S-Index (Cognitive Foundry)

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

S

safeFirstNext() - Method in class gov.sandia.cognition.math.matrix.MatrixUnionIterator: Try to advance the first entry...
safeFirstNext() - Method in class gov.sandia.cognition.math.matrix.VectorUnionIterator: Try to advance the first entry...
safeGetDimensionality(Vectorizable) - Static method in class gov.sandia.cognition.math.matrix.VectorUtil: Gets the dimensionality of the given vector, if it is not null.
safeGetDimensionality(Vector) - Static method in class gov.sandia.cognition.math.matrix.VectorUtil: Gets the dimensionality of the given vector, if it is not null.
safeSecondNext() - Method in class gov.sandia.cognition.math.matrix.MatrixUnionIterator: Try to advance the second entry...
safeSecondNext() - Method in class gov.sandia.cognition.math.matrix.VectorUnionIterator: Try to advance the second entry...
salt(HashFunction, byte[]...) - Static method in class gov.sandia.cognition.hash.HashFunctionUtil: Computes the salted hash of the given inputs, that is, we concatenate the inputs together and compute the hash of the concatenated bytes.
saltInto(byte[], HashFunction, byte[]...) - Static method in class gov.sandia.cognition.hash.HashFunctionUtil: Computes the salted hash of the given inputs, that is, we concatenate the inputs together and compute the hash of the concatenated bytes.
sample(Random) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel
sample(Random, int) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel
sample(Random) - Method in class gov.sandia.cognition.statistics.AbstractDataDistribution
sample(Random, int) - Method in class gov.sandia.cognition.statistics.AbstractDataDistribution
sample(Random) - Method in class gov.sandia.cognition.statistics.AbstractDistribution
sample(Random, int) - Method in class gov.sandia.cognition.statistics.AbstractDistribution
sample(Random) - Method in class gov.sandia.cognition.statistics.AbstractRandomVariable
sample(Random) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling: Draws a single sample by the method of adaptive rejection sampling.
sample(Random, int) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling: Draws samples by the adaptive rejection sampling method, which will have the distribution of the logFunction
sample(Random) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.UpperEnvelope
sample(Random, int) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.UpperEnvelope
sample(ClosedFormDistribution<ObservationType>, String, Distribution<ParameterType>, Random, int) - Static method in class gov.sandia.cognition.statistics.bayesian.BayesianUtil: Samples from the given BayesianParameter by first sampling the prior distribution, then updating the conditional distribution, then sampling from the updated conditional distribution.
sample(BayesianParameter<ParameterType, ? extends Distribution<ObservationType>, ? extends Distribution<ParameterType>>, Random, int) - Static method in class gov.sandia.cognition.statistics.bayesian.BayesianUtil: Samples from the given BayesianParameter by first sampling the prior distribution, then updating the conditional distribution, then sampling from the updated conditional distribution.
Sample(double, ArrayList<DirichletProcessMixtureModel.DPMMCluster<ObservationType>>) - Constructor for class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.Sample: Creates a new instance of Sample
sample(Random) - Method in class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess
sample(double, Random, int) - Static method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution: Samples from a Chi-Square distribution with the given degrees of freedom
sample(Random) - Method in class gov.sandia.cognition.statistics.distribution.DirichletDistribution
sample(double, double, Random, int) - Static method in class gov.sandia.cognition.statistics.distribution.GammaDistribution: Efficiently samples from a Gamma distribution given by the shape and scale parameters.
sample(double, double, Random) - Static method in class gov.sandia.cognition.statistics.distribution.GammaDistribution: Provides a single sample from a Gamma distribution with the given shape and scale.
sample(Random, Vector, Matrix, int) - Static method in class gov.sandia.cognition.statistics.distribution.InverseWishartDistribution: Creates a single sample covariance matrix inverse from the given parameters.
sample(Random) - Method in class gov.sandia.cognition.statistics.distribution.LinearMixtureModel
sample(Vector, Matrix, Random, int) - Static method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian: Returns a collection of draws this Gaussian with the given mean and covariance.
sample(Vector, Matrix, Random) - Static method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian: Returns a single draw from the Gaussian with the given mean and covariance.
sample(Random) - Method in interface gov.sandia.cognition.statistics.Distribution: Draws a single random sample from the distribution.
sample(Random, int) - Method in interface gov.sandia.cognition.statistics.Distribution: Draws multiple random samples from the distribution.
sample(Random) - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution
sample(ProbabilityDensityFunction<ValueType>, Evaluator<ValueType, Double>, Random, int) - Static method in class gov.sandia.cognition.statistics.method.ImportanceSampling: Importance sampling is a technique for estimating properties of a target distribution, while only having samples generated from an "importance" distribution rather than the target distribution.
sample(CumulativeDistributionFunction<Double>, Random, int) - Static method in class gov.sandia.cognition.statistics.method.InverseTransformSampling: Samples from the given CDF using the inverseRootFinder transform sampling method.
sample(ProbabilityFunction<DataType>, Random, int) - Method in class gov.sandia.cognition.statistics.montecarlo.DirectSampler
sample(Evaluator<? super DataType, Double>, Random, int) - Method in class gov.sandia.cognition.statistics.montecarlo.ImportanceSampler
sample(FunctionType, Random, int) - Method in interface gov.sandia.cognition.statistics.montecarlo.MonteCarloSampler: Draws samples according to the distribution of the target function.
sample(ProbabilityMassFunction<DataType>, Random, int) - Static method in class gov.sandia.cognition.statistics.ProbabilityMassFunctionUtil: Samples from the ProbabilityMassFunction.
sample(double[], List<? extends DataType>, Random) - Static method in class gov.sandia.cognition.statistics.ProbabilityMassFunctionUtil: Samples an element from the domain proportionately to the cumulative weights in the given weight array using a fast binary search algorithm.
sample(Random) - Method in class gov.sandia.cognition.statistics.UnivariateRandomVariable
sample(Random, int) - Method in class gov.sandia.cognition.statistics.UnivariateRandomVariable
sampleAsBoolean(Random) - Method in class gov.sandia.cognition.statistics.distribution.BernoulliDistribution: Samples from the Bernoulli distribution as a boolean.
sampleAsDouble(Random) - Method in class gov.sandia.cognition.statistics.AbstractClosedFormSmoothUnivariateDistribution
sampleAsDouble(Random) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.UpperEnvelope: Samples from this distribution as a double.
sampleAsDouble(Random) - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution
sampleAsDouble(Random) - Method in class gov.sandia.cognition.statistics.distribution.CauchyDistribution
sampleAsDouble(Random) - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution
sampleAsDouble(double, double, Random) - Static method in class gov.sandia.cognition.statistics.distribution.GammaDistribution: Efficiently samples from a Gamma distribution given by the shape and scale parameters.
sampleAsDouble(Random) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution
sampleAsDouble(Random) - Method in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution
sampleAsDouble(Random) - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution
sampleAsDouble(Random) - Method in class gov.sandia.cognition.statistics.distribution.LogisticDistribution
sampleAsDouble(Random) - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution
sampleAsDouble(Random) - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution
sampleAsDouble(Random) - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel
sampleAsDouble(Random) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian
sampleAsDouble(Random) - Method in class gov.sandia.cognition.statistics.distribution.WeibullDistribution
sampleAsDouble(Random) - Method in interface gov.sandia.cognition.statistics.SmoothUnivariateDistribution: Samples a value from this distribution as a double.
sampleAsDoubles(Random, int) - Method in class gov.sandia.cognition.statistics.AbstractClosedFormSmoothUnivariateDistribution
sampleAsDoubles(double, Random, int) - Static method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution: Samples from a Chi-Square distribution with the given degrees of freedom
sampleAsDoubles(double, double, Random, int) - Static method in class gov.sandia.cognition.statistics.distribution.GammaDistribution: Efficiently samples from a Gamma distribution given by the shape and scale parameters.
sampleAsDoubles(Random, int) - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel
sampleAsDoubles(Random, int) - Method in interface gov.sandia.cognition.statistics.SmoothUnivariateDistribution: Samples values from this distribution as an array of doubles.
sampleAsInt(Random) - Method in class gov.sandia.cognition.statistics.distribution.BernoulliDistribution
sampleAsInt(Random) - Method in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution
sampleAsInt(Random) - Method in class gov.sandia.cognition.statistics.distribution.BinomialDistribution
sampleAsInt(Random) - Method in class gov.sandia.cognition.statistics.distribution.GeometricDistribution
sampleAsInt(Random) - Method in class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution
sampleAsInt(Random) - Method in class gov.sandia.cognition.statistics.distribution.PoissonDistribution
sampleAsInt(Random) - Method in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution
sampleAsInt(Random) - Method in class gov.sandia.cognition.statistics.distribution.YuleSimonDistribution
sampleAsInt(Random) - Method in interface gov.sandia.cognition.statistics.IntegerDistribution: Draws a single random sample from the distribution as an int.
sampleAsInts(Random, int) - Method in class gov.sandia.cognition.statistics.AbstractClosedFormIntegerDistribution
sampleAsInts(Random, int) - Method in interface gov.sandia.cognition.statistics.IntegerDistribution: Samples values from this distribution as an array of ints.
sampleCount - Variable in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler: The number of model parameter samples that have been made.
sampleExp(double) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.LineSegment: Sample from the exponent of the line segment
sampleIndexFromCumulativeProportions(Random, double[]) - Static method in class gov.sandia.cognition.statistics.DiscreteSamplingUtil: Samples a random index from an array of cumulative proportions.
sampleIndexFromProbabilities(Random, double[]) - Static method in class gov.sandia.cognition.statistics.DiscreteSamplingUtil: Samples an random index according to the given array of probabilities.
sampleIndexFromProbabilities(Random, Vector) - Static method in class gov.sandia.cognition.statistics.DiscreteSamplingUtil: Samples an random index according to the given vector of probabilities.
sampleIndexFromProportions(Random, double[]) - Static method in class gov.sandia.cognition.statistics.DiscreteSamplingUtil: Samples a random index according to the given proportions.
sampleIndexFromProportions(Random, double[], double) - Static method in class gov.sandia.cognition.statistics.DiscreteSamplingUtil: Samples a random index according to the given proportions.
sampleIndicesFromCumulativeProportions(Random, double[], int) - Static method in class gov.sandia.cognition.statistics.DiscreteSamplingUtil: Samples a multiple indices with replacement from an array of cumulative proportions.
sampleIndicesFromProportions(Random, double[], int) - Static method in class gov.sandia.cognition.statistics.DiscreteSamplingUtil: Samples an array of indices from a given set of proportions.
sampleIndicesWithReplacementInto(ArrayList<Integer>, ArrayList<? extends DataType>, int, Random, ArrayList<DataType>, int[]) - Static method in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner: Takes the given number of samples from the given list and places them in the given output list.
sampleInto(Random, int, Collection<? super ObservationType>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel
sampleInto(Random, int, Collection<? super ObservationType>) - Method in class gov.sandia.cognition.learning.function.categorization.MaximumAPosterioriCategorizer
sampleInto(Random, int[], int, int) - Method in class gov.sandia.cognition.statistics.AbstractClosedFormIntegerDistribution
sampleInto(Random, int, Collection<? super Double>) - Method in class gov.sandia.cognition.statistics.AbstractClosedFormSmoothUnivariateDistribution
sampleInto(Random, int, Collection<? super KeyType>) - Method in class gov.sandia.cognition.statistics.AbstractDataDistribution
sampleInto(Random, int, Collection<? super Double>) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.UpperEnvelope
sampleInto(Random, int, Collection<? super Number>) - Method in class gov.sandia.cognition.statistics.distribution.BernoulliDistribution
sampleInto(Random, int, Collection<? super Number>) - Method in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution
sampleInto(Random, int[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution
sampleInto(Random, double[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution
sampleInto(Random, int, Collection<? super Number>) - Method in class gov.sandia.cognition.statistics.distribution.BinomialDistribution
sampleInto(Random, int[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.BinomialDistribution
sampleInto(Random, int, Collection<? super Vector>) - Method in class gov.sandia.cognition.statistics.distribution.CategoricalDistribution
sampleInto(Random, double[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.CauchyDistribution
sampleInto(Random, int, Collection<? super Vector>) - Method in class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess
sampleInto(Random, double[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution
sampleInto(Random, int, Collection<? super Double>) - Method in class gov.sandia.cognition.statistics.distribution.DeterministicDistribution
sampleInto(Random, int, Collection<? super Vector>) - Method in class gov.sandia.cognition.statistics.distribution.DirichletDistribution
sampleInto(Random, double[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution
sampleInto(double, double, Random, double[], int, int) - Static method in class gov.sandia.cognition.statistics.distribution.GammaDistribution: Efficiently samples from a Gamma distribution given by the shape and scale parameters.
sampleInto(Random, double[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution
sampleInto(Random, int, Collection<? super Number>) - Method in class gov.sandia.cognition.statistics.distribution.GeometricDistribution
sampleInto(Random, double[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution
sampleInto(Random, int, Collection<? super Matrix>) - Method in class gov.sandia.cognition.statistics.distribution.InverseWishartDistribution
sampleInto(Random, int, Collection<? super Double>) - Method in class gov.sandia.cognition.statistics.distribution.KolmogorovDistribution
sampleInto(Random, double[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution
sampleInto(Random, int, Collection<? super DataType>) - Method in class gov.sandia.cognition.statistics.distribution.LinearMixtureModel
sampleInto(Random, double[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.LogisticDistribution
sampleInto(Random, double[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution
sampleInto(Random, int, Collection<? super Vector>) - Method in class gov.sandia.cognition.statistics.distribution.MultinomialDistribution
sampleInto(Random, int, Collection<? super Vector>) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian
sampleInto(Vector, Matrix, Random, int, Collection<? super Vector>) - Static method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian: Performs a collection of draws this Gaussian with the given mean and covariance.
sampleInto(Random, int, Collection<? super Vector>) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussianInverseGammaDistribution
sampleInto(Random, int, Collection<? super Vector>) - Method in class gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution
sampleInto(Random, int, Collection<? super Vector>) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution
sampleInto(Random, int, Collection<? super Number>) - Method in class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution
sampleInto(Random, int[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution
sampleInto(Random, int, Collection<? super Vector>) - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution
sampleInto(Random, int, Collection<? super Matrix>) - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution
sampleInto(Random, double[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution
sampleInto(Random, int, Collection<? super Number>) - Method in class gov.sandia.cognition.statistics.distribution.PoissonDistribution
sampleInto(Random, int[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.PoissonDistribution
sampleInto(Random, int, Collection<? super DataType>) - Method in interface gov.sandia.cognition.statistics.Distribution: Draws multiple random samples from the distribution and puts the result into the given collection.
sampleInto(Random, double[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel
sampleInto(Random, int, Collection<? super Double>) - Method in class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution
sampleInto(Random, int, Collection<? super Double>) - Method in class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution
sampleInto(Random, double[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution
sampleInto(Random, double[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution
sampleInto(Random, int, Collection<? super Number>) - Method in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution
sampleInto(Random, double[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian
sampleInto(Random, double[], int, int) - Method in class gov.sandia.cognition.statistics.distribution.WeibullDistribution
sampleInto(Random, int, Collection<? super Number>) - Method in class gov.sandia.cognition.statistics.distribution.YuleSimonDistribution
sampleInto(Random, int[], int, int) - Method in interface gov.sandia.cognition.statistics.IntegerDistribution: Samples values from this distribution as an array of ints.
sampleInto(CumulativeDistributionFunction<Double>, Random, int, Collection<? super Double>) - Static method in class gov.sandia.cognition.statistics.method.InverseTransformSampling: Samples from the given CDF using the inverseRootFinder transform sampling method.
sampleInto(ProbabilityMassFunction<DataType>, Random, int, Collection<? super DataType>) - Static method in class gov.sandia.cognition.statistics.ProbabilityMassFunctionUtil: Samples from the ProbabilityMassFunction.
sampleInto(Random, double[], int, int) - Method in interface gov.sandia.cognition.statistics.SmoothUnivariateDistribution: Samples values from this distribution as an array of doubles.
sampleInto(Random, int, Collection<? super Number>) - Method in class gov.sandia.cognition.statistics.UnivariateRandomVariable
sampleMultiple(ProbabilityMassFunction<DataType>, Random, int) - Static method in class gov.sandia.cognition.statistics.ProbabilityMassFunctionUtil: Samples from the ProbabilityMassFunction.
sampleMultiple(double[], List<? extends DataType>, Random, int) - Static method in class gov.sandia.cognition.statistics.ProbabilityMassFunctionUtil: Samples multiple elements from the domain proportionately to the cumulative weights in the given weight array using a fast binary search algorithm
sampleMultipleInto(ProbabilityMassFunction<DataType>, Random, int, Collection<? super DataType>) - Static method in class gov.sandia.cognition.statistics.ProbabilityMassFunctionUtil: Samples from the ProbabilityMassFunction.
sampleMultipleInto(double[], List<? extends DataType>, Random, int, Collection<? super DataType>) - Static method in class gov.sandia.cognition.statistics.ProbabilityMassFunctionUtil: Samples multiple elements from the domain proportionately to the cumulative weights in the given weight array using a fast binary search algorithm
sampleNextCustomer(Collection<Integer>, int, double, Random) - Static method in class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess: Determines where the next customer sits, given the number of customers already sitting at the various tables and the concentration parameter alpha.
sampler - Variable in class gov.sandia.cognition.statistics.bayesian.RejectionSampling.ScalarEstimator.MinimizerFunction: Sampler function
SampleRange(Random, int, SmoothUnivariateDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution.SampleRange: Creates a new instance of SampleRange
sampleSingle(ProbabilityMassFunction<DataType>, Random) - Static method in class gov.sandia.cognition.statistics.ProbabilityMassFunctionUtil: Draws a single sample from the given PMF
sampleSingle(double[], Collection<? extends DataType>, Random) - Static method in class gov.sandia.cognition.statistics.ProbabilityMassFunctionUtil: Samples a single element from the domain proportionately to the given weights
sampleSize - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner: The size of sample to create on each iteration.
sampleSize - Variable in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient: The sample size requested by the user.
sampleStandard(double, Random) - Static method in class gov.sandia.cognition.statistics.distribution.GammaDistribution: Provides a single sample from a Gamma distribution with the given shape and a scale of 1.
sampleTopic(int, int, double[]) - Method in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler: Samples a topic for a given document and term.
sampleWithoutReplacement(Random, List<DataType>, int) - Static method in class gov.sandia.cognition.statistics.DiscreteSamplingUtil: Samples a a given number of items from a list without replacement.
sampleWithReplacement(Random, List<? extends DataType>, int) - Static method in class gov.sandia.cognition.statistics.DiscreteSamplingUtil: Samples a a given number of items from a list with replacement.
sampleWithReplacementInto(Random, List<? extends DataType>, int, Collection<? super DataType>) - Static method in class gov.sandia.cognition.statistics.DiscreteSamplingUtil: Samples a a given number of items from a list with replacement and puts the samples into the given collection.
SamplingImportanceResamplingParticleFilter<ObservationType,ParameterType> - Class in gov.sandia.cognition.statistics.bayesian: An implementation of the standard Sampling Importance Resampling particle filter.
SamplingImportanceResamplingParticleFilter() - Constructor for class gov.sandia.cognition.statistics.bayesian.SamplingImportanceResamplingParticleFilter: Creates a new instance of SamplingImportanceResamplingParticleFilter
saveAsText(File) - Method in class gov.sandia.cognition.text.term.filter.DefaultStopList: Saves the stop list to the given file.
saveAsText(PrintStream) - Method in class gov.sandia.cognition.text.term.filter.DefaultStopList: Saves the stop list to the given stream.
saveFinalClustering() - Method in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer: Saves the final clustering for each data point.
ScalarBasisSet<InputType> - Class in gov.sandia.cognition.learning.function.vector: Collection of scalar basis functions, where the ith function operates on the ith element of the output Vector
ScalarBasisSet(Collection<? extends Evaluator<? super InputType, Double>>) - Constructor for class gov.sandia.cognition.learning.function.vector.ScalarBasisSet: Creates a new instance of ScalarBasisSet
ScalarBasisSet(ScalarBasisSet<InputType>) - Constructor for class gov.sandia.cognition.learning.function.vector.ScalarBasisSet: Copy Constructor
ScalarDataDistribution - Class in gov.sandia.cognition.statistics.distribution: A Data Distribution that uses Doubles as its keys, making it a univariate distribution
ScalarDataDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarDataDistribution: Creates a new instance of ScalarDataDistribution
ScalarDataDistribution(ScalarDataDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarDataDistribution: Copy constructor
ScalarDataDistribution(Iterable<? extends Number>) - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarDataDistribution: Creates a new instance of ScalarDataDistribution
ScalarDataDistribution(Map<Double, MutableDouble>, double) - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarDataDistribution: Creates a new instance of ScalarDataDistribution
ScalarDataDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution: CDF of the ScalarDataDistribution, maintains the keys/domain in sorted order (TreeMap), so it's slower than it's peers.
ScalarDataDistribution.Estimator - Class in gov.sandia.cognition.statistics.distribution: Estimator for a ScalarDataDistribution
ScalarDataDistribution.PMF - Class in gov.sandia.cognition.statistics.distribution: PMF of the ScalarDataDistribution
ScalarEstimator(BayesianParameter<Double, ? extends ProbabilityFunction<ObservationType>, ? extends UnivariateProbabilityDensityFunction>, Iterable<? extends ObservationType>) - Constructor for class gov.sandia.cognition.statistics.bayesian.RejectionSampling.ScalarEstimator: Creates a new instance of ScalarEstimator
scalarFunction - Variable in class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeNode: The function to apply for leaf nodes.
ScalarFunction<InputType> - Interface in gov.sandia.cognition.math: Interface for a function that maps some input onto a double.
ScalarFunctionKernel<InputType> - Class in gov.sandia.cognition.learning.function.kernel: The ScalarFunctionKernel class implements a kernel that applies a scalar function two the two inputs to the kernel and then returns their product.
ScalarFunctionKernel() - Constructor for class gov.sandia.cognition.learning.function.kernel.ScalarFunctionKernel: Creates a new instance of RealFunctionKernel.
ScalarFunctionKernel(Evaluator<? super InputType, Double>) - Constructor for class gov.sandia.cognition.learning.function.kernel.ScalarFunctionKernel: Creates a new instance of RealFunctionKernel.
ScalarFunctionToBinaryCategorizerAdapter<InputType> - Class in gov.sandia.cognition.learning.function.categorization: Adapts a scalar function to be a categorizer using a threshold.
ScalarFunctionToBinaryCategorizerAdapter() - Constructor for class gov.sandia.cognition.learning.function.categorization.ScalarFunctionToBinaryCategorizerAdapter: Creates a new ScalarFunctionToBinaryCategorizerAdapter.
ScalarFunctionToBinaryCategorizerAdapter(Evaluator<? super InputType, Double>) - Constructor for class gov.sandia.cognition.learning.function.categorization.ScalarFunctionToBinaryCategorizerAdapter: Creates a new ScalarFunctionToBinaryCategorizerAdapter with the given evaluator and a default threshold of 0.0.
ScalarFunctionToBinaryCategorizerAdapter(Evaluator<? super InputType, Double>, double) - Constructor for class gov.sandia.cognition.learning.function.categorization.ScalarFunctionToBinaryCategorizerAdapter: Creates a new ScalarFunctionToBinaryCategorizerAdapter with the given evaluator and threshold.
ScalarMap<KeyType> - Interface in gov.sandia.cognition.collection: An interface for a mapping of objects to scalar values represented as doubles.
ScalarMap.Entry<KeyType> - Interface in gov.sandia.cognition.collection: An entry in a scalar map.
ScalarMixtureDensityModel - Class in gov.sandia.cognition.statistics.distribution: ScalarMixtureDensityModel (SMDM) implements just that: a scalar mixture density model.
ScalarMixtureDensityModel() - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel: Creates a new instance of ScalarMixtureDensityModel
ScalarMixtureDensityModel(SmoothUnivariateDistribution...) - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel: Creates a new instance of ScalarMixtureDensityModel
ScalarMixtureDensityModel(Collection<? extends SmoothUnivariateDistribution>) - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel: Creates a new instance of ScalarMixtureDensityModel
ScalarMixtureDensityModel(Collection<? extends SmoothUnivariateDistribution>, double[]) - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel: Creates a new instance of ScalarMixtureDensityModel
ScalarMixtureDensityModel(ScalarMixtureDensityModel) - Constructor for class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel: Copy constructor
ScalarMixtureDensityModel.CDF - Class in gov.sandia.cognition.statistics.distribution: CDFof the SMDM
ScalarMixtureDensityModel.EMLearner - Class in gov.sandia.cognition.statistics.distribution: An EM learner that estimates a mixture model from data
ScalarMixtureDensityModel.PDF - Class in gov.sandia.cognition.statistics.distribution: PDF of the SMDM
ScalarThresholdBinaryCategorizer - Class in gov.sandia.cognition.learning.function.categorization: The ScalarThresholdBinaryCategorizer class implements a binary categorizer that uses a threshold to categorize a given double.
ScalarThresholdBinaryCategorizer() - Constructor for class gov.sandia.cognition.learning.function.categorization.ScalarThresholdBinaryCategorizer: Creates a new instance of ScalarThresholdBinaryCategorizer.
ScalarThresholdBinaryCategorizer(double) - Constructor for class gov.sandia.cognition.learning.function.categorization.ScalarThresholdBinaryCategorizer: Creates a new instance of ScalarThresholdBinaryCategorizer.
