GeneralDataset, LogPrior, double[][]) - Constructor for class edu.stanford.nlp.classify.AdaptedGaussianPriorObjectiveFunction
 
adaptFile - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
NER adapation (Gaussian prior) parameters.
adaptSigma - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
adaptWeights(Dataset, LinearClassifierFactory) - Method in class edu.stanford.nlp.classify.LinearClassifier
 
adaptWeights(double[][], GeneralDataset) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Adapt classifier (adjust the mean of Gaussian prior) under construction -pichuan
add(Datum) - Method in class edu.stanford.nlp.classify.Dataset
 
add(Collection, Object) - Method in class edu.stanford.nlp.classify.Dataset
 
add(Datum) - Method in class edu.stanford.nlp.classify.GeneralDataset
 
add(Datum) - Method in class edu.stanford.nlp.classify.RVFDataset
 
add(Datum, String, String) - Method in class edu.stanford.nlp.classify.RVFDataset
 
add(Datum) - Method in class edu.stanford.nlp.classify.WeightedDataset
 
add(Collection, Object) - Method in class edu.stanford.nlp.classify.WeightedDataset
 
add(Datum, float) - Method in class edu.stanford.nlp.classify.WeightedDataset
 
add(Collection, Object, float) - Method in class edu.stanford.nlp.classify.WeightedDataset
 
add(Array) - Method in interface edu.stanford.nlp.linalg.Array
Returns this + addend does not change receiver.
add(double[], double) - Static method in class edu.stanford.nlp.math.ArrayMath
 
add(float[], double) - Static method in class edu.stanford.nlp.math.ArrayMath
 
add(E) - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
Adds an object to the queue with the minimum priority (Double.NEGATIVE_INFINITY).
add(E, double) - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
Convenience method for if you want to pretend relaxPriority doesn't exist, or if you really want add's return conditions.
add(E, double) - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
Adds a key to the queue with the given priority.
add(E) - Method in class edu.stanford.nlp.util.Index
Adds an object to the Index.
add(int) - Method in class edu.stanford.nlp.util.IntUni
 
add(E, double) - Method in interface edu.stanford.nlp.util.PriorityQueue
Convenience method for if you want to pretend relaxPriority doesn't exist, or if you really want add's return conditions.
addAll(Collection<Datum>) - Method in class edu.stanford.nlp.classify.GeneralDataset
Adds all Datums in the given collection of data to this dataset
addAll(GenericCounter<E>) - Method in class edu.stanford.nlp.stats.Counter
Adds the counts in the given Counter to the counts in this Counter.
addAll(Collection<E>) - Method in class edu.stanford.nlp.stats.Counter
Calls incrementCount(key) on each key in the given collection.
addAll(IntCounter<E>) - Method in class edu.stanford.nlp.stats.IntCounter
Adds the counts in the given Counter to the counts in this Counter.
addAll(TwoDimensionalCounter<K1, K2>) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
addAll(K1, Counter<K2>) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
addAll(Collection<? extends E>) - Method in class edu.stanford.nlp.util.Index
Adds every member of Collection to the Index.
addFeatures(Collection) - Method in class edu.stanford.nlp.classify.Dataset
 
addInPlace(double[], double) - Static method in class edu.stanford.nlp.math.ArrayMath
Shifts the values in this array by b.
addInPlace(float[], double) - Static method in class edu.stanford.nlp.math.ArrayMath
Shifts the values in this array by b.
addKnownLowerCaseWords(Collection) - Static method in class edu.stanford.nlp.process.WordShapeClassifier
 
addLabel(Object) - Method in class edu.stanford.nlp.classify.Dataset
 
addLabel(Object) - Method in class edu.stanford.nlp.ling.BasicDatum
Adds the given Label to the List of labels for this Datum if it is not null.
addMultiple(GenericCounter<E>, double) - Method in class edu.stanford.nlp.stats.Counter
Adds the counts in the given Counter to the counts in this Counter.
addToKeySet(E) - Method in class edu.stanford.nlp.stats.Distribution
Insures that object is in keyset (with possibly zero value)
ADMath - Class in edu.stanford.nlp.math
The class ADMath was created to extend the current calculations of gradient to automatically include a calculation of the hessian vector product with another vector v.
ADMath() - Constructor for class edu.stanford.nlp.math.ADMath
 
after() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Return the String after the word, which is stored in the map under the key AFTER_KEY.
after() - Method in class edu.stanford.nlp.ling.FeatureLabel
Return the String after the word, which is stored in the map under the key AFTER_KEY.
after() - Method in interface edu.stanford.nlp.ling.HasContext
Return the String after the word.
AFTER_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
the standard key for the String that comes after this word (from the InvertiblePTBTokenizer)
allIndices - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
altAnswerFile - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
annealingRate - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
annealingType - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
answer() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
convenience method for getting answer *
answer() - Method in class edu.stanford.nlp.ling.FeatureLabel
convenience method for getting answer *
ANSWER_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for the answer which is a String
answerFile - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
appendAfter(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Append this String to the current after String
appendAfter(String) - Method in class edu.stanford.nlp.ling.FeatureLabel
Append this String to the current after String
appendAfter(String) - Method in interface edu.stanford.nlp.ling.HasContext
Append this String to the current after String
apply(T1) - Method in interface edu.stanford.nlp.process.Function
Converts a T1 to a different T2.
applyFeatureCountThreshold(List<Pair<Pattern, Integer>>) - Method in class edu.stanford.nlp.classify.Dataset
Applies feature count thresholds to the Dataset.
applyFeatureCountThreshold(int) - Method in class edu.stanford.nlp.classify.GeneralDataset
Applies a feature count threshold to the Dataset.
ARG_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for a propbank label which is of type Argument
argmax(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
argmax(float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
argmax(int[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
argmax(Comparator<E>) - Method in class edu.stanford.nlp.stats.Counter
Finds and returns the key in this Counter with the largest count.
argmax() - Method in class edu.stanford.nlp.stats.Counter
Finds and returns the key in this Counter with the largest count.
argmax() - Method in class edu.stanford.nlp.stats.Distribution
 
argmax(Comparator) - Method in class edu.stanford.nlp.stats.IntCounter
Finds and returns the key in this Counter with the largest count.
argmax() - Method in class edu.stanford.nlp.stats.IntCounter
Finds and returns the key in this Counter with the largest count.
argmax_tieLast(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
argmin(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
argmin(float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
argmin(int[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
argmin(Comparator<E>) - Method in class edu.stanford.nlp.stats.Counter
Finds and returns the key in this Counter with the smallest count.
argmin() - Method in class edu.stanford.nlp.stats.Counter
Finds and returns the key in this Counter with the smallest count.
argmin(Comparator) - Method in class edu.stanford.nlp.stats.IntCounter
Finds and returns the key in this Counter with the smallest count.
argmin() - Method in class edu.stanford.nlp.stats.IntCounter
Finds and returns the key in this Counter with the smallest count.
argsToMap(String[]) - Static method in class edu.stanford.nlp.util.StringUtils
Parses command line arguments into a Map.
argsToMap(String[], Map<String, Integer>) - Static method in class edu.stanford.nlp.util.StringUtils
Parses command line arguments into a Map.
argsToProperties(String[]) - Static method in class edu.stanford.nlp.util.StringUtils
In this version each flag has zero or one argument.
argsToProperties(String[], Map<String, Integer>) - Static method in class edu.stanford.nlp.util.StringUtils
Analagous to StringUtils.argsToMap(java.lang.String[]).
Array - Interface in edu.stanford.nlp.linalg
Interface for dense or sparse arrays which store numbers.
ARRAY_LIST_FACTORY - Static variable in class edu.stanford.nlp.util.CollectionFactory
 
ARRAY_MAP_FACTORY - Static variable in class edu.stanford.nlp.util.MapFactory
 
arrayListFactory() - Static method in class edu.stanford.nlp.util.CollectionFactory
 
ArrayMap<K,V> - Class in edu.stanford.nlp.util
ArrayMap: A map that is backed by an Array
ArrayMap() - Constructor for class edu.stanford.nlp.util.ArrayMap
 
ArrayMap(int) - Constructor for class edu.stanford.nlp.util.ArrayMap
 
ArrayMap(Map<? extends K, ? extends V>) - Constructor for class edu.stanford.nlp.util.ArrayMap
 
ArrayMap(K[], V[]) - Constructor for class edu.stanford.nlp.util.ArrayMap
 
ArrayMath - Class in edu.stanford.nlp.math
Class ArrayMath
ArrayMath() - Constructor for class edu.stanford.nlp.math.ArrayMath
 
arrayToFile(double[], String) - Method in class edu.stanford.nlp.optimization.StochasticDiffFunctionTester
 
ArrayUtils - Class in edu.stanford.nlp.util
Static utility methods for operating on arrays.
ArrayUtils() - Constructor for class edu.stanford.nlp.util.ArrayUtils
Should not be instantiated
asBinaryHeapPriorityQueue() - Method in class edu.stanford.nlp.stats.Counter
Builds a priority queue whose elements are the counter's elements, and whose priorities are those elements' counts in the counter.
ASCENDING_COMPARATOR - Static variable in class edu.stanford.nlp.util.ScoredComparator
 
asCounter(FixedPrioritiesPriorityQueue<E>) - Static method in class edu.stanford.nlp.stats.Counters
Returns a counter whose keys are the elements in this priority queue, and whose counts are the priorities in this queue.
asFeatures() - Method in class edu.stanford.nlp.ling.BasicDatum
Returns the collection that this BasicDatum was constructed with.
asFeatures() - Method in interface edu.stanford.nlp.ling.Featurizable
returns Object as a Collection of its features
asFeatures() - Method in class edu.stanford.nlp.ling.RVFDatum
Returns the list of features without values
asFeaturesCounter() - Method in class edu.stanford.nlp.ling.RVFDatum
Returns the Counter of features and values
asList(int[]) - Static method in class edu.stanford.nlp.util.ArrayUtils
needed because Arrays.asList() won't to autoboxing, so if you give it a primitive array you get a singleton list back with just that array as an element.
asList(int[]) - Static method in class edu.stanford.nlp.util.CollectionUtils
 
asList(double[]) - Static method in class edu.stanford.nlp.util.CollectionUtils
 
asList(Object...) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns a new List containing the specified objects.
asPriorityQueue() - Method in class edu.stanford.nlp.stats.Counter
Builds a priority queue whose elements are the counter's elements, and whose priorities are those elements' counts in the counter.
asSet(T[]) - Static method in class edu.stanford.nlp.util.ArrayUtils
Return a set containing the same elements as the specified array.
asSet(Object[]) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns a new Set containing all the objects in the specified array.
average(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
average(GenericCounter<E>, GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
Returns a new Counter with counts averaged from the two given Counters.
averageCount() - Method in class edu.stanford.nlp.stats.Counter
Returns the mean of all the counts (totalCount/size).
averageCount() - Method in class edu.stanford.nlp.stats.IntCounter
Returns the mean of all the counts (totalCount/size).

B

backgroundSymbol - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
baseTestDir - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
baseTrainDir - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
BasicDatum - Class in edu.stanford.nlp.ling
Basic implementation of Datum interface that can be constructed with a Collection of features and one more more labels.
BasicDatum(Collection, Collection) - Constructor for class edu.stanford.nlp.ling.BasicDatum
Constructs a new BasicDatum with the given features and labels.
BasicDatum(Collection, Object) - Constructor for class edu.stanford.nlp.ling.BasicDatum
Constructs a new BasicDatum with the given features and label.
BasicDatum(Collection) - Constructor for class edu.stanford.nlp.ling.BasicDatum
Constructs a new BasicDatum with the given features and no labels.
BasicDatum() - Constructor for class edu.stanford.nlp.ling.BasicDatum
Constructs a new BasicDatum with no features or labels.
beamSize - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
before() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Return the String before the word, which is stored in the map under the key BEFORE_KEY.
before() - Method in class edu.stanford.nlp.ling.FeatureLabel
Return the String before the word, which is stored in the map under the key BEFORE_KEY.
before() - Method in interface edu.stanford.nlp.ling.HasContext
 
BEFORE_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
the standard key for the String that comes before this word (from the InvertiblePTBTokenizer)
BiasedLogConditionalObjectiveFunction - Class in edu.stanford.nlp.classify
Maximizes the conditional likelihood with a given prior.
BiasedLogConditionalObjectiveFunction(GeneralDataset, double[][]) - Constructor for class edu.stanford.nlp.classify.BiasedLogConditionalObjectiveFunction
 
BiasedLogConditionalObjectiveFunction(GeneralDataset, double[][], LogPrior) - Constructor for class edu.stanford.nlp.classify.BiasedLogConditionalObjectiveFunction
 
BiasedLogConditionalObjectiveFunction(int, int, int[][], int[], double[][]) - Constructor for class edu.stanford.nlp.classify.BiasedLogConditionalObjectiveFunction
 
BiasedLogConditionalObjectiveFunction(int, int, int[][], int[], double[][], LogPrior) - Constructor for class edu.stanford.nlp.classify.BiasedLogConditionalObjectiveFunction
 
BiasedLogisticObjectiveFunction - Class in edu.stanford.nlp.classify
 
BiasedLogisticObjectiveFunction(int, int[][], int[]) - Constructor for class edu.stanford.nlp.classify.BiasedLogisticObjectiveFunction
 
BiasedLogisticObjectiveFunction(int, int[][], int[], LogPrior) - Constructor for class edu.stanford.nlp.classify.BiasedLogisticObjectiveFunction
 
BiasedLogisticObjectiveFunction(int, int[][], int[], float[]) - Constructor for class edu.stanford.nlp.classify.BiasedLogisticObjectiveFunction
 
BiasedLogisticObjectiveFunction(int, int[][], int[], LogPrior, float[]) - Constructor for class edu.stanford.nlp.classify.BiasedLogisticObjectiveFunction
 
BiasedLogisticObjectiveFunction(int, int[][], double[][], int[]) - Constructor for class edu.stanford.nlp.classify.BiasedLogisticObjectiveFunction
 
BiasedLogisticObjectiveFunction(int, int[][], double[][], int[], LogPrior) - Constructor for class edu.stanford.nlp.classify.BiasedLogisticObjectiveFunction
 
BiasedLogisticObjectiveFunction(int, int[][], double[][], int[], LogPrior, float[]) - Constructor for class edu.stanford.nlp.classify.BiasedLogisticObjectiveFunction
 
biasedTrainFile - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
BinaryHeapPriorityQueue<E> - Class in edu.stanford.nlp.util
PriorityQueue with explicit double priority values.
BinaryHeapPriorityQueue() - Constructor for class edu.stanford.nlp.util.BinaryHeapPriorityQueue
 
BinaryHeapPriorityQueue(MapFactory) - Constructor for class edu.stanford.nlp.util.BinaryHeapPriorityQueue
 
binnedLengths - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
bioSubmitOutput - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
booleanFeatures - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
box(int[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
box(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
byteValue() - Method in class edu.stanford.nlp.util.MutableDouble
 
byteValue() - Method in class edu.stanford.nlp.util.MutableInteger
 

C

cacheNGrams - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
for Sighan bakeoff 2005, the path to the dictionary of bigrams appeared in corpus
calcTime - Variable in class edu.stanford.nlp.optimization.SQNMinimizer
 
calculate(double[]) - Method in class edu.stanford.nlp.classify.AdaptedGaussianPriorObjectiveFunction
Calculate the conditional likelihood.
calculate(double[]) - Method in class edu.stanford.nlp.classify.BiasedLogConditionalObjectiveFunction
 
calculate(double[]) - Method in class edu.stanford.nlp.classify.BiasedLogisticObjectiveFunction
 
calculate(double[]) - Method in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 
calculate(double[]) - Method in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
Calculate the conditional likelihood.
calculate(double[]) - Method in class edu.stanford.nlp.classify.LogisticObjectiveFunction
 
calculate(double[]) - Method in class edu.stanford.nlp.classify.SemiSupervisedLogConditionalObjectiveFunction
 
calculate(double[]) - Method in class edu.stanford.nlp.optimization.AbstractCachingDiffFunction
Calculate the value at x and the derivative and save them in the respective fields
calculateRVF(double[]) - Method in class edu.stanford.nlp.classify.BiasedLogisticObjectiveFunction
 
calculateRVF(double[]) - Method in class edu.stanford.nlp.classify.LogisticObjectiveFunction
 
calculatesHessianVectorProduct() - Method in enum edu.stanford.nlp.optimization.StochasticCalculateMethods
 
calculateStochastic(double[], double[], int[]) - Method in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
calculateStochastic(double[], double[], int[]) - Method in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
calculateStochastic needs to calculate a stochastic approximation to the derivative and value of of a function for a given batch of the data.
calculateStochasticAlgorithmicDifferentiation(double[], double[], int[]) - Method in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
calculateStochasticFiniteDifference(double[], double[], double, int[]) - Method in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
calculateStochasticGradientOnly(double[], int[]) - Method in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
capitalize(String) - Static method in class edu.stanford.nlp.util.StringUtils
Uppercases the first character of a string.
cardinality() - Method in interface edu.stanford.nlp.linalg.Array
Returns number of nonzero entries in array
castToInt(double[][]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
category() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Return the category of the label (or null if none), which is stored in the map under the key CATEGORY_KEY.
category() - Method in interface edu.stanford.nlp.ling.HasCategory
Return the category value of the label (or null if none).
CATEGORY_FUNCTIONAL_TAG_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for storing category with functional tags.
CATEGORY_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for storing a category in the map, as a String.
CGMinimizer - Class in edu.stanford.nlp.optimization
Conjugate-gradient implementation based on the code in Numerical Recipes in C.
CGMinimizer() - Constructor for class edu.stanford.nlp.optimization.CGMinimizer
Basic constructor, use this.
CGMinimizer(boolean) - Constructor for class edu.stanford.nlp.optimization.CGMinimizer
Pass in false to get per-iteration progress reports (to stderr).
CGMinimizer(Function) - Constructor for class edu.stanford.nlp.optimization.CGMinimizer
Perform minimization with monitoring.
CH_CHAR_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
for Chinese: character level information, segmentation
CH_ORIG_SEG_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
 
CH_SEG_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
 
changeFeatureIndex(Index) - Method in class edu.stanford.nlp.classify.Dataset
 
changeLabelIndex(Index) - Method in class edu.stanford.nlp.classify.Dataset
 
changePriority(E, double) - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
Changes a priority, either up or down, adding the key it if it wasn't there already.
changePriority(E, double) - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
Not supported in this implementation.
changePriority(E, double) - Method in interface edu.stanford.nlp.util.PriorityQueue
Changes a priority, either up or down, adding the key it if it wasn't there already.
charHalfWindow - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
checkNameList - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
chiSquare2by2(int, int, int, int) - Static method in class edu.stanford.nlp.math.SloppyMath
Find a 2x2 chi-square value.
chomp(String) - Static method in class edu.stanford.nlp.util.StringUtils
Returns the supplied string with any trailing '\n' removed.
chomp(Object) - Static method in class edu.stanford.nlp.util.StringUtils
Returns the result of calling toString() on the supplied Object, but with any trailing '\n' removed.
CL - Static variable in class edu.stanford.nlp.classify.NaiveBayesClassifierFactory
 
Classifier - Interface in edu.stanford.nlp.classify
A simple interface for classifying and scoring data points, implemented by most of the classifiers in this package.
ClassifierFactory - Interface in edu.stanford.nlp.classify
A simple interface for training a Classifier from a list of training examples.
classifierType - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
classOf(Datum) - Method in interface edu.stanford.nlp.classify.Classifier
 
classOf(Datum) - Method in class edu.stanford.nlp.classify.LinearClassifier
 
classOf(RVFDatum) - Method in class edu.stanford.nlp.classify.LinearClassifier
 
classOf(Collection) - Method in class edu.stanford.nlp.classify.LogisticClassifier
 
classOf(Datum) - Method in class edu.stanford.nlp.classify.LogisticClassifier
 
classOf(Counter) - Method in class edu.stanford.nlp.classify.LogisticClassifier
 
classOf(RVFDatum) - Method in class edu.stanford.nlp.classify.LogisticClassifier
 
classOf(RVFDatum) - Method in class edu.stanford.nlp.classify.NaiveBayesClassifier
 
classOf(Datum) - Method in class edu.stanford.nlp.classify.NaiveBayesClassifier
 
classOf(RVFDatum) - Method in interface edu.stanford.nlp.classify.RVFClassifier
 
clean() - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
cleanGazette - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
clear() - Method in class edu.stanford.nlp.classify.GeneralDataset
Resets the Dataset so that it is empty and ready to collect data.
clear(int) - Method in class edu.stanford.nlp.classify.GeneralDataset
Resets the Dataset so that it is empty and ready to collect data.
clear() - Method in class edu.stanford.nlp.classify.RVFDataset
Resets the Dataset so that it is empty and ready to collect data.
clear(int) - Method in class edu.stanford.nlp.classify.RVFDataset
Resets the Dataset so that it is empty and ready to collect data.
clear() - Method in class edu.stanford.nlp.stats.Counter
Removes all counts from this Counter.
clear() - Method in class edu.stanford.nlp.stats.IntCounter
Removes all counts from this Counter.
clear() - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
Clears the queue.
clear() - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
 
clear() - Method in class edu.stanford.nlp.util.Index
Clears this Index.
clear() - Method in class edu.stanford.nlp.util.Interner
 
clearCache() - Method in class edu.stanford.nlp.optimization.AbstractCachingDiffFunction
Clears the cache in a way that doesn't require reallocation :-)
clearCache() - Method in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
Clears the cache in a way that doesn't require reallocation :-)
clone() - Method in interface edu.stanford.nlp.linalg.Array
Returns deep copy of Array
clone() - Method in class edu.stanford.nlp.stats.Counter
 
clone() - Method in class edu.stanford.nlp.stats.IntCounter
 
clone() - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
Returns a clone of this priority queue.
CollectionFactory<T> - Class in edu.stanford.nlp.util
Factory for vending Collections.
CollectionFactory() - Constructor for class edu.stanford.nlp.util.CollectionFactory
 
CollectionFactory.ArrayListFactory<T> - Class in edu.stanford.nlp.util
 
CollectionFactory.ArrayListFactory() - Constructor for class edu.stanford.nlp.util.CollectionFactory.ArrayListFactory
 
CollectionFactory.HashSetFactory<T> - Class in edu.stanford.nlp.util
 
CollectionFactory.HashSetFactory() - Constructor for class edu.stanford.nlp.util.CollectionFactory.HashSetFactory
 
CollectionFactory.LinkedListFactory<T> - Class in edu.stanford.nlp.util
 
CollectionFactory.LinkedListFactory() - Constructor for class edu.stanford.nlp.util.CollectionFactory.LinkedListFactory
 
CollectionUtils - Class in edu.stanford.nlp.util
Collection of useful static methods for working with Collections.
ColumnDataClassifier - Class in edu.stanford.nlp.classify
ColumnDataClassifier provides a command-line interface for doing context-free (independent) classification of a series of data items, where each data item is represented by a line of a file, as a list of String variables, in tab-separated columns.
columnIndex() - Method in interface edu.stanford.nlp.linalg.Array
Returns column index of column vector
comboProps - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
comparator(boolean) - Method in class edu.stanford.nlp.stats.Counter
Returns a comparator suitable for sorting this Counter's keys or entries by their respective counts.
comparator(boolean, boolean) - Method in class edu.stanford.nlp.stats.Counter
Returns a comparator suitable for sorting this Counter's keys or entries by their respective value or magnitude (unsigned value).
comparator() - Method in class edu.stanford.nlp.stats.Counter
Comparator that sorts objects by (increasing) count.
comparator() - Method in interface edu.stanford.nlp.stats.GenericCounter
Returns a comparator
comparator(boolean) - Method in class edu.stanford.nlp.stats.IntCounter
Returns a comparator suitable for sorting this Counter's keys or entries by their respective counts.
comparator(boolean, boolean) - Method in class edu.stanford.nlp.stats.IntCounter
Returns a comparator suitable for sorting this Counter's keys or entries by their respective value or magnitude (unsigned value).
comparator() - Method in class edu.stanford.nlp.stats.IntCounter
Comparator that sorts objects by (increasing) count.
compare(Object, Object) - Method in class edu.stanford.nlp.util.EntryValueComparator
Compares the values of the two given Map.Entry objects in the given order.
compare(Object, Object) - Method in class edu.stanford.nlp.util.ScoredComparator
 
compareLists(List<? extends Comparable>, List<? extends Comparable>) - Static method in class edu.stanford.nlp.util.CollectionUtils
 
compareTo(MutableDouble) - Method in class edu.stanford.nlp.util.MutableDouble
Compares two MutableDouble objects numerically.
compareTo(Object) - Method in class edu.stanford.nlp.util.MutableDouble
Compares this MutableDouble object to another object.
compareTo(MutableInteger) - Method in class edu.stanford.nlp.util.MutableInteger
Compares two MutableInteger objects numerically.
compareTo(Object) - Method in class edu.stanford.nlp.util.MutableInteger
Compares this MutableInteger object to another object.
compareTo(Object) - Method in class edu.stanford.nlp.util.Pair
Compares this Pair to another object.
compute(double[], double[]) - Method in class edu.stanford.nlp.classify.LogPrior
Adjust the given grad array by adding the prior's gradient component and return the value of the logPrior
computeAverage(Function<Triple<GeneralDataset, GeneralDataset, CrossValidator.SavedState>, Double>) - Method in class edu.stanford.nlp.classify.CrossValidator
This computes the average over all folds of the function we're trying to optimize.
concat(IntTuple, IntTuple) - Static method in class edu.stanford.nlp.util.IntTuple
 
conditionalize(List) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
returns a GeneralizedCounter conditioned on the objects in the List argument.
conditionalizeOnce(Object) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Returns a GeneralizedCounter conditioned on the given top level object.
confidenceWeightedAccuracy() - Method in class edu.stanford.nlp.stats.MultiClassAccuracyStats
 
confusionMatrix - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
conjoinShapeNGrams - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
contains(int[], int) - Static method in class edu.stanford.nlp.math.ArrayMath
 
contains(T[], T) - Static method in class edu.stanford.nlp.util.ArrayUtils
Returns true iff object o equals (not ==) some element of array a.
contains(Object) - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
Returns whether the queue contains the given key.
contains(Object) - Method in class edu.stanford.nlp.util.Index
Checks whether an Object already has an index in the Index
containsInSubarray(int[], int, int, int) - Static method in class edu.stanford.nlp.math.ArrayMath
 
containsKey(int) - Method in interface edu.stanford.nlp.linalg.Array
Returns true if the array contains index key
containsKey(E) - Method in class edu.stanford.nlp.stats.Counter
 
containsKey(E) - Method in class edu.stanford.nlp.stats.Distribution
 
containsKey(List) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Like Counter, this currently returns true if the count is explicitly 0.0 for something
containsKey(E) - Method in interface edu.stanford.nlp.stats.GenericCounter
 
containsKey(E) - Method in class edu.stanford.nlp.stats.IntCounter
 
containsKey(K1, K2) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
containsObject(Collection, Object) - Static method in class edu.stanford.nlp.util.CollectionUtils
Checks whether a Collection contains a specified Object.
copy(double[], double[]) - Method in class edu.stanford.nlp.optimization.AbstractCachingDiffFunction
 
copy(double[], double[]) - Method in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
copy(int[]) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
copy(int[][]) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
copy(double[]) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
copy(double[][]) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
copy(double[][][]) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
copy(float[]) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
copy(float[][]) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
copy(float[][][]) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
coref() - Method in class edu.stanford.nlp.ling.FeatureLabel
Return the coreferent of the word, which is stored in the map under the key NER_KEY.
COREF_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
the standard key for the coref label.
correct(double, int) - Static method in class edu.stanford.nlp.classify.PRCurve
 
cosine(GenericCounter<E>, GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
 
Counter<E> - Class in edu.stanford.nlp.stats
A specialized kind of hash table (or map) for storing numeric counts for objects.
Counter() - Constructor for class edu.stanford.nlp.stats.Counter
Constructs a new (empty) Counter.
Counter(MapFactory<E, MutableDouble>) - Constructor for class edu.stanford.nlp.stats.Counter
Pass in a MapFactory and the map it vends will back your counter.
Counter(GenericCounter<E>) - Constructor for class edu.stanford.nlp.stats.Counter
Constructs a new Counter with the contents of the given Counter.
Counter(Collection<E>) - Constructor for class edu.stanford.nlp.stats.Counter
Constructs a new Counter by counting the elements in the given Collection.
counter - Variable in class edu.stanford.nlp.stats.Distribution
 
Counters - Class in edu.stanford.nlp.stats
Static methods for operating on Counters.
counterView() - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Returns a read-only synchronous view (not a snapshot) of this as a Counter.
countInfinite(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
countNaN(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
cPosDef - Variable in class edu.stanford.nlp.optimization.SMDMinimizer
 
createCounterFromCollection(Collection<E>) - Static method in class edu.stanford.nlp.stats.Counters
 
createCounterFromList(List<E>) - Static method in class edu.stanford.nlp.stats.Counters
 
createIndex() - Method in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
create an index for each parameter - the prior probs and the features with all of their values
CRForder - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
crfType - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
CRFwindow - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
cross(Set<E>, Set<F>) - Static method in class edu.stanford.nlp.util.Sets
Returns the set cross product of s1 and s2, as Pairs
crossEntropy(GenericCounter<E>, GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
Note that this implementation doesn't normalize the "from" Counter.
crossEntropy(GenericCounter<E>, Counter<E>) - Static method in class edu.stanford.nlp.stats.Counters
Note that this implementation doesn't normalize the "from" Counter.
crossValidateSetSigma(GeneralDataset) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Calls the method LinearClassifierFactory.crossValidateSetSigma(GeneralDataset, int) with 5-fold cross-validation.
crossValidateSetSigma(GeneralDataset, int) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
callls the method LinearClassifierFactory.crossValidateSetSigma(GeneralDataset, int, Scorer, LineSearcher) with multi-class log-likelihood scoring (see MultiClassAccuracyStats) and golden-section line search (see GoldenSectionLineSearch).
crossValidateSetSigma(GeneralDataset, int, Scorer) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
crossValidateSetSigma(GeneralDataset, int, LineSearcher) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
crossValidateSetSigma(GeneralDataset, int, Scorer, LineSearcher) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Sets the sigma parameter to a value that optimizes the cross-validation score given by scorer.
CrossValidator - Class in edu.stanford.nlp.classify
This class is meant to simplify performing cross validation on classifiers for hyper-parameters.
CrossValidator(GeneralDataset) - Constructor for class edu.stanford.nlp.classify.CrossValidator
 
CrossValidator(GeneralDataset, int) - Constructor for class edu.stanford.nlp.classify.CrossValidator
 
CrossValidator.SavedState - Class in edu.stanford.nlp.classify
 
CrossValidator.SavedState() - Constructor for class edu.stanford.nlp.classify.CrossValidator.SavedState
 
curElement - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
current() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Return the String which is the unmangled word, which is stored in the map under the key CURRENT_KEY.
current() - Method in class edu.stanford.nlp.ling.FeatureLabel
Return the String which is the unmangled word, which is stored in the map under the key CURRENT_KEY.
current() - Method in interface edu.stanford.nlp.ling.HasContext
Return the String which is the unmangled word.
CURRENT_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
the standard key for the actual, unmangled, pre-PTB'd word (from the InvertiblePTBTokenizer)
cwa() - Method in class edu.stanford.nlp.classify.PRCurve
confidence weighted accuracy assuming the scores are probabilities and using .5 as treshold
cwaArray() - Method in class edu.stanford.nlp.classify.PRCurve
confidence weighted accuracy assuming the scores are probabilities and using .5 as treshold

D

data - Variable in class edu.stanford.nlp.classify.BiasedLogConditionalObjectiveFunction
 
data - Variable in class edu.stanford.nlp.classify.GeneralDataset
 
data - Variable in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 
data - Variable in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
dataDimension() - Method in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
dataDimension() - Method in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
Data dimension must return the size of the data used by the function.
Dataset - Class in edu.stanford.nlp.classify
An interfacing class for ClassifierFactory that incrementally builds a more memory-efficent representation of a List of Datum objects for the purposes of training a Classifier with a ClassifierFactory.
Dataset() - Constructor for class edu.stanford.nlp.classify.Dataset
 
Dataset(int, Index, Index) - Constructor for class edu.stanford.nlp.classify.Dataset
 
Dataset(int) - Constructor for class edu.stanford.nlp.classify.Dataset
 
Dataset(Index, int[], Index, int[][]) - Constructor for class edu.stanford.nlp.classify.Dataset
Constructor that fully specifies a Dataset.
Dataset(Index, int[], Index, int[][], int) - Constructor for class edu.stanford.nlp.classify.Dataset
Constructor that fully specifies a Dataset.
dataweights - Variable in class edu.stanford.nlp.classify.BiasedLogisticObjectiveFunction
 
dataweights - Variable in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
dataweights - Variable in class edu.stanford.nlp.classify.LogisticObjectiveFunction
 
Datum - Interface in edu.stanford.nlp.ling
Interface for Objects which can be described by their features.
decreasePriority(E, double) - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
Demotes a key in the queue, adding it if it wasn't there already.
decrementBatch(int) - Method in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
decrementBatch - This decrements the curElement variable by the amount batchSize.
decrementCount(E, double) - Method in class edu.stanford.nlp.stats.Counter
Subtracts the given count from the current count for the given key.
decrementCount(E) - Method in class edu.stanford.nlp.stats.Counter
Subtracts 1.0 from the count for the given key.
decrementCount(E, int) - Method in class edu.stanford.nlp.stats.IntCounter
Subtracts the given count from the current count for the given key.
decrementCount(E) - Method in class edu.stanford.nlp.stats.IntCounter
Subtracts 1 from the count for the given key.
decrementCount(K1, K2) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
decrementCount(K1, K2, double) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
decrementCounts(Collection<E>, double) - Method in class edu.stanford.nlp.stats.Counter
Subtracts the given count from the current counts for each of the given keys.
decrementCounts(Collection<E>) - Method in class edu.stanford.nlp.stats.Counter
Subtracts 1.0 from the counts of each of the given keys.
decrementCounts(Collection<E>, int) - Method in class edu.stanford.nlp.stats.IntCounter
Subtracts the given count from the current counts for each of the given keys.
decrementCounts(Collection<E>) - Method in class edu.stanford.nlp.stats.IntCounter
Subtracts 1 from the counts of each of the given keys.
deepCopy(int[][]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
deepCopy(MapFactory) - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
 
deepCopy() - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
 
DEFAULT_BACKGROUND_SYMBOL - Static variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
dehyphenateNGrams - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
deleteBlankLines - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
depth() - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Returns the depth of the GeneralizedCounter (i.e., the dimension of the distribution).
derivative - Variable in class edu.stanford.nlp.optimization.AbstractCachingDiffFunction
 
derivativeAD - Variable in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
derivativeAt(double[]) - Method in class edu.stanford.nlp.optimization.AbstractCachingDiffFunction
 
derivativeAt(double[], int) - Method in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
derivativeAt(double[], double[], int) - Method in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
derivativeAt(float[]) - Method in interface edu.stanford.nlp.optimization.DiffFloatFunction
Returns the first-derivative vector at the input location.
derivativeAt(double[]) - Method in interface edu.stanford.nlp.optimization.DiffFunction
Returns the first-derivative vector at the input location.
derivativeNumerator - Variable in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
DESCENDING_COMPARATOR - Static variable in class edu.stanford.nlp.util.ScoredComparator
 
deserializeCounter(String) - Static method in class edu.stanford.nlp.stats.Counters
 
devFile - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
diag(int[][]) - Static method in class edu.stanford.nlp.math.ArrayMath
Returns diagonal elements of the given (square) matrix.
diff(Counter<T>, Counter<T>) - Static method in class edu.stanford.nlp.stats.Counters
 
diff(Set<E>, Set<E>) - Static method in class edu.stanford.nlp.util.Sets
Returns the difference of sets s1 and s2.
DiffFloatFunction - Interface in edu.stanford.nlp.optimization
An interface for once-differentiable double-valued functions over double arrays.
DiffFunction - Interface in edu.stanford.nlp.optimization
An interface for once-differentiable double-valued functions over double arrays.
discretizeCompute(Function<Double, Double>, int, double, double) - Method in class edu.stanford.nlp.optimization.GoldenSectionLineSearch
 
disjunctionWidth - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
distance(Array) - Method in interface edu.stanford.nlp.linalg.Array
Returns Euclidean distance from this to other
Distribution<E> - Class in edu.stanford.nlp.stats
Immutable class for representing normalized, smoothed discrete distributions from Counters.
distributionFromLogisticCounter(GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Distribution
Maps a counter representing the linear weights of a multiclass logistic regression model to the probabilities of each class.
distributionWithDirichletPrior(GenericCounter<E>, Distribution<E>, double) - Static method in class edu.stanford.nlp.stats.Distribution
Returns a Distribution that uses prior as a Dirichlet prior weighted by weight.
distSimLexicon - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
divide(Array) - Method in interface edu.stanford.nlp.linalg.Array
Returns componentwise divide: this/dividend
divide(double) - Method in interface edu.stanford.nlp.linalg.Array
Scalar divide
divide(DoubleAD, DoubleAD) - Static method in class edu.stanford.nlp.math.ADMath
 
divideBy(double) - Method in class edu.stanford.nlp.stats.Counter
Divides every count by the given divisor.
divideBy(Counter<E>) - Method in class edu.stanford.nlp.stats.Counter
Divides every non-zero count by the count for the corresponding key in the argument Counter.
divideConst(DoubleAD, double) - Static method in class edu.stanford.nlp.math.ADMath
 
division(GenericCounter<E>, GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
Returns c1 divided by c2.
doAdaptation - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
documentReader - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
doGibbs - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
doing(String) - Method in class edu.stanford.nlp.util.Timing
Print the start of timing message to stderr and start the timer.
domainDimension() - Method in class edu.stanford.nlp.classify.BiasedLogConditionalObjectiveFunction
 
domainDimension() - Method in class edu.stanford.nlp.classify.BiasedLogisticObjectiveFunction
 
domainDimension() - Method in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 
domainDimension() - Method in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
domainDimension() - Method in class edu.stanford.nlp.classify.LogisticObjectiveFunction
 
domainDimension() - Method in class edu.stanford.nlp.classify.SemiSupervisedLogConditionalObjectiveFunction
 
domainDimension() - Method in class edu.stanford.nlp.optimization.AbstractCachingDiffFunction
 
domainDimension() - Method in interface edu.stanford.nlp.optimization.FloatFunction
Returns the number of dimensions in the function's domain
domainDimension() - Method in interface edu.stanford.nlp.optimization.Function
Returns the number of dimensions in the function's domain
done() - Method in class edu.stanford.nlp.util.Timing
Finish the line from startDoing with the end of the timing done message and elapsed time in x.y seconds.
done(String) - Method in class edu.stanford.nlp.util.Timing
Give a line saying that something is " done".
dontExtendTaggy - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
dot(Array) - Method in interface edu.stanford.nlp.linalg.Array
Returns dot product of this with other
dotProduct(GenericCounter<E>, GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
Returns the product of c1 and c2.
DoubleAD - Class in edu.stanford.nlp.math
The class DoubleAD was created to extend the current calculations of gradient to automatically include a calculation of the hessian vector product with another vector v.
DoubleAD() - Constructor for class edu.stanford.nlp.math.DoubleAD
 
DoubleAD(double, double) - Constructor for class edu.stanford.nlp.math.DoubleAD
 
doubleArrayToFloatArray(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
doubleArrayToFloatArray(double[][]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
doubleMax() - Method in class edu.stanford.nlp.stats.Counter
 
doubleMax() - Method in interface edu.stanford.nlp.stats.GenericCounter
Returns the value of the maximum entry in this counter, as a double.
doubleMax() - Method in class edu.stanford.nlp.stats.IntCounter
 
doubleValue() - Method in class edu.stanford.nlp.math.DoubleAD
 
doubleValue() - Method in class edu.stanford.nlp.util.MutableDouble
 
doubleValue() - Method in class edu.stanford.nlp.util.MutableInteger
 
drawSample() - Method in class edu.stanford.nlp.stats.Distribution
Exactly the same as sampleFrom(), needed for the Sampler interface.
drawSample() - Method in interface edu.stanford.nlp.stats.Sampler
 
dropGaz - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
dump() - Method in class edu.stanford.nlp.classify.LinearClassifier
Print all features in the classifier and the weight that they assign to each class.
dump - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
dumpMemory() - Method in class edu.stanford.nlp.optimization.GoldenSectionLineSearch
dump the pairs it computed found
dumpSorted() - Method in class edu.stanford.nlp.classify.LinearClassifier
Print all features in the classifier and the weight that they assign to each class.
dynamicCounterWithDirichletPrior(GenericCounter<E>, Distribution<E>, double) - Static method in class edu.stanford.nlp.stats.Distribution
Like normalizedCounterWithDirichletPrior except probabilities are computed dynamically from the counter and prior instead of all at once up front.

E

editDistance(String, String) - Static method in class edu.stanford.nlp.util.StringUtils
Computes the Levenshtein (edit) distance of the two given Strings.
edu.stanford.nlp.classify - package edu.stanford.nlp.classify
The classify package provides facilities for training classifiers.
edu.stanford.nlp.io - package edu.stanford.nlp.io
 
edu.stanford.nlp.linalg - package edu.stanford.nlp.linalg
 
edu.stanford.nlp.ling - package edu.stanford.nlp.ling
 
edu.stanford.nlp.math - package edu.stanford.nlp.math
 
edu.stanford.nlp.optimization - package edu.stanford.nlp.optimization
 
edu.stanford.nlp.process - package edu.stanford.nlp.process
 
edu.stanford.nlp.sequences - package edu.stanford.nlp.sequences
 
edu.stanford.nlp.stats - package edu.stanford.nlp.stats
 
edu.stanford.nlp.util - package edu.stanford.nlp.util
 
elems() - Method in class edu.stanford.nlp.util.IntTuple
 
EMPTY_STRING_ARRAY - Static variable in class edu.stanford.nlp.util.StringUtils
 
endDoing() - Static method in class edu.stanford.nlp.util.Timing
Finish the line from startDoing with the end of the timing done message and elapsed time in x.y seconds.
endDoing(String) - Static method in class edu.stanford.nlp.util.Timing
Finish the line from startDoing with the end of the timing done message and elapsed time in x.y seconds.
endFold - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
endTime() - Static method in class edu.stanford.nlp.util.Timing
Return elapsed time on (static) timer (without stopping timer).
endTime(String, PrintStream) - Static method in class edu.stanford.nlp.util.Timing
Print elapsed time on (static) timer (without stopping timer).
endTime(String) - Static method in class edu.stanford.nlp.util.Timing
Print elapsed time on (static) timer to System.err (without stopping timer).
ensureSize() - Method in class edu.stanford.nlp.classify.Dataset
 
ensureSize() - Method in class edu.stanford.nlp.classify.WeightedDataset
 
entitySubclassification - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
entropy(GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
Calculates the entropy of the given counter (in bits).
entrySet() - Method in interface edu.stanford.nlp.linalg.Array
Returns a set view of the Entries contained in this array.
entrySet() - Method in class edu.stanford.nlp.stats.Counter
 
entrySet() - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Returns the set of entries in the GeneralizedCounter.
entrySet() - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
entrySet() - Method in class edu.stanford.nlp.util.ArrayMap
 
EntryValueComparator - Class in edu.stanford.nlp.util
Comparator designed for the values of Map entries.
EntryValueComparator() - Constructor for class edu.stanford.nlp.util.EntryValueComparator
Constructs a new EntryValueComparator using ascending (normal) order that works on Map.Entry objects.
EntryValueComparator(boolean) - Constructor for class edu.stanford.nlp.util.EntryValueComparator
Constructs a new EntryValueComparator that will sort in the given order and works on Map.Entry objects.
EntryValueComparator(Map) - Constructor for class edu.stanford.nlp.util.EntryValueComparator
Constructs a new EntryValueComparator that will sort keys for the given Map in ascending (normal) order.
EntryValueComparator(Map, boolean) - Constructor for class edu.stanford.nlp.util.EntryValueComparator
Constructs a new EmptyValueComparator to sort keys or entries of the given map in the given order.
EntryValueComparator(Map, boolean, boolean) - Constructor for class edu.stanford.nlp.util.EntryValueComparator
Constructs a new EmptyValueComparator to sort keys or entries of the given map in the given order.
epsilon - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
equalContents(int[][], int[][]) - Static method in class edu.stanford.nlp.util.ArrayUtils
Tests two int[][] arrays for having equal contents.
equalContents(int[], int[]) - Static method in class edu.stanford.nlp.util.ArrayUtils
tests two int[] arrays for having equal contents
equals(Object) - Method in interface edu.stanford.nlp.linalg.Array
Returns true if o==this
equals(Object) - Method in class edu.stanford.nlp.ling.BasicDatum
Returns whether the given Datum contains the same features as this Datum.
equals(Object) - Method in class edu.stanford.nlp.ling.FeatureLabel
 
equals(Object) - Method in class edu.stanford.nlp.ling.RVFDatum
Returns whether the given Datum contains the same features as this Datum.
equals(DoubleAD) - Method in class edu.stanford.nlp.math.DoubleAD
 
equals(double, double) - Method in class edu.stanford.nlp.math.DoubleAD
 
equals(double, double, double) - Method in class edu.stanford.nlp.math.DoubleAD
 
equals(Object) - Method in class edu.stanford.nlp.stats.Counter
 
equals(Object) - Method in class edu.stanford.nlp.stats.Distribution
 
equals(Distribution) - Method in class edu.stanford.nlp.stats.Distribution
 
equals(Object) - Method in class edu.stanford.nlp.stats.IntCounter
 
equals(Object) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
equals(Object) - Method in class edu.stanford.nlp.util.ArrayMap
 
equals(double[][], double[][]) - Static method in class edu.stanford.nlp.util.ArrayUtils
Tests two double[][] arrays for having equal contents.
equals(boolean[][], boolean[][]) - Static method in class edu.stanford.nlp.util.ArrayUtils
Tests two boolean[][] arrays for having equal contents.
equals(Object) - Method in class edu.stanford.nlp.util.IntTuple
 
equals(Object) - Method in class edu.stanford.nlp.util.MutableDouble
Compares this object to the specified object.
equals(Object) - Method in class edu.stanford.nlp.util.MutableInteger
Compares this object to the specified object.
equals(Object) - Method in class edu.stanford.nlp.util.Pair
 
equals(Object) - Method in class edu.stanford.nlp.util.ScoredComparator
 
equals(Object) - Method in class edu.stanford.nlp.util.Triple
 
escapeString(String, char[], char) - Static method in class edu.stanford.nlp.util.StringUtils
 
estimateInitial - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
exactBinomial(int, int, double) - Static method in class edu.stanford.nlp.math.SloppyMath
Find a one tailed exact binomial test probability.
exp(DoubleAD) - Static method in class edu.stanford.nlp.math.ADMath
 
exp(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
exp(Counter<T>) - Static method in class edu.stanford.nlp.stats.Counters
 
expInPlace(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
extFiniteDiffDerivative - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 

F

f1(int, int, int) - Static method in class edu.stanford.nlp.classify.PRCurve
 
factorial(int) - Static method in class edu.stanford.nlp.math.SloppyMath
Uses floating point so that it can represent the really big numbers that come up.
factory() - Static method in class edu.stanford.nlp.ling.FeatureLabel
 
fakeDataset - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
featThreshFile - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
featureCountThreshold - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
featureDiffThresh - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
featureFactory - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
featureIndex - Variable in class edu.stanford.nlp.classify.GeneralDataset
 
featureIndex() - Method in class edu.stanford.nlp.classify.GeneralDataset
 
featureIndex() - Method in class edu.stanford.nlp.classify.LinearClassifier
 
FeatureLabel - Class in edu.stanford.nlp.ling
An AbstractMapLabel implementation which defines equality as equality of the internal map.
FeatureLabel() - Constructor for class edu.stanford.nlp.ling.FeatureLabel
 
FeatureLabel(MapFactory) - Constructor for class edu.stanford.nlp.ling.FeatureLabel
 
FeatureLabel(String[], String[]) - Constructor for class edu.stanford.nlp.ling.FeatureLabel
 
FeatureLabel(AbstractMapLabel) - Constructor for class edu.stanford.nlp.ling.FeatureLabel
Copy constructor.
FeatureLabel(Map) - Constructor for class edu.stanford.nlp.ling.FeatureLabel
Copy constructor.
features() - Method in class edu.stanford.nlp.classify.LinearClassifier
 
features - Variable in class edu.stanford.nlp.ling.FeatureLabel
 
FEATURES_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for the features which is a Collection
featureThreshold - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
featureWeightThreshold - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
Featurizable - Interface in edu.stanford.nlp.ling
Interface for Objects that can be described by their features.
femaleNameList - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
fileNameClean(String) - Static method in class edu.stanford.nlp.util.StringUtils
Returns a "clean" version of the given filename in which spaces have been converted to dashes and all non-alphanumeric chars are underscores.
fill(double[][], double) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
fill(double[][][], double) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
fill(double[][][][], double) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
fill(boolean[][], boolean) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
fill(boolean[][][], boolean) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
fill(boolean[][][][], boolean) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
Filter<T> - Interface in edu.stanford.nlp.util
Filter is an interface for predicate objects which respond to the accept method.
filterInfinite(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
filterNaN(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
filterNaNAndInfinite(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
finalVal - Variable in class edu.stanford.nlp.optimization.SMDMinimizer
 
find(String, String) - Static method in class edu.stanford.nlp.util.StringUtils
Say whether this regular expression can be found inside this String.
finiteDifferenceStepSize - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
finiteDifferenceStepSize - this is the fixed step size for the finite difference approximation.
first - Variable in class edu.stanford.nlp.util.Pair
Direct access is deprecated.
first() - Method in class edu.stanford.nlp.util.Pair
 
first - Variable in class edu.stanford.nlp.util.Triple
 
first() - Method in class edu.stanford.nlp.util.Triple
 
firstKeySet() - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
FixedPrioritiesPriorityQueue<E> - Class in edu.stanford.nlp.util
A priority queue based on a binary heap.
FixedPrioritiesPriorityQueue() - Constructor for class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
 
FixedPrioritiesPriorityQueue(int) - Constructor for class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
 
flatten() - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
flatten(double[][]) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
floatArrayToDoubleArray(float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
floatArrayToDoubleArray(float[][]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
FloatFunction - Interface in edu.stanford.nlp.optimization
An interface for double-valued functions over double arrays.
floatValue() - Method in class edu.stanford.nlp.math.DoubleAD
 
floatValue() - Method in class edu.stanford.nlp.util.MutableDouble
 
floatValue() - Method in class edu.stanford.nlp.util.MutableInteger
 
fmeasure(int, int) - Method in class edu.stanford.nlp.classify.PRCurve
the f-measure if we just guess as negativ the first numleft and guess as poitive the last numright
fmeasure(int) - Method in class edu.stanford.nlp.classify.PRCurve
what is the f-measure at this recall if we look at the score as the probability of class 1 given x as if coming from logistic regression same as logPrecision but calculating f-measure
fromString(String) - Static method in class edu.stanford.nlp.stats.Counter
converts from format printed by toString method back into a Counter.
Function - Interface in edu.stanford.nlp.optimization
An interface for double-valued functions over double arrays.
Function<T1,T2> - Interface in edu.stanford.nlp.process
An interface for classes that act as a function transforming one object to another.