ScalarThresholdBinaryCategorizer(ScalarThresholdBinaryCategorizer) - Constructor for class gov.sandia.cognition.learning.function.categorization.ScalarThresholdBinaryCategorizer: Copy constructor.
scale(double) - Method in class gov.sandia.cognition.math.AbstractRing
scale(double) - Method in class gov.sandia.cognition.math.LogNumber
scale(double) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
scale(double) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
scale(double) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
scale(double) - Method in class gov.sandia.cognition.math.MutableDouble
scale(double) - Method in class gov.sandia.cognition.math.MutableInteger
scale(double) - Method in class gov.sandia.cognition.math.MutableLong
scale(double) - Method in interface gov.sandia.cognition.math.Ring: Element-wise scaling of this by scaleFactor
scale(double) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
scale - Variable in class gov.sandia.cognition.statistics.bayesian.RejectionSampling.DefaultUpdater: Scale factor to multiply the sampler function by to envelop the conjunctive distribution.
scale - Variable in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution: Scale, similar to the beta parameter scale, must be greater than zero
scale - Variable in class gov.sandia.cognition.statistics.distribution.CauchyDistribution: Scale of the distribution, must be greater than zero.
scale - Variable in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution: Scale parameter, must be greater than zero.
scale - Variable in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution: Scale factor of the distribution, must be greater than zero.
scale - Variable in class gov.sandia.cognition.statistics.distribution.LogisticDistribution: Scale of the distribution, proportionate to the standard deviation, must be greater than zero.
scale(Matrix) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian: Scales the MultivariateGaussian by premultiplying by the given Matrix
scale - Variable in class gov.sandia.cognition.statistics.distribution.ParetoDistribution: Scale parameter, must be greater than zero.
scale - Variable in class gov.sandia.cognition.statistics.distribution.WeibullDistribution: Scale parameter, must be greater than 0.0
SCALE_FACTOR - Static variable in class gov.sandia.cognition.learning.algorithm.root.RootBracketExpander: Default scale factor on expansion, the golden ratio, 1.618034
scaledMinus(double, RingType) - Method in class gov.sandia.cognition.math.AbstractRing
scaledMinus(double, LogNumber) - Method in class gov.sandia.cognition.math.LogNumber
scaledMinus(double, InfiniteVector<KeyType>) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
scaledMinus(double, MutableDouble) - Method in class gov.sandia.cognition.math.MutableDouble
scaledMinus(double, MutableInteger) - Method in class gov.sandia.cognition.math.MutableInteger
scaledMinus(double, MutableLong) - Method in class gov.sandia.cognition.math.MutableLong
scaledMinus(double, RingType) - Method in interface gov.sandia.cognition.math.Ring: Arithmetic subtraction other after element-wise scaling of other by scaleFactor from this.
scaledMinus(double, UnsignedLogNumber) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
scaledMinusEquals(double, RingType) - Method in class gov.sandia.cognition.math.AbstractRing
scaledMinusEquals(double, LogNumber) - Method in class gov.sandia.cognition.math.LogNumber
scaledMinusEquals(double, InfiniteVector<KeyType>) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
scaledMinusEquals(double, AbstractMTJMatrix) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix: Subtracts from this matrix the scaled version of the other given matrix.
scaledMinusEquals(double, AbstractMTJVector) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector: Subtracts from this vector the scaled version of the other given vector.
scaledMinusEquals(double, MutableDouble) - Method in class gov.sandia.cognition.math.MutableDouble
scaledMinusEquals(double, MutableInteger) - Method in class gov.sandia.cognition.math.MutableInteger
scaledMinusEquals(double, MutableLong) - Method in class gov.sandia.cognition.math.MutableLong
scaledMinusEquals(double, RingType) - Method in interface gov.sandia.cognition.math.Ring: Inline arithmetic subtraction of other after element-wise scaling of other by scaleFactor from this.
scaledMinusEquals(double, UnsignedLogNumber) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
scaledPlus(double, RingType) - Method in class gov.sandia.cognition.math.AbstractRing
scaledPlus(double, LogNumber) - Method in class gov.sandia.cognition.math.LogNumber
scaledPlus(double, InfiniteVector<KeyType>) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
scaledPlus(double, MutableDouble) - Method in class gov.sandia.cognition.math.MutableDouble
scaledPlus(double, MutableInteger) - Method in class gov.sandia.cognition.math.MutableInteger
scaledPlus(double, MutableLong) - Method in class gov.sandia.cognition.math.MutableLong
scaledPlus(double, RingType) - Method in interface gov.sandia.cognition.math.Ring: Arithmetic addition of this and other after element-wise scaling of other by scaleFactor.
scaledPlus(double, UnsignedLogNumber) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
scaledPlusEquals(double, ComplexNumber) - Method in class gov.sandia.cognition.math.ComplexNumber
scaledPlusEquals(double, LogNumber) - Method in class gov.sandia.cognition.math.LogNumber
scaledPlusEquals(double, Matrix) - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
scaledPlusEquals(double, Vector) - Method in class gov.sandia.cognition.math.matrix.AbstractVector
scaledPlusEquals(SparseMatrix, double) - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
scaledPlusEquals(DenseMatrix, double) - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
scaledPlusEquals(DiagonalMatrix, double) - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
scaledPlusEquals(DenseVector, double) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
scaledPlusEquals(SparseVector, double) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
scaledPlusEquals(SparseMatrix, double) - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix: Type-specific version of scaledPlusEquals for combining whatever type this is with the input sparse matrix.
scaledPlusEquals(DenseMatrix, double) - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix: Type-specific version of scaledPlusEquals for combining whatever type this is with the input dense matrix.
scaledPlusEquals(DiagonalMatrix, double) - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
scaledPlusEquals(SparseMatrix, double) - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix: Type-specific version of scaledPlusEquals for combining whatever type this is with the input sparse matrix.
scaledPlusEquals(DenseMatrix, double) - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix: Type-specific version of scaledPlusEquals for combining whatever type this is with the input dense matrix.
scaledPlusEquals(DiagonalMatrix, double) - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix: Type-specific version of scaledPlusEquals for combining whatever type this is with the input diagonal matrix.
scaledPlusEquals(DenseVector, double) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector: Type-specific version of scaledPlusEquals for combining whatever type this is with the input dense vector.
scaledPlusEquals(SparseVector, double) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
scaledPlusEquals(double, InfiniteVector<KeyType>) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
scaledPlusEquals(double, Matrix) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix
scaledPlusEquals(double, AbstractMTJMatrix) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix: Adds to this vector the scaled version of the other given vector.
scaledPlusEquals(double, Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector
scaledPlusEquals(double, AbstractMTJVector) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector: Adds to this vector the scaled version of the other given vector.
scaledPlusEquals(double, MutableDouble) - Method in class gov.sandia.cognition.math.MutableDouble
scaledPlusEquals(double, MutableInteger) - Method in class gov.sandia.cognition.math.MutableInteger
scaledPlusEquals(double, MutableLong) - Method in class gov.sandia.cognition.math.MutableLong
scaledPlusEquals(double, RingType) - Method in interface gov.sandia.cognition.math.Ring: Inline arithmetic addition of this and other after element-wise scaling of other by scaleFactor.
scaledPlusEquals(double, UnsignedLogNumber) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
scaledPlusEquals(double, RandomVariable<Number>) - Method in class gov.sandia.cognition.statistics.UnivariateRandomVariable
scaleEquals(DefaultKernelBinaryCategorizer<?>, double) - Static method in class gov.sandia.cognition.learning.function.kernel.KernelUtil: Scales all of the weights in the given kernel binary categorizer by the given value.
scaleEquals(double) - Method in class gov.sandia.cognition.math.ComplexNumber
scaleEquals(double) - Method in class gov.sandia.cognition.math.LogNumber
scaleEquals(double) - Method in class gov.sandia.cognition.math.matrix.AbstractVectorSpace
scaleEquals(double) - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
scaleEquals(double) - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
scaleEquals(double) - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix: Inline element-wise scaling of this by scaleFactor
scaleEquals(double) - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
scaleEquals(double) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix
scaleEquals(double) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector
scaleEquals(double) - Method in class gov.sandia.cognition.math.MutableDouble
scaleEquals(double) - Method in class gov.sandia.cognition.math.MutableInteger
scaleEquals(double) - Method in class gov.sandia.cognition.math.MutableLong
scaleEquals(double) - Method in interface gov.sandia.cognition.math.Ring: Inline element-wise scaling of this by scaleFactor
scaleEquals(double) - Method in class gov.sandia.cognition.math.UnsignedLogNumber
scaleEquals(double) - Method in class gov.sandia.cognition.statistics.UnivariateRandomVariable
scaleSum(double) - Method in class gov.sandia.cognition.math.RingAccumulator: Returns the sum scaled by the given scale factor.
searchForBetter(EvaluatedGenome<GenomeType>, Collection<EvaluatedGenome<GenomeType>>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.GeneticAlgorithm: Searches the provided population of genomes for one whose cost is lower than the provided best so far genome.
SECOND - Static variable in class gov.sandia.cognition.time.DefaultDuration: A second in duration.
second - Variable in class gov.sandia.cognition.util.DefaultPair: The second object.
second - Variable in class gov.sandia.cognition.util.DefaultTriple: The second object.
secondChild - Variable in class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.BinaryClusterHierarchyNode: The second child node.
secondLearner - Variable in class gov.sandia.cognition.learning.algorithm.CompositeBatchLearnerPair: The second learner that is trained on the output of the evaluator created by the first learner.
SECONDS_PER_MINUTE - Static variable in class gov.sandia.cognition.time.DefaultDuration: There are 60 seconds per minute.
sectioningStep() - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.AbstractAnytimeLineMinimizer
sectioningStep() - Method in interface gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizer: Continues the sectioning phase of the algorihtm.
sectioningStep() - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerBacktracking
sectioningStep() - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeBased
sectioningStep() - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeFree
seedScale - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner: The standard deviation for initializing the factors.
seek(Date) - Method in interface gov.sandia.cognition.data.temporal.SeekableTemporalDataReadChannel: Seeks to the given time in the data.
SeekableTemporalDataReadChannel<DataType extends Temporal> - Interface in gov.sandia.cognition.data.temporal: The SeekableTemporalDataReadChannel interface extends the TemporalDataReadChannel interface to give the ability to seek around in the temporal data based on a time coordinate.
segmentCDF - Variable in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.UpperEnvelope: Cumulative sums of the normalized weights of the lines...
select(Collection<EvaluatedGenome<GenomeType>>) - Method in interface gov.sandia.cognition.learning.algorithm.genetic.selector.Selector: Selects and returns a portion of the given population.
select(Collection<EvaluatedGenome<GenomeType>>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.selector.TournamentSelector: Uses tournament selection to create a new population.
selectNextClusterIndex(double[], boolean[]) - Method in class gov.sandia.cognition.learning.algorithm.clustering.initializer.AbstractMinDistanceFixedClusterInitializer: Select the index for the next cluster based on the given minimum distances and array indicating which clusters have already been selected.
selectNextClusterIndex(double[], boolean[]) - Method in class gov.sandia.cognition.learning.algorithm.clustering.initializer.DistanceSamplingClusterInitializer
selectNextClusterIndex(double[], boolean[]) - Method in class gov.sandia.cognition.learning.algorithm.clustering.initializer.GreedyClusterInitializer
Selector<GenomeType> - Interface in gov.sandia.cognition.learning.algorithm.genetic.selector: The Selector interface defines a type of reproducer that can select a portion of a population for reproduction.
selfKernel - Variable in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation.Entry: This is the value of the kernel applied to the example and itself.
SemanticIdentifier - Interface in gov.sandia.cognition.framework: The SemanticIdentifier class holds a SemanticLabel along with the unique integer that can be used to identify the SemanticLabel within a model.
semanticIdentifierAdded(SemanticIdentifierMapEvent) - Method in interface gov.sandia.cognition.framework.SemanticIdentifierMapListener: This event is triggered when a SemanticIdentifier has been added to the map.
semanticIdentifierMap - Variable in class gov.sandia.cognition.framework.learning.converter.AbstractCogxelConverter: The SemanticIdentifierMap for the converter.
SemanticIdentifierMap - Interface in gov.sandia.cognition.framework: The SemanticIdentifierMap defines the functionality of a class that assigns identifiers to SemanticLabels and keeps track of them.
SemanticIdentifierMapEvent - Class in gov.sandia.cognition.framework: The SemanticIdentifierMapEvent class implements an event object for the SemanticIdentifierMapListener interface to make use of.
SemanticIdentifierMapEvent(SemanticIdentifierMap, SemanticIdentifierMapEventType, SemanticIdentifier) - Constructor for class gov.sandia.cognition.framework.SemanticIdentifierMapEvent: Creates a new instance of CognitiveModelStateChangeEvent.
SemanticIdentifierMapEventType - Enum in gov.sandia.cognition.framework: The SemanticIdentifierMapEventType
SemanticIdentifierMapListener - Interface in gov.sandia.cognition.framework: The SemanticIdentifierMapListener defines an EventListener for the SemanticIdentifierMap.
SemanticLabel - Interface in gov.sandia.cognition.framework: This interface defines what a semantic label should have.
SemanticMemory - Interface in gov.sandia.cognition.framework: The SemanticMemory interface defines the general functionality required of a module in the CognitiveModel that is a semantic memory.
SemanticNetwork - Interface in gov.sandia.cognition.framework: The SemanticNetwork interface defines the functionality required for a network that is used as part of the paramterization to a SemanticMemory.
Semimetric<InputType> - Interface in gov.sandia.cognition.math: A semimetric is a divergence function that takes inputs from the same set (domain) and is positive definite and symmetric.
sequenceGammas - Variable in class gov.sandia.cognition.learning.algorithm.hmm.BaumWelchAlgorithm: The list of all gammas from each sequence
SequencePredictionLearner<DataType,LearnedType> - Class in gov.sandia.cognition.learning.algorithm: A wrapper learner that converts an unlabeled sequence of data into a sequence of prediction data using a fixed prediction horizon.
SequencePredictionLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.SequencePredictionLearner: Creates a new SequencePredictionLearner with default parameters.
SequencePredictionLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends DataType, DataType>>, ? extends LearnedType>, int) - Constructor for class gov.sandia.cognition.learning.algorithm.SequencePredictionLearner: Creates a new SequencePredictionLearner with the given learner and prediction horizon.
SequentialDataMultiPartitioner - Class in gov.sandia.cognition.learning.data: This partitioner splits a Collection of data into a pre-defined number of approximately equal sequential partitions, with the nonzero remainder elements going into the final partition.
SequentialDataMultiPartitioner() - Constructor for class gov.sandia.cognition.learning.data.SequentialDataMultiPartitioner
SequentialMinimalOptimization<InputType> - Class in gov.sandia.cognition.learning.algorithm.svm: An implementation of the Sequential Minimal Optimization (SMO) algorithm for training a Support Vector Machine (SVM), which is a kernel-based binary categorizer.
SequentialMinimalOptimization() - Constructor for class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization: Creates a new instance of Sequential Minimal Optimization.
SequentialMinimalOptimization(Kernel<? super InputType>) - Constructor for class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization: Creates a new instance of Sequential Minimal Optimization with the given kernel.
SequentialMinimalOptimization(Kernel<? super InputType>, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization: Creates a new instance of Sequential Minimal Optimization with the given kernel and random number generator.
SequentialMinimalOptimization(Kernel<? super InputType>, double, double, double, int, int, Random) - Constructor for class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization: Creates a new instance of Sequential Minimal Optimization with the given kernel and random number generator.
serialize(String, DenseMemoryGraph<?>) - Static method in class gov.sandia.cognition.graph.DenseMemoryGraph: Helper method for serializing the input to file.
serialize(String, WeightedDenseMemoryGraph<?>) - Static method in class gov.sandia.cognition.graph.WeightedDenseMemoryGraph: Helper method for serializing the input to file.
SerializedModelHandler - Class in gov.sandia.cognition.framework.io: The SerializedModelHandler class implements some utility methods for dealing with models that have been serialized using the Java serialization API.
SerializedModelHandler() - Constructor for class gov.sandia.cognition.framework.io.SerializedModelHandler: Creates a new instance of SerializedModelHandler
set(KeyType, double) - Method in class gov.sandia.cognition.collection.AbstractLogNumberMap
set(KeyType, double) - Method in class gov.sandia.cognition.collection.AbstractMutableDoubleMap
set(int, double) - Method in class gov.sandia.cognition.collection.DoubleArrayList: Sets the element at idx
set(DataType) - Method in class gov.sandia.cognition.collection.FiniteCapacityBuffer.InternalIterator
set(int, DataType) - Method in class gov.sandia.cognition.collection.FiniteCapacityBuffer
set(int, int) - Method in class gov.sandia.cognition.collection.IntArrayList: Sets the element at idx
set(KeyType, double) - Method in interface gov.sandia.cognition.collection.ScalarMap: Sets the value associated with a given key.
set(int, int, double) - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
set(int, double) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
set(int, int, double) - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
set(int, int, double) - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix: Sets the value of the element of the matrix at the zero-based row and column indices.
set(int, double) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
set(int, int, double) - Method in interface gov.sandia.cognition.math.matrix.Matrix: Sets the value of the element of the matrix at the zero-based row and column indices.
set(int, int, double) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix
set(int, double) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector
set(int, double) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVector
set(int, double) - Method in interface gov.sandia.cognition.math.matrix.Vector: Sets the value for an element at the zero-based index from the vector.
set(KeyType, double) - Method in class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution
setA(Matrix) - Method in class gov.sandia.cognition.math.signals.LinearDynamicalSystem: Setter for A.
setAccessedDate(long) - Method in class gov.sandia.cognition.text.document.DefaultDocument: Sets the last accessed date of the document.
setAccessedDate(Date) - Method in class gov.sandia.cognition.text.document.DefaultDocument: Sets the last accessed date of the document.
setActivated(boolean) - Method in class gov.sandia.cognition.framework.lite.BooleanActivatableCogxel: Sets a boolean indicating whether the cogxel is activated.
setActivation(double) - Method in interface gov.sandia.cognition.framework.Cogxel: Sets the activation level of the Cogxel.
setActivation(double) - Method in class gov.sandia.cognition.framework.DefaultCogxel: Sets the activation level of the Cogxel.
setActualCommand(String) - Method in class gov.sandia.cognition.io.ProcessLauncher: Setter for actualCommand
setAggressiveness(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlinePassiveAggressivePerceptron.AbstractSoftMargin: Sets the aggressiveness parameter (C), which is the trade-off between aggressive updating to meet an incorrect example and keeping history around.
setAlgorithm(InternalAlgorithm) - Method in class gov.sandia.cognition.algorithm.AnytimeAlgorithmWrapper: Sets the underlying algorithm.
setAll(Iterable<? extends KeyType>, double) - Method in class gov.sandia.cognition.collection.AbstractScalarMap
setAll(ScalarMap<? extends KeyType>) - Method in class gov.sandia.cognition.collection.AbstractScalarMap
setAll(Iterable<? extends KeyType>, double) - Method in interface gov.sandia.cognition.collection.ScalarMap: Sets all the given keys to the given value.
setAll(ScalarMap<? extends KeyType>) - Method in interface gov.sandia.cognition.collection.ScalarMap: Sets all the given keys to the given value.
setAllowedTerms(Set<Term>) - Method in class gov.sandia.cognition.text.term.filter.DictionaryFilter: Sets the set of allowed terms.
setAlpha(double) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.Sample: Setter for alpha
setAlpha(double) - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution: Setter for alpha
setAlpha(double) - Method in class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess: Setter for alpha.
setAlpha(double) - Method in class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment: Setter for alpha
setAlpha(double) - Method in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler: Sets the alpha parameter controlling the Dirichlet distribution for the document-topic probabilities.
setAlphabet(char[]) - Method in class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector.Learner: Sets the alphabet of lower-case characters that can be used for replaces and inserts.
setAlphabet(char[]) - Method in class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector: Sets the alphabet of lower-case characters that can be used for replaces and inserts.
setAmplitude(double) - Method in class gov.sandia.cognition.learning.function.scalar.CosineFunction: Setter for amplitude
setAreaUnderCurve(double) - Method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic.Statistic: Setter for areaUnderCurve
setAssignment(int, int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer: Sets the assignment of the given element to the new cluster index, updating the cluster counts as well.
setAssignments(int[]) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: Sets the assignments of examples to exemplars (clusters).
setAssociation(SemanticLabel, SemanticLabel, double) - Method in class gov.sandia.cognition.framework.DefaultSemanticNetwork: Sets the association between two nodes in the network.
setAssociation(SemanticLabel, SemanticLabel, double) - Method in interface gov.sandia.cognition.framework.lite.MutablePatternRecognizerLite: Sets the association between nodes in the recognizer.
setAssociation(SemanticLabel, SemanticLabel, double) - Method in class gov.sandia.cognition.framework.lite.MutableSemanticMemoryLite: Sets the association between nodes in the recognizer.
setAssociation(SemanticLabel, SemanticLabel, double) - Method in class gov.sandia.cognition.framework.lite.SimplePatternRecognizer: Sets the association between nodes in the recognizer.
setAuthor(String) - Method in class gov.sandia.cognition.text.document.DefaultDocument: Sets the author field of the document to the given string.
setAutoregressiveCoefficients(Vector) - Method in class gov.sandia.cognition.math.signals.AutoRegressiveMovingAverageFilter: Setter for autoregressiveCoefficients
setAvailabilities(double[][]) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: Sets the availability values.
setAverager(Summarizer<? super OutputType, ? extends OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.nearest.AbstractKNearestNeighbor: Setter for averager
setAverager(Summarizer<? super OutputType, ? extends OutputType>) - Method in interface gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighbor: Setter for averager.
setB(Matrix) - Method in class gov.sandia.cognition.math.signals.LinearDynamicalSystem: Setter for B.
setBag(ArrayList<InputOutputPair<? extends InputType, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner: Sets the most recently created bag.
setBaseHandler(StreamSerializationHandler<SerializedType>) - Method in class gov.sandia.cognition.io.serialization.GZIPSerializationHandler: Sets the base handler whose output is serialized.
setBasisFunctions(ArrayList<? extends Evaluator<? super InputType, ? extends OutputType>>) - Method in class gov.sandia.cognition.learning.function.LinearCombinationFunction: Setter for basisFunctions
setBasisFunctions(Collection<? extends Evaluator<? super InputType, Double>>) - Method in class gov.sandia.cognition.learning.function.vector.ScalarBasisSet: Setter for basisFunctions
setBestSoFar(AnnealedType) - Method in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer: Sets the best state found so far.
setBestSoFar(EvaluatedGenome<GenomeType>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.GeneticAlgorithm: Sets the best genome found so far.
setBestSoFarScore(double) - Method in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer: Sets the score for the best state found so far.
setBeta(double) - Method in class gov.sandia.cognition.statistics.distribution.BetaDistribution: Setter for beta
setBeta(double) - Method in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler: Sets the beta parameter controlling the Dirichlet distribution for the topic-term probabilities.
setBias(double) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AdditiveEnsemble: Sets the initial offset value (bias) to which the output of the ensemble members are added when computing a result.
setBias(double) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedAdditiveEnsemble: Sets the initial offset value (bias) to which the output of the ensemble members are added when computing a result.
setBias(double) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachine: Sets the bias value.
setBias(double) - Method in class gov.sandia.cognition.learning.function.categorization.KernelBinaryCategorizer: Sets the bias term.
setBias(double) - Method in class gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer: Sets the bias term.
setBias(double) - Method in class gov.sandia.cognition.learning.function.scalar.KernelScalarFunction: Sets the bias term.
setBias(double) - Method in class gov.sandia.cognition.learning.function.scalar.LinearDiscriminantWithBias: Setter for bias.
setBias(double) - Method in class gov.sandia.cognition.learning.function.scalar.LinearVectorScalarFunction: Sets the bias term.
setBias(Vector) - Method in class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminantWithBias: Setter for bias
setBiasEnabled(boolean) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner: Sets whether or not the bias term is enabled.
setBiasRegularization(double) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner: Sets the value for the parameter controlling the bias regularization.
setBlockExperimentComparison(BlockExperimentComparison<Number>) - Method in class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment: Setter for blockExperimentComparison
setBody(String) - Method in class gov.sandia.cognition.text.document.DefaultDocument: Sets the body field of the document to the given string.
setBooleanConverter(DataToVectorEncoder<Boolean>) - Method in class gov.sandia.cognition.data.convert.vector.UniqueBooleanVectorEncoder: Sets the boolean converter used to encode the equality comparison between each of the unique values and a given input.
setBounds(Rectangle2D.Double) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node: Sets the bounding box of the region represented by this node.
setBracket(LineBracket) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.AbstractAnytimeLineMinimizer: Setter for bracket
setBracket(LineBracket) - Method in class gov.sandia.cognition.learning.algorithm.root.RootBracketExpander: Setter for bracket.
setBracketer(RootBracketer) - Method in class gov.sandia.cognition.learning.algorithm.root.AbstractBracketedRootFinder: Gets the root-bracketing algorithm that will be used to initially bracket the root.
setBudget(int) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.AbstractOnlineBudgetedKernelBinaryCategorizerLearner: Sets the budget.
setBudget(int) - Method in interface gov.sandia.cognition.learning.algorithm.perceptron.kernel.BudgetedKernelBinaryCategorizerLearner: Sets the budget.
setBufferedReader(BufferedReader) - Method in class gov.sandia.cognition.io.ReaderTokenizer: Setter for bufferedReader
setBurnInIterations(int) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo
setBurnInIterations(int) - Method in interface gov.sandia.cognition.statistics.bayesian.MarkovChainMonteCarlo: Sets the number of iterations that must transpire before the algorithm begins collection the samples.
setBurnInIterations(int) - Method in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler: Sets he number of burn-in iterations for the Markov Chain Monte Carlo algorithm to run before sampling begins.
setC(Matrix) - Method in class gov.sandia.cognition.math.signals.LinearDynamicalSystem: Setter for C.
setCapacity(int) - Method in class gov.sandia.cognition.collection.FiniteCapacityBuffer: Sets the capacity of the buffer, which is the maximum number of elements that can be stored in it.
setCapacity(int) - Method in class gov.sandia.cognition.learning.function.scalar.KolmogorovSmirnovEvaluator: Setter for capacity
setCategories(Set<OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.tree.CategorizationTree: Sets the possible output categories.
setCategories(Set<CategoryType>) - Method in class gov.sandia.cognition.learning.function.categorization.AbstractCategorizer: Sets the Set of possible categories, which are the output values.
setCategories(Set<CategoryType>) - Method in class gov.sandia.cognition.learning.function.categorization.EvaluatorToCategorizerAdapter
setCategories(Set<CategoryType>) - Method in class gov.sandia.cognition.learning.function.categorization.WinnerTakeAllCategorizer
setCategorizedVector(Vector) - Method in class gov.sandia.cognition.statistics.ChiSquaredSimilarity: Basic setter for the categorized vector.
setCategorizer(Categorizer<? super IntermediateType, ? extends CategoryType>) - Method in class gov.sandia.cognition.learning.function.categorization.CompositeCategorizer: Sets the categorizer, which takes the output of the preprocessor and categorizes it.
setCategorizers(Collection<BinaryCategorizer<? super InputType>>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.BinaryCategorizerSelector: Gets the collection of categorizers that the learner selects from.
setCategoryPairsToEvaluatorMap(Map<Pair<CategoryType, CategoryType>, Evaluator<? super InputType, Boolean>>) - Method in class gov.sandia.cognition.learning.function.categorization.BinaryVersusCategorizer: Sets the mapping of false-true category pairs to the binary categorizer that distinguishes them.
setCategoryPriors(Map<OutputType, Double>) - Method in class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeLearner: Set prior category probabilities.
setCDF(CumulativeDistributionFunction<Double>) - Method in class gov.sandia.cognition.learning.function.scalar.KolmogorovSmirnovEvaluator: Setter for cdf
setCenterData(boolean) - Method in class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis.Function: Sets whether or not the data needs to be centered in the kernel space before applying the function.
setCenterData(boolean) - Method in class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis: Sets whether or not the data needs to be centered in the kernel space before applying the algorithm.
setCentralValue(double) - Method in class gov.sandia.cognition.statistics.method.ConfidenceInterval: Setter for centralValue
setCentroid(ClusterType) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.CentroidCluster: Sets the centroid of the cluster.
setChangedCount(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: Sets the number of cluster assignments that have changed in the most recent iteration.
setChildMap(Map<InteriorType, DecisionTreeNode<InputType, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeNode: Sets the mapping of decision values to child nodes.
setChildren(List<ClusterHierarchyNode<DataType, ClusterType>>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.DefaultClusterHierarchyNode: Sets the children of this node.
setChildren(ClusterHierarchyNode<DataType, ClusterType>, ClusterHierarchyNode<DataType, ClusterType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.DefaultClusterHierarchyNode: Sets the children of this node.
setChildren(ArrayList<Quadtree<DataType>.Node>) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node: Sets the list of child nodes of this node.
setChildrenDivergence(double) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer.HierarchyNode: Sets the divergence between the two children.
setChiSquare(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearRegression.Statistic: Setter for chiSquare
setClassifier(ScalarThresholdBinaryCategorizer) - Method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic.DataPoint: Setter for classifier
setCluster(ClusterType) - Method in class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.AbstractClusterHierarchyNode: Sets the cluster associated with the node.
setClusterCount(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer: Sets the number of clusters.
setClusters(HashMap<Integer, CentroidCluster<DataType>>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: Sets the current clusters, which is a sparse mapping of exemplar identifier to cluster object.
setClusters(ArrayList<ClusterType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer: Sets the clusters.
setClusters(ArrayList<ClusterType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer: Sets the current clusters, which is a sparse mapping of exemplar identifier to cluster object.
setClusters(ArrayList<ClusterType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer: Sets the clusters.
setClusters(ArrayList<ClusterType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer: Sets the clusters cache to the provided value.
setClusters(ArrayList<DirichletProcessMixtureModel.DPMMCluster<ObservationType>>) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.Sample: Setter for clusters
setClustersHierarchy(ArrayList<AgglomerativeClusterer.HierarchyNode<DataType, ClusterType>>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer: Sets the hierarchy of clusters.
setClustersHierarchy(ArrayList<BinaryClusterHierarchyNode<DataType, ClusterType>>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer: Sets the hierarchy of clusters.
setCoefficients(Vector) - Method in class gov.sandia.cognition.learning.function.LinearCombinationFunction: Setter for coefficients
setCognitiveState(CognitiveModelLiteState) - Method in class gov.sandia.cognition.framework.lite.AbstractCognitiveModelLite: Sets the cognitive state to the given state.
setCogxelFactory(CogxelFactory) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelBooleanConverter: Gets the CogxelFactory used to create the cogxels used by the converter.
setCogxelFactory(CogxelFactory) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelDoubleConverter: Gets the CogxelFactory used to create the Cogxels used by the converter.
setCogxelFactory(CogxelFactory) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter: Gets the CogxelFactory used to create the Cogxels used by the converter.
setCogxelFactory(CogxelFactory) - Method in class gov.sandia.cognition.framework.lite.ArrayBasedPerceptionModule: Sets the cogxel factory to use.
setCogxelFactory(CogxelFactory) - Method in class gov.sandia.cognition.framework.lite.VectorBasedPerceptionModule: Setter for cogxelFactory
setCogxelFactory(CogxelFactory) - Method in class gov.sandia.cognition.framework.lite.VectorBasedPerceptionModuleFactory: Setter for cogxelFactory
setCogxels(CogxelStateLite) - Method in class gov.sandia.cognition.framework.lite.CognitiveModelLiteState: Setter for cogxels
setCogxelVectorConverters(Collection<CogxelVectorConverter>) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorCollectionConverter: Setter for cogxelVectorConverters
setColumn(int, Vector) - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
setColumn(int, Vector) - Method in interface gov.sandia.cognition.math.matrix.Matrix: Sets the specified column from the given columnVector
setColumn(int, SparseVector) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseColumnMatrix: Sets the column of the matrix using the given SparseVector, using MTJ's internal routine to speed things up
setColumnConverters(ArrayList<CogxelVectorConverter>) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelMatrixConverter: Setter for columnConverters
setColumnIndex(int) - Method in interface gov.sandia.cognition.math.matrix.MatrixEntry: Sets the column index to which the entry points
setColumnIndex(int) - Method in class gov.sandia.cognition.math.matrix.mtj.TwoMatrixEntryMTJ: Setter for columnIndex.
setColumnIndex(int) - Method in interface gov.sandia.cognition.math.matrix.TwoMatrixEntry: Sets the column index to which the entry points
setComponentCount(int) - Method in class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis: Gets the number of components the analysis attempts to find.
setComponents(Matrix) - Method in class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis.Function: Sets the matrix of components from the analysis.
setConditionalDistribution(ConditionalType) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractBayesianParameter: Setter for conditionalDistribution
setConditionalDistribution(ConditionalType) - Method in class gov.sandia.cognition.statistics.DefaultDistributionParameter: Setter for conditionalDistribution
setConditionalLearner(BatchLearner<Collection<? extends ObservationType>, ? extends ComputableDistribution<ObservationType>>) - Method in class gov.sandia.cognition.learning.function.categorization.MaximumAPosterioriCategorizer.Learner: Setter for conditionalLearner
setConditionals(Map<CategoryType, List<DistributionType>>) - Method in class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer: Sets the conditional distributions, which is a mapping of category to the list of probability density functions, one for each dimension of the vector.
setConfidence(double) - Method in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalDeviation: Gets the confidence to use for updating.