G

gain - Variable in class edu.stanford.nlp.optimization.SGDMinimizer
 
gain - Variable in class edu.stanford.nlp.optimization.SGDToQNMinimizer
 
gainSGD - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
gamma(double) - Static method in class edu.stanford.nlp.math.SloppyMath
 
GAZETTEER_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for the gazetteer information
gazettes - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
gazFilesFile - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
GeneralDataset - Class in edu.stanford.nlp.classify
The purpose of this interface is to unify Dataset and RVFDataset.
GeneralDataset() - Constructor for class edu.stanford.nlp.classify.GeneralDataset
 
GeneralizedCounter - Class in edu.stanford.nlp.stats
A class for keeping double counts of Lists of a prespecified length.
GeneralizedCounter(int) - Constructor for class edu.stanford.nlp.stats.GeneralizedCounter
Constructs a new GeneralizedCounter of a specified depth
GenericCounter<E> - Interface in edu.stanford.nlp.stats
Interface to a generic (type-independent) Counter.
get(int) - Method in interface edu.stanford.nlp.linalg.Array
Gets double value of element at index i
get(Object) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Returns the value to which the map contained in this label maps the specified key.
get(Object) - Method in class edu.stanford.nlp.util.ArrayMap
 
get(int) - Method in class edu.stanford.nlp.util.Index
Gets the object whose index is the integer argument.
get(int) - Method in class edu.stanford.nlp.util.IntTuple
 
getAccCoverage() - Method in class edu.stanford.nlp.stats.MultiClassAccuracyStats
 
getAdaptationPrior(double[], LogPrior) - Static method in class edu.stanford.nlp.classify.LogPrior
 
getBaseName(String) - Static method in class edu.stanford.nlp.util.StringUtils
Strip directory from filename.
getBaseName(String, String) - Static method in class edu.stanford.nlp.util.StringUtils
Strip directory and suffix from filename.
getBoolean(List, int) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns ((Boolean)list.get(index)).booleanValue().
getBoolean(Map, Object) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns ((Boolean)map.get(key)).booleanValue().
getCopy() - Method in class edu.stanford.nlp.util.IntPair
 
getCopy() - Method in class edu.stanford.nlp.util.IntQuadruple
 
getCopy() - Method in class edu.stanford.nlp.util.IntTriple
 
getCopy() - Method in class edu.stanford.nlp.util.IntTuple
 
getCopy() - Method in class edu.stanford.nlp.util.IntUni
 
getCount(E) - Method in class edu.stanford.nlp.stats.Counter
Returns the current count for the given key, which is 0 if it hasn't been seen before.
getCount(E) - Method in class edu.stanford.nlp.stats.Distribution
Returns the current count for the given key, which is 0 if it hasn't been seen before.
getCount(Object) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Equivalent to GeneralizedCounter.getCounts(java.util.List)({o}); works only for depth 1 GeneralizedCounters
getCount(Object, Object) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
A convenience method equivalent to GeneralizedCounter.getCounts(java.util.List)({o1,o2}); works only for depth 2 GeneralizedCounters
getCount(Object, Object, Object) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
A convenience method equivalent to GeneralizedCounter.getCounts(java.util.List)({o1,o2,o3}); works only for depth 3 GeneralizedCounters
getCount(E) - Method in interface edu.stanford.nlp.stats.GenericCounter
Returns the count for this key as a double.
getCount(E) - Method in class edu.stanford.nlp.stats.IntCounter
Returns the current count for the given key, which is 0 if it hasn't been seen before.
getCount(K1, K2) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
getCountAsString(E) - Method in class edu.stanford.nlp.stats.Counter
 
getCountAsString(E) - Method in interface edu.stanford.nlp.stats.GenericCounter
Returns the count for this key as a String.
getCountAsString(E) - Method in class edu.stanford.nlp.stats.IntCounter
 
getCountCounts(GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
 
getCounter() - Method in class edu.stanford.nlp.stats.Distribution
 
getCounter(K1) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
getCounts(List) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
returns a double[] array of length depth+1, containing the conditional counts on a depth-length list given each level of conditional distribution from 0 to depth.
getDataArray() - Method in class edu.stanford.nlp.classify.GeneralDataset
 
getDatum(int) - Method in class edu.stanford.nlp.classify.Dataset
 
getDescription(int) - Method in class edu.stanford.nlp.stats.AccuracyStats
 
getDescription(int) - Method in class edu.stanford.nlp.stats.MultiClassAccuracyStats
 
getDescription(int) - Method in interface edu.stanford.nlp.stats.Scorer
 
getDistribution(GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Distribution
Creates a Distribution from the given counter, ie makes an internal copy of the counter and divides all counts by the total count.
getDistributionFromLogValues(GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Distribution
Creates a Distribution from the given counter, ie makes an internal copy of the counter and divides all counts by the total count.
getDistributionFromPartiallySpecifiedCounter(Counter<E>, int) - Static method in class edu.stanford.nlp.stats.Distribution
Assuming that c has a total count < 1, returns a new Distribution using the counts in c as probabilities.
getDistributionWithReservedMass(GenericCounter<E>, double) - Static method in class edu.stanford.nlp.stats.Distribution
 
getdot() - Method in class edu.stanford.nlp.math.DoubleAD
 
getDouble(List, int) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns (Double)list.get(index).
getdouble(List, int) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns ((Double)list.get(index)).doubleValue().
getDouble(Map, Object) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns (Double)map.get(key).
getdouble(Map, Object) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns ((Double)map.get(key)).doubleValue().
getEpsilon() - Method in class edu.stanford.nlp.classify.LogPrior
 
getFeatureCounter() - Method in class edu.stanford.nlp.classify.Dataset
Get Number of datums a given feature appears in.
getFeatureCounts() - Method in class edu.stanford.nlp.classify.GeneralDataset
Get the total count (over all data instances) of each feature
getFeatureCounts() - Method in class edu.stanford.nlp.classify.WeightedDataset
 
getFeatures() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
 
getFirst() - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
Finds the object with the highest priority and returns it, without modifying the queue.
getFirst() - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
Returns the highest-priority element without removing it from the queue.
getFirst() - Method in interface edu.stanford.nlp.util.PriorityQueue
Finds the object with the highest priority and returns it, without modifying the queue.
getGlobal() - Static method in class edu.stanford.nlp.util.Interner
For getting the instance that global methods use.
getIndex(List, Object) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns the index of the first occurrence in the list of the specified object, using object identity (==) not equality as the criterion for object presence.
getInformationGains() - Method in class edu.stanford.nlp.classify.Dataset
 
getInnerMapFactory() - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
getInt(List, int) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns ((Integer)list.get(index)).intValue().
getInt(Map, Object) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns ((Integer)map.get(key)).intValue().
getIntCount(Object) - Method in class edu.stanford.nlp.stats.IntCounter
Returns the current count for the given key, which is 0 if it hasn't been seen before.
getInteger(List, int) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns (Integer)list.get(index).
getInteger(Map, Object) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns (Integer)map.get(key).
getIntTuple(int) - Static method in class edu.stanford.nlp.util.IntTuple
 
getIntTuple(ArrayList) - Static method in class edu.stanford.nlp.util.IntTuple
 
getKnownLowerCaseWords() - Static method in class edu.stanford.nlp.process.WordShapeClassifier
 
getLabelsArray() - Method in class edu.stanford.nlp.classify.GeneralDataset
 
getLemma() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
 
getListComparator() - Static method in class edu.stanford.nlp.util.CollectionUtils
 
getMapFactory() - Method in class edu.stanford.nlp.stats.Counter
 
getMapFactory() - Method in interface edu.stanford.nlp.stats.GenericCounter
Returns the MapFactory used by this counter.
getMapFactory() - Method in class edu.stanford.nlp.stats.IntCounter
 
getMapFromString(String, Class, Class, MapFactory) - Static method in class edu.stanford.nlp.util.CollectionUtils
 
getMiddle() - Method in class edu.stanford.nlp.util.IntQuadruple
 
getMiddle() - Method in class edu.stanford.nlp.util.IntTriple
 
getNormalizedCount(E) - Method in class edu.stanford.nlp.stats.Counter
Return the proportion of the Counter mass under this key.
getNormalizedCount(E) - Method in class edu.stanford.nlp.stats.IntCounter
This has been de-deprecated in order to reduce compilation warnings, but really you should create a Distribution instead of using this method.
getNumberOfKeys() - Method in class edu.stanford.nlp.stats.Distribution
 
getObject(E) - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
Searches for the object in the queue and returns it.
getOuterMapFactory() - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
getPerturbedDistribution(GenericCounter<E>, Random) - Static method in class edu.stanford.nlp.stats.Distribution
 
getPerturbedUniformDistribution(Set<E>, Random) - Static method in class edu.stanford.nlp.stats.Distribution
 
getPriority() - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
Gets the priority of the highest-priority element of the queue.
getPriority(E) - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
Get the priority of a key -- if the key is not in the queue, Double.NEGATIVE_INFINITY is returned.
getPriority(Object) - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
Note that this method will be linear (not constant) time in this implementation! Better not to use it.
getPriority() - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
Gets the priority of the highest-priority element of the queue.
getPriority() - Method in interface edu.stanford.nlp.util.PriorityQueue
Gets the priority of the highest-priority element of the queue (without modifying the queue).
getPriority(E) - Method in interface edu.stanford.nlp.util.PriorityQueue
Get the priority of a key.
getRandomSubDataset(double, int) - Method in class edu.stanford.nlp.classify.Dataset
 
getReservedMass() - Method in class edu.stanford.nlp.stats.Distribution
 
getRole() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
 
getRVFDatum(int) - Method in class edu.stanford.nlp.classify.Dataset
 
getRVFDatum(int) - Method in class edu.stanford.nlp.classify.GeneralDataset
 
getRVFDatum(int) - Method in class edu.stanford.nlp.classify.RVFDataset
 
getRVFDatumId(int) - Method in class edu.stanford.nlp.classify.RVFDataset
 
getRVFDatumSource(int) - Method in class edu.stanford.nlp.classify.RVFDataset
 
getSemanticTag() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Returns the semantic head pos of the phrase if it exists, and null otherwise
getSemanticWord() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Returns the semantic head of the phrase if it exists, and null otherwise
getShortClassName(Object) - Static method in class edu.stanford.nlp.util.StringUtils
Returns a short class name for an object.
getSigma() - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
getSigma() - Method in class edu.stanford.nlp.classify.LogPrior
 
getSource() - Method in class edu.stanford.nlp.util.IntPair
 
getSource() - Method in class edu.stanford.nlp.util.IntQuadruple
 
getSource() - Method in class edu.stanford.nlp.util.IntTriple
 
getSource() - Method in class edu.stanford.nlp.util.IntUni
 
getString(Object) - Method in class edu.stanford.nlp.ling.FeatureLabel
Return the String value of the FeatureLabel for an arbitrary key.
getString(List, int) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns (String)list.get(index).
getString(Map, Object) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns (String)map.get(key).
getTarget() - Method in class edu.stanford.nlp.util.IntPair
 
getTarget() - Method in class edu.stanford.nlp.util.IntQuadruple
 
getTarget() - Method in class edu.stanford.nlp.util.IntTriple
 
getTarget2() - Method in class edu.stanford.nlp.util.IntQuadruple
 
getType(String) - Static method in class edu.stanford.nlp.classify.LogPrior
 
getType() - Method in class edu.stanford.nlp.classify.LogPrior
 
getUniformDistribution(Set<E>) - Static method in class edu.stanford.nlp.stats.Distribution
 
getval() - Method in class edu.stanford.nlp.math.DoubleAD
 
getValuesArray() - Method in class edu.stanford.nlp.classify.Dataset
 
getValuesArray() - Method in class edu.stanford.nlp.classify.GeneralDataset
 
getValuesArray() - Method in class edu.stanford.nlp.classify.RVFDataset
 
getVariance(double[]) - Method in class edu.stanford.nlp.optimization.StochasticDiffFunctionTester
 
getVariance(double[], int) - Method in class edu.stanford.nlp.optimization.StochasticDiffFunctionTester
 
getWeights() - Method in class edu.stanford.nlp.classify.WeightedDataset
 
globalIntern(Object) - Static method in class edu.stanford.nlp.util.Interner
Returns a unique object o' that .equals the argument o.
goldAnswer() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
convenience method for getting gold answer *
goldAnswer() - Method in class edu.stanford.nlp.ling.FeatureLabel
convenience method for getting gold answer *
GOLDANSWER_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for gold answer which is a String
GoldenSectionLineSearch - Class in edu.stanford.nlp.optimization
A class to do golden section line search.
GoldenSectionLineSearch(double, double, double) - Constructor for class edu.stanford.nlp.optimization.GoldenSectionLineSearch
 
GoldenSectionLineSearch(boolean) - Constructor for class edu.stanford.nlp.optimization.GoldenSectionLineSearch
 
GoldenSectionLineSearch(boolean, double, double, double) - Constructor for class edu.stanford.nlp.optimization.GoldenSectionLineSearch
 
goodTuringSmoothedCounter(GenericCounter<E>, int) - Static method in class edu.stanford.nlp.stats.Distribution
Creates a Good-Turing smoothed Distribution from the given counter.
goodTuringWithExplicitUnknown(GenericCounter<E>, E) - Static method in class edu.stanford.nlp.stats.Distribution
Creates a Good-Turing smoothed Distribution from the given counter without creating any reserved mass-- instead, the special object UNK in the counter is assumed to be the count of "UNSEEN" items.
gradPerturbed - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
greekifyNGrams - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 

H

HasCategory - Interface in edu.stanford.nlp.ling
Something that implements the HasCategory interface knows about categories.
HasContext - Interface in edu.stanford.nlp.ling
 
HASH_MAP_FACTORY - Static variable in class edu.stanford.nlp.util.MapFactory
 
HASH_SET_FACTORY - Static variable in class edu.stanford.nlp.util.CollectionFactory
 
hashCode() - Method in class edu.stanford.nlp.ling.FeatureLabel
 
hashCode() - Method in class edu.stanford.nlp.stats.Counter
 
hashCode() - Method in class edu.stanford.nlp.stats.Distribution
 
hashCode() - Method in class edu.stanford.nlp.stats.IntCounter
 
hashCode() - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
hashCode() - Method in class edu.stanford.nlp.util.ArrayMap
 
hashCode() - Method in class edu.stanford.nlp.util.IntPair
 
hashCode() - Method in class edu.stanford.nlp.util.IntQuadruple
 
hashCode() - Method in class edu.stanford.nlp.util.IntTriple
 
hashCode() - Method in class edu.stanford.nlp.util.IntTuple
 
hashCode() - Method in class edu.stanford.nlp.util.IntUni
 
hashCode() - Method in class edu.stanford.nlp.util.MutableDouble
 
hashCode() - Method in class edu.stanford.nlp.util.MutableInteger
 
hashCode() - Method in class edu.stanford.nlp.util.Pair
 
hashCode() - Method in class edu.stanford.nlp.util.ScoredComparator
Return the hashCode: there are only two distinct comparators by equals().
hashCode() - Method in class edu.stanford.nlp.util.Triple
 
hashCodeCache - Variable in class edu.stanford.nlp.util.ArrayMap
 
hashSetFactory() - Static method in class edu.stanford.nlp.util.CollectionFactory
This method allows type safety in calling code.
hasInfinite(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
HasInitial - Interface in edu.stanford.nlp.optimization
Indicates that a function has a method for supplying an intitial value.
hasNaN(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
hasNewVals - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
hasNext() - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
 
hasNext() - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
Returns true if the priority queue is non-empty
HasTag - Interface in edu.stanford.nlp.ling
Something that implements the HasTag interface knows about part-of-speech tags.
HasWord - Interface in edu.stanford.nlp.ling
Something that implements the HasWord interface knows about words.
HdotV - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
HdotVAt(double[], double[], int) - Method in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
HdotVAt will return the hessian vector product H.v at the point x for a batchSize subset of the data There are several ways to perform this calculation, as of now Finite Difference, and Algorithmic Differentiation are the methods that have been used.
HdotVAt(double[], double[], double[], int) - Method in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
HdotVAt(double[], double[]) - Method in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
HEAD_TAG_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for storing a head tag in the map as a pointer to another node.
HEAD_WORD_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for storing a head word in the map as a pointer to another node.
headTag() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
 
headWord() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Return the head word of the label (or null if none), which is stored in the map under the key HEAD_WORD_KEY.
heldOutSetSigma(GeneralDataset) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
heldOutSetSigma(GeneralDataset, Scorer) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
heldOutSetSigma(GeneralDataset, GeneralDataset) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
heldOutSetSigma(GeneralDataset, GeneralDataset, Scorer) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
heldOutSetSigma(GeneralDataset, GeneralDataset, LineSearcher) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
heldOutSetSigma(GeneralDataset, GeneralDataset, Scorer, LineSearcher) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Sets the sigma parameter to a value that optimizes the held-out score given by scorer.
HUBER_PRIOR - Static variable in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 
hybridCutoffIteration - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
HybridMinimizer - Class in edu.stanford.nlp.optimization
Hybrid Minimizer is set up as a combination of two minimizers.
HybridMinimizer(Minimizer, Minimizer, int) - Constructor for class edu.stanford.nlp.optimization.HybridMinimizer
 
hypergeometric(int, int, int, int) - Static method in class edu.stanford.nlp.math.SloppyMath
Find a hypergeometric distribution.

I

IDENTITY_HASH_MAP_FACTORY - Static variable in class edu.stanford.nlp.util.MapFactory
 
incrementAll(double) - Method in class edu.stanford.nlp.stats.Counter
Adds the same amount to every count, that is to every key currently stored in the counter (with no lookups).
incrementBatch(int) - Method in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
incrementBatch will shift the curElement variable to mark the next batch.
incrementCount(E, double) - Method in class edu.stanford.nlp.stats.Counter
Adds the given count to the current count for the given key.
incrementCount(E) - Method in class edu.stanford.nlp.stats.Counter
Adds 1.0 to the count for the given key.
incrementCount(List, Object) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
equivalent to incrementCount(l,o,1.0).
incrementCount(List, Object, double) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
same as incrementCount(List, double) but as if Object o were at the end of the list
incrementCount(List) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Equivalent to incrementCount(l, 1.0).
incrementCount(List, double) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
adds to count for the GeneralizedCounter.depth()-dimensional key l.
incrementCount(E, int) - Method in class edu.stanford.nlp.stats.IntCounter
Adds the given count to the current count for the given key.
incrementCount(E) - Method in class edu.stanford.nlp.stats.IntCounter
Adds 1 to the count for the given key.
incrementCount(K1, K2) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
incrementCount(K1, K2, double) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
incrementCount(Map, Object, int) - Static method in class edu.stanford.nlp.util.CollectionUtils
Adds the given delta to the Integer value stored for the given key in the given Map.
incrementCount(Map, Object) - Static method in class edu.stanford.nlp.util.CollectionUtils
Increments the Integer count of the given key in the given Map by 1.
incrementCount1D(Object) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Equivalent to incrementCount1D(o, 1.0).
incrementCount1D(Object, double) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Equivalent to GeneralizedCounter.incrementCount(java.util.List, java.lang.Object)({o}, count); only works for a depth 1 GeneralizedCounter.
incrementCount2D(Object, Object) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Equivalent to incrementCount2D(first,second,1.0).
incrementCount2D(Object, Object, double) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Equivalent to incrementCount( new Object[] { first, second }, count ).
incrementCount3D(Object, Object, Object) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Equivalent to incrementCount3D(first,second,1.0).
incrementCount3D(Object, Object, Object, double) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Equivalent to incrementCount( new Object[] { first, second, third }, count ).
incrementCounts(Collection<E>, double) - Method in class edu.stanford.nlp.stats.Counter
Adds the given count to the current counts for each of the given keys.
incrementCounts(Collection<E>) - Method in class edu.stanford.nlp.stats.Counter
Adds 1.0 to the counts for each of the given keys.
incrementCounts(Collection<E>, int) - Method in class edu.stanford.nlp.stats.IntCounter
Adds the given count to the current counts for each of the given keys.
incrementCounts(Collection<E>) - Method in class edu.stanford.nlp.stats.IntCounter
Adds 1 to the counts for each of the given keys.
incValue(int) - Method in class edu.stanford.nlp.util.MutableInteger
Add the argument to the value of this integer.
index() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Return the index of the label (or -1 if none), which is stored in the map under the key INDEX_KEY.
Index<E> - Class in edu.stanford.nlp.util
An Index is a collection that maps between an Object vocabulary and a contiguous non-negative integer index series beginning (inclusively) at 0.
Index() - Constructor for class edu.stanford.nlp.util.Index
Creates a new Index.
Index(Collection<? extends E>) - Constructor for class edu.stanford.nlp.util.Index
Creates a new Index and adds every member of c to it.
INDEX_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for storing an integer index in the map.
indexOf(int, int) - Method in class edu.stanford.nlp.classify.BiasedLogConditionalObjectiveFunction
 
indexOf(int) - Method in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 
indexOf(int, int, int) - Method in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 
indexOf(int, int) - Method in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
indexOf(int, int[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
indexOf(E) - Method in class edu.stanford.nlp.util.Index
Returns the integer index of the Object in the Index or -1 if the Object is not already in the Index.
indexOf(E, boolean) - Method in class edu.stanford.nlp.util.Index
Takes an Object and returns the integer index of the Object, perhaps adding it to the index first.
indices(Collection<E>) - Method in class edu.stanford.nlp.util.Index
Returns the index of each elem in a List.
inferenceType - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
init(ArrayList<Pair<Double, Integer>>) - Method in class edu.stanford.nlp.classify.PRCurve
 
initial() - Method in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
use a random starting point uniform -1 1
initial() - Method in class edu.stanford.nlp.optimization.AbstractCachingDiffFunction
 
initial() - Method in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
initial() - Method in interface edu.stanford.nlp.optimization.HasInitial
Returns the intitial point in the domain (but not necessarily a feasible one).
initialGain - Variable in class edu.stanford.nlp.optimization.SMDMinimizer
 
initialGain - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
initialize(int) - Method in class edu.stanford.nlp.classify.Dataset
 
initialize(int) - Method in class edu.stanford.nlp.classify.GeneralDataset
This method takes care of resetting values of the dataset such that it is empty with an initial capacity of numDatums Should be accessed only by appropriate methods within the class, such as clear(), which take care of other parts of the emptying of data
initialize(int) - Method in class edu.stanford.nlp.classify.RVFDataset
 
initialWeights - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
initMC(ArrayList<Triple<Double, Integer, Integer>>) - Method in class edu.stanford.nlp.classify.PRCurve
 
initMC(ProbabilisticClassifier, GeneralDataset) - Method in class edu.stanford.nlp.stats.MultiClassAccuracyStats
 
initViterbi - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
innerProduct(double[], double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
innerProduct(float[], float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
inputEncoding - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
IntCounter<E> - Class in edu.stanford.nlp.stats
A specialized kind of hash table (or map) for storing numeric counts for objects.
IntCounter() - Constructor for class edu.stanford.nlp.stats.IntCounter
Constructs a new (empty) Counter.
IntCounter(MapFactory) - Constructor for class edu.stanford.nlp.stats.IntCounter
Pass in a MapFactory and the map it vends will back your counter.
IntCounter(IntCounter<E>) - Constructor for class edu.stanford.nlp.stats.IntCounter
Constructs a new Counter with the contents of the given Counter.
interimOutputFreq - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
intern - Variable in class edu.stanford.nlp.classify.LinearClassifier
 
intern - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
intern(T) - Method in class edu.stanford.nlp.util.Interner
Returns a unique object o' that .equals the argument o.
intern2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
internAll(Set<T>) - Method in class edu.stanford.nlp.util.Interner
Returns a unique object o' that .equals the argument o.
internedStringPair(String, String) - Static method in class edu.stanford.nlp.util.Pair
Returns an InternedPair where the Strings have been interned.
Interner<T> - Class in edu.stanford.nlp.util
For interning (canonicalizing) things.
Interner() - Constructor for class edu.stanford.nlp.util.Interner
 
interner - Static variable in class edu.stanford.nlp.util.Interner
 
internValues(Interner) - Method in class edu.stanford.nlp.ling.FeatureLabel
Interns all of the keys and values in the underlying map of this FeatureLabel.
interpretation() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
 
INTERPRETATION_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for the semantic interpretation
intersection(GenericCounter<E>, GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
Returns a counter that is the intersection of c1 and c2.
intersection(Set<E>, Set<E>) - Static method in class edu.stanford.nlp.util.Sets
Returns the intersection of sets s1 and s2.
IntPair - Class in edu.stanford.nlp.util
 
IntPair() - Constructor for class edu.stanford.nlp.util.IntPair
 
IntPair(int, int) - Constructor for class edu.stanford.nlp.util.IntPair
 
intPow(int, int) - Static method in class edu.stanford.nlp.math.SloppyMath
exponentiation like we learned in grade school: multiply b by itself e times.
intPow(float, int) - Static method in class edu.stanford.nlp.math.SloppyMath
exponentiation like we learned in grade school: multiply b by itself e times.
intPow(double, int) - Static method in class edu.stanford.nlp.math.SloppyMath
exponentiation like we learned in grade school: multiply b by itself e times.
IntQuadruple - Class in edu.stanford.nlp.util
 
IntQuadruple() - Constructor for class edu.stanford.nlp.util.IntQuadruple
 
IntQuadruple(int, int, int, int) - Constructor for class edu.stanford.nlp.util.IntQuadruple
 
IntTriple - Class in edu.stanford.nlp.util
 
IntTriple() - Constructor for class edu.stanford.nlp.util.IntTriple
 
IntTriple(int, int, int) - Constructor for class edu.stanford.nlp.util.IntTriple
 
IntTuple - Class in edu.stanford.nlp.util
A tuple of int.
IntTuple(int[]) - Constructor for class edu.stanford.nlp.util.IntTuple
 
IntTuple(int) - Constructor for class edu.stanford.nlp.util.IntTuple
 
IntTuple() - Constructor for class edu.stanford.nlp.util.IntTuple
 
IntUni - Class in edu.stanford.nlp.util
Just a single integer
IntUni() - Constructor for class edu.stanford.nlp.util.IntUni
 
IntUni(int) - Constructor for class edu.stanford.nlp.util.IntUni
 
intValue() - Method in class edu.stanford.nlp.math.DoubleAD
 
intValue() - Method in class edu.stanford.nlp.util.MutableDouble
 
intValue() - Method in class edu.stanford.nlp.util.MutableInteger
 
iobTags - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
iobWrapper - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
isCapitalized(String) - Static method in class edu.stanford.nlp.util.StringUtils
Check if a string begins with an uppercase.
isCloseTo(double, double) - Static method in class edu.stanford.nlp.math.SloppyMath
 
isDangerous(double) - Static method in class edu.stanford.nlp.math.SloppyMath
Returns true if the argument is a "dangerous" double to have around, namely one that is infinite, NaN or zero.
isEmpty() - Method in class edu.stanford.nlp.stats.Counter
 
isEmpty() - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Returns true if nothing has a count.
isEmpty() - Method in class edu.stanford.nlp.stats.IntCounter
 
isEmpty() - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
isEmpty() - Method in class edu.stanford.nlp.util.ArrayMap
 
isEmpty() - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
Checks if the queue is empty.
isLocked() - Method in class edu.stanford.nlp.util.Index
Queries the Index for whether it's locked or not.
isSubList(List, List) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns true iff l1 is a sublist of l (i.e., every member of l1 is in l, and for every e1 < e2 in l1, there is an e1 < e2 occurrence in l).
isVeryDangerous(double) - Static method in class edu.stanford.nlp.math.SloppyMath
Returns true if the argument is a "very dangerous" double to have around, namely one that is infinite or NaN.
iterator() - Method in interface edu.stanford.nlp.linalg.Array
Returns an iterator over the entries in the matrix
iterator() - Method in class edu.stanford.nlp.stats.Counter
 
iterator() - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
 
iterator() - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
 
iterator() - Method in class edu.stanford.nlp.util.Index
Returns an iterator over the elements of the collection.

J

jaccardCoefficient(GenericCounter<E>, GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
Returns the Jaccard Coefficient of the two counters.
jensenShannonDivergence(GenericCounter<E>, GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
Calculates the Jensen-Shannon divergence between the two counters.
JL - Static variable in class edu.stanford.nlp.classify.NaiveBayesClassifierFactory
 
join(Iterable, String) - Static method in class edu.stanford.nlp.util.StringUtils
Joins each elem in the Collection with the given glue.
join(List, String) - Static method in class edu.stanford.nlp.util.StringUtils
Joins each elem in the List with the given glue.
join(Object[], String) - Static method in class edu.stanford.nlp.util.StringUtils
Joins each elem in the array with the given glue.
join(List) - Static method in class edu.stanford.nlp.util.StringUtils
Joins elems with a space.
join(Object[]) - Static method in class edu.stanford.nlp.util.StringUtils
Joins elems with a space.
justificationOf(RVFDatum) - Method in class edu.stanford.nlp.classify.LinearClassifier
 
justificationOf(RVFDatum, PrintWriter) - Method in class edu.stanford.nlp.classify.LinearClassifier
Print all features active for a particular datum and the weight that the classifier assigns to each class for those features.
justificationOf(Datum) - Method in class edu.stanford.nlp.classify.LinearClassifier
 
justificationOf(Datum, PrintWriter, Function) - Method in class edu.stanford.nlp.classify.LinearClassifier
 
justificationOf(Datum, PrintWriter, Function, boolean) - Method in class edu.stanford.nlp.classify.LinearClassifier
Print all features active for a particular datum and the weight that the classifier assigns to each class for those features.
justificationOf(Datum, PrintWriter) - Method in class edu.stanford.nlp.classify.LinearClassifier
Print all features active for a particular datum and the weight that the classifier assigns to each class for those features.
justificationOf(Datum, PrintWriter, boolean) - Method in class edu.stanford.nlp.classify.LinearClassifier
Print all features active for a particular datum and the weight that the classifier assigns to each class for those features.
justify - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 

K

keepAllWhitespaces - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
Keep all the whitespaces words in testFile when printing out answers.
keepEnglishWhitespaces - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
Keep the whitespaces between English words in testFile when printing out answers.
keepOBInMemory - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
keysAbove(double) - Method in class edu.stanford.nlp.stats.Counter
Returns the set of keys whose counts are at or above the given threshold.
keysAbove(int) - Method in class edu.stanford.nlp.stats.IntCounter
Returns the set of keys whose counts are at or above the given threshold.
keysAt(double) - Method in class edu.stanford.nlp.stats.Counter
Returns the set of keys that have exactly the given count.
keysAt(int) - Method in class edu.stanford.nlp.stats.IntCounter
Returns the set of keys that have exactly the given count.
keysBelow(double) - Method in class edu.stanford.nlp.stats.Counter
Returns the set of keys whose counts are at or below the given threshold.
keysBelow(int) - Method in class edu.stanford.nlp.stats.IntCounter
Returns the set of keys whose counts are at or below the given threshold.
keySet() - Method in class edu.stanford.nlp.ling.FeatureLabel
 
keySet() - Method in class edu.stanford.nlp.stats.Counter
 
keySet() - Method in class edu.stanford.nlp.stats.Distribution
 
keySet() - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Returns the set of keys, as read-only Lists of size equal to the depth of the GeneralizedCounter.
keySet() - Method in interface edu.stanford.nlp.stats.GenericCounter
Returns the Set of keys in this counter.
keySet() - Method in class edu.stanford.nlp.stats.IntCounter
 
klDivergence(double[], double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
klDivergence(GenericCounter<E>, GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
Calculates the KL divergence between the two counters.

L

L2Normalize(GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
L2 normalize a counter.
label() - Method in class edu.stanford.nlp.ling.BasicDatum
Returns the first label for this Datum, or null if none have been set.
Label - Interface in edu.stanford.nlp.ling
Something that implements the Label interface can act as a constituent, node, or word label with linguistic attributes.
label() - Method in interface edu.stanford.nlp.ling.Labeled
Returns the primary label for this Object, or null if none have been set.
label() - Method in class edu.stanford.nlp.ling.RVFDatum
 
Labeled - Interface in edu.stanford.nlp.ling
Interface for Objects that have a label, whose label is an Object.
labelFactory() - Method in class edu.stanford.nlp.ling.FeatureLabel
 
labelFactory() - Method in interface edu.stanford.nlp.ling.Label
Returns a factory that makes labels of the exact same type as this one.
LabelFactory - Interface in edu.stanford.nlp.ling
A LabelFactory object acts as a factory for creating objects of class Label, or some descendant class.
labelIndex - Variable in class edu.stanford.nlp.classify.GeneralDataset
 
labelIndex() - Method in class edu.stanford.nlp.classify.GeneralDataset
 
labelIndex() - Method in class edu.stanford.nlp.classify.LinearClassifier
 
labelIterator() - Method in class edu.stanford.nlp.classify.GeneralDataset
Returns an iterator over the class labels of the Dataset
labels - Variable in class edu.stanford.nlp.classify.BiasedLogConditionalObjectiveFunction
 
labels() - Method in interface edu.stanford.nlp.classify.Classifier
 
labels - Variable in class edu.stanford.nlp.classify.GeneralDataset
 
labels() - Method in class edu.stanford.nlp.classify.LinearClassifier
 
labels - Variable in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 
labels - Variable in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
labels() - Method in class edu.stanford.nlp.classify.LogisticClassifier
 
labels() - Method in class edu.stanford.nlp.classify.NaiveBayesClassifier
 
labels() - Method in class edu.stanford.nlp.ling.BasicDatum
Returns the complete List of labels for this Datum, which may be empty.
labels() - Method in interface edu.stanford.nlp.ling.Labeled
Returns the complete list of labels for this Object, which may be empty.
labels() - Method in class edu.stanford.nlp.ling.RVFDatum
 
lam - Variable in class edu.stanford.nlp.optimization.SMDMinimizer
 
laplaceSmoothedDistribution(GenericCounter<E>, int) - Static method in class edu.stanford.nlp.stats.Distribution
Creates an Laplace smoothed Distribution from the given counter, ie adds one count to every item, including unseen ones, and divides by the total count.
laplaceSmoothedDistribution(GenericCounter<E>, int, double) - Static method in class edu.stanford.nlp.stats.Distribution
Creates a smoothed Distribution using Lidstone's law, ie adds lambda (typically between 0 and 1) to every item, including unseen ones, and divides by the total count.
laplaceWithExplicitUnknown(GenericCounter<E>, double, E) - Static method in class edu.stanford.nlp.stats.Distribution
Creates a smoothed Distribution with Laplace smoothing, but assumes an explicit count of "UNKNOWN" items.
largeChSegFile - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
lastBatch - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
lastBatchSize - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
lastElement - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
lastNameList - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
lastValue() - Method in class edu.stanford.nlp.optimization.AbstractCachingDiffFunction
 
lastValue() - Method in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
lastVBatch - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
lastXBatch - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
LEFT_TERM_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The Standard key for storing the left terminal number relative to the root of the tree of the leftmost terminal dominated by the current node
lemma() - Method in class edu.stanford.nlp.ling.FeatureLabel
 
LEMMA_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for storing a lemma in the map.
length() - Method in class edu.stanford.nlp.util.IntTuple
 
LinearClassifier - Class in edu.stanford.nlp.classify
Implements a multiclass linear classifier.
LinearClassifier(double[][], Index, Index) - Constructor for class edu.stanford.nlp.classify.LinearClassifier
 
LinearClassifier(double[][], Index, Index, double[]) - Constructor for class edu.stanford.nlp.classify.LinearClassifier
 
LinearClassifier(Counter<Pair>) - Constructor for class edu.stanford.nlp.classify.LinearClassifier
 
LinearClassifier(Counter<Pair>, Counter) - Constructor for class edu.stanford.nlp.classify.LinearClassifier
 
LinearClassifierFactory - Class in edu.stanford.nlp.classify
Builds various types of linear classifiers, with functionality for setting objective function, optimization method, and other parameters.
LinearClassifierFactory() - Constructor for class edu.stanford.nlp.classify.LinearClassifierFactory
 
LinearClassifierFactory(Minimizer) - Constructor for class edu.stanford.nlp.classify.LinearClassifierFactory
 
LinearClassifierFactory(boolean) - Constructor for class edu.stanford.nlp.classify.LinearClassifierFactory
 
LinearClassifierFactory(double) - Constructor for class edu.stanford.nlp.classify.LinearClassifierFactory
 
LinearClassifierFactory(Minimizer, boolean) - Constructor for class edu.stanford.nlp.classify.LinearClassifierFactory
 
LinearClassifierFactory(Minimizer, double, boolean) - Constructor for class edu.stanford.nlp.classify.LinearClassifierFactory
 
LinearClassifierFactory(double, boolean, double) - Constructor for class edu.stanford.nlp.classify.LinearClassifierFactory
 
LinearClassifierFactory(Minimizer, double, boolean, double) - Constructor for class edu.stanford.nlp.classify.LinearClassifierFactory
 
LinearClassifierFactory(Minimizer, double, boolean, int, double) - Constructor for class edu.stanford.nlp.classify.LinearClassifierFactory
 
LinearClassifierFactory(double, boolean, int, double, double) - Constructor for class edu.stanford.nlp.classify.LinearClassifierFactory
 
LinearClassifierFactory(double, boolean, int, double, double, int) - Constructor for class edu.stanford.nlp.classify.LinearClassifierFactory
 
LinearClassifierFactory(Minimizer, double, boolean, int, double, double) - Constructor for class edu.stanford.nlp.classify.LinearClassifierFactory
Create a factory that builds linear classifiers from training data.
LinearClassifierFactory(Minimizer, double, boolean, LogPrior) - Constructor for class edu.stanford.nlp.classify.LinearClassifierFactory
 
linearCombination(GenericCounter<E>, double, GenericCounter<E>, double) - Static method in class edu.stanford.nlp.stats.Counters
Returns a Counter which is a weighted average of c1 and c2.
LineSearcher - Interface in edu.stanford.nlp.optimization
The interface for one variable function minimizers.
LINKED_LIST_FACTORY - Static variable in class edu.stanford.nlp.util.CollectionFactory
 
linkedListFactory() - Static method in class edu.stanford.nlp.util.CollectionFactory
 
listToFile(List<double[]>, String) - Method in class edu.stanford.nlp.optimization.StochasticDiffFunctionTester
 
load2DMatrixFromFile(String) - Static method in class edu.stanford.nlp.math.ArrayMath
 
loadAuxClassifier - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
loadClassifier - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
loadCollection(String, Class, CollectionFactory) - Static method in class edu.stanford.nlp.util.CollectionUtils
 
loadCollection(File, Class, CollectionFactory) - Static method in class edu.stanford.nlp.util.CollectionUtils
 
loadCounter(String, Class<E>) - Static method in class edu.stanford.nlp.stats.Counters
Loads a Counter from a text file.
loadDatasetsDir - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
loadFromFilename(String) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Given the path to a file representing the text based serialization of a Linear Classifier, reconstitutes and returns that LinearClassifier.
loadFromFilename(String) - Static method in class edu.stanford.nlp.util.Index
 
loadFromReader(BufferedReader) - Static method in class edu.stanford.nlp.util.Index
This is the analogue of loadFromFilename, and is intended to be included in a routine that unpacks a text-serialized form of an object that incorporates an Index.
loadIntCounter(String, Class<E>) - Static method in class edu.stanford.nlp.stats.Counters
Loads a Counter from a text file.
loadJarClassifier - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
loadProcessedData - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
lock() - Method in class edu.stanford.nlp.util.Index
Locks the Index.
log(DoubleAD) - Static method in class edu.stanford.nlp.math.ADMath
 
log(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
logAdd(float, float) - Static method in class edu.stanford.nlp.math.SloppyMath
Returns the log of the sum of two numbers, which are themselves input in log form.
logAdd(double, double) - Static method in class edu.stanford.nlp.math.SloppyMath
Returns the log of the sum of two numbers, which are themselves input in log form.
LogConditionalEqConstraintFunction - Class in edu.stanford.nlp.classify
Maximizes the conditional likelihood with a given prior.
LogConditionalEqConstraintFunction(int, int, int[][], int[]) - Constructor for class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 
LogConditionalEqConstraintFunction(int, int, int[][], int[], double) - Constructor for class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 
LogConditionalEqConstraintFunction(int, int, int[][], int[], int, double, double) - Constructor for class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 
LogConditionalObjectiveFunction - Class in edu.stanford.nlp.classify
Maximizes the conditional likelihood with a given prior.
LogConditionalObjectiveFunction(GeneralDataset) - Constructor for class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
LogConditionalObjectiveFunction(GeneralDataset, LogPrior) - Constructor for class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
LogConditionalObjectiveFunction(GeneralDataset, LogPrior, boolean) - Constructor for class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
LogConditionalObjectiveFunction(int, int, int[][], int[], boolean) - Constructor for class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
LogConditionalObjectiveFunction(int, int, int[][], int[]) - Constructor for class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
LogConditionalObjectiveFunction(int, int, int[][], int[], LogPrior) - Constructor for class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
LogConditionalObjectiveFunction(int, int, int[][], int[], float[]) - Constructor for class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
LogConditionalObjectiveFunction(int, int, int[][], int[], float[], LogPrior) - Constructor for class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
LogConditionalObjectiveFunction(int, int, int[][], int[], int, double, double) - Constructor for class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
LogConditionalObjectiveFunction(int, int, int[][], double[][], int[], int, double, double) - Constructor for class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
logIncrementCount(E, double) - Method in class edu.stanford.nlp.stats.Counter
If the current count for the object is c1, and you call logIncrementCount with a value of c2, then the new value will be log(e^c1 + e^c2).
logInPlace(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
LogisticClassifier - Class in edu.stanford.nlp.classify
A classifier for binary logistic regression problems.
LogisticClassifier() - Constructor for class edu.stanford.nlp.classify.LogisticClassifier
 
LogisticClassifier(boolean) - Constructor for class edu.stanford.nlp.classify.LogisticClassifier
 
LogisticClassifier(LogPrior) - Constructor for class edu.stanford.nlp.classify.LogisticClassifier
 
LogisticClassifier(LogPrior, boolean) - Constructor for class edu.stanford.nlp.classify.LogisticClassifier
 
LogisticObjectiveFunction - Class in edu.stanford.nlp.classify
Maximizes the conditional likelihood with a given prior.
LogisticObjectiveFunction(int, int[][], int[]) - Constructor for class edu.stanford.nlp.classify.LogisticObjectiveFunction
 
LogisticObjectiveFunction(int, int[][], int[], LogPrior) - Constructor for class edu.stanford.nlp.classify.LogisticObjectiveFunction
 
LogisticObjectiveFunction(int, int[][], int[], float[]) - Constructor for class edu.stanford.nlp.classify.LogisticObjectiveFunction
 
LogisticObjectiveFunction(int, int[][], int[], LogPrior, float[]) - Constructor for class edu.stanford.nlp.classify.LogisticObjectiveFunction
 
LogisticObjectiveFunction(int, int[][], double[][], int[]) - Constructor for class edu.stanford.nlp.classify.LogisticObjectiveFunction
 
LogisticObjectiveFunction(int, int[][], double[][], int[], LogPrior) - Constructor for class edu.stanford.nlp.classify.LogisticObjectiveFunction
 
LogisticObjectiveFunction(int, int[][], double[][], int[], LogPrior, float[]) - Constructor for class edu.stanford.nlp.classify.LogisticObjectiveFunction
 
logLikelihood() - Method in class edu.stanford.nlp.classify.PRCurve
assuming the scores are probability of 1 given x
logNormalize(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Makes the values in this array sum to 1.0.
logNormalize() - Method in class edu.stanford.nlp.stats.Counter
 
logPrecision(int) - Method in class edu.stanford.nlp.classify.PRCurve
what is the precision at this recall if we look at the score as the probability of class 1 given x as if coming from logistic regression
LogPrior - Class in edu.stanford.nlp.classify
A Prior for functions.
LogPrior() - Constructor for class edu.stanford.nlp.classify.LogPrior
 
LogPrior(int) - Constructor for class edu.stanford.nlp.classify.LogPrior
 
LogPrior(LogPrior.LogPriorType) - Constructor for class edu.stanford.nlp.classify.LogPrior
 
LogPrior(int, double, double) - Constructor for class edu.stanford.nlp.classify.LogPrior
 
LogPrior(LogPrior.LogPriorType, double, double) - Constructor for class edu.stanford.nlp.classify.LogPrior
 
LogPrior.LogPriorType - Enum in edu.stanford.nlp.classify
 
logProbabilityOf(Datum) - Method in class edu.stanford.nlp.classify.LinearClassifier
Returns a counter mapping from each class name to the log probability of that class for a certain example.
logProbabilityOf(RVFDatum) - Method in class edu.stanford.nlp.classify.LinearClassifier
Returns a counter for the log probability of each of the classes looking at the the sum of e^v for each count v, should be 1
logProbabilityOf(Datum) - Method in interface edu.stanford.nlp.classify.ProbabilisticClassifier
 
logSum(DoubleAD[]) - Static method in class edu.stanford.nlp.math.ADMath
 
logSum(DoubleAD[], int, int) - Static method in class edu.stanford.nlp.math.ADMath
 
logSum(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Returns the log of the sum of an array of numbers, which are themselves input in log form.
logSum(double[], int, int) - Static method in class edu.stanford.nlp.math.ArrayMath
Returns the log of the portion between fromIndex, inclusive, and toIndex, exclusive, of an array of numbers, which are themselves input in log form.
logSum(List<Double>) - Static method in class edu.stanford.nlp.math.ArrayMath
 
logSum(List<Double>, int, int) - Static method in class edu.stanford.nlp.math.ArrayMath
 
logSum(float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Returns the log of the sum of an array of numbers, which are themselves input in log form.
logSum() - Method in class edu.stanford.nlp.stats.Counter
 
longestCommonContiguousSubstring(String, String) - Static method in class edu.stanford.nlp.util.StringUtils
Computes the longest common contiguous substring of s and t.
longestCommonSubstring(String, String) - Static method in class edu.stanford.nlp.util.StringUtils
Computes the longest common substring of s and t.
longValue() - Method in class edu.stanford.nlp.math.DoubleAD
 
longValue() - Method in class edu.stanford.nlp.util.MutableDouble
 
longValue() - Method in class edu.stanford.nlp.util.MutableInteger
 
lookingAt(String, String) - Static method in class edu.stanford.nlp.util.StringUtils
Say whether this regular expression can be found at the beginning of this String.
lookupShaper(String) - Static method in class edu.stanford.nlp.process.WordShapeClassifier
 
lowercaseNGrams - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
lowerNewgeneThreshold - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
lowestLevelCounterEntrySet() - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Returns a set of entries, where each key is a read-only List of size one less than the depth of the GeneralizedCounter, and each value is a Counter.