setConfidence(double) - Method in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalVariance: Gets the confidence to use for updating.
setConfidence(ConfidenceStatistic) - Method in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment.Result: Sets the confidence statistic for the learners.
setConfidence(ConfidenceStatistic) - Method in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment: Sets the confidence statistic that the two learners are different.
setConfidence(double) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrixConfidenceInterval: Setter for confidence
setConfidence(double) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrixConfidenceInterval.Summary: Sets the confidence for created the interval.
setConfidence(double) - Method in class gov.sandia.cognition.statistics.method.ConfidenceInterval: Setter for confidence
setConfidence(double) - Method in class gov.sandia.cognition.statistics.method.StudentTConfidence.Summary: Sets the confidence for created the interval.
setConfusionMatrix(DefaultBinaryConfusionMatrix) - Method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic.DataPoint: Setter for confusionMatrix
setConjuctive(BayesianParameter<ParameterType, ? extends ProbabilityFunction<ObservationType>, ? extends ProbabilityFunction<ParameterType>>) - Method in class gov.sandia.cognition.statistics.bayesian.ImportanceSampling.DefaultUpdater: Setter for conjunctive
setConjuctive(BayesianParameter<ParameterType, ? extends ProbabilityFunction<ObservationType>, ? extends ProbabilityFunction<ParameterType>>) - Method in class gov.sandia.cognition.statistics.bayesian.RejectionSampling.DefaultUpdater: Setter for conjunctive
setConstant(double) - Method in class gov.sandia.cognition.learning.function.kernel.PolynomialKernel: Gets the constant of the polynomial.
setConstant(double) - Method in class gov.sandia.cognition.learning.function.kernel.SigmoidKernel: Sets the constant inside of the sigmoid kernel.
setConstantValue(double) - Method in class gov.sandia.cognition.learning.function.scalar.LocallyWeightedKernelScalarFunction: Sets the constant value.
setConstantWeight(double) - Method in class gov.sandia.cognition.learning.function.scalar.LocallyWeightedKernelScalarFunction: Sets the constant weight.
setConstructor(Constructor<? extends CreatedType>) - Method in class gov.sandia.cognition.factory.ConstructorBasedFactory: Sets the constructor to use to create new objects.
setConverter(DataConverter<? super InputType, ? extends Number>) - Method in class gov.sandia.cognition.data.convert.vector.NumberConverterToVectorAdapter: Sets the number converter being adapted to work with vectors.
setConverter(SingleTextualConverter<? super InputType, ? extends OutputType>) - Method in class gov.sandia.cognition.text.convert.SingleToMultiTextualConverterAdapter: Sets the internal single textual converter being wrapped.
setConvexHull(ArrayList<ReceiverOperatingCharacteristic.DataPoint>) - Method in class gov.sandia.cognition.statistics.method.ConvexReceiverOperatingCharacteristic: Setter for convexHull
setCoolingFactor(double) - Method in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer: Sets the cooling factor.
setCost(double) - Method in class gov.sandia.cognition.learning.algorithm.genetic.EvaluatedGenome: Sets the cost of the genome.
setCostFunction(CostFunction<? super AnnealedType, ? super CostParametersType>) - Method in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer: Sets the cost function.
setCostFunction(CostFunction<? super GenomeType, ? super CostParametersType>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.GeneticAlgorithm: Sets the cost function.
setCostFunction(SupervisedCostFunction<Vector, Vector>) - Method in class gov.sandia.cognition.learning.algorithm.regression.AbstractMinimizerBasedParameterCostMinimizer: Setter for costFunction
setCostFunction(CostFunctionType) - Method in class gov.sandia.cognition.learning.algorithm.regression.AbstractParameterCostMinimizer: Setter for costFunction
setCostFunction(ParallelizableCostFunction) - Method in class gov.sandia.cognition.learning.function.cost.ParallelizedCostFunctionContainer: Setter for costFunction
setCostParameters(CostParametersType) - Method in class gov.sandia.cognition.learning.function.cost.AbstractCostFunction
setCostParameters(Collection<? extends InputOutputPair<? extends InputType, TargetType>>) - Method in class gov.sandia.cognition.learning.function.cost.AbstractSupervisedCostFunction
setCostParameters(ClusterDivergenceFunction<? super ClusterType, ? super DataType>) - Method in class gov.sandia.cognition.learning.function.cost.ClusterDistortionMeasure
setCostParameters(CostParametersType) - Method in interface gov.sandia.cognition.learning.function.cost.CostFunction: Sets the parameters of the cost function used to evaluate the cost of a target.
setCostParameters(Vectorizable) - Method in class gov.sandia.cognition.learning.function.cost.EuclideanDistanceCostFunction
setCostParameters(Collection<? extends InputOutputPair<? extends Vector, Vector>>) - Method in class gov.sandia.cognition.learning.function.cost.ParallelizedCostFunctionContainer
setCount(long) - Method in class gov.sandia.cognition.statistics.AbstractSufficientStatistic: Setter for count
setCounterFactory(Factory<? extends DataDistribution<CategoryType>>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner: Sets the factory used for creating the object for counting the votes of the learned ensemble members.
setCovariance(Matrix) - Method in class gov.sandia.cognition.learning.algorithm.annealing.VectorizablePerturber: Sets the covariance of the perturber.
setCovariance(Matrix) - Method in class gov.sandia.cognition.learning.function.categorization.DefaultConfidenceWeightedBinaryCategorizer: Sets the covariance matrix.
setCovariance(Matrix) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian: Sets the covariance matrix.
setCovariance(Matrix, double) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian: Sets the covariance matrix.
setCovarianceDivisor(double) - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution: Setter for covarianceDivisor
setCovarianceInverse(Matrix) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian: Sets the covariance inverse
setCovarianceInverse(Matrix, double) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian: Sets the covariance inverse
setCovarianceSqrt(Matrix) - Method in class gov.sandia.cognition.learning.algorithm.annealing.VectorizablePerturber: Sets the covariance square root matrix.
setCreatedClass(Class<? extends CreatedType>) - Method in class gov.sandia.cognition.factory.DefaultFactory: Sets the class whose default constructor is used to create new objects.
setCreator(ClusterCreator<ClusterType, DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer: Sets the cluster creator.
setCreator(ClusterCreator<ClusterType, DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer: Sets the cluster creator.
setCreator(KDTree<DataType, Double, InputOutputPair<DataType, Double>>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer: Sets the spatial index.
setCreator(ClusterCreator<ClusterType, DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.initializer.AbstractMinDistanceFixedClusterInitializer: Sets the cluster creator used to create the initial clusters.
setCreator(ClusterCreator<ClusterType, DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer: Sets the cluster creator.
setCreator(IncrementalClusterCreator<ClusterType, DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer: Sets the cluster creator.
setCrossoverFunction(CrossoverFunction<GenomeType>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.reproducer.CrossoverReproducer: Sets the CrossoverFunction.
setCurrent(AnnealedType) - Method in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer: Sets the current state of the system.
setCurrentInput(Vector) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter: Setter for currentInput
setCurrentLine(String) - Method in class gov.sandia.cognition.io.ProcessLauncherEvent: Setter for currentLine
setCurrentParameter(ParameterType) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo: Setter for currentParameter.
setCurrentScore(double) - Method in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer: Sets the score of the current state.
setCurvatureCondition(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.WolfeConditions: Setter for curvatureCondition
setD(double) - Method in class gov.sandia.cognition.statistics.method.KolmogorovSmirnovConfidence.Statistic: Setter for D
setDampingFactor(double) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: Sets the damping factor.
setDampingFactor(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.LevenbergMarquardtEstimation: Setter for dampingFactor
setDampingFactorDivisor(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.FletcherXuHybridEstimation: Setter for dampingFactorDivisor
setDampingFactorDivisor(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.LevenbergMarquardtEstimation: Setter for dampingFactorDivisor
setData(DataType) - Method in class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner: Gets the data to use for learning.
setData(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer
setData(Collection<? extends Vector>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer: Set the data to be clustered.
setData(EvaluatorType) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.AbstractAnytimeLineMinimizer
setData(Collection<InputOutputPair<? extends InputType, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborExhaustive: Setter for data
setData(KDTree<InputType, OutputType, InputOutputPair<? extends InputType, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborKDTree: Setter for data
setData(LinkedList<InputOutputPair<? extends InputType, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborExhaustive: Sets the data that the object performs nearest-neighbor lookup on.
setData(KDTree<InputType, OutputType, InputOutputPair<? extends InputType, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborKDTree: Setter for data
setData(List<? extends DataType>) - Method in class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis.Function: Sets the data that was used in the analysis.
setDataInBag(int[]) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner: Sets the array of counts of the number of samples of each example in the current bag.
setDataList(ArrayList<? extends InputOutputPair<? extends InputType, OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner: Sets the data the learner is using as an array list.
setDate(Date) - Method in class gov.sandia.cognition.text.document.DefaultDateField: Sets the date stored in the field.
setDecider(Categorizer<? super InputType, ? extends InteriorType>) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeNode: Sets the decider used at this node.
setDeciderLearner(DeciderLearner<? super InputType, OutputType, ?, ?>) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeLearner: Sets the learner for the decision function.
setDefaultCovariance(double) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.GaussianClusterCreator: Setter for defaultCovariance
setDefaultCovariance(double) - Method in class gov.sandia.cognition.learning.algorithm.clustering.initializer.NeighborhoodGaussianClusterInitializer: Setter for defaultCovariance
setDefaultCovariance(double) - Method in class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator.DiagonalCovarianceLearner: Sets the default covariance value.
setDefaultCovariance(double) - Method in class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator.FullCovarianceLearner: Sets the default covariance value.
setDefaultCovariance(double) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.IncrementalEstimator: Setter for defaultCovariance
setDefaultCovariance(double) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatistic: Setter for defaultCovariance
setDefaultCovariance(double) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatisticCovarianceInverse: Setter for defaultCovarianceInverse
setDefaultCovarianceInverse(double) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.IncrementalEstimatorCovarianceInverse: Setter for defaultCovarianceInverse
setDefaultNumThreads(int) - Static method in class gov.sandia.cognition.algorithm.ParallelUtil: Sets the current default number of threads to use when calling the default createThreadPool() method
setDefaultVariance(double) - Method in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalDeviation: Sets the default variance, which the diagonal of the covariance matrix is initialized to.
setDefaultVariance(double) - Method in class gov.sandia.cognition.learning.algorithm.confidence.ConfidenceWeightedDiagonalVariance: Sets the default variance, which the diagonal of the covariance matrix is initialized to.
setDefaultVariance(double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.IncrementalEstimator: Sets the default variance, which is the amount added to the variance.
setDefaultVariance(double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.MaximumLikelihoodEstimator: Sets the default variance, which is the amount added to the variance.
setDegree(int) - Method in class gov.sandia.cognition.learning.function.kernel.PolynomialKernel: Sets the degree of the polynomial.
setDegreesOfFreedom(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearRegression.Statistic: Setter for degreesOfFreedom
setDegreesOfFreedom(double) - Method in class gov.sandia.cognition.statistics.distribution.ChiSquareDistribution: Setter for degrees of freedom
setDegreesOfFreedom(int) - Method in class gov.sandia.cognition.statistics.distribution.InverseWishartDistribution: Setter for degreesOfFreedom
setDegreesOfFreedom(double) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution: Setter for degreesOfFreedom
setDegreesOfFreedom(double) - Method in class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution: Setter for degreesOfFreedom
setDegreesOfFreedom(double) - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution: Setter for degreesOfFreedom
setDegreesOfFreedom(double) - Method in class gov.sandia.cognition.statistics.method.StudentTConfidence.Statistic: Setter for degreesOfFreedom
setDelaySamples(int) - Method in class gov.sandia.cognition.learning.data.feature.DelayFunction: Setter for delaySamples
setDelta(double) - Method in class gov.sandia.cognition.math.matrix.NumericalDifferentiator: Setter for delta
setDeltaSize(double) - Method in class gov.sandia.cognition.learning.algorithm.gradient.GradientDescendableApproximator: Setter for deltaSize
setDemoteToZero(boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Winnow: Sets whether or not the algorithm will demote features involved in an incorrect categorization to zero (Winnow1).
setDepth(int) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node: Sets the depth in the tree of this node.
setDerivativeGain(double) - Method in class gov.sandia.cognition.math.signals.PIDController: Setter for derivativeGain
setDFbetween(double) - Method in class gov.sandia.cognition.statistics.method.AnalysisOfVarianceOneWay.Statistic: Setter for DFbetween
setDFwithin(double) - Method in class gov.sandia.cognition.statistics.method.AnalysisOfVarianceOneWay.Statistic: Setter for DFwithin
setDimensionReducer(MultivariateDiscriminant) - Method in class gov.sandia.cognition.learning.algorithm.pca.PrincipalComponentsAnalysisFunction: Setter for dimensionReducer
setDimensionsToConsider(int...) - Method in interface gov.sandia.cognition.learning.algorithm.DimensionFilterableLearner: Gets the dimensions that the learner is to consider.
setDimensionsToConsider(int...) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractVectorThresholdMaximumGainLearner
setDimensionsToConsider(int...) - Method in class gov.sandia.cognition.learning.algorithm.tree.RandomSubVectorThresholdLearner
setDimensionsToConsider(int...) - Method in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdVarianceLearner
setDirection(Vector) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToScalarFunction: Setter for direction
setDiscriminant(DiscriminantType) - Method in class gov.sandia.cognition.learning.data.DefaultValueDiscriminantPair: Sets the discriminant.
setDiscriminant(LinearDiscriminant) - Method in class gov.sandia.cognition.learning.function.scalar.VectorFunctionLinearDiscriminant: Setter for discriminant
setDiscriminant(MultivariateDiscriminant) - Method in class gov.sandia.cognition.learning.function.vector.GeneralizedLinearModel: Setter for discriminant
setDiscriminant(Matrix) - Method in class gov.sandia.cognition.learning.function.vector.MultivariateDiscriminant: Setter for discriminant
setDistribution(UnivariateDistribution<? extends Number>) - Method in class gov.sandia.cognition.statistics.UnivariateRandomVariable: Setter for distribution
setDistributionEstimator(DistributionEstimator<? super Double, ? extends DistributionType>) - Method in class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer.Learner: Sets the estimation method for the distribution of each dimension of each category.
setDistributionLearner(IncrementalLearner<? super Double, DistributionType>) - Method in class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer.OnlineLearner: Sets the learner used for the distribution representing each dimension.
setDistributionLearner(BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>, ? extends ComputableDistribution<ObservationType>>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.AbstractBaumWelchAlgorithm: Setter for distributionLearner
setDistributions(ArrayList<? extends DistributionType>) - Method in class gov.sandia.cognition.statistics.distribution.LinearMixtureModel: Setter for distributions
setDistributions(Collection<? extends ClosedFormComputableDistribution<DataType>>) - Method in class gov.sandia.cognition.statistics.method.MaximumLikelihoodDistributionEstimator: Setter for distributions
setDivergence(DivergenceFunction<? super DataType, ? super DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: Sets the divergence function used by the algorithm.
setDivergenceFunction(ClusterToClusterDivergenceFunction<? super ClusterType, ? super DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer: Sets the divergence function.
setDivergenceFunction(ClusterDivergenceFunction<? super ClusterType, ? super DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer: Sets the divergence function.
setDivergenceFunction(DivergenceFunction<? super ClusterType, ? super DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer: Use a metric between a cluster and a point to update the metric on clusters.
setDivergenceFunction(DivergenceFunction<? super InputType, ? super InputType>) - Method in class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborKDTree
setDivergenceFunction(Metric<? super InputType>) - Method in class gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighborKDTree: Sets the Metric to use.
setDivergenceFunction(DivergenceFunction<? super InputType, ? super InputType>) - Method in class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborKDTree
setDivergenceFunction(Metric<? super InputType>) - Method in class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborKDTree: Sets the Metric to use.
setDivergenceFunction(DivergenceFunction<? super FirstType, ? super SecondType>) - Method in class gov.sandia.cognition.learning.function.distance.DefaultDivergenceFunctionContainer: Sets the divergence function used by this object.
setDivergenceFunction(DivergenceFunction<? super ValueType, ? super InputType>) - Method in class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator.Learner: Sets the divergence function to use from the values to the inputs.
setDivergenceFunction(DivergenceFunction<? super ValueType, ? super InputType>) - Method in class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator: Sets the divergence function to use from the values to the inputs.
setDocumentCount(int) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.AbstractFrequencyBasedGlobalTermWeighter: Sets the document count.
setDocumentTopicProbabilities(double[][]) - Method in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler.Result: Sets the topic-term probabilities, which are the often called the phi model parameters.
setDominance(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.DominanceGlobalTermWeighter: Sets the cached dominance weight vector.
setDPrime(double) - Method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic.Statistic: Setter for dPrime
setEffectiveZero(double) - Method in class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization: Sets the effective value for zero to use in the computation to deal with numerical imprecision.
setEffectiveZero(double) - Method in class gov.sandia.cognition.text.term.relation.TermVectorSimilarityNetworkCreator: Sets the value to treat as zero.
setEigenDecomposition(ComplexNumber[], Matrix, Matrix, boolean) - Method in class gov.sandia.cognition.math.matrix.decomposition.AbstractEigenDecomposition: Sets the eigen decomposition for this
setEigenValues(ComplexNumber[]) - Method in class gov.sandia.cognition.math.matrix.decomposition.AbstractEigenDecomposition: setter for eigenValues
setEigenVectorsImaginaryPart(Matrix) - Method in class gov.sandia.cognition.math.matrix.decomposition.AbstractEigenDecomposition: setter for the imaginary part of the eienvector, where the ith eigenvector is the ith column
setEigenVectorsRealPart(Matrix) - Method in class gov.sandia.cognition.math.matrix.decomposition.AbstractEigenDecomposition: setter for eigenVectorsRealPart, where the ith eigenvector is the ith column
setElement(int, int, double) - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
setElement(int, double) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
setElement(int, int, double) - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix: Set the value stored at the input locations to the input value
setElement(int, int, double) - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix: Sets the Matrix element at the specified zero-based indices throws ArrayIndexOutOfBoundsException if either rowIndex or columnIndex are less than 0, or greater than the number of rows (columns) minus one (0 <= index <= num-1)
setElement(int, double) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
setElement(int, double) - Method in interface gov.sandia.cognition.math.matrix.DiagonalMatrix: Sets the zero-based index diagonal element into the diagonal matrix
setElement(int, int, double) - Method in interface gov.sandia.cognition.math.matrix.Matrix: Sets the Matrix element at the specified zero-based indices throws ArrayIndexOutOfBoundsException if either rowIndex or columnIndex are less than 0, or greater than the number of rows (columns) minus one (0 <= index <= num-1)
setElement(int, int, double) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix
setElement(int, double) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector
setElement(int, int, double) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractSparseMatrix
setElement(int, double) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVector
setElement(int, int, double) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixMTJ
setElement(int, double) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixMTJ
setElement(int, double) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVector
setElement(int, double) - Method in interface gov.sandia.cognition.math.matrix.Vector: Sets the zero-based indexed element in the Vector from the specified value
setElements(Quaternion) - Method in interface gov.sandia.cognition.math.matrix.Quaternion: Sets the elements of this quaternion equal to the elements of the given quaternion.
setElementsScore(int, int, double, double) - Method in class gov.sandia.cognition.learning.algorithm.semisupervised.valence.MultipartiteValenceMatrix: Sets elements of the group with their score (+1/-1 or similar) and how much to trust that weight.
setEmissionFunctions(Collection<? extends ComputableDistribution<ObservationType>>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel: Setter for emissionFunctions.
setEnsemble(EnsembleType) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner: Sets the ensemble created by this learner.
setEnsemble(WeightedBinaryEnsemble<InputType, Evaluator<? super InputType, ? extends Boolean>>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AdaBoost: Sets the ensemble created by this learner.
setEnsembleSize(int) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.OnlineBaggingCategorizerLearner: Sets the size of the ensemble to create.
setEntries(ArrayList<SuccessiveOverrelaxation<InputType>.Entry>) - Method in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation: Gets the data that the algorithm keeps for each training instance.
setEntropy(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.EntropyGlobalTermWeighter: Sets the cached entropy weight vector.
setErrorCount(int) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.BatchMultiPerceptron: Sets the error count of the most recent iteration.
setErrorCount(long) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Forgetron.Result: Sets the error count.
setErrorCount(int) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelAdatron: Sets the error count of the most recent iteration.
setErrorCount(int) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelPerceptron: Sets the error count of the most recent iteration.
setErrorCount(long) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineShiftingPerceptron.LinearResult: Sets the error count.
setErrorCount(int) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Perceptron: Sets the error count of the most recent iteration.
setErrorCount(int) - Method in class gov.sandia.cognition.learning.algorithm.regression.KernelBasedIterativeRegression: Sets the error count of the most recent iteration.
setErrorTolerance(double) - Method in class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization: Sets the error tolerance for the algorithm.
setErrSum(double) - Method in class gov.sandia.cognition.math.signals.PIDController.State: Setter for errSum
setEstimate(EstimateType) - Method in class gov.sandia.cognition.learning.data.DefaultTargetEstimatePair: Sets the estimate, which is the prediction or guess.
setEstimateConverter(CogxelConverter<EstimateType>) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelTargetEstimatePairConverter: Sets the converter for the estimate value.
setEta(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Projectron: Sets the eta parameter that controls how many supports are allowed.
setEvaluator(Evaluator<? super InputType, ? extends OutputType>) - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleSettings: Sets the evaluator to be used by the module.
setEvaluator(Evaluator<? super InputType, ? extends CategoryType>) - Method in class gov.sandia.cognition.learning.function.categorization.EvaluatorToCategorizerAdapter: Sets the evaluator that is to be adapted to be a categorizer.
setEvaluator(Evaluator<? super InputType, Double>) - Method in class gov.sandia.cognition.learning.function.categorization.ScalarFunctionToBinaryCategorizerAdapter: Sets the scalar function that the adapter wraps.
setEvaluator(Evaluator<? super InputType, ? extends Vectorizable>) - Method in class gov.sandia.cognition.learning.function.categorization.WinnerTakeAllCategorizer: Sets the wrapped evaluator.
setEvaluator(Evaluator<String, String>) - Method in class gov.sandia.cognition.text.term.filter.StringEvaluatorSingleTermFilter: Sets the evaluator being used as a filter.
setEvaluators(Collection<? extends Evaluator<?, ?>>) - Method in class gov.sandia.cognition.evaluator.CompositeEvaluatorList: Sets the list of evaluators to compose together.
setEvaluators(Evaluator<?, ?>...) - Method in class gov.sandia.cognition.evaluator.CompositeEvaluatorList: Sets the array of evaluators to compose together.
setEventType(SemanticIdentifierMapEventType) - Method in class gov.sandia.cognition.framework.SemanticIdentifierMapEvent: Sets the type of the event.
setExamples(ArrayList<DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: Sets the array list of examples to cluster.
setExamples(Collection<EntryType>) - Method in class gov.sandia.cognition.learning.function.categorization.KernelBinaryCategorizer: Sets the list of weighted examples that categorizer is using.
setExamples(Collection<? extends WeightedValue<? extends InputType>>) - Method in class gov.sandia.cognition.learning.function.scalar.KernelScalarFunction: Sets the list of weighted examples that categorizer is using.
setExponent(double) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction: Setter for exponent
setF(double) - Method in class gov.sandia.cognition.statistics.method.AnalysisOfVarianceOneWay.Statistic: Setter for F
setFactorCount(int) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner: Sets the number of factors.
setFactorRegularization(double) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner: Sets the value for the parameter controlling the factor matrix regularization.
setFactors(Matrix) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachine: Sets the matrix of factors.
setFactory(Factory<? extends ConfusionMatrix<CategoryType>>) - Method in class gov.sandia.cognition.learning.performance.categorization.ConfusionMatrixPerformanceEvaluator: Sets the factory for the confusion matrix the evaluator creates.
setFalseNegativesCount(double) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix: Sets the number of false negatives in the matrix.
setFalseNegativesRate(ConfidenceInterval) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrixConfidenceInterval: Setter for falseNegativesRate
setFalsePositivesCount(double) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix: Sets the number of false positives in the matrix.
setFalsePositivesRate(ConfidenceInterval) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrixConfidenceInterval: Setter for falsePositivesRate
setFalseValue(Number) - Method in class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter: Sets the number that represents a falue value.
setField(Field) - Method in class gov.sandia.cognition.statistics.method.FieldConfidenceInterval: Setter for field
setFieldMap(HashMap<String, Field>) - Method in class gov.sandia.cognition.text.document.AbstractDocument: Sets the mapping of field name to the field.
setFieldName(String) - Method in class gov.sandia.cognition.text.convert.DocumentSingleFieldConverter: Sets the name of the field to extract.
setFieldNames(List<String>) - Method in class gov.sandia.cognition.text.convert.DocumentFieldConcatenator: Sets the list of field names whose text are to be concatenated together.
setFieldSeparator(String) - Method in class gov.sandia.cognition.text.convert.DocumentFieldConcatenator: Sets the string used as a separator between field text values.
setFirst(FirstType) - Method in class gov.sandia.cognition.util.DefaultPair: Sets the first object.
setFirst(FirstType) - Method in class gov.sandia.cognition.util.DefaultTriple: Sets the first object.
setFirstChild(ClusterHierarchyNode<DataType, ClusterType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.BinaryClusterHierarchyNode: Sets the first child node.
setFirstConverter(CogxelConverter<FirstType>) - Method in class gov.sandia.cognition.framework.learning.converter.AbstractCogxelPairConverter: Setter for firstConverter
setFirstInternalEntry(MatrixEntry) - Method in class gov.sandia.cognition.math.matrix.MatrixUnionIterator: setter for firstInternalEntry
setFirstInternalEntry(VectorEntry) - Method in class gov.sandia.cognition.math.matrix.VectorUnionIterator: setter for firstInternalEntry
setFirstIterator(Iterator<MatrixEntry>) - Method in class gov.sandia.cognition.math.matrix.MatrixUnionIterator: setter for firstInterator
setFirstIterator(Iterator<VectorEntry>) - Method in class gov.sandia.cognition.math.matrix.VectorUnionIterator: setter for firstIterator
setFirstLearner(BatchLearner<? super Collection<? extends InputType>, ? extends Evaluator<? super InputType, ? extends IntermediateType>>) - Method in class gov.sandia.cognition.learning.algorithm.CompositeBatchLearnerPair: Sets the first learner that is applied to the input data.
setFirstMatrix(AbstractMTJMatrix) - Method in class gov.sandia.cognition.math.matrix.mtj.TwoMatrixEntryMTJ: Setter for firstMatrix.
setFirstValue(double) - Method in class gov.sandia.cognition.math.matrix.DefaultTwoVectorEntry: Sets the entry value to the first underlying vector.
setFirstValue(double) - Method in class gov.sandia.cognition.math.matrix.mtj.TwoMatrixEntryMTJ: Sets the first value of the entry into the first underlying matrix.
setFirstValue(double) - Method in interface gov.sandia.cognition.math.matrix.TwoMatrixEntry: Sets the first value to which this entry points
setFirstValue(double) - Method in interface gov.sandia.cognition.math.matrix.TwoVectorEntry: Sets the first value to which this entry points
setFirstVector(Vector) - Method in class gov.sandia.cognition.math.matrix.DefaultTwoVectorEntry: Setter for firstVector.
setFoldCreator(ValidationFoldCreator<InputDataType, FoldDataType>) - Method in class gov.sandia.cognition.learning.experiment.AbstractValidationFoldExperiment: Sets the fold creator.
setForward(ForwardType) - Method in class gov.sandia.cognition.evaluator.ForwardReverseEvaluatorPair: Sets the forward evaluator that maps input type to output type.
setFractionValue(double) - Method in class gov.sandia.cognition.math.LentzMethod: Setter for fractionValue
setFrequency(double) - Method in class gov.sandia.cognition.learning.function.scalar.CosineFunction: Setter for frequency
setFunction(VectorizableVectorFunction) - Method in class gov.sandia.cognition.learning.algorithm.gradient.GradientDescendableApproximator: Setter for function
setFunction(Evaluator<? super InputType, Double>) - Method in class gov.sandia.cognition.learning.function.kernel.ScalarFunctionKernel: Sets the scalar function for the kernel to use.
setFunction(VectorFunction) - Method in class gov.sandia.cognition.learning.function.kernel.VectorFunctionKernel: Sets the vector function for the kernel to use.
setFunction(EvaluatorType) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.LogEvaluator: Setter for function
setGammas(ArrayList<Vector>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelBaumWelchAlgorithm.DistributionEstimatorTask: Sets the gamma samples pointer.
setGaussian(MultivariateGaussian.PDF) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.GaussianCluster: Sets the Gaussian representing the cluster.
setGaussian(MultivariateGaussian) - Method in class gov.sandia.cognition.learning.data.feature.MultivariateDecorrelator: Sets the underlying multivariate Gaussian.
setGaussian(MultivariateGaussian) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussianInverseGammaDistribution: Setter for gaussian
setGaussian(MultivariateGaussian) - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution: Setter for gaussian
setGaussianMixture(MixtureOfGaussians.PDF) - Method in class gov.sandia.cognition.learning.function.vector.GaussianContextRecognizer: Setter for gaussianMixture
setGenome(GenomeType) - Method in class gov.sandia.cognition.learning.algorithm.genetic.EvaluatedGenome: Sets the genome.
setGeometricDecrease(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerBacktracking: Setter for geometricDecrease
setGlobalWeighter(GlobalTermWeighter) - Method in class gov.sandia.cognition.text.term.vector.weighter.CompositeLocalGlobalTermWeighter: Sets the weighting scheme for the global weights.