M

main(String[]) - Static method in class edu.stanford.nlp.classify.ColumnDataClassifier
Runs the ColumnDataClassifier.
main(String[]) - Static method in class edu.stanford.nlp.classify.CrossValidator
 
main(String[]) - Static method in class edu.stanford.nlp.classify.Dataset
 
main(String[]) - Static method in class edu.stanford.nlp.classify.LogisticClassifier
 
main(String[]) - Static method in class edu.stanford.nlp.classify.NaiveBayesClassifierFactory
 
main(String[]) - Static method in class edu.stanford.nlp.classify.PRCurve
 
main(String[]) - Static method in class edu.stanford.nlp.classify.RVFDataset
 
main(String[]) - Static method in class edu.stanford.nlp.math.ArrayMath
For testing only.
main(String[]) - Static method in class edu.stanford.nlp.math.SloppyMath
Tests the hypergeometric distribution code, or other functions provided in this module.
main(String[]) - Static method in class edu.stanford.nlp.optimization.GoldenSectionLineSearch
 
main(String[]) - Static method in class edu.stanford.nlp.optimization.QNMinimizer
 
main(String[]) - Static method in class edu.stanford.nlp.optimization.SGDMinimizer
 
main(String[]) - Static method in class edu.stanford.nlp.optimization.SMDMinimizer
 
main(String[]) - Static method in class edu.stanford.nlp.process.WordShapeClassifier
Usage: java edu.stanford.nlp.process.WordShapeClassifier [-wordShape name] string+
where name is an argument to lookupShaper.
main(String[]) - Static method in class edu.stanford.nlp.stats.Counter
For internal debugging purposes only.
main(String[]) - Static method in class edu.stanford.nlp.stats.Distribution
For internal testing purposes only.
main(String[]) - Static method in class edu.stanford.nlp.stats.GeneralizedCounter
for testing purposes only
main(String[]) - Static method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
main(String[]) - Static method in class edu.stanford.nlp.util.ArrayUtils
For internal debugging purposes only
main(String[]) - Static method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
 
main(String[]) - Static method in class edu.stanford.nlp.util.CollectionUtils
 
main(String[]) - Static method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
 
main(String[]) - Static method in class edu.stanford.nlp.util.Index
 
main(String[]) - Static method in class edu.stanford.nlp.util.Interner
Test method: interns its arguments and says whether they == themselves.
main(String[]) - Static method in class edu.stanford.nlp.util.Sets
 
main(String[]) - Static method in class edu.stanford.nlp.util.StringUtils
Tests the string edit distance function.
makeConsistent - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
makeHTMLTable(String[][], String[], String[]) - Static method in class edu.stanford.nlp.util.StringUtils
Returns an HTML table containing the matrix of Strings passed in.
makeIntFromByte2(byte[], int) - Static method in class edu.stanford.nlp.math.ArrayMath
 
makeIntFromByte4(byte[], int) - Static method in class edu.stanford.nlp.math.ArrayMath
 
makeList(T) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns a new List containing the given object.
makeList(T, T) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns a new List containing the given objects.
makeList(T, T, T) - Static method in class edu.stanford.nlp.util.CollectionUtils
Returns a new List containing the given objects.
maleNameList - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
map - Variable in class edu.stanford.nlp.ling.AbstractMapLabel
The Map which stores all of the attributes for this label, and the label value itself.
map() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Return the Map contained in this label.
map() - Method in class edu.stanford.nlp.ling.FeatureLabel
 
map - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
map - Variable in class edu.stanford.nlp.util.Interner
 
mapFactory - Variable in class edu.stanford.nlp.ling.AbstractMapLabel
The MapFactory which will be used to make new Maps in this AbstractMapLabel.
MapFactory<K,V> - Class in edu.stanford.nlp.util
A factory class for vending different sorts of Maps.
mapStringToArray(String) - Static method in class edu.stanford.nlp.ling.FeatureLabel
 
MARKING_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
Another key used for propbank - to signify core arg nodes or predicate nodes
matches(String, String) - Static method in class edu.stanford.nlp.util.StringUtils
Say whether this regular expression matches this String.
max(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
max(float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
max(int[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
max(int, int, int) - Static method in class edu.stanford.nlp.math.SloppyMath
Returns the minimum of three int values.
max(float, float) - Static method in class edu.stanford.nlp.math.SloppyMath
Returns the greater of two float values.
max(double, double) - Static method in class edu.stanford.nlp.math.SloppyMath
Returns the greater of two double values.
max() - Method in class edu.stanford.nlp.stats.Counter
Finds and returns the largest count in this Counter.
max() - Method in class edu.stanford.nlp.stats.IntCounter
Finds and returns the largest count in this Counter.
maxDocSize - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
maxIterations - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
maxLeft - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
maxNGramLeng - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
maxRight - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
mean(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
memoryThrift - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
mergeTags - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
method - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
min(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
min(float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
min(int[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
min(int, int, int) - Static method in class edu.stanford.nlp.math.SloppyMath
Returns the minimum of three int values.
min(float, float) - Static method in class edu.stanford.nlp.math.SloppyMath
Returns the smaller of two float values.
min(double, double) - Static method in class edu.stanford.nlp.math.SloppyMath
Returns the smaller of two double values.
min() - Method in class edu.stanford.nlp.stats.Counter
Finds and returns the smallest count in this Counter.
min() - Method in class edu.stanford.nlp.stats.IntCounter
Finds and returns the smallest count in this Counter.
minimize(DiffFunction, double, double[]) - Method in class edu.stanford.nlp.optimization.CGMinimizer
 
minimize(DiffFunction, double, double[], int) - Method in class edu.stanford.nlp.optimization.CGMinimizer
 
minimize(Function<Double, Double>, double, double, double) - Method in class edu.stanford.nlp.optimization.GoldenSectionLineSearch
 
minimize(Function<Double, Double>) - Method in class edu.stanford.nlp.optimization.GoldenSectionLineSearch
 
minimize(Function, double, double[]) - Method in class edu.stanford.nlp.optimization.HybridMinimizer
 
minimize(Function, double, double[], int) - Method in class edu.stanford.nlp.optimization.HybridMinimizer
 
minimize(Function<Double, Double>) - Method in interface edu.stanford.nlp.optimization.LineSearcher
Attempts to find an unconstrained minimum of the objective function starting at initial, within functionTolerance.
minimize(T, double, double[]) - Method in interface edu.stanford.nlp.optimization.Minimizer
Attempts to find an unconstrained minimum of the objective function starting at initial, within functionTolerance.
minimize(T, double, double[], int) - Method in interface edu.stanford.nlp.optimization.Minimizer
 
minimize(DiffFunction, double, double[]) - Method in class edu.stanford.nlp.optimization.QNMinimizer
 
minimize(DiffFunction, double, double[], int) - Method in class edu.stanford.nlp.optimization.QNMinimizer
 
minimize(FloatFunction, float, float[]) - Method in class edu.stanford.nlp.optimization.QNMinimizer
 
minimize(Function, double, double[]) - Method in class edu.stanford.nlp.optimization.SGDMinimizer
 
minimize(Function, double, double[], int) - Method in class edu.stanford.nlp.optimization.SGDMinimizer
 
minimize(DiffFunction, double, double[]) - Method in class edu.stanford.nlp.optimization.SGDToQNMinimizer
 
minimize(DiffFunction, double, double[], int) - Method in class edu.stanford.nlp.optimization.SGDToQNMinimizer
 
minimize(Function, double, double[]) - Method in class edu.stanford.nlp.optimization.SMDMinimizer
 
minimize(Function, double, double[], int) - Method in class edu.stanford.nlp.optimization.SMDMinimizer
 
minimize(DiffFunction, double, double[]) - Method in class edu.stanford.nlp.optimization.SQNMinimizer
 
minimize(DiffFunction, double, double[], int) - Method in class edu.stanford.nlp.optimization.SQNMinimizer
 
Minimizer<T extends Function> - Interface in edu.stanford.nlp.optimization
The interface for unconstrained function minimizers.
minus(DoubleAD, DoubleAD) - Static method in class edu.stanford.nlp.math.ADMath
 
minusConst(DoubleAD, double) - Static method in class edu.stanford.nlp.math.ADMath
 
minusEquals(DoubleAD) - Method in class edu.stanford.nlp.math.DoubleAD
 
minusEqualsConst(double) - Method in class edu.stanford.nlp.math.DoubleAD
 
morphFeatureFile - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
mu - Variable in class edu.stanford.nlp.optimization.SMDMinimizer
 
mult(DoubleAD, DoubleAD) - Static method in class edu.stanford.nlp.math.ADMath
 
multConst(DoubleAD, double) - Static method in class edu.stanford.nlp.math.ADMath
 
MultiClassAccuracyStats - Class in edu.stanford.nlp.stats
 
MultiClassAccuracyStats() - Constructor for class edu.stanford.nlp.stats.MultiClassAccuracyStats
 
MultiClassAccuracyStats(int) - Constructor for class edu.stanford.nlp.stats.MultiClassAccuracyStats
 
MultiClassAccuracyStats(String) - Constructor for class edu.stanford.nlp.stats.MultiClassAccuracyStats
 
MultiClassAccuracyStats(String, int) - Constructor for class edu.stanford.nlp.stats.MultiClassAccuracyStats
 
MultiClassAccuracyStats(ProbabilisticClassifier, GeneralDataset, String) - Constructor for class edu.stanford.nlp.stats.MultiClassAccuracyStats
 
MultiClassAccuracyStats(ProbabilisticClassifier, GeneralDataset, String, int) - Constructor for class edu.stanford.nlp.stats.MultiClassAccuracyStats
 
multiply(Array) - Method in interface edu.stanford.nlp.linalg.Array
Returns componentwise multiply
multiply(double) - Method in interface edu.stanford.nlp.linalg.Array
Scalar multiply
multiply(double[], double) - Static method in class edu.stanford.nlp.math.ArrayMath
Scales the values in this array by c.
multiply(float[], float) - Static method in class edu.stanford.nlp.math.ArrayMath
Scales the values in this array by c.
multiplyBy(double) - Method in class edu.stanford.nlp.stats.Counter
Multiplies every count by the given multiplier.
multiplyInPlace(double[], double) - Static method in class edu.stanford.nlp.math.ArrayMath
Scales the values in this array by b.
multiplyInPlace(float[], double) - Static method in class edu.stanford.nlp.math.ArrayMath
Scales the values in this array by b.
MutableDouble - Class in edu.stanford.nlp.util
A class for Double objects that you can change.
MutableDouble() - Constructor for class edu.stanford.nlp.util.MutableDouble
 
MutableDouble(double) - Constructor for class edu.stanford.nlp.util.MutableDouble
 
MutableInteger - Class in edu.stanford.nlp.util
A class for Integer objects that you can change.
MutableInteger() - Constructor for class edu.stanford.nlp.util.MutableInteger
 
MutableInteger(int) - Constructor for class edu.stanford.nlp.util.MutableInteger
 

N

NaiveBayesClassifier - Class in edu.stanford.nlp.classify
 
NaiveBayesClassifier(Counter, Counter, Set, Set, boolean) - Constructor for class edu.stanford.nlp.classify.NaiveBayesClassifier
 
NaiveBayesClassifier(Counter, Counter, Set) - Constructor for class edu.stanford.nlp.classify.NaiveBayesClassifier
 
NaiveBayesClassifierFactory - Class in edu.stanford.nlp.classify
 
NaiveBayesClassifierFactory() - Constructor for class edu.stanford.nlp.classify.NaiveBayesClassifierFactory
 
NaiveBayesClassifierFactory(double, double, double, int, int) - Constructor for class edu.stanford.nlp.classify.NaiveBayesClassifierFactory
 
NBLinearClassifierFactory - Class in edu.stanford.nlp.classify
Provides a medium-weight implementation of Bernoulli (or binary) Naive Bayes via a linear classifier.
NBLinearClassifierFactory() - Constructor for class edu.stanford.nlp.classify.NBLinearClassifierFactory
Create a ClassifierFactory.
NBLinearClassifierFactory(double) - Constructor for class edu.stanford.nlp.classify.NBLinearClassifierFactory
Create a ClassifierFactory.
NBLinearClassifierFactory(double, boolean) - Constructor for class edu.stanford.nlp.classify.NBLinearClassifierFactory
Create a ClassifierFactory.
nChooseK(int, int) - Static method in class edu.stanford.nlp.math.SloppyMath
Computes n choose k in an efficient way.
ner() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Return the NER type of the word, which is stored in the map under the key NER_KEY.
ner() - Method in class edu.stanford.nlp.ling.FeatureLabel
Return the NER type of the word, which is stored in the map under the key NER_KEY.
NER_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
the standard key for the NER label.
newCollection() - Method in class edu.stanford.nlp.util.CollectionFactory.ArrayListFactory
 
newCollection() - Method in class edu.stanford.nlp.util.CollectionFactory.HashSetFactory
 
newCollection() - Method in class edu.stanford.nlp.util.CollectionFactory.LinkedListFactory
 
newCollection() - Method in class edu.stanford.nlp.util.CollectionFactory
 
newEmptyCollection() - Method in class edu.stanford.nlp.util.CollectionFactory.ArrayListFactory
 
newEmptyCollection() - Method in class edu.stanford.nlp.util.CollectionFactory.HashSetFactory
 
newEmptyCollection() - Method in class edu.stanford.nlp.util.CollectionFactory.LinkedListFactory
 
newEmptyCollection() - Method in class edu.stanford.nlp.util.CollectionFactory
 
newgeneThreshold - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
newInstance(int) - Method in interface edu.stanford.nlp.linalg.Array
Returns an empty Array of the same dynamic type with given size
newLabel(String) - Method in interface edu.stanford.nlp.ling.LabelFactory
Make a new label with this String as the value.
newLabel(String, int) - Method in interface edu.stanford.nlp.ling.LabelFactory
Make a new label with this String as the value, and the type determined in an implementation-dependent way from the options value.
newLabel(Label) - Method in interface edu.stanford.nlp.ling.LabelFactory
Create a new Label, where the label is formed from the Label object passed in.
newLabelFromString(String) - Method in interface edu.stanford.nlp.ling.LabelFactory
Make a new label.
newMap() - Method in class edu.stanford.nlp.util.MapFactory
Returns a new non-parameterized map of a particular sort.
newMap(int) - Method in class edu.stanford.nlp.util.MapFactory
Returns a new non-parameterized map of a particular sort with an initial capacity.
next() - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
 
next() - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
Returns the element in the queue with highest priority, and pops it from the queue.
NO_PRIOR - Static variable in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 
noMidNGrams - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
NominalDataReader - Class in edu.stanford.nlp.classify
 
NominalDataReader() - Constructor for class edu.stanford.nlp.classify.NominalDataReader
 
norm() - Method in interface edu.stanford.nlp.linalg.Array
Returns L2 norm of vector
norm(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Computes 2-norm of vector
norm(float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Computes 2-norm of vector
norm_1(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Computes 1-norm of vector
norm_1(float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Computes 1-norm of vector
norm_inf(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Computes inf-norm of vector
norm_inf(float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Computes inf-norm of vector
normalizationTable - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
normalize() - Method in interface edu.stanford.nlp.linalg.Array
Returns vector with euclidian distance normalized to 1.
normalize(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Makes the values in this array sum to 1.0.
normalize(float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Makes the values in this array sum to 1.0.
normalize - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
normalize() - Method in class edu.stanford.nlp.stats.Counter
This has been de-deprecated in order to reduce compilation warnings, but really you should create a Distribution instead.
normalizeTerms - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
normalizeTimex - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
normTableEncoding - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
NOWORDSHAPE - Static variable in class edu.stanford.nlp.process.WordShapeClassifier
 
nthIndex(String, char, int) - Static method in class edu.stanford.nlp.util.StringUtils
Returns the index of the nth occurrence of ch in s, or -1 if there are less than n occurrences of ch.
numBatches - Variable in class edu.stanford.nlp.optimization.StochasticDiffFunctionTester
 
numClasses - Variable in class edu.stanford.nlp.classify.BiasedLogConditionalObjectiveFunction
 
numClasses() - Method in class edu.stanford.nlp.classify.GeneralDataset
 
numClasses - Variable in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 
numClasses - Variable in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
numCorrect(int) - Method in class edu.stanford.nlp.stats.MultiClassAccuracyStats
how many correct do we have if we return the most confident num recall ones
numDatasetsPerFile - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
numFeatures - Variable in class edu.stanford.nlp.classify.BiasedLogConditionalObjectiveFunction
 
numFeatures() - Method in class edu.stanford.nlp.classify.GeneralDataset
 
numFeatures - Variable in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 
numFeatures - Variable in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
numFeatureTokens() - Method in class edu.stanford.nlp.classify.GeneralDataset
returns the number of feature tokens in the Dataset.
numFeatureTypes() - Method in class edu.stanford.nlp.classify.GeneralDataset
returns the number of distinct feature types in the Dataset.
numFolds - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
numRows(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
numRuns - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
numSamples - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
numSamples() - Method in class edu.stanford.nlp.stats.MultiClassAccuracyStats
 
numStartLayers - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
numTimesPruneFeatures - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
numTimesRemoveTopN - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
numValues - Variable in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 

O

object() - Method in class edu.stanford.nlp.util.ScoredObject
 
objects(int[]) - Method in class edu.stanford.nlp.util.Index
Looks up the objects corresponding to an array of indices, and returns them in a Collection.
objectsList() - Method in class edu.stanford.nlp.util.Index
Returns a complete List of indexed objects, in the order of their indices.
ocrFold - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
ocrTrain - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
oneDimensionalCounterView() - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Returns a read-only synchronous view (not a snapshot) of this as a Counter.
oneTailedFishersExact(int, int, int, int) - Static method in class edu.stanford.nlp.math.SloppyMath
Find a one-tailed Fisher's exact probability.
opFmeasure() - Method in class edu.stanford.nlp.classify.PRCurve
 
optFmeasure(int) - Method in class edu.stanford.nlp.classify.PRCurve
what is the optimal f-measure we can achieve given recall guesses using the optimal monotonic function
optimalAccuracy() - Method in class edu.stanford.nlp.classify.PRCurve
 
optimalCwa() - Method in class edu.stanford.nlp.classify.PRCurve
optimal confidence weighted accuracy assuming for each recall we can fit an optimal monotonic function
optimalCwaArray() - Method in class edu.stanford.nlp.classify.PRCurve
confidence weighted accuracy assuming the scores are probabilities and using .5 as treshold
outDict2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
outputFormat - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
outputFrequency - Variable in class edu.stanford.nlp.optimization.SGDMinimizer
 
outputFrequency - Variable in class edu.stanford.nlp.optimization.SGDToQNMinimizer
 
outputFrequency - Variable in class edu.stanford.nlp.optimization.SMDMinimizer
 
outputFrequency - Variable in class edu.stanford.nlp.optimization.SQNMinimizer
 
outputIterationsToFile - Variable in class edu.stanford.nlp.optimization.SGDMinimizer
 
outputIterationsToFile - Variable in class edu.stanford.nlp.optimization.SGDToQNMinimizer
 
outputIterationsToFile - Variable in class edu.stanford.nlp.optimization.SMDMinimizer
 
outputIterationsToFile - Variable in class edu.stanford.nlp.optimization.SQNMinimizer
 
outputIterationsToFile - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 

P

pad - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
pad(String, int) - Static method in class edu.stanford.nlp.util.StringUtils
Return a String of length a minimum of totalChars characters by padding the input String str at the right end with spaces.
pad(Object, int) - Static method in class edu.stanford.nlp.util.StringUtils
Pads the toString value of the given Object.
padLeft(String, int, char) - Static method in class edu.stanford.nlp.util.StringUtils
Pads the given String to the left with the given character to ensure that it's at least totalChars long.
padLeft(String, int) - Static method in class edu.stanford.nlp.util.StringUtils
Pads the given String to the left with spaces to ensure that it's at least totalChars long.
padLeft(Object, int) - Static method in class edu.stanford.nlp.util.StringUtils
 
padLeft(int, int) - Static method in class edu.stanford.nlp.util.StringUtils
 
padLeft(double, int) - Static method in class edu.stanford.nlp.util.StringUtils
 
padLeftOrTrim(String, int) - Static method in class edu.stanford.nlp.util.StringUtils
Pad or trim so as to produce a string of exactly a certain length.
padOrTrim(String, int) - Static method in class edu.stanford.nlp.util.StringUtils
Pad or trim so as to produce a string of exactly a certain length.
padOrTrim(Object, int) - Static method in class edu.stanford.nlp.util.StringUtils
Pad or trim the toString value of the given Object.
Pair<T1,T2> - Class in edu.stanford.nlp.util
Pair: A Class for holding a pair of objects.
Pair() - Constructor for class edu.stanford.nlp.util.Pair
 
Pair(T1, T2) - Constructor for class edu.stanford.nlp.util.Pair
 
pairwiseAdd(int[], int[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
pairwiseAdd(double[], double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
pairwiseAdd(float[], float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
pairwiseAddInPlace(double[], double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
pairwiseMultiply(double[], double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Assumes that both arrays have same length.
pairwiseMultiply(float[], float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Assumes that both arrays have same length.
pairwiseMultiply(double[], double[], double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Puts the result in the result array.
pairwiseMultiply(float[], float[], float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Puts the result in the result array.
pairwiseSubtract(double[], double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
pairwiseSubtract(float[], float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
pairwiseSubtractInPlace(double[], double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
PARENT_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for the parent which is a String
parseCommandLineArguments(String[]) - Static method in class edu.stanford.nlp.util.StringUtils
A simpler form of command line argument parsing.
parseCommandLineArguments(String[], boolean) - Static method in class edu.stanford.nlp.util.StringUtils
A simpler form of command line argument parsing.
parseMethod(String) - Method in enum edu.stanford.nlp.optimization.StochasticCalculateMethods
 
pennPOSToWordnetPOS(String) - Static method in class edu.stanford.nlp.util.StringUtils
Computes the WordNet 2.0 POS tag corresponding to the PTB POS tag s.
perturbCounts(GenericCounter<E>, Random, double) - Static method in class edu.stanford.nlp.stats.Counters
 
plus(DoubleAD, DoubleAD) - Static method in class edu.stanford.nlp.math.ADMath
 
plusConst(DoubleAD, double) - Static method in class edu.stanford.nlp.math.ADMath
 
plusEquals(DoubleAD) - Method in class edu.stanford.nlp.math.DoubleAD
 
plusEqualsConst(double) - Method in class edu.stanford.nlp.math.DoubleAD
 
poisson(int, double) - Static method in class edu.stanford.nlp.math.SloppyMath
 
POLARITY_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
 
pow(double[], double) - Static method in class edu.stanford.nlp.math.ArrayMath
Scales the values in this array by c.
pow(float[], float) - Static method in class edu.stanford.nlp.math.ArrayMath
Scales the values in this array by c.
pow(Counter<T>, double) - Static method in class edu.stanford.nlp.stats.Counters
 
powerSet(Set<E>) - Static method in class edu.stanford.nlp.util.Sets
Returns the powerset (the set of all subsets) of set s.
powInPlace(double[], double) - Static method in class edu.stanford.nlp.math.ArrayMath
Scales the values in this array by c.
powInPlace(float[], float) - Static method in class edu.stanford.nlp.math.ArrayMath
Sets the values in this array by to their value taken to cth power.
powNormalized(Counter<T>, double) - Static method in class edu.stanford.nlp.stats.Counters
Returns a counter where each element corresponds to the normalized count of the corresponding element in c raised to the given power.
PRCurve - Class in edu.stanford.nlp.classify
 