setGoldenInterpolator(LineBracketInterpolatorGoldenSection) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.LineBracketInterpolatorBrent: Setter for goldenInterpolator
setHashFunction(HashFunction) - Method in class gov.sandia.cognition.learning.data.feature.FeatureHashing: Gets the hash function to use.
setHiddenDimensionality(int) - Method in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork: Sets the number of hidden units, not including the bias term, by re-initializing the neural net's weights.
setHighValue(double) - Method in class gov.sandia.cognition.learning.function.scalar.ThresholdFunction: Setter for highValue
setIdentifier(SemanticIdentifier) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelBooleanConverter: Gets the semantic identifier of the cogxel to convert.
setIdentifier(SemanticIdentifier) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelDoubleConverter: Gets the semantic identifier of the Cogxel to convert.
setIdentifier(SemanticIdentifier) - Method in class gov.sandia.cognition.framework.SemanticIdentifierMapEvent: Sets the SemanticIdentifier involved in the event.
setIdentifier(IdentifierType) - Method in class gov.sandia.cognition.util.DefaultIdentifiedValue: Sets the identifier.
setIdentifierCounter(int) - Method in class gov.sandia.cognition.framework.DefaultSemanticIdentifierMap: Setter for identifier Counter.
setIdentifiers(ArrayList<SemanticIdentifier>) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter: Sets the list of cached SemanticIdentifiers.
setIdentifiers(SemanticIdentifier[]) - Method in class gov.sandia.cognition.framework.lite.VectorBasedCognitiveModelInput: Setter for identifiers
setIdentifierToIndexMap(HashMap<SemanticIdentifier, Integer>) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter: Sets the cached mapping of SemanticIdentifier to vector index.
setImaginaryPart(double) - Method in class gov.sandia.cognition.math.ComplexNumber: Sets the imaginary portion of the complex number
setImportanceDistribution(ProbabilityFunction<DataType>) - Method in class gov.sandia.cognition.statistics.montecarlo.ImportanceSampler: Setter for importanceDistribution.
setIncomingValue(Object) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeNode: Sets the incoming value for the node.
setIndex(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.DefaultCluster: Sets the index of the cluster.
setIndex(int) - Method in class gov.sandia.cognition.learning.function.categorization.VectorElementThresholdCategorizer: Sets the vector index that the threshold is being applied to.
setIndex(int) - Method in class gov.sandia.cognition.learning.function.scalar.VectorEntryFunction: Sets the index into the vector that the function gets the value for.
setIndex(int) - Method in class gov.sandia.cognition.math.matrix.DefaultTwoVectorEntry: Sets the current index into the underlying vectors.
setIndex(int) - Method in interface gov.sandia.cognition.math.matrix.TwoVectorEntry: Sets the current index into the Vector to which this entry points
setIndex(int) - Method in interface gov.sandia.cognition.math.matrix.VectorEntry: Sets the current index into the Vector to which this entry points
setIndex(int) - Method in class gov.sandia.cognition.math.matrix.VectorizableIndexComparator: Setter for index.
setIndex(int) - Method in class gov.sandia.cognition.text.term.DefaultIndexedTerm: Sets the index of the term.
setIndexComparator(EntryIndexComparator<MatrixEntry>) - Method in class gov.sandia.cognition.math.matrix.MatrixUnionIterator: Sets the index comparator.
setInitialAlpha(double) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel: Setter for initialAlpha
setInitialDomainCapacity(int) - Method in class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution.DefaultFactory: Sets the initial domain capacity.
setInitialGuess(HiddenMarkovModel<ObservationType>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.AbstractBaumWelchAlgorithm: Setter for initialGuess.
setInitialGuess(InputType) - Method in class gov.sandia.cognition.learning.algorithm.minimization.AbstractAnytimeFunctionMinimizer: Setter for initialGuess
setInitialGuess(InputType) - Method in interface gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizer: Sets the initial guess of the minimization routine
setInitialGuess(Double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.AbstractAnytimeLineMinimizer
setInitialGuess(Vector) - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.IterativeMatrixSolver: Sets the initial guess ("x0")
setInitialGuess(double) - Method in class gov.sandia.cognition.learning.algorithm.root.AbstractRootFinder
setInitialGuess(double) - Method in class gov.sandia.cognition.learning.algorithm.root.MinimizerBasedRootFinder
setInitialGuess(double) - Method in interface gov.sandia.cognition.learning.algorithm.root.RootBracketer: Sets the initial guess of the root location.
setInitialGuess(double) - Method in class gov.sandia.cognition.learning.algorithm.root.RootBracketExpander
setInitialGuess(double) - Method in interface gov.sandia.cognition.learning.algorithm.root.RootFinder: Sets the initial guess of the root (zero-crossing), which is supplied as input to the function to find the zero-crossings of.
setInitialGuessFunctionValue(Double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.AbstractAnytimeLineMinimizer: Setter for initialGuessFunctionValue
setInitialGuessSlope(Double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.AbstractAnytimeLineMinimizer: Setter for initialGuessSlope
setInitializationRange(double) - Method in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork: Setter for initializationRange
setInitialized(boolean) - Method in class gov.sandia.cognition.framework.lite.CognitiveModelLiteState: Sets whether or not this state has been initialized.
setInitializer(FixedClusterInitializer<ClusterType, DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer: Sets the cluster initializer.
setInitialPopulation(Collection<GenomeType>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.GeneticAlgorithm: Setter for initialPopulation.
setInitialProbability(Vector) - Method in class gov.sandia.cognition.learning.algorithm.hmm.MarkovChain: Setter for initialProbability
setInput(CognitiveModelInput) - Method in interface gov.sandia.cognition.framework.CognitiveModelState: Sets the input for this model state.
setInput(CognitiveModelInput) - Method in class gov.sandia.cognition.framework.lite.CognitiveModelLiteState: Sets the input for this model state.
setInput(InputType) - Method in class gov.sandia.cognition.learning.data.DefaultInputOutputPair: Sets the input.
setInputConverter(CogxelConverter<InputType>) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelInputOutputPairConverter: Sets the input converter
setInputConverter(CogxelConverter<InputType>) - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactoryLearner: Sets the CogxelConverter used to convert from a CogxelState to InputType.
setInputConverter(CogxelConverter<InputType>) - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleSettings: Sets the CogxelConverter used to convert from a CogxelState to InputType.
setInputDimensionality(int) - Method in class gov.sandia.cognition.learning.algorithm.bayes.DiscreteNaiveBayesCategorizer: Setter for inputDimensionality.
setInputDimensionality(int) - Method in class gov.sandia.cognition.learning.function.vector.SubVectorEvaluator: Sets the expected input dimensionality.
setInputDimensionality(int) - Method in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork: Sets the number of input units, not counting the bias term, by re-initializing the neural net's parameters.
setInputLearner(BatchLearner<? super Collection<? extends InputType>, ? extends Evaluator<? super InputType, ? extends TransformedInputType>>) - Method in class gov.sandia.cognition.learning.algorithm.InputOutputTransformedBatchLearner: Sets the unsupervised learning algorithm for creating the input transformation.
setInputToVectorMap(Evaluator<? super InputType, Vector>) - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearBasisRegression: Setter for inputToVectorMap
setIntegralGain(double) - Method in class gov.sandia.cognition.math.signals.PIDController: Setter for integralGain
setInternalEntry(TwoMatrixEntry) - Method in class gov.sandia.cognition.math.matrix.MatrixUnionIterator: setter for internalEntry
setInternalEntry(TwoVectorEntry) - Method in class gov.sandia.cognition.math.matrix.VectorUnionIterator: setter for internalEntry
setInternalFunction(Evaluator<Double, Double>) - Method in class gov.sandia.cognition.learning.algorithm.root.SolverFunction: Setter for internalFunction.
setInternalFunction(Evaluator<? super InputType, OutputType>) - Method in class gov.sandia.cognition.math.matrix.NumericalDifferentiator: Setter for internalFunction
setInternalMatrix(Matrix) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix: Setter for internalMatrix
setInternalMatrix(Matrix) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixMTJ
setInternalReader(VectorReader) - Method in class gov.sandia.cognition.math.matrix.MatrixReader: Setter for internalReader
setInternalState(CloneableSerializable) - Method in class gov.sandia.cognition.framework.lite.CognitiveModuleStateWrapper: Sets the internal state object.
setInternalVector(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector: Setter for internalVector
setInternalVector(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVector
setInternalVector(DenseVector) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVector: Sets the internalVector using MTJ's DenseVector
setInternalVector(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVector
setInternalVector(SparseVector) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVector: Setter for the internal MTJ vector.
setInterpolator(LineBracketInterpolator<? super EvaluatorType>) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.AbstractAnytimeLineMinimizer: Setter for interpolator
setInverseDocumentFrequency(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.InverseDocumentFrequencyGlobalTermWeighter: Sets the cached inverse-document-frequency (IDF) global weight values.
setInverseGamma(InverseGammaDistribution) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussianInverseGammaDistribution: Setter for inverseGamma
setInverseScale(Matrix) - Method in class gov.sandia.cognition.statistics.distribution.InverseWishartDistribution: Setter for inverseScale
setInverseWishart(InverseWishartDistribution) - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseWishartDistribution: Setter for inverseWishart
setIteration(int) - Method in class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm: Sets the current iteration number.
setIterationLearner(SupervisedBatchLearner<InputType, OutputType, ?>) - Method in class gov.sandia.cognition.learning.algorithm.regression.KernelWeightedRobustRegression
setIterationsPerSample(int) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo
setIterationsPerSample(int) - Method in interface gov.sandia.cognition.statistics.bayesian.MarkovChainMonteCarlo: Sets the number of iterations that must transpire between capturing samples from the distribution.
setIterationsPerSample(int) - Method in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler: Sets the number of iterations to the Markov Chain Monte Carlo algorithm between samples (after the burn-in iterations).
setIterationsWithoutImprovement(int) - Method in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer: Sets the current number of iterations without improvement.
setIterationsWithoutImprovement(int) - Method in class gov.sandia.cognition.learning.algorithm.genetic.GeneticAlgorithm: Sets the current number of iterations without improvement.
setIterationsWithoutImprovement(int) - Method in class gov.sandia.cognition.learning.algorithm.regression.LevenbergMarquardtEstimation: Setter for iterationsWithoutImprovement
setIterativeSolverTolerance(double) - Method in class gov.sandia.cognition.text.algorithm.ValenceSpreader: The tolerance that between-iteration error must be below before considering the iterative solver "done".
setK(int) - Method in class gov.sandia.cognition.learning.algorithm.nearest.AbstractKNearestNeighbor: Setter for k
setK(int) - Method in interface gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighbor: Setter for k
setKappa(double) - Method in class gov.sandia.cognition.learning.function.kernel.SigmoidKernel: Sets kappa, the value multiplied by the dot product of the two vectors before it is passed to the hyperbolic tangent function.
setKeepGoing(boolean) - Method in class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner: Sets the keep going value, which indicates if the algorithm should continue on to another step.
setKernel(Kernel<? super InputType>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.AbstractOnlineKernelBinaryCategorizerLearner: Sets the kernel used by this learner.
setKernel(Kernel<? super InputType>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelAdatron: Sets the kernel to use.
setKernel(Kernel<? super InputType>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelPerceptron: Sets the kernel to use.
setKernel(Kernel<? super InputType>) - Method in class gov.sandia.cognition.learning.algorithm.regression.KernelBasedIterativeRegression: Sets the kernel to use.
setKernel(Kernel<? super InputType>) - Method in class gov.sandia.cognition.learning.algorithm.regression.LocallyWeightedFunction.Learner: Setter for kernel
setKernel(Kernel<? super InputType>) - Method in class gov.sandia.cognition.learning.algorithm.regression.LocallyWeightedFunction: Setter for kernel
setKernel(Kernel<? super InputType>) - Method in class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization: Sets the kernel to use in training the SVM.
setKernel(Kernel<? super InputType>) - Method in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation: Sets the kernel to use.
setKernel(Kernel<? super InputType>) - Method in class gov.sandia.cognition.learning.function.categorization.KernelBinaryCategorizer: Sets the internal kernel.
setKernel(Kernel<? super InputType>) - Method in class gov.sandia.cognition.learning.function.kernel.DefaultKernelContainer: Sets the internal kernel.
setKernelCacheSize(int) - Method in class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization: Sets the size of the kernel cache or 0 if no kernel cache is to be used.
setKernelMatrix(Matrix) - Method in class gov.sandia.cognition.learning.algorithm.pca.KernelPrincipalComponentsAnalysis.Function: Sets the kernel matrix for the data that the analysis was done over.
setKernels(Collection<? extends Kernel<? super InputType>>) - Method in class gov.sandia.cognition.learning.function.kernel.DefaultKernelsContainer: Sets the collection of kernels.
setKernelWeightingFunction(Kernel<? super OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.regression.KernelWeightedRobustRegression: Getter for kernelWeightingFunction
setKey(KeyType) - Method in class gov.sandia.cognition.util.DefaultKeyValuePair: Sets the key element of the pair.
setKnownCovarianceInverse(Matrix) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanBayesianEstimator: Setter for knownCovarianceInverse.
setKnownVariance(double) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanBayesianEstimator: Setter for knownVariance.
setLabel(SemanticLabel) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelBooleanConverter: Sets the label of the cogxel to convert.
setLabel(SemanticLabel) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelDoubleConverter: Sets the label of the Cogxel to convert.
setLabel(NodeNameType, LabelType) - Method in class gov.sandia.cognition.graph.inference.CostSpeedupEnergyFunction
setLabel(NodeNameType, LabelType) - Method in class gov.sandia.cognition.graph.inference.EdgeMergingEnergyFunction
setLabel(NodeNameType, LabelType) - Method in class gov.sandia.cognition.graph.inference.GraphWrappingEnergyFunction
setLabel(NodeNameType, LabelType) - Method in interface gov.sandia.cognition.graph.inference.NodeNameAwareEnergyFunction: Set the label for the input node
setLabels(HashMap<String, DefaultSemanticLabel>) - Method in class gov.sandia.cognition.framework.io.CSVDefaultCognitiveModelLiteHandler: Sets the label mapping.
setLabels(ArrayList<SemanticLabel>) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter: Sets the labels to be used by the converter, each one corresponding to one dimension in the converted Vector.
setLabels(Collection<SemanticLabel>) - Method in class gov.sandia.cognition.framework.lite.SimplePatternRecognizerState: Sets the labels for the state to keep as the labels of the dimensions of the state.
setLambda(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineShiftingPerceptron: Sets the lambda parameter, which controls how much shifting and decay there is in the weight vector.
setLastErr(double) - Method in class gov.sandia.cognition.math.signals.PIDController.State: Setter for lastErr
setLastGradient(InputOutputPair<Vector, Vector>) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToDifferentiableScalarFunction: Setter for lastGradient
setLastModifiedDate(long) - Method in class gov.sandia.cognition.text.document.DefaultDocument: Sets the last modified date of the document.
setLastModifiedDate(Date) - Method in class gov.sandia.cognition.text.document.DefaultDocument: Sets the last modified date of the document.
setLastTokenNum(int) - Method in class gov.sandia.cognition.io.ReaderTokenizer: Setter for lastTokenNum
setLayers(ArrayList<? extends GeneralizedLinearModel>) - Method in class gov.sandia.cognition.learning.function.vector.FeedforwardNeuralNetwork: Setter for layers
setLeafCountThreshold(int) - Method in class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeLearner: Sets the leaf count threshold, which determines the number of elements at which to make an element into a leaf.
setLeafCountThreshold(int) - Method in class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeLearner: Sets the leaf count threshold, which determines the number of elements at which to learn a regression function.
setLeakage(double) - Method in class gov.sandia.cognition.learning.function.scalar.LeakyRectifiedLinearFunction: Sets the leakage, which is the multiplier for the value when it is less than zero.
setLearned(KernelBinaryCategorizer<InputType, DefaultWeightedValue<InputType>>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelAdatron: Sets the object currently being result.
setLearned(Evaluator<InputType, OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.regression.KernelWeightedRobustRegression: Getter for result
setLearner(BatchLearner<? super Collection<LearningDataType>, ? extends Evaluator<? super InputType, ? extends OutputType>>) - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactoryLearner: Sets the learner used to create the Evaluator for the module.
setLearner(LearnerType) - Method in class gov.sandia.cognition.learning.algorithm.AbstractBatchLearnerContainer: Sets the wrapped learner.
setLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, OutputType>>, ? extends MemberType>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner: Sets the learner used to learn each ensemble member.
setLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, CategoryType>>, ? extends Evaluator<? super InputType, ? extends CategoryType>>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner: Sets the learner used to learn each ensemble member.
setLearner(IncrementalLearner<? super InputOutputPair<? extends InputType, CategoryType>, MemberType>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.OnlineBaggingCategorizerLearner: Sets the incremental (online) learning algorithm to use to learn all of the ensemble members.
setLearner(KernelizableBinaryCategorizerOnlineLearner) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelBinaryCategorizerOnlineLearnerAdapter: Sets the kernelizable learner that this adapter is wrapping.
setLearner(SupervisedBatchLearner<InputType, OutputType, ?>) - Method in class gov.sandia.cognition.learning.algorithm.regression.LocallyWeightedFunction.Learner: Setter for learner
setLearner(SupervisedBatchLearner<InputType, OutputType, ?>) - Method in class gov.sandia.cognition.learning.algorithm.regression.LocallyWeightedFunction: Setter for learner
setLearner(BatchLearner<? super Collection<? extends InputDataType>, ? extends LearnedType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerRepeatExperiment: Sets the learner the experiment is being run on.
setLearner(BatchLearner<? super Collection<? extends FoldDataType>, ? extends LearnedType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerValidationExperiment: Sets the learner the experiment is being run on.
setLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, CategoryType>>, ? extends Evaluator<? super InputType, ? extends CategoryType>>) - Method in class gov.sandia.cognition.learning.function.categorization.EvaluatorToCategorizerAdapter.Learner: Sets the learner whose output is to be adapted to be a categorizer.
setLearners(Pair<BatchLearner<? super Collection<? extends FoldDataType>, ? extends LearnedType>, BatchLearner<? super Collection<? extends FoldDataType>, ? extends LearnedType>>) - Method in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment: Sets the learners the experiment is being run on.
setLearners(Collection<? extends DistributionWeightedEstimator<Double, ? extends SmoothUnivariateDistribution>>) - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.EMLearner: Setter for learners
setLearningDataConverter(CogxelConverter<LearningDataType>) - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactoryLearner: Sets the CogxelConverter used to convert from a CogxelState to LearningDataType.
setLearningRate(double) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineStochasticGradient: Gets the learning rate.
setLearningRate(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerGradientDescent: Setter for learningRate
setLearningRate(double) - Method in class gov.sandia.cognition.learning.algorithm.pca.GeneralizedHebbianAlgorithm: Setter for learningRate
setLength(int) - Method in class gov.sandia.cognition.text.AbstractOccurrenceInText: Sets the length of the occurrence.
setLineMinimizer(LineMinimizer<?>) - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerConjugateGradient: Setter for lineMinimizer
setLineMinimizer(LineMinimizer<?>) - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerDirectionSetPowell: Setter for lineMinimizer
setLineMinimizer(LineMinimizer<?>) - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerQuasiNewton: Setter for lineMinimizer
setLineMinimizer(LineMinimizer<?>) - Method in class gov.sandia.cognition.learning.algorithm.regression.FletcherXuHybridEstimation: Setter for lineMinimizer
setLineMinimizer(LineMinimizer<?>) - Method in class gov.sandia.cognition.learning.algorithm.regression.GaussNewtonAlgorithm: Setter for lineMinimizer
setListeners(LinkedList<IterativeAlgorithmListener>) - Method in class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm: Sets the list of listeners for this algorithm.
setListeners(LinkedList<LearningExperimentListener>) - Method in class gov.sandia.cognition.learning.experiment.AbstractLearningExperiment: Sets the listeners for this experiment.
setLocalApproximator(Evaluator<? super InputType, ? extends OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.regression.LocallyWeightedFunction: Setter for localApproximator
setLocalItems(LinkedList<DataType>) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node: Gets the list of items stored locally at the node in the tree.
setLocalWeighter(LocalTermWeighter) - Method in class gov.sandia.cognition.text.term.vector.weighter.CompositeLocalGlobalTermWeighter: Sets the weighting scheme for the local weights.
setLocation(double) - Method in class gov.sandia.cognition.statistics.distribution.CauchyDistribution: Setter for location
setLocation(double) - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution: Setter for location.
setLog(KeyType, LogNumber) - Method in class gov.sandia.cognition.collection.AbstractLogNumberMap: Sets the LogNumber at the given key
setLogFunction(AdaptiveRejectionSampling.LogEvaluator<?>) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling: Setter for logFunction
setLogNormalMean(double) - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution: Setter for logNormalMean
setLogNormalVariance(double) - Method in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution: Setter for logNormalVariance
setLogValue(double) - Method in class gov.sandia.cognition.math.LogNumber: Sets the log of the value represented by this object, which is what is stored in the object.
setLogValue(double) - Method in class gov.sandia.cognition.math.UnsignedLogNumber: Sets the log of the value represented by this object, which is what is stored in the object.
setLowerBound(InputOutputSlopeTriplet) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineBracket: Setter for lowerBound
setLowerBound(double) - Method in class gov.sandia.cognition.statistics.method.ConfidenceInterval: Setter for lowerBound
setLowerLeft(Quadtree<DataType>.Node) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node: Sets the lower-left child.
setLowerRight(Quadtree<DataType>.Node) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node: Sets the lower-right child.
setLowValue(double) - Method in class gov.sandia.cognition.learning.function.scalar.ThresholdFunction: Setter for lowValue
setMap(SemanticIdentifierMap) - Method in class gov.sandia.cognition.framework.SemanticIdentifierMapEvent: Sets the SemanticIdentifierMap that the event happened in.
setMapping(LinkedHashMap<SemanticLabel, SemanticIdentifier>) - Method in class gov.sandia.cognition.framework.DefaultSemanticIdentifierMap: Sets the mapping used by the object.
setMargin(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelPerceptron: Sets both the positive and negative margin to the same value.
setMargin(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Perceptron: Sets both the positive and negative margin to the same value.
setMarginNegative(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelPerceptron: Sets the negative margin that is enforced.
setMarginNegative(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Perceptron: Sets the negative margin that is enforced.
setMarginPositive(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelPerceptron: Sets the positive margin that is enforced.
setMarginPositive(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Perceptron: Sets the positive margin that is enforced.
setMatrix(Matrix) - Method in class gov.sandia.cognition.framework.lite.SimplePatternRecognizer: Sets the underlying matrix.
setMatrixFactory(MatrixFactory<? extends Matrix>) - Method in class gov.sandia.cognition.text.term.relation.TermVectorSimilarityNetworkCreator: Sets the matrix factory to create the matrix that backs the similarity network.
setMatrixFactory(MatrixFactory<? extends Matrix>) - Method in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis: Sets the matrix factory to use.
setMaxBufferSize(int) - Method in class gov.sandia.cognition.learning.data.feature.LinearRegressionCoefficientExtractor: Setter for maxBufferSize
setMaxCriterionDecrease(double) - Method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer: Sets the maximum decrease in the training criterion allowed following a bisection.
setMaxDepth(int) - Method in class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeLearner: Sets the maximum depth to grow the tree.
setMaxDepth(int) - Method in class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeLearner: Sets the maximum depth to grow the tree.
setMaxDistance(double) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer: The maximum distance between clusters that is allowed for the two clusters to be merged.
setMaximum(DataType) - Method in class gov.sandia.cognition.evaluator.ValueClamper: Sets the maximum value to allow.
setMaximumLength(Integer) - Method in class gov.sandia.cognition.text.term.filter.TermLengthFilter: Gets the maximum length allowed for a term (inclusive).
setMaxIterations(int) - Method in class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm
setMaxIterations(int) - Method in interface gov.sandia.cognition.algorithm.AnytimeAlgorithm: Sets the maximum number of total iterations before stopping.
setMaxIterations(int) - Method in class gov.sandia.cognition.algorithm.AnytimeAlgorithmWrapper
setMaxIterations(int) - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.IterativeMatrixSolver: Sets the maximum number of iterations before this will stop iterating.
setMaxIterationsWithoutImprovement(int) - Method in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer: Sets the maximum number of iterations to go without improvement before stopping.
setMaxIterationsWithoutImprovement(int) - Method in class gov.sandia.cognition.learning.algorithm.genetic.GeneticAlgorithm: Sets the maximum number of iterations to go without improvement before stopping.
setMaxIterationsWithoutImprovement(int) - Method in class gov.sandia.cognition.learning.algorithm.regression.LevenbergMarquardtEstimation: Setter for maxIterationsWithoutImprovement
setMaxMagnitude(double) - Method in class gov.sandia.cognition.learning.function.scalar.AtanFunction: Setter for maxMagnitude.
setMaxMinDistance(double) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer: Deprecated.
Use setMaxDistance
setMaxNumPoints(int) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling: Setter for maxNumPoints
setMaxPenalty(double) - Method in class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization: Sets the maximum penalty parameter for the algorithm, which is also known as C in the paper and in other related literature.
setMaxSupport(double) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling: Setter for maxSupport
setMaxSupport(double) - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution: Setter for maxSupport
setMaxSupport(int) - Method in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution: Sets the maximum support.
setMaxValue(int) - Method in class gov.sandia.cognition.collection.IntegerSpan: Setter for maxValue
setMaxWeight(double) - Method in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation: Sets the maximum weight allowed on an instance (support vector).
setMean(Vector) - Method in class gov.sandia.cognition.learning.algorithm.pca.PrincipalComponentsAnalysisFunction: Setter for mean
setMean(double) - Method in class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer: Sets the mean.
setMean(Vector) - Method in class gov.sandia.cognition.learning.function.categorization.AbstractConfidenceWeightedBinaryCategorizer: Sets the mean of the categorizer, which is the weight vector.
setMean(double) - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution: Setter for mean
setMean(double) - Method in class gov.sandia.cognition.statistics.distribution.LogisticDistribution: Setter for mean
setMean(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateGaussian: Sets the mean vector.
setMean(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution: Setter for mean
setMean(double) - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution: Setter for mean
setMean(double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian: Setter for mean
setMeanL1Error(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearRegression.Statistic: Setter for meanL1Error
setMeasurementCovariance(Matrix) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter: Setter for measurementCovariance
setMembers(List<MemberType>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractUnweightedEnsemble: Sets the list of ensemble members.
setMembers(List<WeightedValue<MemberType>>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractWeightedEnsemble: Sets the members of the ensemble.
setMembers(List<MemberType>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.VotingCategorizerEnsemble: Sets the list of ensemble members.
setMembers(List<WeightedValue<MemberType>>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedBinaryEnsemble: Sets the members of the ensemble.
setMembers(List<WeightedValue<MemberType>>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.WeightedVotingCategorizerEnsemble: Sets the members of the ensemble.
setMetric(Semimetric<? super DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer: Sets the distance metric the clustering uses.
setMinChange(double) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineAlternatingLeastSquares: Sets the minimum change allowed in an iteration.
setMinChange(double) - Method in class gov.sandia.cognition.learning.algorithm.pca.GeneralizedHebbianAlgorithm: Setter for minChange
setMinChange(double) - Method in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation: Sets the minimum total weight change allowed for the algorithm to continue.
setMinClusters(ArrayList<Integer>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer: Sets the index of the closest cluster.
setMinClusterSize(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer: Sets the minimum number of elements per cluster to allow.
setMinDenominator(double) - Method in class gov.sandia.cognition.math.LentzMethod: Setter for minDenominator
setMinDistances(ArrayList<Double>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer: Sets the minimum distances for each clusters.
setMinFunctionValue(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeBased: Setter for minFunctionValue
setMinibatchSize(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer: Set the size of the mini-batches.
setMinimum(DataType) - Method in class gov.sandia.cognition.evaluator.ValueClamper: Gets the minimum value to allow.
setMinimumChange(double) - Method in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis: Sets the minimum change in log-likelihood to allow before stopping the algorithm.
setMinimumLength(Integer) - Method in class gov.sandia.cognition.text.term.filter.TermLengthFilter: Gets the minimum length allowed for a term (inclusive).
setMinMargin(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.BatchMultiPerceptron: Gets the minimum margin to enforce.
setMinMargin(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineBinaryMarginInfusedRelaxedAlgorithm: Gets the minimum margin to enforce.
setMinMargin(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineMultiPerceptron.ProportionalUpdate
setMinMargin(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineMultiPerceptron: Gets the minimum margin to enforce.
setMinNumClusters(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer: The minimum number of clusters to allow.
setMinSamples(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer: Sets the minimum number of samples.
setMinSensitivity(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.KernelBasedIterativeRegression: Sets the minimum sensitivity that an example can have on the result function.
setMinSplitSize(int) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractVectorThresholdMaximumGainLearner: Sets the minimum split size.
setMinSplitSize(int) - Method in class gov.sandia.cognition.learning.algorithm.tree.VectorThresholdVarianceLearner: Sets the minimum split size.
setMinSupport(double) - Method in class gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling: Setter for minSupport
setMinSupport(double) - Method in class gov.sandia.cognition.statistics.distribution.UniformDistribution: Setter for minSupport
setMinSupport(int) - Method in class gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution: Sets the minimum support.
setMinValue(int) - Method in class gov.sandia.cognition.collection.IntegerSpan: Setter for minValue
setModel(CognitiveModel) - Method in class gov.sandia.cognition.framework.CognitiveModelStateChangeEvent: Sets the model that changed.
setModel(LinearDynamicalSystem) - Method in class gov.sandia.cognition.statistics.bayesian.KalmanFilter: Setter for model
setModelCovariance(Matrix) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractKalmanFilter: Setter for modelCovariance
setModuleFactories(ArrayList<CognitiveModuleFactory>) - Method in class gov.sandia.cognition.framework.AbstractCognitiveModelFactory: Sets the list of module factories to use.
setModuleStatesArray(CognitiveModuleState[]) - Method in class gov.sandia.cognition.framework.lite.CognitiveModelLiteState: Sets the array of module states.
setMomentum(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerGradientDescent: Getter for momentum
setMotionModel(StatefulEvaluator<Vector, Vector, Vector>) - Method in class gov.sandia.cognition.statistics.bayesian.ExtendedKalmanFilter: Setter for motionModel
setMovingAverageCoefficients(Vector) - Method in class gov.sandia.cognition.math.signals.AutoRegressiveMovingAverageFilter: Setter for movingAverageCoefficients
setMovingAverageCoefficients(Vector) - Method in class gov.sandia.cognition.math.signals.MovingAverageFilter: Setter for movingAverageCoefficients
setN(int) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.BinomialBayesianEstimator: Sets the number of samples in the experiment
setN(int) - Method in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution: Setter for n
setN(int) - Method in class gov.sandia.cognition.statistics.distribution.BinomialDistribution: Setter for N
setN1(int) - Method in class gov.sandia.cognition.statistics.method.MannWhitneyUConfidence.Statistic: Setter for N1
setN2(int) - Method in class gov.sandia.cognition.statistics.method.MannWhitneyUConfidence.Statistic: Setter for N2
setName(String) - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModule: Sets the name of the module
setName(String) - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactory: Setter for name
setName(String) - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactoryLearner: Setter for name
setName(String) - Method in class gov.sandia.cognition.statistics.DefaultDistributionParameter
setName(String) - Method in class gov.sandia.cognition.util.AbstractNamed: Sets the name of this Object.
setName(String) - Method in class gov.sandia.cognition.util.DefaultNamedValue
setNe(double) - Method in class gov.sandia.cognition.statistics.method.KolmogorovSmirnovConfidence.Statistic: Setter for Ne
setNegative(boolean) - Method in class gov.sandia.cognition.math.LogNumber: Sets whether or not this value has a negative sign.
setNeighborhoodRadius(double) - Method in class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer: Sets the neighborhood radius.
setNetwork(DefaultSemanticNetwork) - Method in class gov.sandia.cognition.framework.io.CSVDefaultCognitiveModelLiteHandler: Set the network being parsed.
setNodes(ArrayList<SemanticLabel>) - Method in class gov.sandia.cognition.framework.lite.SimplePatternRecognizer: Sets the nodes in the recognizer.
setNodeToIDMap(HashMap<SemanticLabel, Integer>) - Method in class gov.sandia.cognition.framework.lite.SimplePatternRecognizer: Sets the mapping of nodes to their vector indices.
setNormalizedCentroid(ClusterType) - Method in class gov.sandia.cognition.learning.algorithm.clustering.cluster.NormalizedCentroidCluster: Sets the normalized centroid of the cluster.
setNormalizer(TermWeightNormalizer) - Method in class gov.sandia.cognition.text.term.vector.weighter.CompositeLocalGlobalTermWeighter: Sets the weight normalizer.
setNullHypothesisProbability(double) - Method in class gov.sandia.cognition.statistics.method.AbstractConfidenceStatistic: Setter for nullHypothesisProbability
setNullValue(Number) - Method in class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter: Sets the number that represents a null value.
setNumChanged(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer: Setter for numChanged
setNumClusters(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansFactory: Sets the number of clusters for the algorithm to attempt to create.
setNumComponents(int) - Method in class gov.sandia.cognition.learning.algorithm.pca.AbstractPrincipalComponentsAnalysis: Setter for numComponents
setNumComponents(int) - Method in class gov.sandia.cognition.learning.algorithm.pca.GeneralizedHebbianAlgorithm: Setter for numComponents
setNumCustomers(int) - Method in class gov.sandia.cognition.statistics.distribution.ChineseRestaurantProcess: Setter for numCustomers
setNumDifferent(int) - Method in class gov.sandia.cognition.statistics.method.FisherSignConfidence.Statistic: Setter for numDifferent
setNumericalDerivative(boolean) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerBacktracking: Setter for numericalDerivative
setNumFolds(int) - Method in class gov.sandia.cognition.learning.experiment.CrossFoldCreator: Sets the number of folds to create.
setNumFolds(int) - Method in class gov.sandia.cognition.learning.experiment.RandomFoldCreator: Sets the number of folds to create.
setNumInitialClusters(int) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel: Getter for numInitialClusters
setNumNonZero(int) - Method in class gov.sandia.cognition.statistics.method.WilcoxonSignedRankConfidence.Statistic: Setter for numNonZero
setNumParameters(int) - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearRegression.Statistic: Setter for numParameters
setNumParticles(int) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractParticleFilter
setNumParticles(int) - Method in interface gov.sandia.cognition.statistics.bayesian.ParticleFilter: Sets the number of particles
setNumPositiveSign(int) - Method in class gov.sandia.cognition.statistics.method.FisherSignConfidence.Statistic: Setter for numPositiveSign
setNumRequestedClusters(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer: Sets the number of requested clusters.
setNumRequestedClusters(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer: Sets the number of clusters requested.
setNumSamples(int) - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearRegression.Statistic: Setter for numSamples
setNumSamples(int) - Method in class gov.sandia.cognition.statistics.bayesian.ImportanceSampling: Setter for numSamples
setNumSamples(int) - Method in class gov.sandia.cognition.statistics.bayesian.RejectionSampling: Setter for numSamples
setNumSamples(int) - Method in class gov.sandia.cognition.statistics.method.ConfidenceInterval: Setter for numSamples
setNumSamples(int) - Method in class gov.sandia.cognition.statistics.UnivariateRandomVariable: Setter for numSamples
setNumSplits(int) - Method in class gov.sandia.cognition.learning.experiment.RandomByTwoFoldCreator: Sets the number of splits to perform.
setNumThreads(int) - Method in class gov.sandia.cognition.text.algorithm.ValenceSpreader: Specifies how many threads to use in the matrix/vector multiplies in the iterative solver.
setNumTrials(int) - Method in class gov.sandia.cognition.learning.experiment.AbstractValidationFoldExperiment: Sets the number of trials in the experiment.
setNumTrials(int) - Method in class gov.sandia.cognition.learning.experiment.LearnerRepeatExperiment: Sets the number of trials for the experiment to repeatedly call the learning algorithm.
setNumTrials(int) - Method in class gov.sandia.cognition.learning.experiment.OnlineLearnerValidationExperiment: Sets the number of trials.
setNumTrials(int) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultinomialBayesianEstimator: Setter for numTrials
setNumTrials(int) - Method in class gov.sandia.cognition.statistics.distribution.MultinomialDistribution: Setter for numTrials
setNumTrials(int) - Method in class gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution: Setter for numTrials
setObjectToOptimize(FunctionType) - Method in class gov.sandia.cognition.learning.algorithm.regression.AbstractLogisticRegression: Setter for objectToOptimize
setObjectToOptimize(ResultType) - Method in class gov.sandia.cognition.learning.algorithm.regression.AbstractMinimizerBasedParameterCostMinimizer
setObjectToOptimize(ResultType) - Method in class gov.sandia.cognition.learning.algorithm.regression.AbstractParameterCostMinimizer: Setter for objectToOptimize
setObjectToOptimize(LogisticRegression.Function) - Method in class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression: Setter for objectToOptimize
setObjectToOptimize(ResultType) - Method in interface gov.sandia.cognition.learning.algorithm.regression.ParameterCostMinimizer: Set the object to optimize
setObservationModel(Evaluator<Vector, Vector>) - Method in class gov.sandia.cognition.statistics.bayesian.ExtendedKalmanFilter: Setter for observationModel
setOffset(double) - Method in class gov.sandia.cognition.learning.function.scalar.LinearFunction: Sets the offset of the function, which is the b term in: f(x) = m*x + b.
setOptimalThreshold(ReceiverOperatingCharacteristic.DataPoint) - Method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic.Statistic: Setter for optimalThreshold
setOriginalPoint(InputOutputSlopeTriplet) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.WolfeConditions: Setter for originalPoint
setOtherPoint(InputOutputSlopeTriplet) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineBracket: Setter for otherPoint
setOutlierPercent(double) - Method in class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer.Learner: Sets the percentage of outliers to exclude from learning.
setOutput(OutputType) - Method in class gov.sandia.cognition.learning.data.DefaultInputOutputPair: Sets the output.
setOutputCategory(OutputType) - Method in class gov.sandia.cognition.learning.algorithm.tree.CategorizationTreeNode: Sets the output category for the node.
setOutputConverter(CogxelConverter<OutputType>) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelInputOutputPairConverter: Sets the output converter
setOutputConverter(CogxelConverter<OutputType>) - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactoryLearner: Sets the CogxelConverter used to convert OutputType to a CogxelState.
setOutputConverter(CogxelConverter<OutputType>) - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleSettings: Sets the CogxelConverter used to convert OutputType to a CogxelState.
setOutputDimensionality(int) - Method in class gov.sandia.cognition.learning.data.feature.FeatureHashing: Sets the output dimensionality, which is the size of the output vector that the input is hashed into.
setOutputDimensionality(int) - Method in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork: Sets the output dimensionality of the neural net by re-initializing the weights.
setOutputLearner(BatchLearner<? super Collection<? extends OutputType>, ? extends ReversibleEvaluator<OutputType, TransformedOutputType, ?>>) - Method in class gov.sandia.cognition.learning.algorithm.InputOutputTransformedBatchLearner: Gets the unsupervised learning algorithm for creating the output transformation, which must be reversible for evaluation.
setOutputVariance(double) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression: Setter for outputVariance
setOutputVariance(InverseGammaDistribution) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression: Setter for outputVariance
setOutputVariance(double) - Method in class gov.sandia.cognition.statistics.bayesian.GaussianProcessRegression: Getter for outputVariance
setOverrelaxation(double) - Method in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation: Gets the overrelaxation parameter for the algorithm.
setP(double) - Method in class gov.sandia.cognition.statistics.distribution.BernoulliDistribution: Setter for p
setP(double) - Method in class gov.sandia.cognition.statistics.distribution.BinomialDistribution: Setter for p
setP(double) - Method in class gov.sandia.cognition.statistics.distribution.GeometricDistribution: Setter for p
setP(double) - Method in class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution: Setter for p
setPairConverter(CogxelInputOutputPairConverter<InputType, OutputType>) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelWeightedInputOutputPairConverter: Setter for pairConverter
setPairwiseTest(NullHypothesisEvaluator<Collection<? extends Number>>) - Method in class gov.sandia.cognition.statistics.method.AbstractPairwiseMultipleHypothesisComparison: Setter for pairwiseTest
setParabolicInterpolator(LineBracketInterpolatorParabola) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.LineBracketInterpolatorBrent: Setter for parabolicInterpolator
setParameter(BayesianParameter<ParameterType, ConditionalType, BeliefType>) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.AbstractConjugatePriorBayesianEstimator: Setter for parameter
setParameterAdapters(LinkedList<ParameterAdapter<? super LearnerType, ? super DataType>>) - Method in class gov.sandia.cognition.learning.parameter.ParameterAdaptableBatchLearnerWrapper: Sets the list of parameter adapters for the learning algorithm.
setParameterPrior(PriorType) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractBayesianParameter: Setter for parameterPrior
setParameterPrior(PriorType) - Method in class gov.sandia.cognition.statistics.bayesian.DefaultBayesianParameter: Sets the Distribution of values that the parameter is assumed to take.
setParameters(Object...) - Method in class gov.sandia.cognition.factory.ConstructorBasedFactory: Sets the parameters that are passed to the constructor to create new objects.
setParameters(Vector) - Method in class gov.sandia.cognition.statistics.distribution.CategoricalDistribution: Setter for parameters
setParameters(Vector) - Method in class gov.sandia.cognition.statistics.distribution.DirichletDistribution: Setter for parameters
setParameters(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultinomialDistribution: Setter for parameters
setParameters(Vector) - Method in class gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution: Setter for parameters
setParent(DecisionTreeNode<InputType, OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeNode: Sets the parent node for this node.
setParent(Quadtree<DataType>.Node) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node: Sets the parent node of this node.
setParticlePctThreadhold(double) - Method in class gov.sandia.cognition.statistics.bayesian.SamplingImportanceResamplingParticleFilter: Setter for particlePctThreadhold
setPartitioner(RandomizedDataPartitioner<DataType>) - Method in class gov.sandia.cognition.learning.experiment.RandomFoldCreator: Sets the randomized partitioner to use.
setPercent(double) - Method in class gov.sandia.cognition.learning.algorithm.genetic.selector.TournamentSelector: Sets the percent of the population to select.
setPercentToSample(double) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner: Sets the percentage of the data to sample (with replacement) on each iteration.
setPercentToSample(double) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner: Sets the percentage of the data to sample (with replacement) on each iteration.
setPercentToSample(double) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.OnlineBaggingCategorizerLearner: Sets the percent of the data to attempt to sample for each ensemble member.
setPercentToSample(double) - Method in class gov.sandia.cognition.learning.algorithm.tree.RandomSubVectorThresholdLearner: Sets the percent of the dimensionality to sample.
setPerformanceEvaluator(PerformanceEvaluator<? super LearnedType, ? super Collection<? extends FoldDataType>, ? extends StatisticType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment: Sets the performance evaluator to apply to each fold.
setPerformanceEvaluator(PerformanceEvaluator<? super LearnedType, ? super Collection<? extends InputDataType>, ? extends StatisticType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerRepeatExperiment: Sets the performance evaluator to apply to each fold.
setPerformanceEvaluator(PerformanceEvaluator<? super LearnedType, ? super Collection<? extends FoldDataType>, ? extends StatisticType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerValidationExperiment: Sets the performance evaluator to apply to each fold.
setPerformanceEvaluator(PerformanceEvaluator<? super LearnedType, ? super Collection<? extends DataType>, ? extends StatisticType>) - Method in class gov.sandia.cognition.learning.experiment.OnlineLearnerValidationExperiment: Sets the performance evaluator to apply to each fold.
setPerformanceEvaluator(PerformanceEvaluator<? super ObjectType, ? super DataType, ?>) - Method in class gov.sandia.cognition.learning.performance.AnytimeBatchLearnerValidationPerformanceReporter: Sets the performance evaluator.
setPerturber(Perturber<AnnealedType>) - Method in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer: Sets the perturber.
setPerturber(Perturber<GenomeType>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.reproducer.MutationReproducer: Sets the perturber used for mutation.
setPhase(double) - Method in class gov.sandia.cognition.learning.function.scalar.CosineFunction: Setter for phase
setPoint(double) - Method in class gov.sandia.cognition.statistics.distribution.DeterministicDistribution: Setter for point
setPointIndex(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer: Sets the point index.
setPoints(ArrayList<DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer: Sets the list of points.
setPolynomials(ScalarBasisSet<Double>) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Regression: Setter for polynomials
setPopulation(Collection<EvaluatedGenome<GenomeType>>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.GeneticAlgorithm: Sets the population of genomes.
setPosteriorLogLikelihood(Double) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.Sample: sets the posterior log-likelihood.
setPostHocTest(MultipleHypothesisComparison<Collection<? extends Number>>) - Method in class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment: Setter for postHocTest
setPower(double) - Method in class gov.sandia.cognition.learning.function.distance.MinkowskiDistanceMetric: Sets the power used for the distance.
setPrecision(Matrix) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution.PDF
setPrecision(Matrix) - Method in class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution: Setter for precision
setPrecision(double) - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution: Setter for precision.
setPrecision(double) - Method in class gov.sandia.cognition.statistics.distribution.StudentTDistribution: Setter for precision
setPrecision(double) - Method in class gov.sandia.cognition.text.evaluation.DefaultPrecisionRecallPair: Sets the precision.
setPredictionHorizon(int) - Method in class gov.sandia.cognition.learning.algorithm.SequencePredictionLearner: Sets the prediction horizon, which is the number of samples ahead in time that the learner is to predict.
setPredictionHorizon(int) - Method in class gov.sandia.cognition.learning.algorithm.TimeSeriesPredictionLearner: Setter for predictionHorizon
setPreprocessor(Evaluator<? super InputType, ? extends IntermediateType>) - Method in class gov.sandia.cognition.learning.function.categorization.CompositeCategorizer: Sets the preprocessor, which takes the input and produces an intermediate value that is then passed to the categorizer.
setPriors(DataDistribution<CategoryType>) - Method in class gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer: Sets the prior distribution over the categories.
setPriorWeights(double[]) - Method in class gov.sandia.cognition.statistics.distribution.LinearMixtureModel: Getter for priorWeights
setProbabilityCrossover(double) - Method in class gov.sandia.cognition.learning.algorithm.genetic.reproducer.VectorizableCrossoverFunction: Setter for probabilityCrossover.
setProbabilityFunction(ProbabilityFunction<? super ObservationType>) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel.DPMMCluster: Setter for probabilityFunction
setProcess(Process) - Method in class gov.sandia.cognition.io.ProcessLauncher: Setter for process
setProcess(Process) - Method in class gov.sandia.cognition.io.ProcessLauncherEvent: Setter for process
setProportionalGain(double) - Method in class gov.sandia.cognition.math.signals.PIDController: Setter for proportionalGain
setProportionIncorrectInSample(double) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner: Sets the proportion of incorrect examples to place in each sample.
setProposals(int) - Method in class gov.sandia.cognition.statistics.bayesian.RejectionSampling.DefaultUpdater: Setter for proposals
setPrototype(CreatedType) - Method in class gov.sandia.cognition.factory.PrototypeFactory: Sets the prototype object that is cloned to create new objects.
setPrototypes(Map<CategoryType, LinearBinaryCategorizer>) - Method in class gov.sandia.cognition.learning.function.categorization.LinearMultiCategorizer: Sets the mapping of categories to prototypes.
setQ(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Forgetron.Result: Gets the value Q updated by the algorithm.
setQ0(double) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Linear: Setter for q0
setQ1(double) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Linear: Setter for q1
setQ2(double) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Quadratic: Setter for q2
setQ3(double) - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Cubic: Setter for q3
setR(double) - Method in class gov.sandia.cognition.learning.algorithm.confidence.AdaptiveRegularizationOfWeights: Sets the regularization parameter.
setR(DenseMatrix) - Method in class gov.sandia.cognition.math.matrix.mtj.decomposition.CholeskyDecompositionMTJ: Setter for R
setR(double) - Method in class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution: Setter for r.
setRadius(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Ballseptron: Sets the radius parameter.
setRandom(Random) - Method in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer: Sets the random number generator.
setRandom(Random) - Method in class gov.sandia.cognition.learning.algorithm.clustering.DirichletProcessClustering
setRandom(Random) - Method in class gov.sandia.cognition.learning.algorithm.clustering.initializer.AbstractMinDistanceFixedClusterInitializer
setRandom(Random) - Method in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer
setRandom(Random) - Method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer
setRandom(Random) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner
setRandom(Random) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner
setRandom(Random) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.OnlineBaggingCategorizerLearner
setRandom(Random) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner
setRandom(Random) - Method in class gov.sandia.cognition.learning.algorithm.genetic.selector.TournamentSelector: Sets the random number generator to use.
setRandom(Random) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.OnlineKernelRandomizedBudgetPerceptron
setRandom(Random) - Method in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient
setRandom(Random) - Method in class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization
setRandom(Random) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo
setRandom(Random) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractParticleFilter
setRandom(Random) - Method in class gov.sandia.cognition.statistics.distribution.MixtureOfGaussians.EMLearner
setRandom(Random) - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.EMLearner
setRandom(Random) - Method in class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution
setRandom(Random) - Method in class gov.sandia.cognition.statistics.UnivariateRandomVariable
setRandom(Random) - Method in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler
setRandom(Random) - Method in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis
setRandom(Random) - Method in class gov.sandia.cognition.util.AbstractRandomized
setRandom(Random) - Method in interface gov.sandia.cognition.util.Randomized: Sets the random number generator used by this object.
setRandomRange(double) - Method in class gov.sandia.cognition.learning.algorithm.clustering.initializer.NeighborhoodGaussianClusterInitializer: Setter for randomRange
setRandomSet(long) - Method in class gov.sandia.cognition.graph.community.Louvain: Initialize the random number generator with the input seed.
setRandomSet(long) - Method in class gov.sandia.cognition.graph.community.PersonalizedPageRank: Initialize the random number generator with the input seed.
setRate(double) - Method in class gov.sandia.cognition.statistics.distribution.ExponentialDistribution: Setter for rate.
setRate(double) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution: Sets the rate parameter, which is just the inverse of the scale parameter.
setRate(double) - Method in class gov.sandia.cognition.statistics.distribution.PoissonDistribution: Setter for rate
setRealPart(double) - Method in class gov.sandia.cognition.math.ComplexNumber: Sets the real part of the complex number
setRecall(double) - Method in class gov.sandia.cognition.text.evaluation.DefaultPrecisionRecallPair: Sets the recall.
setRecognizer(MutablePatternRecognizerLite) - Method in class gov.sandia.cognition.framework.lite.MutableSemanticMemoryLiteFactory: Sets the settings used by the factory.
setReductionTest(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.FletcherXuHybridEstimation: Setter for reduction test.
setReestimateAlpha(boolean) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel: Setter for reestimateAlpha
setReestimateInitialProbabilities(boolean) - Method in class gov.sandia.cognition.learning.algorithm.hmm.AbstractBaumWelchAlgorithm: Setter for reestimateInitialProbabilities
setReference(DocumentReference) - Method in class gov.sandia.cognition.text.document.AbstractDocument: Sets the reference to where the document can be found.
setRegressionLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, Double>>, ? extends Evaluator<? super InputType, Double>>) - Method in class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeLearner: Sets the regression learner that is to be used to fit a function approximator to the values in the tree.
setRegularization(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.AbstractLogisticRegression: Setter for regularization
setRegularization(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearRegression: Setter for regularization
setRegularization(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression: Setter for regularization
setRegularization(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.MultivariateLinearRegression: Setter for regularization
setRegularizationWeight(double) - Method in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient: Sets the regularization weight (lambda) assigned to the regularization term of the algorithm.
setRemovalThreshold(double) - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClustererWithRemoval: Setter for removalThreshold
setReproducer(Reproducer<GenomeType>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.GeneticAlgorithm: Sets the reproducer.
setReproducers(Collection<Reproducer<GenomeType>>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.reproducer.MultiReproducer: Sets the reproducers to use for reproducing.
setRequestedRank(int) - Method in class gov.sandia.cognition.text.topic.LatentSemanticAnalysis: Sets the requested rank of the analysis.
setRequestedRank(int) - Method in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis: Sets the requested rank to conduct the analysis for.
setResponsibilities(double[][]) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: Sets the responsibility values.
setResult(InputOutputPair<InputType, OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.minimization.AbstractAnytimeFunctionMinimizer: Setter for result
setResult(PrincipalComponentsAnalysisFunction) - Method in class gov.sandia.cognition.learning.algorithm.pca.AbstractPrincipalComponentsAnalysis: Setter for result
setResult(PrincipalComponentsAnalysisFunction) - Method in class gov.sandia.cognition.learning.algorithm.pca.GeneralizedHebbianAlgorithm: Setter for result
setResult(LinearMultiCategorizer<CategoryType>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.BatchMultiPerceptron: Sets the result of the algorithm.
setResult(DefaultKernelBinaryCategorizer<InputType>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelPerceptron: Sets the object currently being result.
setResult(LinearBinaryCategorizer) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Perceptron: Sets the object currently being result.
setResult(ResultType) - Method in class gov.sandia.cognition.learning.algorithm.regression.AbstractMinimizerBasedParameterCostMinimizer: Setter for result
setResult(ResultType) - Method in class gov.sandia.cognition.learning.algorithm.regression.AbstractParameterCostMinimizer: Setter for result
setResult(KernelScalarFunction<InputType>) - Method in class gov.sandia.cognition.learning.algorithm.regression.KernelBasedIterativeRegression: Sets the object currently being result.
setResult(LogisticRegression.Function) - Method in class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression: Setter for result
setResult(KernelBinaryCategorizer<InputType, DefaultWeightedValue<InputType>>) - Method in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation: Sets the object currently being result.
setResult(DefaultDataDistribution<ParameterType>) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo: Setter for result
setResultCost(Double) - Method in class gov.sandia.cognition.learning.algorithm.regression.AbstractParameterCostMinimizer: Setter for resultCost
setReverse(ReverseType) - Method in class gov.sandia.cognition.evaluator.ForwardReverseEvaluatorPair: Sets the reverse evaluator that maps output type to input type.
setRootBracket(LineBracket) - Method in class gov.sandia.cognition.learning.algorithm.root.AbstractBracketedRootFinder: Setter for rootBracket.
setRootMeanSquaredError(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearRegression.Statistic: Setter fpr rootMeanSquaredError
setRootNode(DecisionTreeNode<InputType, OutputType>) - Method in class gov.sandia.cognition.learning.algorithm.tree.DecisionTree: Sets the root node of the decision tree.
setRow(int, Vector) - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
setRow(int, Vector) - Method in interface gov.sandia.cognition.math.matrix.Matrix: Sets the specified row from the given rowVector
setRow(int, SparseVector) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseRowMatrix: Sets the specified row of the matrix using rowVector, using MTJ's internal routine to speed things up.
setRowIndex(int) - Method in interface gov.sandia.cognition.math.matrix.MatrixEntry: Sets the current row index to which this entry points
setRowIndex(int) - Method in class gov.sandia.cognition.math.matrix.mtj.TwoMatrixEntryMTJ: Setter for rowIndex.
setRowIndex(int) - Method in interface gov.sandia.cognition.math.matrix.TwoMatrixEntry: Sets the current row index to which this entry points
setS(Matrix) - Method in class gov.sandia.cognition.math.matrix.decomposition.AbstractSingularValueDecomposition: setter for the singular values matrix
setSampler(ProbabilityFunction<ParameterType>) - Method in class gov.sandia.cognition.statistics.bayesian.RejectionSampling.DefaultUpdater: Setter for sampler
setSampleSize(int) - Method in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient: Sets the sample size, which is the number of examples sampled without replacement on each iteration of the algorithm.
setScalarFunction(Evaluator<? super InputType, Double>) - Method in class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeNode: Sets the scalar function applied to the input when the node is a leaf node.
setScalarFunction(UnivariateScalarFunction) - Method in class gov.sandia.cognition.learning.function.vector.ElementWiseDifferentiableVectorFunction
setScalarFunction(UnivariateScalarFunction) - Method in class gov.sandia.cognition.learning.function.vector.ElementWiseVectorFunction: Setter for scalarFunction
setScale(Double) - Method in class gov.sandia.cognition.statistics.bayesian.RejectionSampling.DefaultUpdater: Setter for scale
setScale(double) - Method in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution: Setter for scale
setScale(double) - Method in class gov.sandia.cognition.statistics.distribution.CauchyDistribution: Setter for scale
setScale(double) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution: Setter for scale
setScale(double) - Method in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution: Setter for scale
setScale(double) - Method in class gov.sandia.cognition.statistics.distribution.LaplaceDistribution: Setter for scale
setScale(double) - Method in class gov.sandia.cognition.statistics.distribution.LogisticDistribution: Setter for scale
setScale(double) - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution: Setter for scale
setScale(double) - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution: Setter for scale
setScale(double) - Method in class gov.sandia.cognition.statistics.distribution.WeibullDistribution: Setter for scale
setScaleFactor(double) - Method in class gov.sandia.cognition.learning.function.vector.LinearVectorFunction: Sets the linear scale factor.
setSecond(SecondType) - Method in class gov.sandia.cognition.util.DefaultPair: Sets the second object.
setSecond(SecondType) - Method in class gov.sandia.cognition.util.DefaultTriple: Sets the second object.
setSecondChild(ClusterHierarchyNode<DataType, ClusterType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.hierarchy.BinaryClusterHierarchyNode: Sets the second child node.
setSecondConverter(CogxelConverter<SecondType>) - Method in class gov.sandia.cognition.framework.learning.converter.AbstractCogxelPairConverter: Setter for secondConverter
setSecondInternalEntry(MatrixEntry) - Method in class gov.sandia.cognition.math.matrix.MatrixUnionIterator: setter for secondInternalEntry
setSecondInternalEntry(VectorEntry) - Method in class gov.sandia.cognition.math.matrix.VectorUnionIterator: setter for secondInternalEntry
setSecondIterator(Iterator<MatrixEntry>) - Method in class gov.sandia.cognition.math.matrix.MatrixUnionIterator: setter for secondIterator
setSecondIterator(Iterator<VectorEntry>) - Method in class gov.sandia.cognition.math.matrix.VectorUnionIterator: setter for secondIterator
setSecondLearner(BatchLearner<? super Collection<? extends IntermediateType>, ? extends Evaluator<? super IntermediateType, ? extends OutputType>>) - Method in class gov.sandia.cognition.learning.algorithm.CompositeBatchLearnerPair: Sets the second learner that is applied to the output of the first learner.
setSecondMatrix(AbstractMTJMatrix) - Method in class gov.sandia.cognition.math.matrix.mtj.TwoMatrixEntryMTJ: Setter for secondMatrix.