PRCurve(String) - Constructor for class edu.stanford.nlp.classify.PRCurve
reads scores with classes from a file, sorts by score and creates the arrays
PRCurve(String, boolean) - Constructor for class edu.stanford.nlp.classify.PRCurve
reads scores with classes from a file, sorts by score and creates the arrays
PRCurve(ArrayList<Pair<Double, Integer>>) - Constructor for class edu.stanford.nlp.classify.PRCurve
 
precision(int) - Method in class edu.stanford.nlp.classify.PRCurve
what is the best precision at the given recall
predProp - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
prependBefore(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Prepend this String to the current before String
prependBefore(String) - Method in class edu.stanford.nlp.ling.FeatureLabel
Prepend this String to the current before String
prependBefore(String) - Method in interface edu.stanford.nlp.ling.HasContext
Prepend this String to the before String.
prettyPrint() - Method in class edu.stanford.nlp.stats.GeneralizedCounter
pretty-prints the GeneralizedCounter to System.out.
prettyPrint(PrintWriter) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
pretty-prints the GeneralizedCounter, using a buffer increment of two spaces.
prettyPrint(PrintWriter, String) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
pretty-prints the GeneralizedCounter.
primeFactors(long) - Static method in class edu.stanford.nlp.optimization.SMDMinimizer
 
print(PrintStream) - Method in class edu.stanford.nlp.classify.NaiveBayesClassifier
 
print() - Method in class edu.stanford.nlp.classify.NaiveBayesClassifier
 
print() - Method in class edu.stanford.nlp.util.IntTuple
 
printClassifier - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
printClassifierParam - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
printCounterComparison(GenericCounter<E>, GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
Great for debugging.
printCounterComparison(GenericCounter<E>, GenericCounter<E>, PrintStream) - Static method in class edu.stanford.nlp.stats.Counters
Great for debugging.
printCounterSortedByKeys(GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
 
printFeatures - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
printFirstOrderProbs - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
printFullFeatureMatrix(PrintWriter) - Method in class edu.stanford.nlp.classify.Dataset
prints the full feature matrix in tab-delimited form.
printFullFeatureMatrix(PrintWriter) - Method in class edu.stanford.nlp.classify.RVFDataset
prints the full feature matrix in tab-delimited form.
printFullFeatureMatrixWithValues(PrintWriter) - Method in class edu.stanford.nlp.classify.RVFDataset
Modification of printFullFeatureMatrix to correct bugs & print values (Rajat).
printGazFeatures - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
printMinMax - Variable in class edu.stanford.nlp.optimization.SMDMinimizer
 
printProbs - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
printSparseFeatureMatrix() - Method in class edu.stanford.nlp.classify.Dataset
prints the sparse feature matrix using Dataset.printSparseFeatureMatrix() to System.out.
printSparseFeatureMatrix(PrintWriter) - Method in class edu.stanford.nlp.classify.Dataset
prints a sparse feature matrix representation of the Dataset.
printSparseFeatureMatrix() - Method in class edu.stanford.nlp.classify.RVFDataset
prints the sparse feature matrix using RVFDataset.printSparseFeatureMatrix() to System.out.
printSparseFeatureMatrix(PrintWriter) - Method in class edu.stanford.nlp.classify.RVFDataset
prints a sparse feature matrix representation of the Dataset.
printStringOneCharPerLine(String) - Static method in class edu.stanford.nlp.util.StringUtils
 
printSVMLightFormat(PrintWriter, Counter<Integer>, int) - Static method in class edu.stanford.nlp.classify.Dataset
Need to sort the counter by feature keys and dump it
printSVMLightFormat() - Method in class edu.stanford.nlp.classify.GeneralDataset
Dumps the Dataset as a training/test file for SVMLight.
printSVMLightFormat(PrintWriter) - Method in class edu.stanford.nlp.classify.GeneralDataset
Print SVM Light Format file.
printToFile(File, String, boolean) - Static method in class edu.stanford.nlp.util.StringUtils
Prints to a file.
printToFile(File, String) - Static method in class edu.stanford.nlp.util.StringUtils
Prints to a file.
printToFile(String, String, boolean) - Static method in class edu.stanford.nlp.util.StringUtils
Prints to a file.
printToFile(String, String) - Static method in class edu.stanford.nlp.util.StringUtils
Prints to a file.
printToFileLn(File, String, boolean) - Static method in class edu.stanford.nlp.util.StringUtils
Prints to a file.
printToFileLn(String, String, boolean) - Static method in class edu.stanford.nlp.util.StringUtils
Prints to a file.
printXML - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
prior - Variable in class edu.stanford.nlp.classify.BiasedLogConditionalObjectiveFunction
 
prior - Variable in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
priorDerivative - Variable in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
PriorityQueue<E> - Interface in edu.stanford.nlp.util
A Set that also represents an ordering of its elements, and responds quickly to add(), changePriority(), removeFirst(), and getFirst() method calls.
priors(double[]) - Method in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 
priorType - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
ProbabilisticClassifier - Interface in edu.stanford.nlp.classify
 
probabilityOf(Datum) - Method in class edu.stanford.nlp.classify.LinearClassifier
Returns a counter mapping from each class name to the probability of that class for a certain example.
probabilityOf(RVFDatum) - Method in class edu.stanford.nlp.classify.LinearClassifier
Returns a counter mapping from each class name to the probability of that class for a certain example.
probabilityOf(Datum) - Method in class edu.stanford.nlp.classify.LogisticClassifier
 
probabilityOf(Collection, Object) - Method in class edu.stanford.nlp.classify.LogisticClassifier
 
probabilityOf(RVFDatum) - Method in class edu.stanford.nlp.classify.LogisticClassifier
 
probabilityOf(Counter, Object) - Method in class edu.stanford.nlp.classify.LogisticClassifier
 
probabilityOf(Datum) - Method in interface edu.stanford.nlp.classify.ProbabilisticClassifier
 
probabilityOf(E) - Method in class edu.stanford.nlp.stats.Distribution
Returns the normalized count of the given object.
probs - Variable in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
product(GenericCounter<E>, GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
Returns the product of c1 and c2.
PROJ_CAT_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for storing a projected category in the map, as a String.
purgeDatasets - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
purgeFeatures - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
pushDir - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
put(Object, Object) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Associates the specified value with the specified key in the map contained in this label.
put(K, V) - Method in class edu.stanford.nlp.util.ArrayMap
 

Q

QNMinimizer - Class in edu.stanford.nlp.optimization
Limited-Memory Quasi-Newton BFGS implementation based on the algorithms in
QNMinimizer(int) - Constructor for class edu.stanford.nlp.optimization.QNMinimizer
 
QNMinimizer() - Constructor for class edu.stanford.nlp.optimization.QNMinimizer
 
QNMinimizer(Function) - Constructor for class edu.stanford.nlp.optimization.QNMinimizer
 
QNMinimizer(Function, int) - Constructor for class edu.stanford.nlp.optimization.QNMinimizer
 
QNMinimizer(FloatFunction) - Constructor for class edu.stanford.nlp.optimization.QNMinimizer
 
QNPasses - Variable in class edu.stanford.nlp.optimization.SGDToQNMinimizer
 
QNPasses - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
QNsize - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
QNsize2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
QUADRATIC_PRIOR - Static variable in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 
QUARTIC_PRIOR - Static variable in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 

R

randGenerator - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
randomizedRatio - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
readClassifier(String) - Static method in class edu.stanford.nlp.classify.LinearClassifier
 
readerAndWriter - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
readStringPair(DataInputStream) - Static method in class edu.stanford.nlp.util.Pair
Read a string representation of a Pair from a DataStream.
readSVMLightFormat(String) - Static method in class edu.stanford.nlp.classify.Dataset
Constructs a Dataset by reading in a file in SVM light format.
readSVMLightFormat(String, List<String>) - Static method in class edu.stanford.nlp.classify.Dataset
Constructs a Dataset by reading in a file in SVM light format.
readSVMLightFormat(String, Index, Index) - Static method in class edu.stanford.nlp.classify.Dataset
Constructs a Dataset by reading in a file in SVM light format.
readSVMLightFormat(String, Index, Index, List<String>) - Static method in class edu.stanford.nlp.classify.Dataset
Constructs a Dataset by reading in a file in SVM light format.
readSVMLightFormat(String) - Static method in class edu.stanford.nlp.classify.RVFDataset
Constructs a Dataset by reading in a file in SVM light format.
readSVMLightFormat(String, List<String>) - Static method in class edu.stanford.nlp.classify.RVFDataset
Constructs a Dataset by reading in a file in SVM light format.
readSVMLightFormat(String, Index, Index) - Static method in class edu.stanford.nlp.classify.RVFDataset
Constructs a Dataset by reading in a file in SVM light format.
recalculatePrevBatch - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
relaxPriority(E, double) - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
Promotes a key in the queue, adding it if it wasn't there already.
relaxPriority(E, double) - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
Not supported in this implementation.
relaxPriority(E, double) - Method in interface edu.stanford.nlp.util.PriorityQueue
Increases the priority of the E key to the new priority if the old priority was lower than the new priority.
remove(String) - Method in class edu.stanford.nlp.ling.FeatureLabel
 
remove(E) - Method in class edu.stanford.nlp.stats.Counter
Removes the given key from this Counter.
remove(E) - Method in class edu.stanford.nlp.stats.IntCounter
Removes the given key from this Counter.
remove(K1, K2) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
remove(K1) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
remove(Object) - Method in class edu.stanford.nlp.util.ArrayMap
 
remove() - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
 
remove(Object) - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
 
remove() - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
Not supported -- next() already removes the head of the queue.
remove(Object) - Method in class edu.stanford.nlp.util.Index
Removes an object from the index, if it exists (otherwise nothing happens).
removeAll(Collection<E>) - Method in class edu.stanford.nlp.stats.Counter
Removes all the given keys from this Counter.
removeAll(Collection<E>) - Method in class edu.stanford.nlp.stats.IntCounter
Removes all the given keys from this Counter.
removeAt(double[], int) - Static method in class edu.stanford.nlp.util.ArrayUtils
Removes the element at the specified index from the array, and returns a new array containing the remaining elements.
removeAt(Object[], int) - Static method in class edu.stanford.nlp.util.ArrayUtils
Removes the element at the specified index from the array, and returns a new array containing the remaining elements.
removeBackgroundSingletonFeatures - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
removeFirst() - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
Finds the object with the highest priority, removes it, and returns it.
removeFirst() - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
Returns the highest-priority element and removes it from the queue.
removeFirst() - Method in interface edu.stanford.nlp.util.PriorityQueue
Finds the object with the highest priority, removes it, and returns it.
removeObject(List, Object) - Static method in class edu.stanford.nlp.util.CollectionUtils
Removes the first occurrence in the list of the specified object, using object identity (==) not equality as the criterion for object presence.
removeTopN - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
removeTopNPercent - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
removeZeroCounts() - Method in class edu.stanford.nlp.stats.Counter
Removes all keys whose count is 0.
removeZeroCounts() - Method in class edu.stanford.nlp.stats.IntCounter
Removes all keys whose count is 0.
removeZeroCounts() - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
repeat(String, int) - Static method in class edu.stanford.nlp.util.StringUtils
 
report() - Method in class edu.stanford.nlp.util.Timing
Return elapsed time (without stopping timer).
report(String, PrintStream) - Method in class edu.stanford.nlp.util.Timing
Print elapsed time (without stopping timer).
report(String) - Method in class edu.stanford.nlp.util.Timing
Print elapsed time to System.err (without stopping timer).
report(String, PrintWriter) - Method in class edu.stanford.nlp.util.Timing
Print elapsed time (without stopping timer).
resetWeight() - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
resetWeight sets the restWeight flag.
restart() - Method in class edu.stanford.nlp.util.Timing
Restart timer.
restart(String, PrintStream) - Method in class edu.stanford.nlp.util.Timing
Print elapsed time and restart timer.
restart(String) - Method in class edu.stanford.nlp.util.Timing
Print elapsed time to System.err and restart timer.
restart(String, PrintWriter) - Method in class edu.stanford.nlp.util.Timing
Print elapsed time and restart timer.
restrictedArgMax(Counter<E>, Collection<E>) - Static method in class edu.stanford.nlp.stats.Counters
 
restrictSteps - Variable in class edu.stanford.nlp.optimization.SMDMinimizer
 
restrictTransitionsTimit - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
retainEntitySubclassification - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
retainTop(int) - Method in class edu.stanford.nlp.stats.Counter
 
returnPreviousValues - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
reverse(int[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
reverseIndexOrder(TwoDimensionalCounter<K1, K2>) - Static method in class edu.stanford.nlp.stats.TwoDimensionalCounter
Produces a new ConditionalCounter.
reverseKeys() - Method in class edu.stanford.nlp.stats.GeneralizedCounter
 
ROLE_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for the semantic role label
round(double, int) - Static method in class edu.stanford.nlp.math.SloppyMath
E.g.
RuntimeIOException - Exception in edu.stanford.nlp.io
An unchecked version of IOException.
RuntimeIOException() - Constructor for exception edu.stanford.nlp.io.RuntimeIOException
Creates a new exception.
RuntimeIOException(String) - Constructor for exception edu.stanford.nlp.io.RuntimeIOException
Creates a new exception with a message.
RuntimeIOException(Throwable) - Constructor for exception edu.stanford.nlp.io.RuntimeIOException
Creates a new exception with an embedded cause.
RuntimeIOException(String, Throwable) - Constructor for exception edu.stanford.nlp.io.RuntimeIOException
Creates a new exception with a message and an embedded cause.
rvfcalculate(double[]) - Method in class edu.stanford.nlp.classify.AdaptedGaussianPriorObjectiveFunction
 
rvfcalculate(double[]) - Method in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
Calculate conditional likelihood for datasets with real-valued features.
RVFClassifier - Interface in edu.stanford.nlp.classify
A simple interface for classifying and scoring data points with real values features.
RVFDataset - Class in edu.stanford.nlp.classify
An interfacing class for ClassifierFactory that incrementally builds a more memory-efficent representation of a List of RVFDatum objects for the purposes of training a Classifier with a ClassifierFactory.
RVFDataset() - Constructor for class edu.stanford.nlp.classify.RVFDataset
 
RVFDataset(int, Index, Index) - Constructor for class edu.stanford.nlp.classify.RVFDataset
 
RVFDataset(int) - Constructor for class edu.stanford.nlp.classify.RVFDataset
 
RVFDataset(Index, int[], Index, int[][], double[][]) - Constructor for class edu.stanford.nlp.classify.RVFDataset
Constructor that fully specifies a Dataset.
RVFDatum - Class in edu.stanford.nlp.ling
Basic implementation of Datum interface that can be constructed with a Collection of features and one more more labels.
RVFDatum(Counter, Object) - Constructor for class edu.stanford.nlp.ling.RVFDatum
Constructs a new RVFDatum with the given features and label.
RVFDatum(Datum) - Constructor for class edu.stanford.nlp.ling.RVFDatum
Constructs a new RVFDatum taking the data from a Datum
RVFDatum(Counter) - Constructor for class edu.stanford.nlp.ling.RVFDatum
Constructs a new RVFDatum with the given features and no labels.
RVFDatum() - Constructor for class edu.stanford.nlp.ling.RVFDatum
Constructs a new RVFDatum with no features or labels.

S

safeMax(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Returns the largest value in a vector of doubles.
safeMean(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Returns the mean of a vector of doubles.
safeMin(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Returns the largest value in a vector of doubles.
safeStdev(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Returns the standard deviation of a vector of doubles.
sample(Counter<T>, Random) - Static method in class edu.stanford.nlp.stats.Counters
Does not assumes c is normalized.
sample(Counter<T>) - Static method in class edu.stanford.nlp.stats.Counters
Does not assumes c is normalized.
sampleFrom() - Method in class edu.stanford.nlp.stats.Distribution
Returns an object sampled from the distribution.
sampleFromDistribution(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Samples from the distribution over values 0 through d.length given by d.
sampleFromDistribution(double[], Random) - Static method in class edu.stanford.nlp.math.ArrayMath
Samples from the distribution over values 0 through d.length given by d.
sampleFromDistribution(float[], Random) - Static method in class edu.stanford.nlp.math.ArrayMath
Samples from the distribution over values 0 through d.length given by d.
sampleMethod - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
Sampler<T> - Interface in edu.stanford.nlp.stats
An interace for drawing samples from the label space of an object.
sampleWithoutReplacement(int[], int) - Static method in class edu.stanford.nlp.math.ArrayMath
Fills the array with sample from 0 to numArgClasses-1 without replacement.
sampleWithoutReplacement(int[], int, Random) - Static method in class edu.stanford.nlp.math.ArrayMath
Fills the array with sample from 0 to numArgClasses-1 without replacement.
sampleWithoutReplacement(Collection<E>, int) - Static method in class edu.stanford.nlp.util.CollectionUtils
Samples without replacement from a collection.
sampleWithoutReplacement(Collection<E>, int, Random) - Static method in class edu.stanford.nlp.util.CollectionUtils
Samples without replacement from a collection, using your own Random number generator.
sampleWithReplacement(Collection<E>, int) - Static method in class edu.stanford.nlp.util.CollectionUtils
Samples with replacement from a collection
sampleWithReplacement(Collection<E>, int, Random) - Static method in class edu.stanford.nlp.util.CollectionUtils
Samples with replacement from a collection, using your own Random number generator
save(DataOutputStream) - Method in class edu.stanford.nlp.util.Pair
Write a string representation of a Pair from a DataStream.
saveCounter(GenericCounter<E>, String) - Static method in class edu.stanford.nlp.stats.Counters
Saves a Counter to a text file.
saveFeatureIndexToDisk - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
saveToFilename(String) - Method in class edu.stanford.nlp.classify.LinearClassifier
Saves this out to a standard text file, instead of as a serialized Java object.
saveToFilename(String) - Method in class edu.stanford.nlp.util.Index
 
saveToWriter(Writer) - Method in class edu.stanford.nlp.util.Index
This saves the contents of this index into string form, as part of a larger text-serialization.
scale(double) - Method in interface edu.stanford.nlp.linalg.Array
Returns this * factor does not change receiver.
scale(GenericCounter<E>, double) - Static method in class edu.stanford.nlp.stats.Counters
Returns a new Counter which is scaled by the given scale factor.
scale(TwoDimensionalCounter<T1, T2>, double) - Static method in class edu.stanford.nlp.stats.Counters
Creates a new TwoDimensionalCounter where all the counts are scaled by d.
score(ProbabilisticClassifier, GeneralDataset) - Method in class edu.stanford.nlp.stats.AccuracyStats
 
score(ProbabilisticClassifier, GeneralDataset) - Method in class edu.stanford.nlp.stats.MultiClassAccuracyStats
 
score() - Method in class edu.stanford.nlp.stats.MultiClassAccuracyStats
 
score(ProbabilisticClassifier, GeneralDataset) - Method in interface edu.stanford.nlp.stats.Scorer
 
score() - Method in interface edu.stanford.nlp.util.Scored
 
score() - Method in class edu.stanford.nlp.util.ScoredObject
 
Scored - Interface in edu.stanford.nlp.util
Scored: This is a simple interface that says that an object can answer requests for the score, or goodness of the object.
ScoredComparator - Class in edu.stanford.nlp.util
ScoredComparator allows one to compare Scored things.
ScoredObject<T> - Class in edu.stanford.nlp.util
Scored Object: Wrapper class for holding a scored object
ScoredObject() - Constructor for class edu.stanford.nlp.util.ScoredObject
 
ScoredObject(T, double) - Constructor for class edu.stanford.nlp.util.ScoredObject
 
scoreOf(Datum, Object) - Method in class edu.stanford.nlp.classify.LinearClassifier
Returns of the score of the Datum for the specified label.
scoreOf(RVFDatum, Object) - Method in class edu.stanford.nlp.classify.LinearClassifier
Returns the score of the RVFDatum for the specified label.
scoreOf(Collection) - Method in class edu.stanford.nlp.classify.LogisticClassifier
 
scoreOf(Counter<String>) - Method in class edu.stanford.nlp.classify.LogisticClassifier
 
Scorer - Interface in edu.stanford.nlp.stats
 
scoresOf(Datum) - Method in interface edu.stanford.nlp.classify.Classifier
 
scoresOf(Datum) - Method in class edu.stanford.nlp.classify.LinearClassifier
Construct a counter with keys the labels of the classifier and values the score (unnormalized log probability) of each class.
scoresOf(RVFDatum) - Method in class edu.stanford.nlp.classify.LinearClassifier
Construct a counter with keys the labels of the classifier and values the score (unnormalized log probability) of each class for an RVFDatum.
scoresOf(Datum, Collection) - Method in class edu.stanford.nlp.classify.LinearClassifier
 
scoresOf(Datum) - Method in class edu.stanford.nlp.classify.LogisticClassifier
 
scoresOf(RVFDatum) - Method in class edu.stanford.nlp.classify.LogisticClassifier
 
scoresOf(RVFDatum) - Method in class edu.stanford.nlp.classify.NaiveBayesClassifier
 
scoresOf(Datum) - Method in class edu.stanford.nlp.classify.NaiveBayesClassifier
 
scoresOf(RVFDatum) - Method in interface edu.stanford.nlp.classify.RVFClassifier
 
searchAndReplace(String, String, String) - Static method in class edu.stanford.nlp.util.StringUtils
 
second - Variable in class edu.stanford.nlp.util.Pair
Direct access is deprecated.
second() - Method in class edu.stanford.nlp.util.Pair
 
second - Variable in class edu.stanford.nlp.util.Triple
 
second() - Method in class edu.stanford.nlp.util.Triple
 
secondKeySet() - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
selectFeaturesBinaryInformationGain(int) - Method in class edu.stanford.nlp.classify.Dataset
 
selfTest - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
selfTrainConfidenceThreshold - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
selfTrainFile - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
selfTrainIterations - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
selfTrainWindowSize - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
SEMANTIC_HEAD_POS_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for Semantic Head Word POS which is a String
SEMANTIC_HEAD_WORD_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for Semantic Head Word which is a String
SemiSupervisedLogConditionalObjectiveFunction - Class in edu.stanford.nlp.classify
Maximizes the conditional likelihood with a given prior.
SemiSupervisedLogConditionalObjectiveFunction(LogConditionalObjectiveFunction, BiasedLogConditionalObjectiveFunction, LogPrior) - Constructor for class edu.stanford.nlp.classify.SemiSupervisedLogConditionalObjectiveFunction
 
SeqClassifierFlags - Class in edu.stanford.nlp.sequences
Flags for sequence classifiers.
SeqClassifierFlags() - Constructor for class edu.stanford.nlp.sequences.SeqClassifierFlags
 
serializeCounter(GenericCounter, String) - Static method in class edu.stanford.nlp.stats.Counters
 
serializeDatasetsDir - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
serializeTo - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
serialVersionUID - Static variable in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
set(int, double) - Method in interface edu.stanford.nlp.linalg.Array
Sets value of element at index i to val
set(Object, Object) - Method in class edu.stanford.nlp.ling.FeatureLabel
 
set(double, double) - Method in class edu.stanford.nlp.math.DoubleAD
 
set(int, int) - Method in class edu.stanford.nlp.util.IntTuple
 
set(double) - Method in class edu.stanford.nlp.util.MutableDouble
 
set(int) - Method in class edu.stanford.nlp.util.MutableInteger
 
setAfter(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Set the String after the word by storing it in the map under the key AFTER_KEY.
setAfter(String) - Method in class edu.stanford.nlp.ling.FeatureLabel
Set the String after the word by storing it in the map under the key AFTER_KEY.
setAfter(String) - Method in interface edu.stanford.nlp.ling.HasContext
Set the String after the word.
setAll(double) - Method in interface edu.stanford.nlp.linalg.Array
Sets all elements = value (in sparse matrices, sets all nonzero elements = value)
setAnswer(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
convenience method for setting answer *
setAnswer(String) - Method in class edu.stanford.nlp.ling.FeatureLabel
convenience method for setting answer *
setBatchSize(int) - Method in class edu.stanford.nlp.optimization.SGDMinimizer
 
setBatchSize(int) - Method in class edu.stanford.nlp.optimization.SGDToQNMinimizer
 
setBatchSize(int) - Method in class edu.stanford.nlp.optimization.SMDMinimizer
 
setBefore(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Set the String before the word by storing it in the map under the key BEFORE_KEY.
setBefore(String) - Method in class edu.stanford.nlp.ling.FeatureLabel
Set the String before the word by storing it in the map under the key BEFORE_KEY.
setBefore(String) - Method in interface edu.stanford.nlp.ling.HasContext
Set the String before the word.
setCategory(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Set the category for the label.
setCategory(String) - Method in interface edu.stanford.nlp.ling.HasCategory
Set the category value for the label (if one is stored).
setColumnIndex(int) - Method in interface edu.stanford.nlp.linalg.Array
Sets column index of column vector.
setCount(E, double) - Method in class edu.stanford.nlp.stats.Counter
Sets the current count for the given key.
setCount(E, String) - Method in class edu.stanford.nlp.stats.Counter
 
setCount(E, String) - Method in interface edu.stanford.nlp.stats.GenericCounter
Sets the count for this key to be the number encoded in the given String.
setCount(E, int) - Method in class edu.stanford.nlp.stats.IntCounter
Sets the current count for the given key.
setCount(E, String) - Method in class edu.stanford.nlp.stats.IntCounter
 
setCount(K1, K2, double) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
setCounter(K1, Counter<K2>) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
setCounts(Collection<E>, double) - Method in class edu.stanford.nlp.stats.Counter
Sets the current count for each of the given keys.
setCounts(Collection<E>, int) - Method in class edu.stanford.nlp.stats.IntCounter
Sets the current count for each of the given keys.
setCurrent(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Set the String which is the unmangled word, which is stored in the map under the key CURRENT_KEY.
setCurrent(String) - Method in class edu.stanford.nlp.ling.FeatureLabel
Set the String which is the unmangled word, which is stored in the map under the key CURRENT_KEY.
setCurrent(String) - Method in interface edu.stanford.nlp.ling.HasContext
Set the String which is the unmangled word.
setdot(double) - Method in class edu.stanford.nlp.math.DoubleAD
 
setEpsilon(double) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Sets the epsilon value for LogConditionalObjectiveFunction.
setEpsilon(double) - Method in class edu.stanford.nlp.classify.LogPrior
 
setFeatures(Collection) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
 
setFirst(T1) - Method in class edu.stanford.nlp.util.Pair
 
setFirst(T1) - Method in class edu.stanford.nlp.util.Triple
 
setFromString(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Set value for the label from a String.
setFromString(String) - Method in class edu.stanford.nlp.ling.FeatureLabel
 
setFromString(String) - Method in interface edu.stanford.nlp.ling.Label
Set the contents of this label to this String representing the complete contents of the label.
setGlobal(Interner) - Static method in class edu.stanford.nlp.util.Interner
For supplying a new instance for the global methods to use.
setGoldAnswer(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
convenience method for setting gold answer *
setGoldAnswer(String) - Method in class edu.stanford.nlp.ling.FeatureLabel
convenience method for setting gold answer *
setHeadTag(Object) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Set a pointer to the head-word for the label.
setHeadWord(Object) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Set a pointer to the head-word for the label.
setHeldOutSearcher(LineSearcher) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Set the LineSearcher to be used in LinearClassifierFactory.heldOutSetSigma(GeneralDataset, GeneralDataset).
setHessSampleSize(int) - Method in class edu.stanford.nlp.optimization.SGDToQNMinimizer
 
setHistory(List<double[]>, List<double[]>) - Method in class edu.stanford.nlp.optimization.QNMinimizer
 
setIndex(int) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Set the index for the label by storing it in the contained map under the key INDEX_KEY.
setInterpretation(Object) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
 
setKnownLowerCaseWords(Set) - Static method in class edu.stanford.nlp.process.WordShapeClassifier
 
setLabel(Object) - Method in class edu.stanford.nlp.ling.BasicDatum
Removes all currently assigned Labels for this Datum then adds the given Label.
setLabel(Object) - Method in class edu.stanford.nlp.ling.RVFDatum
Removes all currently assigned Labels for this Datum then adds the given Label.
setLabels(Collection) - Method in class edu.stanford.nlp.ling.BasicDatum
Removes all currently assigned labels for this Datum then adds all of the given Labels.
setLemma(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
 
setM(int) - Method in class edu.stanford.nlp.optimization.QNMinimizer
 
setM(int) - Method in class edu.stanford.nlp.optimization.SQNMinimizer
 
setMap(Map<K1, V1>) - Method in class edu.stanford.nlp.util.MapFactory
A method to get a parameterized (genericized) map out.
setMap(Map<K1, V1>, int) - Method in class edu.stanford.nlp.util.MapFactory
 
setMem(int) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Set the mem value for QNMinimizer.
setMinimizer(Minimizer) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Sets the minimizer.
setNER(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Set the NER label for the word, using the key NER_KEY.
setNER(String) - Method in class edu.stanford.nlp.ling.FeatureLabel
Set the NER label for the word, using the key NER_KEY.
setObject(T) - Method in class edu.stanford.nlp.util.ScoredObject
 
setPrior(LogPrior) - Method in class edu.stanford.nlp.classify.BiasedLogConditionalObjectiveFunction
 
setPrior(LogPrior) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Set the prior.
setPrior(LogPrior) - Method in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
setPrior(LogPrior) - Method in class edu.stanford.nlp.classify.SemiSupervisedLogConditionalObjectiveFunction
 
setProperties(Properties) - Method in class edu.stanford.nlp.sequences.SeqClassifierFlags
Initialize this object using values in Properties file.
setProperties(Properties, boolean) - Method in class edu.stanford.nlp.sequences.SeqClassifierFlags
Initialize using values in Properties file.
setQNMem(int) - Method in class edu.stanford.nlp.optimization.SGDToQNMinimizer
 
setRetrainFromScratchAfterSigmaTuning(boolean) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
If set to true, then when training a classifier, after an optimal sigma is chosen a model is relearned from scratch.
setRole(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
 
Sets - Class in edu.stanford.nlp.util
Utilities for sets.
setScore(double) - Method in class edu.stanford.nlp.util.ScoredObject
 
setSecond(T2) - Method in class edu.stanford.nlp.util.Pair
 
setSecond(T2) - Method in class edu.stanford.nlp.util.Triple
 
setSemanticTag(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Set the semantic head pos of the phrase
setSemanticWord(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Set the semantic head of the phrase
setShape(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Set the shape property for the word, using the key SHAPE_KEY.
setSigma(double) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
setSigma(double) - Method in class edu.stanford.nlp.classify.LogPrior
 
setSource(int) - Method in class edu.stanford.nlp.util.IntUni
 
setSpan(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
 
setTag(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Set the head tag for the label by storing it in the map under the key HEAD_TAG_KEY.
setTag(String) - Method in interface edu.stanford.nlp.ling.HasTag
Set the tag value for the label (if one is stored).
setThird(T3) - Method in class edu.stanford.nlp.util.Triple
 
setTol(double) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Set the tolerance.
setToLogDeterministic(float[], int) - Static method in class edu.stanford.nlp.math.ArrayMath
 
setToLogDeterministic(double[], int) - Static method in class edu.stanford.nlp.math.ArrayMath
 
setTuneSigmaCV(int) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
setTuneSigmaCV sets the tuneSigmaCV flag: when turned on, the sigma is tuned by cross-validation.
setTuneSigmaCV(int) - Method in class edu.stanford.nlp.classify.NBLinearClassifierFactory
setTuneSigmaCV sets the tuneSigma flag: when turned on, the sigma is tuned by cross-validation.
setTuneSigmaHeldOut() - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
setTuneSigmaHeldOut sets the tuneSigmaHeldOut flag: when turned on, the sigma is tuned by means of held-out (70%-30%).
setUseSum(boolean) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
SetUseSum sets the useSum flag: when turned on, the Summed Conditional Objective Function is used.
setUseSumCondObjFun(boolean) - Method in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
setval(double) - Method in class edu.stanford.nlp.math.DoubleAD
 
setValue(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Set the value for the label.
setValue(String) - Method in class edu.stanford.nlp.ling.FeatureLabel
 
setValue(String) - Method in interface edu.stanford.nlp.ling.Label
Set the value for the label (if one is stored).
setValue(double) - Method in class edu.stanford.nlp.optimization.AbstractCachingDiffFunction
 
setValue(double) - Method in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 
setVerbose(boolean) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Set the verbose flag for CGMinimizer.
setWeights(double[][]) - Method in class edu.stanford.nlp.classify.LinearClassifier
 
setWord(String) - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Set the word for the label.
setWord(String) - Method in class edu.stanford.nlp.ling.FeatureLabel
convenience method for setting word *
setWord(String) - Method in interface edu.stanford.nlp.ling.HasWord
Set the word value for the label (if one is stored).
SGD2QNhessSamples - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
SGDMinimizer - Class in edu.stanford.nlp.optimization
Stochastic Gradient Descent Minimizer The basic way to use the minimizer is with a null constructor, then the simple minimize method:

SGDMinimizer() - Constructor for class edu.stanford.nlp.optimization.SGDMinimizer
 
SGDMinimizer(double, int) - Constructor for class edu.stanford.nlp.optimization.SGDMinimizer
 
SGDPasses - Variable in class edu.stanford.nlp.optimization.SGDToQNMinimizer
 
SGDPasses - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
SGDToQNMinimizer - Class in edu.stanford.nlp.optimization
Stochastic Gradient Descent To Quasi Newton Minimizer An experimental minimizer which takes a stochastic function (one implementing AbstractStochasticCachingDiffFunction) and executes SGD for the first couple passes, During the final iterations a series of approximate hessian vector products are built up...
SGDToQNMinimizer(SeqClassifierFlags) - Constructor for class edu.stanford.nlp.optimization.SGDToQNMinimizer
 
SGDToQNMinimizer(double, int, int, int, int, int) - Constructor for class edu.stanford.nlp.optimization.SGDToQNMinimizer
 
SGDToQNMinimizer(double, int, int, int) - Constructor for class edu.stanford.nlp.optimization.SGDToQNMinimizer
 
shape() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Return the shape attribute of the word, which is stored in the map under the key SHAPE_KEY.
SHAPE_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for the "shape" of a word: a String representing the type of characters in a word, such as "Xx" for a capitalized word.
shiftLeft() - Method in class edu.stanford.nlp.util.IntTuple
 
shortValue() - Method in class edu.stanford.nlp.util.MutableDouble
 
shortValue() - Method in class edu.stanford.nlp.util.MutableInteger
 
shuffle(int[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
shuffle(int[], Random) - Static method in class edu.stanford.nlp.math.ArrayMath
 
shutUp() - Method in class edu.stanford.nlp.optimization.QNMinimizer
 
shutUp() - Method in class edu.stanford.nlp.optimization.SGDMinimizer
 
shutUp() - Method in class edu.stanford.nlp.optimization.SGDToQNMinimizer
 
shutUp() - Method in class edu.stanford.nlp.optimization.SMDMinimizer
 
shutUp() - Method in class edu.stanford.nlp.optimization.SQNMinimizer
 
sighanCorporaDict - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
for Sighan bakeoff 2005, the path to the dictionary of bigrams appeared in corpus
sighanPostProcessing - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
sigLevelByApproxRand(double[], double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Computes the significance level by approximate randomization, using a default value of 1000 iterations.
sigLevelByApproxRand(double[], double[], int) - Static method in class edu.stanford.nlp.math.ArrayMath
Takes a pair of arrays, A and B, which represent corresponding outcomes of a pair of random variables: say, results for two different classifiers on a sequence of inputs.
sigLevelByApproxRand(int[], int[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
sigLevelByApproxRand(int[], int[], int) - Static method in class edu.stanford.nlp.math.ArrayMath
 
sigLevelByApproxRand(boolean[], boolean[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
sigLevelByApproxRand(boolean[], boolean[], int) - Static method in class edu.stanford.nlp.math.ArrayMath
 
sigma - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
sigmasToTry - Static variable in class edu.stanford.nlp.classify.LinearClassifierFactory
 
sigmoid(double) - Static method in class edu.stanford.nlp.math.SloppyMath
Compute the sigmoid function with mean zero.
size - Variable in class edu.stanford.nlp.classify.GeneralDataset
 
size() - Method in class edu.stanford.nlp.classify.GeneralDataset
Returns the number of examples (Datums) in the Dataset.
size() - Method in interface edu.stanford.nlp.linalg.Array
Returns number of elements in Array
size() - Method in class edu.stanford.nlp.stats.Counter
Returns the number of keys stored in the counter.
size() - Method in interface edu.stanford.nlp.stats.GenericCounter
Returns the number of entries in this counter.
size() - Method in class edu.stanford.nlp.stats.IntCounter
 
size() - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
size() - Method in class edu.stanford.nlp.util.ArrayMap
 
size() - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
Get the number of elements in the queue.
size() - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
Number of elements in the queue.
size() - Method in class edu.stanford.nlp.util.Index
Returns the number of indexed objects.
size() - Method in class edu.stanford.nlp.util.Interner
 
skewDivergence(GenericCounter<E>, GenericCounter<E>, double) - Static method in class edu.stanford.nlp.stats.Counters
Calculates the skew divergence between the two counters.
sloppyGazette - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
SloppyMath - Class in edu.stanford.nlp.math
The class SloppyMath contains methods for performing basic numeric operations.
SloppyMath() - Constructor for class edu.stanford.nlp.math.SloppyMath
 
slurpFile(File) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text in the given File.
slurpFile(String, String) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text in the given file with the given encoding.
slurpFile(String) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text in the given file
slurpFileNoExceptions(String, String) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text in the given file with the given encoding.
slurpFileNoExceptions(File) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text in the given File.
slurpFileNoExceptions(String) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text in the given File.
slurpGBFile(String) - Static method in class edu.stanford.nlp.util.StringUtils
 
slurpGBFileNoExceptions(String) - Static method in class edu.stanford.nlp.util.StringUtils
 
slurpGBURL(URL) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text at the given URL.
slurpGBURLNoExceptions(URL) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text at the given URL.
slurpGZippedFile(String) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text in the given File.
slurpGZippedFile(File) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text in the given File.
slurpReader(Reader) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text from the given Reader.
slurpURL(URL, String) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text at the given URL.
slurpURL(URL) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text at the given URL.
slurpURL(String) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text at the given URL.
slurpURLNoExceptions(URL, String) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text at the given URL.
slurpURLNoExceptions(URL) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text at the given URL.
slurpURLNoExceptions(String) - Static method in class edu.stanford.nlp.util.StringUtils
Returns all the text at the given URL.
SMDMinimizer - Class in edu.stanford.nlp.optimization
Stochastic Meta Descent Minimizer based on
SMDMinimizer() - Constructor for class edu.stanford.nlp.optimization.SMDMinimizer
 
SMDMinimizer(double, int, StochasticCalculateMethods) - Constructor for class edu.stanford.nlp.optimization.SMDMinimizer
 
span() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
The span of this node as begin and end positions if it exists
SPAN_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for span which is a String
split(double) - Method in class edu.stanford.nlp.classify.Dataset
 
split(int, int) - Method in class edu.stanford.nlp.classify.Dataset
 
split(int, int) - Method in class edu.stanford.nlp.classify.GeneralDataset
 
split(double) - Method in class edu.stanford.nlp.classify.GeneralDataset
 
split(double) - Method in class edu.stanford.nlp.classify.RVFDataset
 
split(int, int) - Method in class edu.stanford.nlp.classify.RVFDataset
 
split(String) - Static method in class edu.stanford.nlp.util.StringUtils
Splits on whitespace (\\s+).
split(String, String) - Static method in class edu.stanford.nlp.util.StringUtils
Splits the given string using the given regex as delimiters.
splitDocuments - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
splitOnCharWithQuoting(String, char, char, char) - Static method in class edu.stanford.nlp.util.StringUtils
This function splits the String s into multiple Strings using the splitChar.
splitOnHead - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
SQNMinimizer - Class in edu.stanford.nlp.optimization
Online Limited-Memory Quasi-Newton BFGS implementation based on the algorithms in
SQNMinimizer(int) - Constructor for class edu.stanford.nlp.optimization.SQNMinimizer
 
SQNMinimizer() - Constructor for class edu.stanford.nlp.optimization.SQNMinimizer
 
SQNMinimizer(int, double, int) - Constructor for class edu.stanford.nlp.optimization.SQNMinimizer
 
SQNMinimizer(Function, int) - Constructor for class edu.stanford.nlp.optimization.SQNMinimizer
 
SQNMinimizer(FloatFunction) - Constructor for class edu.stanford.nlp.optimization.SQNMinimizer
 
standardErrorOfMean(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
start() - Method in class edu.stanford.nlp.util.Timing
Start timer.
startDoing(String) - Static method in class edu.stanford.nlp.util.Timing
Print the start of timing message to stderr and start the timer.
startFold - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
startTime() - Static method in class edu.stanford.nlp.util.Timing
Start (static) timer.
state - Variable in class edu.stanford.nlp.classify.CrossValidator.SavedState
 
stdev(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
STEM_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
 
stochasticBatchSize - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
StochasticCalculateMethods - Enum in edu.stanford.nlp.optimization
This enumeratin was created to organize the selection of different methods for stochastic calculations.
StochasticDiffFunctionTester - Class in edu.stanford.nlp.optimization
 
StochasticDiffFunctionTester(Function) - Constructor for class edu.stanford.nlp.optimization.StochasticDiffFunctionTester
 
stochasticMethod - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
stop() - Method in class edu.stanford.nlp.util.Timing
Stop timer.
stop(String, PrintStream) - Method in class edu.stanford.nlp.util.Timing
Print elapsed time and stop timer.
stop(String) - Method in class edu.stanford.nlp.util.Timing
Print elapsed time to System.err and stop timer.
stop(String, PrintWriter) - Method in class edu.stanford.nlp.util.Timing
Print elapsed time and stop timer.
strictlyFirstOrder - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
strictlySecondOrder - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
strictlyThirdOrder - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
strictlyZeroethOrder - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
stringIntern(Pair<String, String>) - Static method in class edu.stanford.nlp.util.Pair
If first and second are Strings, then this returns an InternedPair where the Strings have been interned, and if this Pair is serialized and then deserialized, first and second are interned upon deserialization.
stringToProperties(String) - Static method in class edu.stanford.nlp.util.StringUtils
This method converts a comma-separated String (with whitespace optionally allowed after the comma) representing properties to a Properties object.
stringToProperties(String, Properties) - Static method in class edu.stanford.nlp.util.StringUtils
This method updates a Properties object based on a comma-separated String (with whitespace optionally allowed after the comma) representing properties to a Properties object.
StringUtils - Class in edu.stanford.nlp.util
StringUtils is a class for random String things, including output formatting and command line argument parsing.
stripNonAlphaNumerics(String) - Static method in class edu.stanford.nlp.util.StringUtils
 
stripSGML(String) - Static method in class edu.stanford.nlp.util.StringUtils
 
subArray(int[], int, int) - Static method in class edu.stanford.nlp.math.ArrayMath
 
subCWGaz - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
subtractAll(GenericCounter<E>) - Method in class edu.stanford.nlp.stats.Counter
Subtracts the counts in the given Counter to the counts in this Counter.
subtractAll(GenericCounter<E>, boolean) - Method in class edu.stanford.nlp.stats.Counter
Subtracts the counts in the given Counter from the counts in this Counter.
subtractAll(IntCounter<E>) - Method in class edu.stanford.nlp.stats.IntCounter
Subtracts the counts in the given Counter from the counts in this Counter.
subtractAll(K1, Counter<K2>) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
subtractAll(TwoDimensionalCounter<K1, K2>, boolean) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
subtractMultiple(GenericCounter<E>, double) - Method in class edu.stanford.nlp.stats.Counter
Subtracts the counts in the given Counter to the counts in this Counter.
sum(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
Returns the sum of an array of numbers.
sum(double[], int, int) - Static method in class edu.stanford.nlp.math.ArrayMath
Returns the sum of the portion of an array of numbers between fromIndex, inclusive, and toIndex, exclusive.
sum(int[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
sum(float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
sum(int[][]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
summaryStatistics() - Method in class edu.stanford.nlp.classify.Dataset
Prints some summary statistics to stderr for the Dataset.
summaryStatistics() - Method in class edu.stanford.nlp.classify.GeneralDataset
Print some statistics summarizing the dataset
summaryStatistics() - Method in class edu.stanford.nlp.classify.RVFDataset
Prints some summary statistics to stderr for the Dataset.
sums - Variable in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
sumSquaredError(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
svmLightLineToDatum(String) - Static method in class edu.stanford.nlp.classify.Dataset
 
svmLightLineToRVFDatum(String) - Static method in class edu.stanford.nlp.classify.RVFDataset
 
svmModelFile - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 

T

tag() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Return the head tag of the label (or null if none), which is stored in the map under the key TAG_KEY.
tag() - Method in class edu.stanford.nlp.ling.FeatureLabel
 
tag() - Method in interface edu.stanford.nlp.ling.HasTag
Return the tag value of the label (or null if none).
TAG_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for storing a tag in the map.
testBatchSize - Variable in class edu.stanford.nlp.optimization.StochasticDiffFunctionTester
 
testConditionNumber(int) - Method in class edu.stanford.nlp.optimization.StochasticDiffFunctionTester
 
testDerivatives(double[], double) - Method in class edu.stanford.nlp.optimization.StochasticDiffFunctionTester
This function tests to make sure that the sum of the stochastic calculated gradients is equal to the full gradient.
testDirs - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
testFile - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
testFiles - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
testHessSamples - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
testObjectiveFunction(Function, double[], double) - Method in class edu.stanford.nlp.optimization.SMDMinimizer
testObjectiveFunction This function was written to provide a test for accuracy of stochastic objective functions.
testObjFunc - Variable in class edu.stanford.nlp.optimization.SMDMinimizer
 
testObjFunction - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
testSumOfBatches(double[], double) - Method in class edu.stanford.nlp.optimization.StochasticDiffFunctionTester
This function tests to make sure that the sum of the stochastic calculated gradients is equal to the full gradient.
testVariance(double[]) - Method in class edu.stanford.nlp.optimization.StochasticDiffFunctionTester
 
testVariance - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
TEXT_SERIALIZATION_DELIMITER - Static variable in class edu.stanford.nlp.classify.LinearClassifier
 
textFile - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
third - Variable in class edu.stanford.nlp.util.Triple
 
third() - Method in class edu.stanford.nlp.util.Triple
 
thisFunc - Variable in class edu.stanford.nlp.optimization.StochasticDiffFunctionTester
 
tick() - Static method in class edu.stanford.nlp.util.Timing
Restart (static) timer.
tick(String, PrintStream) - Static method in class edu.stanford.nlp.util.Timing
Print elapsed time and restart (static) timer.
tick(String) - Static method in class edu.stanford.nlp.util.Timing
Print elapsed time to System.err and restart (static) timer.
Timing - Class in edu.stanford.nlp.util
A class for measuring how long things take.
Timing() - Constructor for class edu.stanford.nlp.util.Timing
Constructs new Timing object and starts the timer.
timitDatum - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
to1D(double[][]) - Method in class edu.stanford.nlp.classify.AdaptedGaussianPriorObjectiveFunction
 
to2D(double[]) - Method in class edu.stanford.nlp.classify.BiasedLogConditionalObjectiveFunction
 
to2D(double[]) - Method in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
to2D(double[], int) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
to2D(double[], int, int) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
to3D(double[]) - Method in class edu.stanford.nlp.classify.LogConditionalEqConstraintFunction
 
toAllWeightsString() - Method in class edu.stanford.nlp.classify.LinearClassifier
 
toAscii(String) - Static method in class edu.stanford.nlp.util.StringUtils
 
toBiggestValuesFirstString(Counter<E>) - Static method in class edu.stanford.nlp.stats.Counters
 
toBiggestValuesFirstString(Counter<E>, int) - Static method in class edu.stanford.nlp.stats.Counters
 
toBiggestWeightFeaturesString(boolean, int, boolean) - Method in class edu.stanford.nlp.classify.LinearClassifier
Return a String that prints features with large weights.
toBinaryString(byte[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
toCounter(double[], Index<T>) - Static method in class edu.stanford.nlp.stats.Counters
 
toCSVString(NumberFormat) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
toCSVString(String[]) - Static method in class edu.stanford.nlp.util.StringUtils
 
toDistributionString(int) - Method in class edu.stanford.nlp.classify.LinearClassifier
Similar to histogram but exact values of the weights to see whether there are many equal weights.
toDouble(int[]) - Static method in class edu.stanford.nlp.util.ArrayUtils
Casts to a double array.
toHistogramString() - Method in class edu.stanford.nlp.classify.LinearClassifier
 
toInvocationString(String, String[]) - Static method in class edu.stanford.nlp.util.StringUtils
 
tolerance - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
toLogSpace() - Method in interface edu.stanford.nlp.linalg.Array
Transforms the array to log space.
toMatrix(List<K1>, List<K2>) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
Given an ordering of the first (row) and second (column) keys, will produce a double matrix.
toMatrixString(int) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
toOriginalString(List<FeatureLabel>) - Static method in class edu.stanford.nlp.ling.FeatureLabel
Pieces a List of MapLabels back together using before, after and current.
topLevelKeySet() - Method in class edu.stanford.nlp.stats.GeneralizedCounter
Returns the set of elements that occur in the 0th position of a List key in the GeneralizedCounter.
toPriorityQueue(GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
Returns a PriorityQueue of the c where the score of the object is its priority.
toSecondsString() - Method in class edu.stanford.nlp.util.Timing
 