setSecondValue(double) - Method in class gov.sandia.cognition.math.matrix.DefaultTwoVectorEntry: Sets the entry value for the second underlying vector.
setSecondValue(double) - Method in class gov.sandia.cognition.math.matrix.mtj.TwoMatrixEntryMTJ: Sets the entry value from the second underlying matrix.
setSecondValue(double) - Method in interface gov.sandia.cognition.math.matrix.TwoMatrixEntry: Sets the second value to which this entry points
setSecondValue(double) - Method in interface gov.sandia.cognition.math.matrix.TwoVectorEntry: Sets the second value to which this entry points
setSecondVector(Vector) - Method in class gov.sandia.cognition.math.matrix.DefaultTwoVectorEntry: Setter for secondVector.
setSeedScale(double) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner: Sets the seed initialization scale.
setSelector(Selector<GenomeType>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.reproducer.CrossoverReproducer: Sets the selector.
setSelector(Selector<GenomeType>) - Method in class gov.sandia.cognition.learning.algorithm.genetic.reproducer.MutationReproducer: Sets the selector used to select the population.
setSelfDivergence(double) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: Sets the value used for self-divergence, which controls how many clusters are generated.
setSemanticIdentifierMap(SemanticIdentifierMap) - Method in class gov.sandia.cognition.framework.learning.converter.AbstractCogxelConverter
setSemanticIdentifierMap(SemanticIdentifierMap) - Method in class gov.sandia.cognition.framework.learning.converter.AbstractCogxelPairConverter
setSemanticIdentifierMap(SemanticIdentifierMap) - Method in interface gov.sandia.cognition.framework.learning.converter.CogxelConverter: Sets the SemanticIdentifierMap that the converter is to use.
setSemanticIdentifierMap(SemanticIdentifierMap) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelDoubleConverter: Sets the SemanticIdentifierMap that the converter is to use.
setSemanticIdentifierMap(SemanticIdentifierMap) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelMatrixConverter: Sets the SemanticIdentifierMap that the converter is to use.
setSemanticIdentifierMap(SemanticIdentifierMap) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorCollectionConverter: Sets the SemanticIdentifierMap that the converter is to use.
setSemanticIdentifierMap(SemanticIdentifierMap) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelVectorConverter: Sets the SemanticIdentifierMap that the converter is to use.
setSemanticIdentifierMap(SemanticIdentifierMap) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelWeightedInputOutputPairConverter: Sets the SemanticIdentifierMap that the converter is to use.
setSemanticIdentifierMap(DefaultSemanticIdentifierMap) - Method in class gov.sandia.cognition.framework.lite.AbstractCognitiveModelLite: Sets the semantic identifier map.
setSemanticIdentifierMap(SemanticIdentifierMap) - Method in class gov.sandia.cognition.framework.lite.ArrayBasedPerceptionModule: Sets the semantic identifier map.
setSettings(EvaluatorBasedCognitiveModuleSettings<InputType, OutputType>) - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModule: Sets the settings of the module.
setSettings(EvaluatorBasedCognitiveModuleSettings<InputType, OutputType>) - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModuleFactory: Sets the settings of the module created by the factory.
setShape(double) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.GammaInverseScaleBayesianEstimator: Sets the shape of the conditional distribution
setShape(double) - Method in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution: Setter for shape
setShape(double) - Method in class gov.sandia.cognition.statistics.distribution.GammaDistribution: Setter for shape
setShape(double) - Method in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution: Setter for shape
setShape(double) - Method in class gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution: Setter for shape
setShape(double) - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution: Setter for shape
setShape(double) - Method in class gov.sandia.cognition.statistics.distribution.WeibullDistribution: Setter for shape
setShape(double) - Method in class gov.sandia.cognition.statistics.distribution.YuleSimonDistribution: Setter for shape
setShift(double) - Method in class gov.sandia.cognition.statistics.distribution.ParetoDistribution: Setter for shift.
setSigma(double) - Method in class gov.sandia.cognition.learning.function.kernel.RadialBasisKernel: Sets the sigma value that controls the bandwidth of the kernel.
setSimilarities(double[][]) - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: Sets the array of similarities.
setSimilarities(Matrix) - Method in class gov.sandia.cognition.text.term.relation.MatrixBasedTermSimilarityNetwork: Gets the similarities between terms.
setSimilarity(double) - Method in class gov.sandia.cognition.text.term.relation.IndexedTermSimilarityRelation: Sets the similarity between the two terms.
setSimilarityFunction(SimilarityFunction<? super Vector, ? super Vector>) - Method in class gov.sandia.cognition.text.term.relation.TermVectorSimilarityNetworkCreator: Sets the similarity function between term vectors used to determine the similarity between two terms.
setSingularValues(Matrix) - Method in class gov.sandia.cognition.text.topic.LatentSemanticAnalysis.Transform: Sets the diagonal matrix of singular values.
setSize(int) - Method in class gov.sandia.cognition.learning.data.feature.RandomSubspace: Sets the size of the subspace that will be created.
setSize(int) - Method in class gov.sandia.cognition.text.term.filter.NGramFilter: Sets the size of the n-gram created by the filter.
setSlope(Double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.InputOutputSlopeTriplet: Setter for slope
setSlope(double) - Method in class gov.sandia.cognition.learning.function.scalar.LinearFunction: Sets the slope of the function, which is the m term in: f(x) = m*x + b.
setSlopeCondition(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.WolfeConditions: Setter for slopeCondition
setSmoothingWindowSize(int) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractCategorizerOutOfBagStoppingCriteria: Sets the smoothing window size.
setSortedROCData(ArrayList<ReceiverOperatingCharacteristic.DataPoint>) - Method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic: Setter for srtedROCData
setSource(SourceType) - Method in class gov.sandia.cognition.text.relation.AbstractRelation: Sets the source object of the relation.
setSplitThreshold(int) - Method in class gov.sandia.cognition.math.geometry.Quadtree: Sets the split threshold for the node.
setSquashingFunction(VectorFunction) - Method in class gov.sandia.cognition.learning.function.vector.GeneralizedLinearModel: Setter for squashingFunction
setSquashingFunction(DifferentiableUnivariateScalarFunction) - Method in class gov.sandia.cognition.learning.function.vector.ThreeLayerFeedforwardNeuralNetwork: Setter for squashingFunction
setStart(int) - Method in class gov.sandia.cognition.text.AbstractOccurrenceInText: Sets the starting point of the occurrence.
setStartNode(int) - Method in class gov.sandia.cognition.graph.GraphWalker: Sets the starting node (by id) for the next step (or series of steps)
setStartNode(NodeNameType) - Method in class gov.sandia.cognition.graph.GraphWalker: Sets the start node (by name) for the next step (or series of steps)
setState(StateType) - Method in class gov.sandia.cognition.evaluator.AbstractStatefulEvaluator
setState(StateType) - Method in interface gov.sandia.cognition.evaluator.StatefulEvaluator: Sets the current state of the evaluator.
setState(CognitiveModelState) - Method in class gov.sandia.cognition.framework.CognitiveModelStateChangeEvent: Sets the new state of the model.
setStateVector(Vector) - Method in class gov.sandia.cognition.framework.lite.SimplePatternRecognizerState: Sets the state vector stored in the state object.
setStatisticalTest(NullHypothesisEvaluator<Collection<? extends StatisticType>>) - Method in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment: Sets the statistical test to use to determine if the two learners are significantly different.
setStatistics(DefaultPair<ArrayList<StatisticType>, ArrayList<StatisticType>>) - Method in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment: Sets the performance evaluations for the trials of the experiment.
setStatistics(ArrayList<StatisticType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerRepeatExperiment: Sets the performance evaluations for the trials of the experiment.
setStatistics(ArrayList<StatisticType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerValidationExperiment: Sets the performance evaluations for the trials of the experiment.
setStatistics(ArrayList<StatisticType>) - Method in class gov.sandia.cognition.learning.experiment.OnlineLearnerValidationExperiment: Sets the performance evaluations for the trials of the experiment.
setStopList(StopList) - Method in class gov.sandia.cognition.text.term.filter.StopListFilter: Sets the stop list for the filter to use.
setStoppingCriterion(double) - Method in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer: Set the stopping criterion for this clusterer.
setSubIndices(int[]) - Method in class gov.sandia.cognition.learning.function.vector.SubVectorEvaluator: Sets the array of sub-indices.
setSubLearner(DeciderLearner<Vectorizable, OutputType, Boolean, VectorElementThresholdCategorizer>) - Method in class gov.sandia.cognition.learning.algorithm.tree.RandomSubVectorThresholdLearner: Sets the learner used to learn a threshold function over the subspace.
setSubMatrix(int, int, Matrix) - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
setSubMatrix(int, int, Matrix) - Method in interface gov.sandia.cognition.math.matrix.Matrix: Sets the submatrix inside of the Matrix, specified by the zero-based indices.
setSufficientDecrease(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerBacktracking: Setter for sufficientDecrease
setSummaries(DefaultPair<SummaryType, SummaryType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment.Result: Sets the summary of performance for the learners.
setSummaries(DefaultPair<SummaryType, SummaryType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment: Sets the summaries of the experiment.
setSummarizer(Summarizer<? super StatisticType, ? extends SummaryType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment: Sets the summarizer of the performance evaluations.
setSummarizer(Summarizer<? super StatisticType, ? extends SummaryType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerRepeatExperiment: Sets the summarizer of the performance evaluations.
setSummarizer(Summarizer<? super StatisticType, ? extends SummaryType>) - Method in class gov.sandia.cognition.learning.experiment.LearnerValidationExperiment: Sets the summarizer of the performance evaluations.
setSummarizer(Summarizer<? super StatisticType, ? extends SummaryType>) - Method in class gov.sandia.cognition.learning.experiment.OnlineLearnerValidationExperiment: Sets the summarizer of the performance evaluations.
setSummary(SummaryType) - Method in class gov.sandia.cognition.learning.experiment.LearnerRepeatExperiment: Sets the summary of the experiment.
setSummary(SummaryType) - Method in class gov.sandia.cognition.learning.experiment.LearnerValidationExperiment: Sets the summary of the experiment.
setSummary(SummaryType) - Method in class gov.sandia.cognition.learning.experiment.OnlineLearnerValidationExperiment: Sets the summary of the experiment.
setSupervisedLearner(SupervisedBatchLearner<InputType, OutputType, EvaluatorType>) - Method in class gov.sandia.cognition.learning.algorithm.TimeSeriesPredictionLearner: Setter for supervisedLearner
setSupportsMap(LinkedHashMap<InputOutputPair<? extends InputType, Boolean>, DefaultWeightedValue<InputType>>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelAdatron: Gets the mapping of examples to weight objects (support vectors).
setSupportsMap(LinkedHashMap<InputOutputPair<? extends InputType, ? extends Boolean>, DefaultWeightedValue<InputType>>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelPerceptron: Gets the mapping of examples to weight objects (support vectors).
setSupportsMap(LinkedHashMap<InputOutputPair<? extends InputType, Double>, DefaultWeightedValue<InputType>>) - Method in class gov.sandia.cognition.learning.algorithm.regression.KernelBasedIterativeRegression: Gets the mapping of examples to weight objects (support vectors).
setSupportsMap(LinkedHashMap<InputOutputPair<? extends InputType, ? extends Boolean>, SuccessiveOverrelaxation<InputType>.Entry>) - Method in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation: Gets the mapping of examples to weight objects (support vectors).
setSynonyms(Map<Term, Term>) - Method in class gov.sandia.cognition.text.term.filter.SynonymFilter: Sets the mapping of terms to their synonyms.
setT(double) - Method in class gov.sandia.cognition.statistics.method.StudentTConfidence.Statistic: Setter for t
setT(double) - Method in class gov.sandia.cognition.statistics.method.WilcoxonSignedRankConfidence.Statistic: Getter for T
setTarget(double) - Method in class gov.sandia.cognition.learning.algorithm.root.SolverFunction: Setter for target
setTarget(TargetType) - Method in class gov.sandia.cognition.learning.data.DefaultTargetEstimatePair: Sets the target, which is the ground-truth or actual value.
setTarget(TargetType) - Method in class gov.sandia.cognition.text.relation.AbstractRelation: Sets the target object of the relation.
setTargetConverter(CogxelConverter<TargetType>) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelTargetEstimatePairConverter: Sets the converter for the target value.
setTargetEstimateCorrelation(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearRegression.Statistic: Setter for targetEstimateCorrelation
setTargetInput(double) - Method in class gov.sandia.cognition.math.signals.PIDController: Setter for targetInput
setTemperature(double) - Method in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer: Sets the current temperature of the system.
setTerm(Term) - Method in class gov.sandia.cognition.text.term.DefaultIndexedTerm: Sets the term.
setTerm(Term) - Method in class gov.sandia.cognition.text.term.DefaultTermOccurrence: Sets the term that occurred.
setTermBasis(Matrix) - Method in class gov.sandia.cognition.text.topic.LatentSemanticAnalysis.Transform: Sets the matrix of orthogonal term column vectors.
setTermDocumentFrequencies(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.AbstractFrequencyBasedGlobalTermWeighter: Sets the vector containing the number of documents that each term appears in.
setTermEntropiesSum(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.AbstractEntropyBasedGlobalTermWeighter: Sets the vector containing the sum of the term entropies.
setTermGlobalFrequencies(Vector) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.AbstractFrequencyBasedGlobalTermWeighter: Gets the vector containing the number of times that each term appears.
setTermIndex(TermIndex) - Method in class gov.sandia.cognition.text.term.relation.MatrixBasedTermSimilarityNetwork: Sets the index of terms.
setTermIndex(TermIndex) - Method in class gov.sandia.cognition.text.term.vector.BagOfWordsTransform: Sets the term index that the transform is to use to map terms to their vector indices.
setTermList(List<DefaultIndexedTerm>) - Method in class gov.sandia.cognition.text.term.DefaultTermIndex: Gets the list of terms, ordered by index.
setTermMap(Map<Term, DefaultIndexedTerm>) - Method in class gov.sandia.cognition.text.term.DefaultTermIndex: Sets the mapping of terms to their indices.
setTerms(Term...) - Method in class gov.sandia.cognition.text.term.DefaultTermNGram: Sets the terms that make up the n-gram.
setTermToCountMap(HashMap<Term, Integer>) - Method in class gov.sandia.cognition.text.term.DefaultTermCounts: Gets the mapping of terms to their respective counts.
setTestingSet(Collection<DataType>) - Method in class gov.sandia.cognition.learning.data.DefaultPartitionedDataset: Sets the testing set.
setTestVector(Vector) - Method in class gov.sandia.cognition.statistics.ChiSquaredSimilarity: Basic setter for the test vector.
setText(String) - Method in class gov.sandia.cognition.text.DefaultTextual: Sets the text value in this object.
setText(String) - Method in class gov.sandia.cognition.text.document.DefaultTextField: Sets the text content for the field.
setText(String) - Method in class gov.sandia.cognition.text.term.DefaultTerm: Sets the text of the term.
setText(String) - Method in class gov.sandia.cognition.text.token.DefaultToken: Sets the text of the token.
setThird(ThirdType) - Method in class gov.sandia.cognition.util.DefaultTriple: Sets the third object.
setThreadPool(ThreadPoolExecutor) - Method in class gov.sandia.cognition.algorithm.AbstractParallelAlgorithm
setThreadPool(ThreadPoolExecutor) - Method in interface gov.sandia.cognition.algorithm.ParallelAlgorithm: Sets the thread pool for the algorithm to use.
setThreadPool(ThreadPoolExecutor) - Method in class gov.sandia.cognition.learning.algorithm.clustering.ParallelizedKMeansClusterer
setThreadPool(ThreadPoolExecutor) - Method in class gov.sandia.cognition.learning.algorithm.genetic.ParallelizedGeneticAlgorithm: Setter for threadPool
setThreadPool(ThreadPoolExecutor) - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelBaumWelchAlgorithm
setThreadPool(ThreadPoolExecutor) - Method in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel
setThreadPool(ThreadPoolExecutor) - Method in class gov.sandia.cognition.learning.experiment.ParallelLearnerValidationExperiment
setThreadPool(ThreadPoolExecutor) - Method in class gov.sandia.cognition.learning.function.cost.ParallelClusterDistortionMeasure
setThreadPool(ThreadPoolExecutor) - Method in class gov.sandia.cognition.learning.function.cost.ParallelizedCostFunctionContainer
setThreadPool(ThreadPoolExecutor) - Method in class gov.sandia.cognition.learning.function.cost.ParallelNegativeLogLikelihood
setThreadPool(ThreadPoolExecutor) - Method in class gov.sandia.cognition.statistics.bayesian.ParallelDirichletProcessMixtureModel
setThreadPool(ThreadPoolExecutor) - Method in class gov.sandia.cognition.text.topic.ParallelLatentDirichletAllocationVectorGibbsSampler
setThreshold(double) - Method in class gov.sandia.cognition.learning.function.categorization.AbstractThresholdBinaryCategorizer: Setter for threshold
setThreshold(double) - Method in class gov.sandia.cognition.learning.function.categorization.KernelBinaryCategorizer: Sets the threshold, which is the negative of the bias.
setThreshold(double) - Method in class gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer: Sets the threshold, which is the negative of the bias.
setThreshold(double) - Method in interface gov.sandia.cognition.learning.function.categorization.ThresholdBinaryCategorizer: Sets the threshold between the two categories used in binary categorization.
setThreshold(double) - Method in class gov.sandia.cognition.learning.function.scalar.ThresholdFunction: Setter for threshold
setTime(Date) - Method in class gov.sandia.cognition.util.AbstractTemporal: Sets the time of the Temporal
setTitle(String) - Method in class gov.sandia.cognition.text.document.DefaultDocument: Sets the title field of the document to the given string.
setToJBlas() - Method in class gov.sandia.cognition.math.matrix.custom.NativeBlasHandler: Sets the BLAS library to use the Java version.
setTokenizer(ReaderTokenizer) - Method in class gov.sandia.cognition.math.matrix.VectorReader: Setter for tokenizer
setTolerance(double) - Method in class gov.sandia.cognition.graph.community.PersonalizedPageRank: Set the tolerance to a new value.
setTolerance(double) - Method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer: Sets the tolerance value.
setTolerance(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.AbstractAnytimeFunctionMinimizer: Setter for tolerance
setTolerance(double) - Method in interface gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizer: Sets the tolerance of the minimization algorithm
setTolerance(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.AbstractLineBracketInterpolator: Setter for tolerance
setTolerance(double) - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.IterativeMatrixSolver: Sets the minimum tolerance before iterations complete (must be non-negative).
setTolerance(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.AbstractLogisticRegression: Setter for tolerance
setTolerance(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.AbstractParameterCostMinimizer: Setter for tolerance
setTolerance(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.KernelWeightedRobustRegression: Setter for tolerance
setTolerance(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression: Setter for tolerance
setTolerance(double) - Method in class gov.sandia.cognition.learning.algorithm.root.AbstractRootFinder
setTolerance(double) - Method in class gov.sandia.cognition.learning.algorithm.root.MinimizerBasedRootFinder
setTolerance(double) - Method in interface gov.sandia.cognition.learning.algorithm.root.RootFinder: Sets the tolerance of the algorithm.
setTolerance(double) - Method in class gov.sandia.cognition.math.LentzMethod: Setter for tolerance
setTolerance(double) - Method in class gov.sandia.cognition.statistics.distribution.MixtureOfGaussians.EMLearner: Setter for tolerance
setTolerance(double) - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.EMLearner: Setter for tolerance
setToNativeBlas() - Method in class gov.sandia.cognition.math.matrix.custom.NativeBlasHandler: If native BLAS is available, this sets the BLAS library to use the native version.
setTopicCount(int) - Method in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler: Sets the number of topics (k) created by the topic model.
setTopicTermProbabilities(double[][]) - Method in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler.Result: Sets the document-topic probabilities, which are often called the theta model parameters.
setTotalChange(double) - Method in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation: Gets the total change in weight from the most recent step of the algorithm.
setTotalCount(int) - Method in class gov.sandia.cognition.text.term.DefaultTermCounts: Sets the total count.
setTournamentSize(int) - Method in class gov.sandia.cognition.learning.algorithm.genetic.selector.TournamentSelector: Sets the size for tournaments.
setTrainingPercent(double) - Method in class gov.sandia.cognition.learning.data.RandomDataPartitioner: Sets the percentage of data to put in the training partition.
setTrainingSet(Collection<DataType>) - Method in class gov.sandia.cognition.learning.data.DefaultPartitionedDataset: Sets the training set.
setTransform(Matrix) - Method in class gov.sandia.cognition.text.topic.LatentSemanticAnalysis.Transform: Gets the cached transform matrix.
setTransitionProbability(Matrix) - Method in class gov.sandia.cognition.learning.algorithm.hmm.MarkovChain: Setter for transitionProbability.
setTreatmentCount(int) - Method in class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution: Setter for treatmentCount
setTrueNegativesCount(double) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix: Sets the number of true negatives in the matrix.
setTrueNegativesRate(ConfidenceInterval) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrixConfidenceInterval: Setter for trueNegativesRate
setTruePositivesCount(double) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix: Sets the number of true positives in the matrix.
setTruePositivesRate(ConfidenceInterval) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrixConfidenceInterval: Setter for truePositivesRate
setTrueValue(Number) - Method in class gov.sandia.cognition.data.convert.number.DefaultBooleanToNumberConverter: Sets the number that represents a true value.
setType(ProcessLauncherEvent.EventType) - Method in class gov.sandia.cognition.io.ProcessLauncherEvent: Setter for type
setU(Matrix) - Method in class gov.sandia.cognition.math.matrix.decomposition.AbstractSingularValueDecomposition: setter for left singular vectors
setU(double) - Method in class gov.sandia.cognition.statistics.method.MannWhitneyUConfidence.Statistic: Setter for U
setUnlabeledWeight(double) - Method in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation.Entry: Sets the unlabeled weight.
setUnpredictedErrorFraction(double) - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearRegression.Statistic: Setter for unpredictedErrorFraction
setUnsortedEigenDecomposition(ComplexNumber[], Matrix, Matrix) - Method in class gov.sandia.cognition.math.matrix.decomposition.AbstractEigenDecomposition: Creates a new eigendecomposition using the given eigenvalues and eigenvectors...
setUpdateBias(boolean) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractLinearCombinationOnlineLearner: Sets whether or not the algorithm is updating the bias.
setUpdater(ParticleFilter.Updater<ObservationType, ParameterType>) - Method in class gov.sandia.cognition.statistics.bayesian.AbstractParticleFilter: Setter for updater
setUpdater(DirichletProcessMixtureModel.Updater<ObservationType>) - Method in class gov.sandia.cognition.statistics.bayesian.DirichletProcessMixtureModel: Setter for updater
setUpdater(ImportanceSampling.Updater<ObservationType, ParameterType>) - Method in class gov.sandia.cognition.statistics.bayesian.ImportanceSampling: Setter for updater
setUpdater(MetropolisHastingsAlgorithm.Updater<ObservationType, ParameterType>) - Method in class gov.sandia.cognition.statistics.bayesian.MetropolisHastingsAlgorithm: Sets the object that makes proposal samples from the current location.
setUpdater(RejectionSampling.Updater<ObservationType, ParameterType>) - Method in class gov.sandia.cognition.statistics.bayesian.RejectionSampling: Setter for updater
setUpperBound(InputOutputSlopeTriplet) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.LineBracket: Setter for upperBound
setUpperBound(double) - Method in class gov.sandia.cognition.statistics.method.ConfidenceInterval: Setter for upperBound
setUpperLeft(Quadtree<DataType>.Node) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node: Sets the upper-left child.
setUpperRight(Quadtree<DataType>.Node) - Method in class gov.sandia.cognition.math.geometry.Quadtree.Node: Sets the upper-right child.
setUsePseudoInverse(boolean) - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearBasisRegression: Setter for usePseudoInverse
setUsePseudoInverse(boolean) - Method in class gov.sandia.cognition.learning.algorithm.regression.LinearRegression: Setter for usePseudoInverse
setUsePseudoInverse(boolean) - Method in class gov.sandia.cognition.learning.algorithm.regression.MultivariateLinearRegression: Setter for usePseudoInverse
setUtest(MannWhitneyUConfidence.Statistic) - Method in class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic: Setter for Utest
setV1(double) - Method in class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution: Setter for v1
setV2(double) - Method in class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution: Setter for v2
setValid(boolean) - Method in class gov.sandia.cognition.io.ReaderTokenizer: Setter for valid
setValidationData(DataType) - Method in class gov.sandia.cognition.learning.performance.AnytimeBatchLearnerValidationPerformanceReporter: Sets the validation dataset.
setValidBracket(boolean) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.AbstractAnytimeLineMinimizer: Setter for validBracket
setValue(double) - Method in class gov.sandia.cognition.collection.AbstractLogNumberMap.SimpleEntry
setValue(double) - Method in class gov.sandia.cognition.collection.AbstractMutableDoubleMap.SimpleEntry
setValue(double) - Method in interface gov.sandia.cognition.collection.ScalarMap.Entry: Sets the value associated with the key.
setValue(ValueType) - Method in class gov.sandia.cognition.learning.algorithm.baseline.ConstantLearner: Sets the value that is the result of learning.
setValue(double) - Method in class gov.sandia.cognition.learning.algorithm.tree.RegressionTreeNode: Sets the value stored at the node, which is usually the mean value.
setValue(ValueType) - Method in class gov.sandia.cognition.learning.data.DefaultValueDiscriminantPair: Sets the value.
setValue(OutputType) - Method in class gov.sandia.cognition.learning.function.ConstantEvaluator: Sets the constant output value for the evaluator.
setValue(double) - Method in class gov.sandia.cognition.math.LogNumber: Sets the value represented by the log number.
setValue(double) - Method in interface gov.sandia.cognition.math.matrix.MatrixEntry: Sets the value to which this entry points
setValue(double) - Method in interface gov.sandia.cognition.math.matrix.VectorSpace.Entry: Sets the value to which this entry points
setValue(double) - Method in class gov.sandia.cognition.math.MutableDouble: Sets the value stored in the object.
setValue(int) - Method in class gov.sandia.cognition.math.MutableInteger: Sets the value stored in the object.
setValue(long) - Method in class gov.sandia.cognition.math.MutableLong: Sets the value stored in the object.
setValue(double) - Method in class gov.sandia.cognition.math.UnsignedLogNumber: Sets the value represented by the log number.
setValue(Double) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.BernoulliBayesianEstimator.Parameter
setValue(Double) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.BinomialBayesianEstimator.Parameter
setValue(Double) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.ExponentialBayesianEstimator.Parameter
setValue(Double) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.GammaInverseScaleBayesianEstimator.Parameter
setValue(Vector) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultinomialBayesianEstimator.Parameter
setValue(Vector) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanBayesianEstimator.Parameter
setValue(Matrix) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.MultivariateGaussianMeanCovarianceBayesianEstimator.Parameter
setValue(Double) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.PoissonBayesianEstimator.Parameter
setValue(Double) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.UniformDistributionBayesianEstimator.Parameter
setValue(Double) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanBayesianEstimator.Parameter
setValue(Vector) - Method in class gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanVarianceBayesianEstimator.Parameter
setValue(ParameterType) - Method in class gov.sandia.cognition.statistics.DefaultDistributionParameter
setValue(ParameterType) - Method in interface gov.sandia.cognition.statistics.DistributionParameter: Sets the value of the parameter.
setValue(ValueType) - Method in class gov.sandia.cognition.util.DefaultIdentifiedValue: Sets the value.
setValue(ValueType) - Method in class gov.sandia.cognition.util.DefaultKeyValuePair: Sets the value element of the pair.
setValue(ValueType) - Method in class gov.sandia.cognition.util.DefaultNamedValue: Sets the value stored in the name-value pair.
setValue(ValueType) - Method in class gov.sandia.cognition.util.DefaultTemporalValue: Sets the value.
setValue(ValueType) - Method in class gov.sandia.cognition.util.DefaultWeightedValue: Sets the value.
setValueMap(Map<InputType, OutputType>) - Method in class gov.sandia.cognition.evaluator.ValueMapper: Sets the map of input values to output values.
setValues(List<InputType>) - Method in class gov.sandia.cognition.data.convert.vector.UniqueBooleanVectorEncoder: Sets the list of unique values that the encoder is to use.
setValues(Vector) - Method in class gov.sandia.cognition.framework.lite.VectorBasedCognitiveModelInput: Setter for values
setValues(Collection<ValueType>) - Method in class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator: Sets the values that the divergence is computed from using the divergence function to the input.
setVariance(double) - Method in class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer: Sets the variance.
setVariance(Vector) - Method in class gov.sandia.cognition.learning.function.categorization.DiagonalConfidenceWeightedBinaryCategorizer: Sets the variance vector.
setVariance(double) - Method in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian: Setter for variance
setVectorFactory(VectorFactory<?>) - Method in class gov.sandia.cognition.data.convert.vector.AbstractToVectorEncoder: Sets the vector factory used by this encoder.
setVectorFactory(VectorFactory<?>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.AbstractOnlineLinearBinaryCategorizerLearner: Sets the VectorFactory used to create the weight vector.
setVectorFactory(VectorFactory<?>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.BatchMultiPerceptron: Sets the VectorFactory used to create the weight vector.
setVectorFactory(VectorFactory<?>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineMultiPerceptron: Sets the VectorFactory used to create the weight vector.
setVectorFactory(VectorFactory<?>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.OnlineVotedPerceptron: Sets the VectorFactory used to create the weight vector.
setVectorFactory(VectorFactory<?>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Perceptron: Sets the VectorFactory used to create the weight vector.