toSecondsString(long) - Static method in class edu.stanford.nlp.util.Timing
 
toSentence(List<? extends FeatureLabel>) - Static method in class edu.stanford.nlp.ling.FeatureLabel
Pieces a List of MapLabels back together using word and setting a white space between each word
toSortedList(GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
A List of the keys in c, sorted from highest count to lowest.
toSortedList() - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
 
toSortedList() - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
 
toSortedList() - Method in interface edu.stanford.nlp.util.PriorityQueue
 
toString() - Method in class edu.stanford.nlp.classify.Dataset
 
toString() - Method in class edu.stanford.nlp.classify.LinearClassifier
Print out a partial representation of a linear classifier.
toString(String, int) - Method in class edu.stanford.nlp.classify.LinearClassifier
Print out a partial representation of a linear classifier in one of several ways.
toString() - Method in class edu.stanford.nlp.classify.RVFDataset
 
toString() - Method in interface edu.stanford.nlp.linalg.Array
Returns String representation of Array
toString() - Method in class edu.stanford.nlp.ling.BasicDatum
Returns a String representation of this BasicDatum (lists features and labels).
toString() - Method in class edu.stanford.nlp.ling.FeatureLabel
 
toString(String) - Method in class edu.stanford.nlp.ling.FeatureLabel
 
toString() - Method in interface edu.stanford.nlp.ling.Label
Return a String representation of the label.
toString() - Method in class edu.stanford.nlp.ling.RVFDatum
Returns a String representation of this BasicDatum (lists features and labels).
toString(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
toString(double[], NumberFormat) - Static method in class edu.stanford.nlp.math.ArrayMath
 
toString(float[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
toString(float[], NumberFormat) - Static method in class edu.stanford.nlp.math.ArrayMath
 
toString(int[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
toString(int[], NumberFormat) - Static method in class edu.stanford.nlp.math.ArrayMath
 
toString(byte[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
toString(byte[], NumberFormat) - Static method in class edu.stanford.nlp.math.ArrayMath
 
toString(int[][]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
toString(int[][], Object[], Object[], int, int, NumberFormat, boolean) - Static method in class edu.stanford.nlp.math.ArrayMath
 
toString(double[][]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
toString(double[][], int, Object[], Object[], NumberFormat, boolean) - Static method in class edu.stanford.nlp.math.ArrayMath
 
toString(float[][]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
toString(float[][], int, Object[], Object[], NumberFormat, boolean) - Static method in class edu.stanford.nlp.math.ArrayMath
 
toString() - Method in class edu.stanford.nlp.math.DoubleAD
 
toString() - Method in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
toString() - Method in class edu.stanford.nlp.stats.Counter
 
toString(int) - Method in class edu.stanford.nlp.stats.Counter
Returns a string representation which includes no more than the maxKeysToPrint elements with largest counts.
toString(NumberFormat, String, String, String, String) - Method in class edu.stanford.nlp.stats.Counter
Pretty print a Counter.
toString(NumberFormat) - Method in class edu.stanford.nlp.stats.Counter
Pretty print a Counter.
toString(NumberFormat) - Method in class edu.stanford.nlp.stats.Distribution
 
toString() - Method in class edu.stanford.nlp.stats.Distribution
 
toString() - Method in class edu.stanford.nlp.stats.GeneralizedCounter
 
toString(String) - Method in class edu.stanford.nlp.stats.GeneralizedCounter
 
toString() - Method in class edu.stanford.nlp.stats.IntCounter
 
toString(NumberFormat, String, String, String, String) - Method in class edu.stanford.nlp.stats.IntCounter
 
toString(NumberFormat) - Method in class edu.stanford.nlp.stats.IntCounter
 
toString() - Method in class edu.stanford.nlp.stats.MultiClassAccuracyStats
 
toString() - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
A simple String representation of this TwoDimensionalCounter, which has the String representation of each key pair on a separate line, followed by the count for that pair.
toString(int[][]) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
toString(boolean[][]) - Static method in class edu.stanford.nlp.util.ArrayUtils
 
toString() - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
 
toString(int) - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
 
toString() - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
Returns a representation of the queue in decreasing priority order.
toString(int) - Method in class edu.stanford.nlp.util.FixedPrioritiesPriorityQueue
Returns a representation of the queue in decreasing priority order, displaying at most maxKeysToPring elements.
toString() - Method in class edu.stanford.nlp.util.Index
Returns a readable version of the Index contents
toString(int) - Method in class edu.stanford.nlp.util.Index
Returns a readable version of at least part of the Index contents.
toString() - Method in class edu.stanford.nlp.util.IntTuple
 
toString() - Method in class edu.stanford.nlp.util.MutableDouble
 
toString() - Method in class edu.stanford.nlp.util.MutableInteger
 
toString() - Method in class edu.stanford.nlp.util.Pair
 
toString() - Method in class edu.stanford.nlp.util.ScoredComparator
 
toString() - Method in class edu.stanford.nlp.util.ScoredObject
 
toString() - Method in class edu.stanford.nlp.util.Timing
 
toString() - Method in class edu.stanford.nlp.util.Triple
 
TOSTRING_FORMAT - Static variable in class edu.stanford.nlp.ling.FeatureLabel
 
toStringArr(int[]) - Static method in class edu.stanford.nlp.stats.AccuracyStats
 
toSummaryStatistics() - Method in class edu.stanford.nlp.classify.Dataset
 
toSummaryString() - Method in class edu.stanford.nlp.classify.Dataset
 
toSummaryString() - Method in class edu.stanford.nlp.classify.RVFDataset
 
totalCount() - Method in class edu.stanford.nlp.stats.Counter
Returns the current total count for all objects in this Counter.
totalCount(Filter<E>) - Method in class edu.stanford.nlp.stats.Counter
Returns the total count for all objects in this Counter that pass the given Filter.
totalCount() - Method in class edu.stanford.nlp.stats.Distribution
 
totalCount() - Method in class edu.stanford.nlp.stats.GeneralizedCounter
returns the total count of objects in the GeneralizedCounter.
totalCount() - Method in class edu.stanford.nlp.stats.IntCounter
Returns the current total count for all objects in this Counter.
totalCount(Filter) - Method in class edu.stanford.nlp.stats.IntCounter
Returns the total count for all objects in this Counter that pass the given Filter.
totalCount() - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
Takes linear time.
totalCount(K1) - Method in class edu.stanford.nlp.stats.TwoDimensionalCounter
 
totalDoubleCount() - Method in class edu.stanford.nlp.stats.Counter
Returns the current total count for all objects in this Counter.
totalDoubleCount() - Method in interface edu.stanford.nlp.stats.GenericCounter
Computes the total of all counts in this counter, and returns it as a double.
totalDoubleCount() - Method in class edu.stanford.nlp.stats.IntCounter
 
totalDoubleCount(Filter) - Method in class edu.stanford.nlp.stats.IntCounter
 
totalSize() - Method in class edu.stanford.nlp.classify.LinearClassifier
 
toVerticalString(Counter<E>) - Static method in class edu.stanford.nlp.stats.Counters
 
toVerticalString(Counter<E>, int) - Static method in class edu.stanford.nlp.stats.Counters
 
toVerticalString(Counter<E>, String) - Static method in class edu.stanford.nlp.stats.Counters
 
toVerticalString(Counter<E>, int, String) - Static method in class edu.stanford.nlp.stats.Counters
 
toVerticalString(Counter<E>, int, String, boolean) - Static method in class edu.stanford.nlp.stats.Counters
Returns a String representation of the k keys with the largest counts in the given Counter, using the given format string.
toVerticalString() - Method in class edu.stanford.nlp.util.BinaryHeapPriorityQueue
 
toVerticalString(Map) - Static method in class edu.stanford.nlp.util.CollectionUtils
 
tr(String, String, String) - Static method in class edu.stanford.nlp.util.StringUtils
Swap any occurances of any characters in the from String in the input String with the corresponding character from the to String.
train(GeneralDataset) - Method in class edu.stanford.nlp.classify.LogisticClassifier
 
trainClassifier(Collection<Datum>) - Method in class edu.stanford.nlp.classify.AbstractLinearClassifierFactory
Takes a Collection of Datum objects and gives you back a Classifier trained on it.
trainClassifier(Reference) - Method in class edu.stanford.nlp.classify.AbstractLinearClassifierFactory
Takes a Reference to a Collection of Datum objects and gives you back a Classifier trained on them
trainClassifier(GeneralDataset) - Method in class edu.stanford.nlp.classify.AbstractLinearClassifierFactory
trains a Classifier on a Dataset.
trainClassifier(List) - Method in interface edu.stanford.nlp.classify.ClassifierFactory
 
trainClassifier(GeneralDataset, double[]) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
trainClassifier(List) - Method in class edu.stanford.nlp.classify.NaiveBayesClassifierFactory
The examples are assumed a list of RFVDatum the datums are assumed to contain the zeros as well
trainClassifier(List, Set) - Method in class edu.stanford.nlp.classify.NaiveBayesClassifierFactory
The examples are assumed a list of RFVDatum the datums are assumed to not contain the zeros and then they are added to each instance
trainClassifierSemiSup(GeneralDataset, GeneralDataset, double[][], double[]) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
IMPORTANT: dataset and biasedDataset must have same featureIndex, labelIndex
trainClassifierV(GeneralDataset, GeneralDataset, double, double, boolean) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Train a classifier with a sigma tuned on a validation set.
trainClassifierV(GeneralDataset, double, double, boolean) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Train a classifier with a sigma tuned on a validation set.
trainDirs - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
trainFile - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
trainFiles - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
trainWeights(GeneralDataset) - Method in class edu.stanford.nlp.classify.AbstractLinearClassifierFactory
 
trainWeights(GeneralDataset) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
trainWeights(GeneralDataset, double[]) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
trainWeights(GeneralDataset, double[], boolean) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
trainWeights(GeneralDataset) - Method in class edu.stanford.nlp.classify.NBLinearClassifierFactory
 
trainWeightsSemiSup(GeneralDataset, GeneralDataset, double[][], double[]) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
TREE_MAP_FACTORY - Static variable in class edu.stanford.nlp.util.MapFactory
 
trim(String, int) - Static method in class edu.stanford.nlp.util.StringUtils
Returns s if it's at most maxWidth chars, otherwise chops right side to fit.
trim(Object, int) - Static method in class edu.stanford.nlp.util.StringUtils
 
trimData() - Method in class edu.stanford.nlp.classify.GeneralDataset
 
trimLabels() - Method in class edu.stanford.nlp.classify.GeneralDataset
 
trimToSize(int[]) - Method in class edu.stanford.nlp.classify.GeneralDataset
 
trimToSize(int[][]) - Method in class edu.stanford.nlp.classify.GeneralDataset
 
trimToSize(double[][]) - Method in class edu.stanford.nlp.classify.GeneralDataset
 
Triple<T1,T2,T3> - Class in edu.stanford.nlp.util
Class representing an ordered triple of objects, possibly typed.
Triple(T1, T2, T3) - Constructor for class edu.stanford.nlp.util.Triple
 
truncate(int, int, int) - Static method in class edu.stanford.nlp.util.StringUtils
This returns a string from decimal digit smallestDigit to decimal digit biggest digit.
TwoDimensionalCounter<K1,K2> - Class in edu.stanford.nlp.stats
A class representing a mapping between pairs of typed objects and double values.
TwoDimensionalCounter() - Constructor for class edu.stanford.nlp.stats.TwoDimensionalCounter
 
TwoDimensionalCounter(MapFactory<K1, Counter<K2>>, MapFactory<K2, MutableDouble>) - Constructor for class edu.stanford.nlp.stats.TwoDimensionalCounter
 
twoStage - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
type - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 

U

UCL - Static variable in class edu.stanford.nlp.classify.NaiveBayesClassifierFactory
 
unbox(List<Integer>) - Static method in class edu.stanford.nlp.math.ArrayMath
 
unbox(List<Double>) - Static method in class edu.stanford.nlp.math.ArrayMath
 
union(GenericCounter<E>, GenericCounter<E>) - Static method in class edu.stanford.nlp.stats.Counters
Returns a Counter that is the union of the two Counters passed in (counts are added).
union(Set<E>, Set<E>) - Static method in class edu.stanford.nlp.util.Sets
Returns the union of sets s1 and s2.
unlock() - Method in class edu.stanford.nlp.util.Index
Unlocks the Index.
unmodifiableCounter(GenericCounter<T>) - Static method in class edu.stanford.nlp.stats.Counters
Returns unmodifiable view of the counter.
unmodifiableView() - Method in class edu.stanford.nlp.util.Index
Returns an unmodifiable view of the Index.
USE_ACCURACY - Static variable in class edu.stanford.nlp.stats.MultiClassAccuracyStats
 
USE_LOGLIKELIHOOD - Static variable in class edu.stanford.nlp.stats.MultiClassAccuracyStats
 
useAbbr - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useAbbr1 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useABGENE - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useABSTR - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useABSTRFreq - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useABSTRFreqDict - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useAcqPrior - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useACR - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useAlgorithmicDifferentiation - Variable in class edu.stanford.nlp.optimization.SMDMinimizer
 
useAltGazFeatures - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useANTE - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useAs - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useASBCChar2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useASBCPre1 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useASBCSuf1 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useBeginSent - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useBig5 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useBigramInTwoClique - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useBoundarySequences - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useChPos - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
use POS information (an "open" feature for Chinese segmentation)
useChunks - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useChunkySequences - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useClassFeature - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useConjugateGradientAscent(boolean) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Sets the minimizer to CGMinimizer, with the passed verbose flag.
useConjugateGradientAscent() - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Sets the minimizer to CGMinimizer.
useCTBChar2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useCTBPre1 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useCTBSuf1 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useDict2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useDictASBC2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useDictCTB2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useDictHK2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useDictleng - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useDictPK2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useDisjShape - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useDisjunctive - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useDisjunctiveShapeInteraction - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useDistSim - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useEitherSideDisjunctive - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useEitherSideWord - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useEntityRule - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useEntityTypes - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useEntityTypeSequences - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useExternal - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useExtraTaggySequences - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useFilter - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useFirstWord - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useFloat - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useFREQ - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useGaussNewton - Variable in class edu.stanford.nlp.optimization.SMDMinimizer
 
useGazettePhrases - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useGazettes - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useGazFeatures - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useGENIA - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useHeadGov - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useHk - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useHKChar2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useHKPre1 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useHKSuf1 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useHuber - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useHybrid - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useHybridMinimizer() - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
useHybridMinimizer(double, int, StochasticCalculateMethods, int) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
useInternal - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useIsDateRange - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useIsURL - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useLastRealWord - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useLemmaAsWord - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useLemmas - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useLongSequences - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useMinimalAbbr - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useMinimalAbbr1 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useMoreAbbr - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useMoreGazFeatures - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useMoreTags - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useMsr - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useMSRChar2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useMUCFeatures - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNB - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNegASBCDict2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNegASBCDict3 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNegASBCDict4 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNegCTBDict2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNegCTBDict3 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNegCTBDict4 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNegDict2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNegDict3 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNegDict4 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNegHKDict2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNegHKDict3 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNegHKDict4 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNegPKDict2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNegPKDict3 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNegPKDict4 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNERPrior - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNext - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNextRealWord - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNextSequences - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNextVB - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNGrams - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNPGovernor - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNPHead - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useNumberFeature - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useObservedFeaturesOnly - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useObservedSequencesOnly - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useOccurrencePatterns - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useOnlySeenWeights - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useOrdinal - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useOutDict2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useParenMatching - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
usePk - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
usePKChar2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
usePKPre1 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
usePKSuf1 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
usePosition - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
usePre - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
usePrediction - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
usePrediction2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
usePrev - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
usePrevNextLemmas - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
usePrevSequences - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
usePrevVB - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useQN - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useQuartic - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useQuasiNewton() - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
Sets the minimizer to QuasiNewton.
useRad1 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useRad2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useRadical - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useReverse - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useReverseAffix - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useRule - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useRule2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useSeenFeaturesOnly - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useSegmentation - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useSemPrior - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useSequences - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useSGD - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useSGDtoQN - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useShapeConjunctions - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useShapeStrings - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
usesLC(int) - Static method in class edu.stanford.nlp.process.WordShapeClassifier
Returns true if the specified word shaper uses known lower case words.
useSMD - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useStochasticGradientDescent() - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
useStochasticGradientDescent(double, int) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
useStochasticGradientDescentToQuasiNewton(SeqClassifierFlags) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
useStochasticMetaDescent() - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
useStochasticMetaDescent(double, int, StochasticCalculateMethods) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
useStochasticQN(double, int) - Method in class edu.stanford.nlp.classify.LinearClassifierFactory
 
useStochasticQN - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useSuf - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useSum - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useSummedConditionalLikelihood - Variable in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
useSymTags - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useSymWordPairs - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
useSymWordPairs Has a small negative effect.
useTaggySequences - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useTaggySequencesShapeInteraction - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useTags - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useTitle - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useTOK - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useTypeSeqs - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useTypeSeqs2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useTypeSeqs3 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useTypeySequences - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useUnknown - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useURLSequences - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useVB - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useViterbi - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useWEB - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useWEBFreqDict - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useWideDisjunctive - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useWord - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useWord1 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useWord2 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useWord3 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useWord4 - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useWordLabelCounts - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useWordn - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useWordPairs - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useWordShapeGaz - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
useWordTag - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 

V

value() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
 
value() - Method in class edu.stanford.nlp.ling.FeatureLabel
 
value() - Method in interface edu.stanford.nlp.ling.Label
Return a String representation of just the "main" value of this label.
value - Variable in class edu.stanford.nlp.optimization.AbstractCachingDiffFunction
 
VALUE_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for storing a value in the map, as a String.
valueAt(double[]) - Method in class edu.stanford.nlp.optimization.AbstractCachingDiffFunction
 
valueAt(double[], int) - Method in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
valueAt(x,batchSize) derivativeAt(x,batchSize) invokes the calculateStochastic function to get the current value at x for the next batchSize data points.
valueAt(double[], double[], int) - Method in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
This function will return the stochastic approximation at the point x.
valueAt(float[]) - Method in interface edu.stanford.nlp.optimization.FloatFunction
Returns the value of the function at a single point.
valueAt(double[]) - Method in interface edu.stanford.nlp.optimization.Function
Returns the value of the function at a single point.
valueOf(String) - Static method in enum edu.stanford.nlp.classify.LogPrior.LogPriorType
Returns the enum constant of this type with the specified name.
valueOf(String, MapFactory) - Static method in class edu.stanford.nlp.ling.FeatureLabel
Uses String representation of a Map to populate Map with String keys and String values.
valueOf(String) - Static method in enum edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction.SamplingMethod
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum edu.stanford.nlp.optimization.StochasticCalculateMethods
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in class edu.stanford.nlp.stats.Counter
Returns the Counter over Strings specified by this String.
valueOfIgnoreComments(String) - Static method in class edu.stanford.nlp.stats.Counter
Similar to valueOf in that it returns the Counter over Strings specified by this String.
values - Variable in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
values() - Static method in enum edu.stanford.nlp.classify.LogPrior.LogPriorType
Returns an array containing the constants of this enum type, in the order they're declared.
values() - Static method in enum edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction.SamplingMethod
Returns an array containing the constants of this enum type, in the order they're declared.
values() - Static method in enum edu.stanford.nlp.optimization.StochasticCalculateMethods
Returns an array containing the constants of this enum type, in the order they're declared.
variance(double[]) - Static method in class edu.stanford.nlp.math.ArrayMath
 
VERB_SENSE_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
Probank key for the Verb sense given in the Propbank Annotation, should only be in the verbnode

W

WEAK_HASH_MAP_FACTORY - Static variable in class edu.stanford.nlp.util.MapFactory
 
weight(Object, Object) - Method in class edu.stanford.nlp.classify.LinearClassifier
 
WeightedDataset - Class in edu.stanford.nlp.classify
 
WeightedDataset(Index, int[], Index, int[][], int, float[]) - Constructor for class edu.stanford.nlp.classify.WeightedDataset
 
WeightedDataset() - Constructor for class edu.stanford.nlp.classify.WeightedDataset
 
WeightedDataset(int) - Constructor for class edu.stanford.nlp.classify.WeightedDataset
 
weights() - Method in class edu.stanford.nlp.classify.LinearClassifier
 
weights - Variable in class edu.stanford.nlp.classify.WeightedDataset
 
wideDisjunctionWidth - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
word() - Method in class edu.stanford.nlp.ling.AbstractMapLabel
Return the word of the label, stored in the map under the key WORD_KEY.
word() - Method in class edu.stanford.nlp.ling.FeatureLabel
convenience method for getting word *
word() - Method in interface edu.stanford.nlp.ling.HasWord
Return the word value of the label (or null if none).
WORD_KEY - Static variable in class edu.stanford.nlp.ling.AbstractMapLabel
The standard key for storing a word in the map, as a String.
wordShape(String, int) - Static method in class edu.stanford.nlp.process.WordShapeClassifier
Specify the string and the int identifying which word shaper to use and this returns the result of using that wordshaper on the word.
wordShape(String, int, Set) - Static method in class edu.stanford.nlp.process.WordShapeClassifier
Specify the string and the int identifying which word shaper to use and this returns the result of using that wordshaper on the word.
wordShape(String, int, boolean) - Static method in class edu.stanford.nlp.process.WordShapeClassifier
 
wordShape(String, int, boolean, Set) - Static method in class edu.stanford.nlp.process.WordShapeClassifier
 
wordShape - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
WORDSHAPECHRIS1 - Static variable in class edu.stanford.nlp.process.WordShapeClassifier
 
wordShapeChris1(String) - Static method in class edu.stanford.nlp.process.WordShapeClassifier
 
WORDSHAPECHRIS2 - Static variable in class edu.stanford.nlp.process.WordShapeClassifier
 
wordShapeChris2(String, boolean, boolean) - Static method in class edu.stanford.nlp.process.WordShapeClassifier
This one picks up on Dan2 ideas, but seeks to make less distinctions mid sequence by sorting for long words, but to maintain extra distinctions for short words.
WORDSHAPECHRIS2USELC - Static variable in class edu.stanford.nlp.process.WordShapeClassifier
 
WORDSHAPECHRIS3 - Static variable in class edu.stanford.nlp.process.WordShapeClassifier
 
WORDSHAPECHRIS3USELC - Static variable in class edu.stanford.nlp.process.WordShapeClassifier
 
WORDSHAPECHRIS4 - Static variable in class edu.stanford.nlp.process.WordShapeClassifier
 
wordShapeChris4(String, boolean, boolean) - Static method in class edu.stanford.nlp.process.WordShapeClassifier
This one picks up on Dan2 ideas, but seeks to make less distinctions mid sequence by sorting for long words, but to maintain extra distinctions for short words, by always recording the class of the first and last two characters of the word.
WordShapeClassifier - Class in edu.stanford.nlp.process
Provides static methods which map any String to another String indicative of its "word shape" -- e.g., whether capitalized, numeric, etc.
WORDSHAPEDAN1 - Static variable in class edu.stanford.nlp.process.WordShapeClassifier
 
wordShapeDan1(String) - Static method in class edu.stanford.nlp.process.WordShapeClassifier
A fairly basic 5-way classifier, that notes digits, and upper and lower case, mixed, and non-alphanumeric.
WORDSHAPEDAN2 - Static variable in class edu.stanford.nlp.process.WordShapeClassifier
 
wordShapeDan2(String, boolean) - Static method in class edu.stanford.nlp.process.WordShapeClassifier
A fine-grained word shape classifier, that equivalence classes.
WORDSHAPEDAN2BIO - Static variable in class edu.stanford.nlp.process.WordShapeClassifier
 
wordShapeDan2Bio(String, boolean) - Static method in class edu.stanford.nlp.process.WordShapeClassifier
Returns a fine-grained word shape classifier, that equivalence classes lower and upper case and digits, and collapses sequences of the same type, but keeps all punctuation.
WORDSHAPEDAN2BIOUSELC - Static variable in class edu.stanford.nlp.process.WordShapeClassifier
 
WORDSHAPEDAN2USELC - Static variable in class edu.stanford.nlp.process.WordShapeClassifier
 
wordShapeGaz - Variable in class edu.stanford.nlp.sequences.SeqClassifierFlags
 
WORDSHAPEJENNY1 - Static variable in class edu.stanford.nlp.process.WordShapeClassifier
 
wordShapeJenny1(String, boolean) - Static method in class edu.stanford.nlp.process.WordShapeClassifier
 
WORDSHAPEJENNY1USELC - Static variable in class edu.stanford.nlp.process.WordShapeClassifier
 

X

xAD - Variable in class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
 
xPerturbed - Variable in class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
 

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