setVectorFactory(VectorFactory<? extends Vector>) - Method in class gov.sandia.cognition.learning.algorithm.tree.RandomSubVectorThresholdLearner: Sets the vector factory.
setVectorFactory(VectorFactory<?>) - Method in class gov.sandia.cognition.learning.data.feature.FeatureHashing: Sets the vector factory to use.
setVectorFactory(VectorFactory<?>) - Method in class gov.sandia.cognition.learning.data.feature.RandomSubspace: Sets the vector factory to use.
setVectorFactory(VectorFactory<?>) - Method in class gov.sandia.cognition.learning.function.categorization.WinnerTakeAllCategorizer.Learner: Sets the vector factory.
setVectorFactory(VectorFactory<?>) - Method in class gov.sandia.cognition.learning.function.distance.DivergencesEvaluator.Learner: Sets the vector factory to use.
setVectorFactory(VectorFactory<?>) - Method in class gov.sandia.cognition.learning.function.vector.VectorizableVectorConverterWithBias: Sets the vector factory used to create the vector with the bias.
setVectorFactory(VectorFactory<? extends Vector>) - Method in class gov.sandia.cognition.math.matrix.DefaultVectorFactoryContainer: Sets the vector factory for the object to use to create new vectors.
setVectorFactory(VectorFactory<? extends Vector>) - Method in class gov.sandia.cognition.text.term.vector.weighter.global.AbstractGlobalTermWeighter: Sets the vector factory.
setVectorFactory(VectorFactory<? extends Vector>) - Method in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis: Sets the vector factory to use.
setVectorFunction(Evaluator<? super InputType, Vector>) - Method in class gov.sandia.cognition.learning.function.scalar.VectorFunctionLinearDiscriminant: Setter for vectorFunction
setVectorFunction(Evaluator<? super InputType, ? extends Vectorizable>) - Method in class gov.sandia.cognition.learning.function.scalar.VectorFunctionToScalarFunction: Sets the vector function with a one-dimensional output that is being converted to a scalar function.
setVectorOffset(Vector) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToScalarFunction: Point to use as input to vectorFunction
setVectorScalarFunction(Evaluator<? super Vector, ? extends Double>) - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToScalarFunction: Setter for vectorScalarFunction
setVoteOutOfBagOnly(boolean) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner: Sets whether during learning ensemble members can only vote on items that they are not in their bag (training set).
setVtranspose(Matrix) - Method in class gov.sandia.cognition.math.matrix.decomposition.AbstractSingularValueDecomposition: sets the transpose of the right singular vectors matrix
setW(double) - Method in interface gov.sandia.cognition.math.matrix.Quaternion: Sets the w component of the quaternion.
setWeakLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, Boolean>>, ? extends Evaluator<? super InputType, ? extends Boolean>>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AdaBoost: Sets the weak learner that is passed the weighted training data on each step of the algorithm.
setWeakLearner(BatchLearner<? super Collection<? extends InputOutputPair<? extends InputType, CategoryType>>, ? extends Evaluator<? super InputType, ? extends CategoryType>>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.MultiCategoryAdaBoost: Sets the weak learner that is passed the weighted training data on each step of the algorithm.
setWeight(double) - Method in class gov.sandia.cognition.learning.data.DefaultWeightedInputOutputPair: Sets the weight for the pair.
setWeight(double) - Method in class gov.sandia.cognition.learning.data.DefaultWeightedTargetEstimatePair: Sets the weight.
setWeight(double) - Method in class gov.sandia.cognition.learning.function.kernel.WeightedKernel: Sets the weight used to rescale the kernel's results.
setWeight(double) - Method in class gov.sandia.cognition.util.AbstractWeighted: Setter for weight
setWeight(double) - Method in class gov.sandia.cognition.util.DefaultWeightedPair: Gets the weight of the pair.
setWeightConverter(CogxelConverter<Double>) - Method in class gov.sandia.cognition.framework.learning.converter.CogxelWeightedInputOutputPairConverter: Setter for weightConverter
setWeightedData(ArrayList<DefaultWeightedInputOutputPair<InputType, Boolean>>) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AdaBoost: Sets the weighted version of the data.
setWeightPrior(MultivariateGaussian) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression: Setter for weightPrior
setWeightPrior(MultivariateGaussian) - Method in class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression: Setter for weightPrior
setWeightRegularization(double) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner: Sets the value for the parameter controlling the linear weight regularization.
setWeights(Vector) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachine: Sets the weight vector.
setWeights(Vector) - Method in class gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer: Sets the weight vector.
setWeights(Vector) - Method in class gov.sandia.cognition.learning.function.distance.WeightedEuclideanDistanceMetric: Sets the vector of weights for each dimension.
setWeights(Vector) - Method in class gov.sandia.cognition.learning.function.scalar.LinearVectorScalarFunction: Sets the weight vector.
setWeightsEnabled(boolean) - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner: Sets whether or not the linear weight term is enabled.
setWeightUpdate(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Winnow: Sets the multiplicative weight update term.
setWeightVector(Vector) - Method in class gov.sandia.cognition.learning.function.scalar.LinearDiscriminant: Setter for weightVector
setWithinClusterDivergenceFunction(WithinClusterDivergence<? super ClusterType, ? super DataType>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer: Sets the metric on clusters used for partitioning.
setWordCounts(DefaultDataDistribution<String>) - Method in class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector: Sets the dictionary of words counts.
setWords(Set<String>) - Method in class gov.sandia.cognition.text.term.filter.DefaultStopList: Sets the set of words in the stop list.
setWriter(Writer) - Method in class gov.sandia.cognition.math.matrix.MatrixWriter: Setter for writer
setWriter(Writer) - Method in class gov.sandia.cognition.math.matrix.VectorWriter: Setter for writer
setWXYZ(double, double, double, double) - Method in interface gov.sandia.cognition.math.matrix.Quaternion: Sets all of the elements of the quaternion in w-x-y-z order.
setX(double) - Method in class gov.sandia.cognition.math.matrix.mtj.Vector1
setX(double) - Method in class gov.sandia.cognition.math.matrix.mtj.Vector2
setX(double) - Method in class gov.sandia.cognition.math.matrix.mtj.Vector3
setX(double) - Method in interface gov.sandia.cognition.math.matrix.Quaternion: Sets the x component of the quaternion.
setX(double) - Method in interface gov.sandia.cognition.math.matrix.Vector1D: Sets the value of the first (and only) dimension (x).
setX(double) - Method in interface gov.sandia.cognition.math.matrix.Vector2D: Sets the value of the first dimension (x).
setX(double) - Method in interface gov.sandia.cognition.math.matrix.Vector3D: Sets the value of the first dimension (x).
setXY(double, double) - Method in class gov.sandia.cognition.math.matrix.mtj.Vector2
setXY(double, double) - Method in interface gov.sandia.cognition.math.matrix.Vector2D: Sets the value of both dimensions of the vector.
setXYZ(double, double, double) - Method in class gov.sandia.cognition.math.matrix.mtj.Vector3
setXYZ(double, double, double) - Method in interface gov.sandia.cognition.math.matrix.Vector3D: Sets the value of all three dimensions of this vector.
setXYZW(double, double, double, double) - Method in interface gov.sandia.cognition.math.matrix.Quaternion: Sets all of the elements of the quaternion in x-y-z-w order.
setY(double) - Method in class gov.sandia.cognition.math.matrix.mtj.Vector2
setY(double) - Method in class gov.sandia.cognition.math.matrix.mtj.Vector3
setY(double) - Method in interface gov.sandia.cognition.math.matrix.Quaternion: Sets the y component of the quaternion.
setY(double) - Method in interface gov.sandia.cognition.math.matrix.Vector2D: Sets the value of the second dimension (y).
setY(double) - Method in interface gov.sandia.cognition.math.matrix.Vector3D: Sets the value of the second dimension (y).
setZ(double) - Method in class gov.sandia.cognition.math.matrix.mtj.Vector3
setZ(double) - Method in interface gov.sandia.cognition.math.matrix.Quaternion: Sets the z component of the quaternion.
setZ(double) - Method in interface gov.sandia.cognition.math.matrix.Vector3D: Sets the value of the third dimension (z).
setZ(double) - Method in class gov.sandia.cognition.statistics.method.GaussianConfidence.Statistic: Getter for z
setZ(double) - Method in class gov.sandia.cognition.statistics.method.MannWhitneyUConfidence.Statistic: Setter for z
setZ(double) - Method in class gov.sandia.cognition.statistics.method.WilcoxonSignedRankConfidence.Statistic: Setter for z
SHA1Hash - Class in gov.sandia.cognition.hash: A Java implementation of the Secure Hash Algorithm, SHA-1, as defined in FIPS PUB 180-1.
SHA1Hash() - Constructor for class gov.sandia.cognition.hash.SHA1Hash: Default constructor
SHA256Hash - Class in gov.sandia.cognition.hash: The SHA-2, 256-bit (32 byte) hash function.
SHA256Hash() - Constructor for class gov.sandia.cognition.hash.SHA256Hash: Default constructor
SHA512Hash - Class in gov.sandia.cognition.hash: The SHA-2 512-bit (64-byte) hash function.
SHA512Hash() - Constructor for class gov.sandia.cognition.hash.SHA512Hash: Creates a new instance of SHA512Hash
ShafferStaticCorrection - Class in gov.sandia.cognition.statistics.method: The Shaffer Static Correction uses logical relationships to tighten up the Bonferroni/Sidak corrections when performing pairwise multiple hypothesis comparisons.
ShafferStaticCorrection() - Constructor for class gov.sandia.cognition.statistics.method.ShafferStaticCorrection: Default constructor
ShafferStaticCorrection(NullHypothesisEvaluator<Collection<? extends Number>>) - Constructor for class gov.sandia.cognition.statistics.method.ShafferStaticCorrection: Creates a new instance of BonferroniCorrection
ShafferStaticCorrection.Statistic - Class in gov.sandia.cognition.statistics.method: Test statistic from the Shaffer static multiple-comparison test
shape - Variable in class gov.sandia.cognition.statistics.distribution.BetaBinomialDistribution: Shape, similar to the beta parameter shape, must be greater than zero
shape - Variable in class gov.sandia.cognition.statistics.distribution.InverseGammaDistribution: Shape parameter, must be greater than zero.
shape - Variable in class gov.sandia.cognition.statistics.distribution.ParetoDistribution: Scale parameter, must be greater than zero.
shape - Variable in class gov.sandia.cognition.statistics.distribution.WeibullDistribution: Shape parameter, must be greater than 0.0
shape - Variable in class gov.sandia.cognition.statistics.distribution.YuleSimonDistribution: Shape parameter, must be greater than zero
ShareableCognitiveModuleSettings - Interface in gov.sandia.cognition.framework: The ShareableCognitiveModuleSettings is an interface for module settings that can be shared between two instances of a CognitiveModule.
SharedSemanticMemoryLite - Class in gov.sandia.cognition.framework.lite: The SharedSemanticMemoryLite class implements a semantic memory that uses a shared piece of memory to store the settings.
SharedSemanticMemoryLite(SemanticIdentifierMap, SharedSemanticMemoryLiteSettings) - Constructor for class gov.sandia.cognition.framework.lite.SharedSemanticMemoryLite: Creates a new instance of SharedSemanticMemoryLite.
SharedSemanticMemoryLiteFactory - Class in gov.sandia.cognition.framework.lite: The SharedSemanticMemoryLiteFactory implements a CognitiveModuleFactory for SharedSemanticMemoryLite modules.
SharedSemanticMemoryLiteFactory(PatternRecognizerLite) - Constructor for class gov.sandia.cognition.framework.lite.SharedSemanticMemoryLiteFactory: Creates a new instance of SharedSemanticMemoryLiteFactory.
SharedSemanticMemoryLiteSettings - Class in gov.sandia.cognition.framework.lite: The SharedSemanticMemoryLiteSettings class implements the settings for the SharedSemanticMemoryLite module.
SharedSemanticMemoryLiteSettings(PatternRecognizerLite) - Constructor for class gov.sandia.cognition.framework.lite.SharedSemanticMemoryLiteSettings: Creates a new instance of SharedSemanticMemoryLiteSettings.
shift - Variable in class gov.sandia.cognition.statistics.distribution.ParetoDistribution: Amount to shift the distribution to the left.
shouldSplit(Quadtree<DataType>.Node) - Method in class gov.sandia.cognition.math.geometry.Quadtree: Determines if a given node should be split.
shouldStop - Variable in class gov.sandia.cognition.learning.algorithm.minimization.matrix.IterativeMatrixSolver: If set to true, the algorithm will stop after the current iteration completes.
shouldUpdate(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Projectron.LinearSoftMargin
shouldUpdate(double) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Projectron: Determine if an update should be made.
shrink(Forgetron.Result<InputType>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Forgetron.Greedy
shrink(Forgetron.Result<InputType>) - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Forgetron: Apply the shrinking step of the algorithm.
SidakCorrection - Class in gov.sandia.cognition.statistics.method: The Sidak correction takes a pair-wise null-hypothesis test and generalizes it to multiple comparisons by adjusting the requisite p-value to find significance as alpha / NumComparisons.
SidakCorrection() - Constructor for class gov.sandia.cognition.statistics.method.SidakCorrection: Creates a new instance of SidakCorrection
SidakCorrection(NullHypothesisEvaluator<Collection<? extends Number>>) - Constructor for class gov.sandia.cognition.statistics.method.SidakCorrection: Creates a new instance of SidakCorrection
Sigma - Variable in class gov.sandia.cognition.math.matrix.custom.DenseMatrix.SVD: * The singular value diagonal matrix.
SigmoidFunction - Class in gov.sandia.cognition.learning.function.scalar: An implementation of a sigmoid squashing function.
SigmoidFunction() - Constructor for class gov.sandia.cognition.learning.function.scalar.SigmoidFunction: Creates a new instance of SigmoidFunction
SigmoidKernel - Class in gov.sandia.cognition.learning.function.kernel: The SigmoidKernel class implements a sigmoid kernel based on the hyperbolic tangent.
SigmoidKernel() - Constructor for class gov.sandia.cognition.learning.function.kernel.SigmoidKernel: Creates a new instance of SigmoidKernel with default values of 1.0 for kappa and 0.0 for the constant.
SigmoidKernel(double, double) - Constructor for class gov.sandia.cognition.learning.function.kernel.SigmoidKernel: Creates a new instance of SigmoidKernel from its two needed parameters: kappa and a constant.
SigmoidKernel(SigmoidKernel) - Constructor for class gov.sandia.cognition.learning.function.kernel.SigmoidKernel: Creates a new copy of a SigmoidKernel.
similarities - Variable in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation: The array of example-example similarities.
similarities - Variable in class gov.sandia.cognition.text.term.relation.MatrixBasedTermSimilarityNetwork: The similarities between terms.
similarity - Variable in class gov.sandia.cognition.text.term.relation.IndexedTermSimilarityRelation: The similarity between the two terms.
SimilarityFunction<FromType,ToType> - Interface in gov.sandia.cognition.text.relation: Defines the functionality of a similarity function that computes the similarity between two objects.
similarityFunction - Variable in class gov.sandia.cognition.text.term.relation.TermVectorSimilarityNetworkCreator: The similarity function between term vectors used to determine the similarity between two terms.
SimpleEntry(KeyType, LogNumber) - Constructor for class gov.sandia.cognition.collection.AbstractLogNumberMap.SimpleEntry: Creates a new instance of SimpleEntry
SimpleEntry(KeyType, MutableDouble) - Constructor for class gov.sandia.cognition.collection.AbstractMutableDoubleMap.SimpleEntry: Creates a new instance of SimpleEntry
SimpleEntrySet(Map<KeyType, LogNumber>) - Constructor for class gov.sandia.cognition.collection.AbstractLogNumberMap.SimpleEntrySet: Creates a new instance of SimpleEntrySet
SimpleEntrySet(Map<KeyType, MutableDouble>) - Constructor for class gov.sandia.cognition.collection.AbstractMutableDoubleMap.SimpleEntrySet: Creates a new instance of SimpleEntrySet
SimpleIterator(Iterator<? extends Map.Entry<KeyType, LogNumber>>) - Constructor for class gov.sandia.cognition.collection.AbstractLogNumberMap.SimpleIterator: Default constructor
SimpleIterator(Iterator<? extends Map.Entry<KeyType, MutableDouble>>) - Constructor for class gov.sandia.cognition.collection.AbstractMutableDoubleMap.SimpleIterator: Default constructor
SimpleIterator() - Constructor for class gov.sandia.cognition.math.matrix.DefaultInfiniteVector.SimpleIterator: Default constructor
SimplePatternRecognizer - Class in gov.sandia.cognition.framework.lite: The SimplePatternRecognizer class implements a simple version of the PatternRecognizerLite interface.
SimplePatternRecognizer() - Constructor for class gov.sandia.cognition.framework.lite.SimplePatternRecognizer: Creates a new, empty instance of SimplePatternRecognizer.
SimplePatternRecognizer(SemanticNetwork) - Constructor for class gov.sandia.cognition.framework.lite.SimplePatternRecognizer: Creates a new instance of SimplePatternRecognizer.
SimplePatternRecognizer(SimplePatternRecognizer) - Constructor for class gov.sandia.cognition.framework.lite.SimplePatternRecognizer: Creates a new instance of SimplePatternRecognizer.
SimplePatternRecognizerState - Class in gov.sandia.cognition.framework.lite: The SimplePatternRecognizerState class implements a CognitiveModuleState for the SimplePatternRecognizer.
SimplePatternRecognizerState(Collection<SemanticLabel>) - Constructor for class gov.sandia.cognition.framework.lite.SimplePatternRecognizerState: Creates a new instance of SimplePatternRecognizerState.
SimplePatternRecognizerState(Collection<SemanticLabel>, Vector) - Constructor for class gov.sandia.cognition.framework.lite.SimplePatternRecognizerState: Creats a new instance of SimplePatternRecognizerState.
SimplePatternRecognizerState(Collection<SemanticLabel>, Vector, boolean) - Constructor for class gov.sandia.cognition.framework.lite.SimplePatternRecognizerState: Creats a new instance of SimplePatternRecognizerState.
SimplePatternRecognizerState(SimplePatternRecognizerState) - Constructor for class gov.sandia.cognition.framework.lite.SimplePatternRecognizerState: Creats a new instance of SimplePatternRecognizerState.
SimpleStatisticalSpellingCorrector - Class in gov.sandia.cognition.text.spelling: A simple statistical spelling corrector based on word counts that looks at possible one and two-character edits.
SimpleStatisticalSpellingCorrector() - Constructor for class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector: Creates a new, default SimpleStatisticalSpellingCorrector with a default alphabet.
SimpleStatisticalSpellingCorrector(char[]) - Constructor for class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector: Creates a new SimpleStatisticalSpellingCorrector with a given alphabet.
SimpleStatisticalSpellingCorrector(DefaultDataDistribution<String>, char[]) - Constructor for class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector: Creates a new SimpleStatisticalSpellingCorrector.
SimpleStatisticalSpellingCorrector.Learner - Class in gov.sandia.cognition.text.spelling: A learner for the SimpleStatisticalSpellingCorrector.
SimulatedAnnealer<CostParametersType,AnnealedType> - Class in gov.sandia.cognition.learning.algorithm.annealing: The SimulatedAnnealer class implements the simulated annealing algorithm using the provided cost function and perturbation function.
SimulatedAnnealer(AnnealedType, Perturber<AnnealedType>, CostFunction<? super AnnealedType, ? super CostParametersType>) - Constructor for class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer: Creates a new instance of SimulatedAnnealer.
SimulatedAnnealer(AnnealedType, Perturber<AnnealedType>, CostFunction<? super AnnealedType, ? super CostParametersType>, int) - Constructor for class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer: Creates a new instance of SimulatedAnnealer.
SimulatedAnnealer(AnnealedType, Perturber<AnnealedType>, CostFunction<? super AnnealedType, ? super CostParametersType>, int, int) - Constructor for class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer: Creates a new instance of SimulatedAnnealer.
SingleDocumentExtractor - Interface in gov.sandia.cognition.text.document.extractor: Interface for a DocumentExtractor that only extracts a single document from a file.
SingleTermFilter - Interface in gov.sandia.cognition.text.term.filter: Interface for a term filter that looks at each term individually.
SingleTextualConverter<InputType,OutputType extends Textual> - Interface in gov.sandia.cognition.text.convert: Interface for an TextConverter that converts an input into a single output.
SingleToMultiTextualConverterAdapter<InputType,OutputType extends Textual> - Class in gov.sandia.cognition.text.convert: Adapts a SingleTextualConverter to work within the interface of an MultiTextualConverter.
SingleToMultiTextualConverterAdapter() - Constructor for class gov.sandia.cognition.text.convert.SingleToMultiTextualConverterAdapter: Creates a new SingleToMultiTextualConverterAdapter with no internal converter.
SingleToMultiTextualConverterAdapter(SingleTextualConverter<? super InputType, ? extends OutputType>) - Constructor for class gov.sandia.cognition.text.convert.SingleToMultiTextualConverterAdapter: Creates a new SingleToMultiTextualConverterAdapter with the given internal converter.
SingularValueDecomposition - Interface in gov.sandia.cognition.math.matrix.decomposition: Interface that describes the operations of all SingularValueDecompositions
SingularValueDecompositionMTJ - Class in gov.sandia.cognition.math.matrix.mtj.decomposition: Full singular-value decomposition, based on MTJ's SVD.
singularValues - Variable in class gov.sandia.cognition.text.topic.LatentSemanticAnalysis.Transform: The diagonal matrix of singular values.
size() - Method in class gov.sandia.cognition.collection.AbstractLogNumberMap.SimpleEntrySet
size() - Method in class gov.sandia.cognition.collection.AbstractMutableDoubleMap.SimpleEntrySet
size() - Method in class gov.sandia.cognition.collection.AbstractScalarMap
size(Collection<?>) - Static method in class gov.sandia.cognition.collection.CollectionUtil: Determines the size of the given collection, checking for null.
size(Iterable<?>) - Static method in class gov.sandia.cognition.collection.CollectionUtil: Determines the size of the given iterable.
size() - Method in class gov.sandia.cognition.collection.DefaultIndexer
size() - Method in class gov.sandia.cognition.collection.DefaultMultiCollection
size() - Method in class gov.sandia.cognition.collection.DynamicArrayMap: Runs in O(1).
size() - Method in class gov.sandia.cognition.collection.FiniteCapacityBuffer
size() - Method in interface gov.sandia.cognition.collection.Indexer: Gets the number of items in the index.
size() - Method in class gov.sandia.cognition.collection.IntegerSpan
size() - Method in interface gov.sandia.cognition.collection.NumericMap: Gets the number of items in the map.
size() - Method in class gov.sandia.cognition.collection.RangeExcludedArrayList
size - Variable in class gov.sandia.cognition.learning.data.feature.RandomSubspace: The size of the random subspace to create, which is the number of dimensions that are chosen.
size - Variable in class gov.sandia.cognition.math.Combinations.AbstractCombinationsIterator
size() - Method in class gov.sandia.cognition.math.Combinations
size() - Method in class gov.sandia.cognition.math.geometry.KDTree.Neighborhood
size() - Method in class gov.sandia.cognition.math.geometry.KDTree
size() - Method in class gov.sandia.cognition.statistics.distribution.MultinomialDistribution.Domain
size - Variable in class gov.sandia.cognition.text.term.filter.NGramFilter: The size of the n-gram.
slope - Variable in class gov.sandia.cognition.learning.function.scalar.LinearFunction: The slope (m).
SmoothCumulativeDistributionFunction - Interface in gov.sandia.cognition.statistics: This defines a CDF that has an associated derivative, which is its PDF.
smoothedErrorRates - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractCategorizerOutOfBagStoppingCriteria: The smoothed out-of-bag error rates, per iteration.
smoothingBuffer - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractCategorizerOutOfBagStoppingCriteria: The buffer used for smoothing.
smoothingWindowSize - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractCategorizerOutOfBagStoppingCriteria: The size of window of data to look at to determine if learning has hit a minimum.
SmoothUnivariateDistribution - Interface in gov.sandia.cognition.statistics: A closed-form scalar distribution that is also smooth.
SnedecorFDistribution - Class in gov.sandia.cognition.statistics.distribution: CDF of the Snedecor F-distribution (also known as Fisher F-distribution, Fisher-Snedecor F-distribution, or just plain old F-distribution).
SnedecorFDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution: Default constructor
SnedecorFDistribution(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution: Creates a new instance of CumulativeDistribution
SnedecorFDistribution(SnedecorFDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.SnedecorFDistribution: Copy Constructor
SnedecorFDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution: CDF of the F-distribution.
SoftPlusFunction - Class in gov.sandia.cognition.learning.function.scalar: A smoothed approximation for rectified linear unit.
SoftPlusFunction() - Constructor for class gov.sandia.cognition.learning.function.scalar.SoftPlusFunction: Creates a new SoftPlusFunction.
SoftwareLicenseType - Enum in gov.sandia.cognition.annotation: An enumeration of common types of software licenses.
SoftwareReference - Annotation Type in gov.sandia.cognition.annotation: Describes a reference to software.
SoftwareReferences - Annotation Type in gov.sandia.cognition.annotation: The SoftwareReferences annotation defines a container for one or more references to a publication.
solve() - Method in interface gov.sandia.cognition.graph.inference.EnergyFunctionSolver: Solves for the energy function passed in during initialize, e.g., compute the beliefs.
solve() - Method in class gov.sandia.cognition.graph.inference.SumProductInferencingAlgorithm
solve(Matrix) - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
solve(Vector) - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix
solve(Matrix) - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
solve(Vector) - Method in class gov.sandia.cognition.math.matrix.custom.DiagonalMatrix
solve(Matrix) - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix: Solves for "X" in the equation: this*X = B
solve(Vector) - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix: Solves for "x" in the equation: this*x = b
solve(Matrix) - Method in interface gov.sandia.cognition.math.matrix.Matrix: Solves for "X" in the equation: this*X = B
solve(Vector) - Method in interface gov.sandia.cognition.math.matrix.Matrix: Solves for "x" in the equation: this*x = b
solve(Matrix) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix
solve(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix
solve(AbstractMTJMatrix) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix: Solves for "X" in the equation this * X = B, where X is a DenseMatrix, "this" and "B" will be converted to a DenseMatrix (if not already)
solve(AbstractMTJVector) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix: Solves for "x" in the equation: this*x = b
solve(AbstractMTJVector) - Method in class gov.sandia.cognition.math.matrix.mtj.AbstractSparseMatrix: This sparse-vector solver performs iterative solving for "x" in the equation: this*x = b, and the AbstractSparseMatrix "this" can be unstructured (e.g., asymmetric, indefinite, etc.)
solve(AbstractMTJVector) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixMTJ
solve(Matrix) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixMTJ
solve(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.DiagonalMatrixMTJ
solveCommunities() - Method in class gov.sandia.cognition.graph.community.Louvain: Solves for community detection of the graph passed in during the constructor.
solveCommunities() - Method in class gov.sandia.cognition.graph.community.Permanence: Performs the actual permanence-maximization computation and returns the resulting partitioning.
solveInto(DenseMatrix, DenseMatrix) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseMatrix: Solve for "X" in the equation: this*X = B
solveInto(DenseVector, DenseVector) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseMatrix: Solve for "x" in the equation: this*x = b
SolverFunction - Class in gov.sandia.cognition.learning.algorithm.root: Evaluator that allows RootFinders to solve for nonzero values by setting a "target" parameter.
SolverFunction() - Constructor for class gov.sandia.cognition.learning.algorithm.root.SolverFunction: Creates a new instance of SolverFunction
SolverFunction(double, Evaluator<Double, Double>) - Constructor for class gov.sandia.cognition.learning.algorithm.root.SolverFunction: Creates a new instance of SolverFunction
sortAndSetEigenDecomposition(ComplexNumber[], Matrix, Matrix) - Method in class gov.sandia.cognition.math.matrix.decomposition.AbstractEigenDecomposition: Sorts the eigendecomposition in descending order of the value of the magnitudes of the eigenvalues
sortArrayAscending(double[]) - Static method in class gov.sandia.cognition.util.ArrayIndexSorter: Returns the indices of the array sorted in ascending order
sortArrayDescending(double[]) - Static method in class gov.sandia.cognition.util.ArrayIndexSorter: Returns the indices of the array sorted in ascending order.
Sorter() - Constructor for class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic.DataPoint.Sorter
source - Variable in class gov.sandia.cognition.text.relation.AbstractRelation: The source object of the relation.
sourceVectorPrior - Variable in class gov.sandia.cognition.statistics.TransferEntropy.TransferEntropyDistributionObject: The prior for the source vector.
SparseColumnMatrix - Class in gov.sandia.cognition.math.matrix.mtj: A sparse matrix, represented as a collection of sparse column vectors.
SparseColumnMatrix(int, int) - Constructor for class gov.sandia.cognition.math.matrix.mtj.SparseColumnMatrix: Creates a new empty instance of SparseColumnMatrix.
SparseColumnMatrix(SparseColumnMatrix) - Constructor for class gov.sandia.cognition.math.matrix.mtj.SparseColumnMatrix: Copy constructor for SparseColumnMatrix matrices.
SparseColumnMatrix(Matrix) - Constructor for class gov.sandia.cognition.math.matrix.mtj.SparseColumnMatrix: Copy constructor for general matrices, copies over nonzero values.
SparseColumnMatrix(FlexCompColMatrix) - Constructor for class gov.sandia.cognition.math.matrix.mtj.SparseColumnMatrix: Creates a SparseColumnMatrix based on the appropriate MTJ matrix, does NOT create a copy of internalMatrix.
SparseMatrix - Class in gov.sandia.cognition.math.matrix.custom: A sparse matrix implementation.
SparseMatrix(int, int) - Constructor for class gov.sandia.cognition.math.matrix.custom.SparseMatrix: Creates a new sparse matrix with the specified number of rows and columns.
SparseMatrix(SparseMatrix) - Constructor for class gov.sandia.cognition.math.matrix.custom.SparseMatrix: Creates a new sparse matrix with the same dimensions and data as m (performs a deep copy).
SparseMatrix(DenseMatrix) - Constructor for class gov.sandia.cognition.math.matrix.custom.SparseMatrix: Creates a new sparse matrix with the same dimensions and data as d (performs a deep copy).
SparseMatrix(DiagonalMatrix) - Constructor for class gov.sandia.cognition.math.matrix.custom.SparseMatrix: Creates a new sparse matrix with the same dimensions and data as d (performs a deep copy).
SparseMatrix() - Constructor for class gov.sandia.cognition.math.matrix.custom.SparseMatrix: This should never be called by anything or anyone other than Java's serialization code.
SparseMatrix - Class in gov.sandia.cognition.math.matrix.mtj: A sparse matrix, represented as a collection of sparse row vectors.
SparseMatrix(int, int) - Constructor for class gov.sandia.cognition.math.matrix.mtj.SparseMatrix: Creates a new empty instance of SparseMatrix.
SparseMatrix(Matrix) - Constructor for class gov.sandia.cognition.math.matrix.mtj.SparseMatrix: Converts the given matrix to a SparseMatrix.
SparseMatrixFactoryMTJ - Class in gov.sandia.cognition.math.matrix.mtj: Factory for MTJ's flexible sparse row matrix
SparseMatrixFactoryMTJ() - Constructor for class gov.sandia.cognition.math.matrix.mtj.SparseMatrixFactoryMTJ: Creates a new instance of SparseMatrixFactoryMTJ
SparseRowMatrix - Class in gov.sandia.cognition.math.matrix.mtj: A sparse matrix, represented as a collection of sparse row vectors.
SparseRowMatrix(int, int) - Constructor for class gov.sandia.cognition.math.matrix.mtj.SparseRowMatrix: Creates a new empty instance of SparseRowMatrix.
SparseRowMatrix(SparseRowMatrix) - Constructor for class gov.sandia.cognition.math.matrix.mtj.SparseRowMatrix: Copy constructor for SparseRowMatrix matrices
SparseRowMatrix(Matrix) - Constructor for class gov.sandia.cognition.math.matrix.mtj.SparseRowMatrix: Copy constructor for general matrices, copies over nonzero values.
SparseRowMatrix(FlexCompRowMatrix) - Constructor for class gov.sandia.cognition.math.matrix.mtj.SparseRowMatrix: Creates a SparseRowMatrix based on the appropriate MTJ matrix, does NOT create a copy of internalMatrix.
SparseVector - Class in gov.sandia.cognition.math.matrix.custom: Our sparse vector implementation.
SparseVector(int) - Constructor for class gov.sandia.cognition.math.matrix.custom.SparseVector: Create a new sparse vector.
SparseVector(SparseVector) - Constructor for class gov.sandia.cognition.math.matrix.custom.SparseVector: Copy constructor -- creates a deep copy of the input sparse vector.
SparseVector(DenseVector) - Constructor for class gov.sandia.cognition.math.matrix.custom.SparseVector: Copy constructor -- creates a deep copy of the input sparse vector.
SparseVector() - Constructor for class gov.sandia.cognition.math.matrix.custom.SparseVector: This should never be called by anything or anyone other than Java's serialization code.
SparseVector - Class in gov.sandia.cognition.math.matrix.mtj: A vector that only stores the nonzero elements, relies on MTJ's SparseVector.
SparseVector(int) - Constructor for class gov.sandia.cognition.math.matrix.mtj.SparseVector: Creates a new instance of SparseVector with no initial size.
SparseVector(int, int) - Constructor for class gov.sandia.cognition.math.matrix.mtj.SparseVector: Creates a new instance of SparseVector with a specified initial size.
SparseVector(SparseVector) - Constructor for class gov.sandia.cognition.math.matrix.mtj.SparseVector: Creates a new copy of SparseVector.
SparseVector(Vector) - Constructor for class gov.sandia.cognition.math.matrix.mtj.SparseVector: Creates a new copy of SparseVector.
SparseVectorFactory<VectorType extends Vector> - Class in gov.sandia.cognition.math.matrix: Abstract factory class for creating sparse vectors.
SparseVectorFactory() - Constructor for class gov.sandia.cognition.math.matrix.SparseVectorFactory
SparseVectorFactoryMTJ - Class in gov.sandia.cognition.math.matrix.mtj: Factory for MTJ's SparseVector
SparseVectorFactoryMTJ() - Constructor for class gov.sandia.cognition.math.matrix.mtj.SparseVectorFactoryMTJ: Creates a new instance of SparseVectorFactoryMTJ
split(String, char) - Static method in class gov.sandia.cognition.io.CSVUtility: Splits the given line into the array of character-separated values.
split(Quadtree<DataType>.Node) - Method in class gov.sandia.cognition.math.geometry.Quadtree: Splits the given node.
splitCommas(String) - Static method in class gov.sandia.cognition.io.CSVUtility: Splits the given line into the array of comma-separated values.
splitData(Collection<? extends InputOutputPair<? extends InputType, OutputType>>, Categorizer<? super InputType, ? extends DecisionType>) - Method in class gov.sandia.cognition.learning.algorithm.tree.AbstractDecisionTreeLearner: Splits the data into new lists based on the given decision function.
splitDatasets(Collection<? extends InputOutputPair<? extends DataType, Boolean>>) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil: Splits a dataset of input-output pair into two datasets, one for the inputs that have a "true" output and another for the inputs that have a "false" output
splitOnOutput(Iterable<? extends InputOutputPair<? extends InputType, ? extends CategoryType>>) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil: Splits a dataset according to its output value (usually a category) so that all the inputs for that category are given in a list.
splitThreshold - Variable in class gov.sandia.cognition.math.geometry.Quadtree: The minimum number of items allowed in a leaf node.
spreadValence() - Method in class gov.sandia.cognition.text.algorithm.ValenceSpreader: This method solves the system of equations to determine the valence for all documents input and for all terms in those documents.
spreadValence(int) - Method in class gov.sandia.cognition.text.algorithm.ValenceSpreader: This method solves the system of equations to determine the valence for all documents input and for all terms in those documents.
SQRT2 - Static variable in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian: Square root of 2.0, 0.707...
SQRT2PI - Static variable in class gov.sandia.cognition.statistics.distribution.LogNormalDistribution: Constant value of Math.sqrt(2*Math.PI)
stack(Vector) - Method in class gov.sandia.cognition.math.matrix.AbstractVector
stack(DenseVector) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
stack(SparseVector) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
stack(DenseVector) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
stack(SparseVector) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
stack(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVector
stack(Vector) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVector
stack(Vector) - Method in interface gov.sandia.cognition.math.matrix.Vector: Stacks "other" below "this" and returns the stacked Vector
standardDeviation - Variable in class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer: The cached value of the standard deviation for normalization.
StandardDistributionNormalizer - Class in gov.sandia.cognition.learning.data.feature: The StandardDistributionNormalizer class implements a normalization method where a real value is converted onto a standard distribution.
StandardDistributionNormalizer() - Constructor for class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer: Creates a new instance of StandardNormalization with a mean of 0.0 and a variance of 1.0.
StandardDistributionNormalizer(double, double) - Constructor for class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer: Creates a new instance of StandardDistributionNormalizer with the given mean and variance.
StandardDistributionNormalizer(UnivariateGaussian) - Constructor for class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer: Creates a new instance of StandardDistributionNormalizer from the given Gaussian.
StandardDistributionNormalizer(StandardDistributionNormalizer) - Constructor for class gov.sandia.cognition.learning.data.feature.StandardDistributionNormalizer: Creates a new copy of a StandardDistributionNormalizer.
StandardDistributionNormalizer.Learner - Class in gov.sandia.cognition.learning.data.feature: The Learner class implements a BatchLearner object for a StandardDistributionNormalizer.
standardErrors - Variable in class gov.sandia.cognition.statistics.method.TukeyKramerConfidence.Statistic: Gets the standard errors in the experiment
start - Variable in class gov.sandia.cognition.text.AbstractOccurrenceInText: The starting point of the occurrence.
state - Variable in class gov.sandia.cognition.framework.lite.AbstractCognitiveModelLite: The current state of the model.
State() - Constructor for class gov.sandia.cognition.math.signals.PIDController.State: Default constructor.
State(double, double) - Constructor for class gov.sandia.cognition.math.signals.PIDController.State: Creates a new instance of State
state - Variable in class gov.sandia.cognition.statistics.TransferEntropy.TransferEntropyPartialSumObject: The state.
stateBeliefs(Collection<? extends ObservationType>) - Method in class gov.sandia.cognition.learning.algorithm.hmm.HiddenMarkovModel: Computes the probability distribution over all states for each observation.
StatefulEvaluator<InputType,OutputType,StateType extends CloneableSerializable> - Interface in gov.sandia.cognition.evaluator: The StatefulEvaluator interface defines the functionality of an Evaluator that maintains an internal state.
StatefulEvaluatorBasedCognitiveModule<InputType,OutputType> - Class in gov.sandia.cognition.framework.learning: The StatefulEvaluatorBasedCognitiveModule implements a CognitiveModule that wraps a StatefulEvaluator object.
StatefulEvaluatorBasedCognitiveModule(CognitiveModel, EvaluatorBasedCognitiveModuleSettings<InputType, OutputType>, String) - Constructor for class gov.sandia.cognition.framework.learning.StatefulEvaluatorBasedCognitiveModule: Creates a new instance of StatefulEvaluatorBasedCognitiveModule.
StateObservationLikelihoodTask() - Constructor for class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel.StateObservationLikelihoodTask: Default constructor.
stateObservationLikelihoodTasks - Variable in class gov.sandia.cognition.learning.algorithm.hmm.ParallelHiddenMarkovModel: StateObservationLikelihoodTasks
stationaryPoints() - Method in interface gov.sandia.cognition.learning.function.scalar.PolynomialFunction.ClosedForm: Finds the real-valued stationary points (zero slope) maxima or minima of the polynomial
stationaryPoints() - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Cubic
stationaryPoints(double, double, double, double) - Static method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Cubic: Finds the stationary point of the quadratic equation.
stationaryPoints() - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Linear
stationaryPoints() - Method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Quadratic: Finds the real-valued stationary points (zero-derivatives) of the quadratic.
stationaryPoints(double, double, double) - Static method in class gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Quadratic: Finds the stationary point of the quadratic equation.
Statistic(Collection<Double>, Collection<Double>, int) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LinearRegression.Statistic: Creates a new instance of Statistic
Statistic(Collection<Double>, Collection<Double>, Collection<Double>, int) - Constructor for class gov.sandia.cognition.learning.algorithm.regression.LinearRegression.Statistic: Creates a new instance of Statistic
Statistic() - Constructor for class gov.sandia.cognition.statistics.method.AbstractMultipleHypothesisComparison.Statistic: Default constructor
Statistic(Collection<? extends Collection<? extends Number>>, double, NullHypothesisEvaluator<Collection<? extends Number>>) - Constructor for class gov.sandia.cognition.statistics.method.AbstractPairwiseMultipleHypothesisComparison.Statistic: Creates a new instance of StudentizedMultipleComparisonStatistic
Statistic(double, double, double) - Constructor for class gov.sandia.cognition.statistics.method.AnalysisOfVarianceOneWay.Statistic: Creates a new instance of Statistic
Statistic(double, double) - Constructor for class gov.sandia.cognition.statistics.method.ChiSquareConfidence.Statistic: Creates a new instance of chiSquare
Statistic(int, int) - Constructor for class gov.sandia.cognition.statistics.method.FisherSignConfidence.Statistic: Creates a new instance of Statistic
Statistic(int, int, ArrayList<Double>) - Constructor for class gov.sandia.cognition.statistics.method.FriedmanConfidence.Statistic: Creates a new instance of Statistic
Statistic(int, int, ArrayList<Double>, double) - Constructor for class gov.sandia.cognition.statistics.method.FriedmanConfidence.Statistic: Creates a new instance of Statistic
Statistic(int, int, ArrayList<Double>, double, double) - Constructor for class gov.sandia.cognition.statistics.method.FriedmanConfidence.Statistic: Creates a new instance of Statistic
Statistic(double) - Constructor for class gov.sandia.cognition.statistics.method.GaussianConfidence.Statistic: Creates a new instance of Statistic
Statistic(Collection<? extends Collection<? extends Number>>, double, NullHypothesisEvaluator<Collection<? extends Number>>) - Constructor for class gov.sandia.cognition.statistics.method.HolmCorrection.Statistic: Creates a new instance of Statistic
Statistic(double, double) - Constructor for class gov.sandia.cognition.statistics.method.KolmogorovSmirnovConfidence.Statistic: Creates a new instance of Statistic
Statistic(double, int, double, int) - Constructor for class gov.sandia.cognition.statistics.method.MannWhitneyUConfidence.Statistic: Creates a new instance of Statistic
Statistic(MannWhitneyUConfidence.Statistic) - Constructor for class gov.sandia.cognition.statistics.method.MannWhitneyUConfidence.Statistic: Copy Constructor
Statistic(ConfidenceStatistic, MultipleHypothesisComparison.Statistic) - Constructor for class gov.sandia.cognition.statistics.method.MultipleComparisonExperiment.Statistic: Creates a new instance of Statistic
Statistic(double, int, ArrayList<Double>, double) - Constructor for class gov.sandia.cognition.statistics.method.NemenyiConfidence.Statistic: Creates a new instance of StudentizedMultipleComparisonStatistic
Statistic(ReceiverOperatingCharacteristic) - Constructor for class gov.sandia.cognition.statistics.method.ReceiverOperatingCharacteristic.Statistic: Creates a new instance of Statistic
Statistic(Collection<? extends Collection<? extends Number>>, double, NullHypothesisEvaluator<Collection<? extends Number>>) - Constructor for class gov.sandia.cognition.statistics.method.ShafferStaticCorrection.Statistic: Creates a new instance of Statistic
Statistic(double, double) - Constructor for class gov.sandia.cognition.statistics.method.StudentTConfidence.Statistic: Creates a new instance of Statistic
Statistic(StudentTConfidence.Statistic) - Constructor for class gov.sandia.cognition.statistics.method.StudentTConfidence.Statistic: Copy Constructor
Statistic(double, ArrayList<Integer>, ArrayList<Double>, double) - Constructor for class gov.sandia.cognition.statistics.method.TukeyKramerConfidence.Statistic: Creates a new instance of Statistic
Statistic(double, int) - Constructor for class gov.sandia.cognition.statistics.method.WilcoxonSignedRankConfidence.Statistic: Creates a new instance of Statistic
statistics - Variable in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment: The performance evaluations made during the experiment.
statistics - Variable in class gov.sandia.cognition.learning.experiment.LearnerRepeatExperiment: The performance evaluations made during the experiment.
statistics - Variable in class gov.sandia.cognition.learning.experiment.LearnerValidationExperiment: The performance evaluations made during the experiment.
statistics - Variable in class gov.sandia.cognition.learning.experiment.OnlineLearnerValidationExperiment: The performance evaluations made during the experiment.
StatusPrinter() - Constructor for class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis.StatusPrinter: Creates a new StatusPrinter writing to System.out.
StatusPrinter(PrintStream) - Constructor for class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis.StatusPrinter: Creates a new StatusPrinter writing to the given stream.
SteepestDescentMatrixSolver - Class in gov.sandia.cognition.learning.algorithm.minimization.matrix: Implements a basic Steepest Descent iterative solver for linear systems of equations.
SteepestDescentMatrixSolver(Vector, Vector) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.matrix.SteepestDescentMatrixSolver: Initializes a steepest-descent solver with the minimum values
SteepestDescentMatrixSolver(Vector, Vector, double) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.matrix.SteepestDescentMatrixSolver: Initializes a steepest-descent solver with some additional parameters
SteepestDescentMatrixSolver(Vector, Vector, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.matrix.SteepestDescentMatrixSolver: Initializes a steepest-descent solver with all user-definable parameters
stem(String) - Static method in class gov.sandia.cognition.text.term.filter.stem.PorterEnglishStemmingFilter: Stems the given String according to the Porter stemming algorithm for English words.
step() - Method in class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner: Called to take a single step of the learning algorithm.
step() - Method in class gov.sandia.cognition.learning.algorithm.annealing.SimulatedAnnealer: Takes one step in the Simulated Annealing process.
step() - Method in class gov.sandia.cognition.learning.algorithm.clustering.AffinityPropagation
step() - Method in class gov.sandia.cognition.learning.algorithm.clustering.AgglomerativeClusterer
step() - Method in class gov.sandia.cognition.learning.algorithm.clustering.DBSCANClusterer
step() - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClusterer: Do a step of the clustering algorithm.
step() - Method in class gov.sandia.cognition.learning.algorithm.clustering.KMeansClustererWithRemoval
step() - Method in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer: Do a step of the clustering algorithm.
step() - Method in class gov.sandia.cognition.learning.algorithm.clustering.OptimizedKMeansClusterer
step() - Method in class gov.sandia.cognition.learning.algorithm.clustering.PartitionalClusterer
step() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner
step() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AdaBoost
step() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.IVotingCategorizerLearner
step() - Method in class gov.sandia.cognition.learning.algorithm.ensemble.MultiCategoryAdaBoost
step() - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineAlternatingLeastSquares
step() - Method in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachineStochasticGradient
step() - Method in class gov.sandia.cognition.learning.algorithm.genetic.GeneticAlgorithm
step() - Method in class gov.sandia.cognition.learning.algorithm.hmm.BaumWelchAlgorithm
step() - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerConjugateGradient
step() - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerDirectionSetPowell
step() - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerGradientDescent: Called to take a single step of the learning algorithm.
step() - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerNelderMead
step() - Method in class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerQuasiNewton
step() - Method in class gov.sandia.cognition.learning.algorithm.minimization.line.AbstractAnytimeLineMinimizer
step(InputOutputPair<Double, Double>, InputOutputPair<Double, Double>, double) - Static method in class gov.sandia.cognition.learning.algorithm.minimization.line.interpolator.LineBracketInterpolatorGoldenSection: Takes a Golden Section step between the two points
step() - Method in class gov.sandia.cognition.learning.algorithm.pca.GeneralizedHebbianAlgorithm
step() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.BatchMultiPerceptron
step() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelAdatron
step() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelPerceptron
step() - Method in class gov.sandia.cognition.learning.algorithm.perceptron.Perceptron
step() - Method in class gov.sandia.cognition.learning.algorithm.regression.FletcherXuHybridEstimation
step() - Method in class gov.sandia.cognition.learning.algorithm.regression.GaussNewtonAlgorithm
step() - Method in class gov.sandia.cognition.learning.algorithm.regression.KernelBasedIterativeRegression
step() - Method in class gov.sandia.cognition.learning.algorithm.regression.KernelWeightedRobustRegression
step() - Method in class gov.sandia.cognition.learning.algorithm.regression.LevenbergMarquardtEstimation
step() - Method in class gov.sandia.cognition.learning.algorithm.regression.LogisticRegression
step() - Method in class gov.sandia.cognition.learning.algorithm.root.RootBracketExpander
step() - Method in class gov.sandia.cognition.learning.algorithm.root.RootFinderBisectionMethod
step() - Method in class gov.sandia.cognition.learning.algorithm.root.RootFinderFalsePositionMethod
step() - Method in class gov.sandia.cognition.learning.algorithm.root.RootFinderNewtonsMethod
step() - Method in class gov.sandia.cognition.learning.algorithm.root.RootFinderRiddersMethod
step() - Method in class gov.sandia.cognition.learning.algorithm.root.RootFinderSecantMethod
step() - Method in class gov.sandia.cognition.learning.algorithm.svm.PrimalEstimatedSubGradient
step() - Method in class gov.sandia.cognition.learning.algorithm.svm.SequentialMinimalOptimization
step() - Method in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation
step() - Method in class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo
step() - Method in class gov.sandia.cognition.statistics.distribution.MixtureOfGaussians.EMLearner
step() - Method in class gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.EMLearner
step() - Method in class gov.sandia.cognition.text.topic.LatentDirichletAllocationVectorGibbsSampler
step() - Method in class gov.sandia.cognition.text.topic.ParallelLatentDirichletAllocationVectorGibbsSampler
step(Vector, Matrix, Vector) - Method in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis.Result: Take a step of the expectation-maximization algorithm for computing the probability of the query given each latent variable.
step() - Method in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis
STEP_MAX - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerBacktracking: Maximum step size allowed by a parabolic fit, 100.0
STEP_MAX - Static variable in class gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeFree: Maximum step size allowed by a parabolic fit, 100.0.
STEP_MAX - Static variable in class gov.sandia.cognition.learning.algorithm.regression.GaussNewtonAlgorithm: Maximum step norm allowed under a Gauss-Newton step, 100.0
stepEnded(IterativeAlgorithm) - Method in class gov.sandia.cognition.algorithm.AnytimeAlgorithmWrapper
stepEnded(IterativeAlgorithm) - Method in class gov.sandia.cognition.algorithm.event.AbstractIterativeAlgorithmListener
stepEnded(IterativeAlgorithm) - Method in class gov.sandia.cognition.algorithm.event.IterationMeasurablePerformanceReporter
stepEnded(IterativeAlgorithm) - Method in interface gov.sandia.cognition.algorithm.IterativeAlgorithmListener: This method is called when the algorithm has ended a step of its execution.
stepEnded(IterativeAlgorithm) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.AbstractCategorizerOutOfBagStoppingCriteria
stepEnded(IterativeAlgorithm) - Method in class gov.sandia.cognition.learning.algorithm.ensemble.BaggingCategorizerLearner.OutOfBagErrorStoppingCriteria
stepEnded(IterativeAlgorithm) - Method in class gov.sandia.cognition.learning.performance.AnytimeBatchLearnerValidationPerformanceReporter
stepEnded(IterativeAlgorithm) - Method in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis.StatusPrinter
stepStarted(IterativeAlgorithm) - Method in class gov.sandia.cognition.algorithm.AnytimeAlgorithmWrapper
stepStarted(IterativeAlgorithm) - Method in class gov.sandia.cognition.algorithm.event.AbstractIterativeAlgorithmListener
stepStarted(IterativeAlgorithm) - Method in class gov.sandia.cognition.algorithm.event.IterationStartReporter
stepStarted(IterativeAlgorithm) - Method in interface gov.sandia.cognition.algorithm.IterativeAlgorithmListener: This method is called when the algorithm has started a step in its execution.
stepStarted(IterativeAlgorithm) - Method in class gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis.StatusPrinter
stop() - Method in class gov.sandia.cognition.algorithm.AnytimeAlgorithmWrapper
stop() - Method in interface gov.sandia.cognition.algorithm.StoppableAlgorithm: Requests that the algorithm stop at the next appropriate point.
stop() - Method in class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner
stop() - Method in class gov.sandia.cognition.learning.algorithm.minimization.matrix.IterativeMatrixSolver: Execution will stop after the current iteration completes.
stop() - Method in class gov.sandia.cognition.math.LentzMethod
StopList - Interface in gov.sandia.cognition.text.term.filter: Interface for a list of stop words.
stopList - Variable in class gov.sandia.cognition.text.term.filter.StopListFilter: The stop list for the filter to use.
StopListFilter - Class in gov.sandia.cognition.text.term.filter: A term filter that rejects any term that appears in a given stop list.
StopListFilter() - Constructor for class gov.sandia.cognition.text.term.filter.StopListFilter: Creates a new StopListFilter.
StopListFilter(StopList) - Constructor for class gov.sandia.cognition.text.term.filter.StopListFilter: Creates a new StopListFilter with the given stop list.
StoppableAlgorithm - Interface in gov.sandia.cognition.algorithm: Defines methods for an algorithm that can be stopped early during its execution.
stopProcess() - Method in class gov.sandia.cognition.io.ProcessLauncher: Stops the process.
Stoptron<InputType> - Class in gov.sandia.cognition.learning.algorithm.perceptron.kernel: An online, budgeted, kernel version of the Perceptron algorithm that stops learning once it has reached its budget.
Stoptron() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Stoptron: Creates a new Stoptron with default parameters and a null kernel.
Stoptron(Kernel<? super InputType>, int) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.kernel.Stoptron: Creates a new Stoptron with the given parameters.
StreamSerializationHandler<SerializedType> - Interface in gov.sandia.cognition.io.serialization: Interface for an object that can be used to serialize and deserialize objects.
StringEvaluatorSingleTermFilter - Class in gov.sandia.cognition.text.term.filter: Adapts an evaluator from string to string to be a term filter on individual terms.
StringEvaluatorSingleTermFilter() - Constructor for class gov.sandia.cognition.text.term.filter.StringEvaluatorSingleTermFilter: Creates a new StringEvaluatorSingleTermFilter with a null evaluator.
StringEvaluatorSingleTermFilter(Evaluator<String, String>) - Constructor for class gov.sandia.cognition.text.term.filter.StringEvaluatorSingleTermFilter: Creates a new StringEvaluatorSingleTermFilter with a given evaluator.
StringToDoubleConverter - Class in gov.sandia.cognition.data.convert.number: Converts a String to a Double using the Double.valueOf method.
StringToDoubleConverter() - Constructor for class gov.sandia.cognition.data.convert.number.StringToDoubleConverter: Creates a new StringToDoubleConverter.
StringToIntegerConverter - Class in gov.sandia.cognition.data.convert.number: Converts a String to a Integer using the Integer.valueOf method.
StringToIntegerConverter() - Constructor for class gov.sandia.cognition.data.convert.number.StringToIntegerConverter: Creates a new StringToIntegerConverter.
StringUtil - Class in gov.sandia.cognition.util: The Strings class implements static utility methods for dealing with String objects.
StringUtil() - Constructor for class gov.sandia.cognition.util.StringUtil
StudentizedRangeDistribution - Class in gov.sandia.cognition.statistics.distribution: Implementation of the Studentized Range distribution, which defines the population correction factor when performing multiple comparisons.
StudentizedRangeDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution: Default constructor
StudentizedRangeDistribution(int, double) - Constructor for class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution: Creates a new instance of StudentizedRangeDistribution
StudentizedRangeDistribution(StudentizedRangeDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution: Copy constructor
StudentizedRangeDistribution.APStat - Class in gov.sandia.cognition.statistics.distribution: This is a translation of Fortran code taken from http://lib.stat.cmu.edu/apstat/, and the comments on the individual functions in this class are taken directly from the original.
StudentizedRangeDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution: CDF of the StudentizedRangeDistribution
StudentizedRangeDistribution.SampleRange - Class in gov.sandia.cognition.statistics.distribution: Computes the estimate of the Studentized Range for a single sample
StudentTConfidence - Class in gov.sandia.cognition.statistics.method: This class implements Student's t-tests for different uses.
StudentTConfidence() - Constructor for class gov.sandia.cognition.statistics.method.StudentTConfidence: Creates a new instance of StudentTConfidence
StudentTConfidence.Statistic - Class in gov.sandia.cognition.statistics.method: Confidence statistics for a Student-t test
StudentTConfidence.Summary - Class in gov.sandia.cognition.statistics.method: An implementation of the Summarizer interface for creating a ConfidenceInterval
StudentTDistribution - Class in gov.sandia.cognition.statistics.distribution: Defines a noncentral Student-t Distribution.
StudentTDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.StudentTDistribution: Default degrees of freedom.
StudentTDistribution(double) - Constructor for class gov.sandia.cognition.statistics.distribution.StudentTDistribution: Creates a new instance of StudentTDistribution
StudentTDistribution(double, double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.StudentTDistribution: Creates a new instance of StudentTDistribution
StudentTDistribution(StudentTDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.StudentTDistribution: Copy constructor
StudentTDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution: Evaluator that computes the Cumulative Distribution Function (CDF) of a Student-t distribution with a fixed number of degrees of freedom
StudentTDistribution.MaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution: Estimates the parameters of the Student-t distribution from the given data, where the degrees of freedom are estimated from the Kurtosis of the sample data.
StudentTDistribution.PDF - Class in gov.sandia.cognition.statistics.distribution: Evaluator that computes the Probability Density Function (CDF) of a Student-t distribution with a fixed number of degrees of freedom
StudentTDistribution.WeightedMaximumLikelihoodEstimator - Class in gov.sandia.cognition.statistics.distribution: Creates a UnivariateGaussian from weighted data
subCollections() - Method in class gov.sandia.cognition.collection.DefaultMultiCollection
subCollections() - Method in interface gov.sandia.cognition.collection.MultiCollection: Returns the sub-collections of the multi-collection.
subCollections() - Method in class gov.sandia.cognition.collection.RangeExcludedArrayList
SubCostEvaluate(ParallelizableCostFunction, Evaluator<? super Vector, ? extends Vector>) - Constructor for class gov.sandia.cognition.learning.function.cost.ParallelizedCostFunctionContainer.SubCostEvaluate: Creates a new instance of SubCostEvaluate
SubCostGradient(ParallelizableCostFunction, GradientDescendable) - Constructor for class gov.sandia.cognition.learning.function.cost.ParallelizedCostFunctionContainer.SubCostGradient: Creates a new instance of SubCostGradient
subIndices - Variable in class gov.sandia.cognition.learning.function.vector.SubVectorEvaluator: The indices to pull out of an input vector to create a new vector from.
subjectCounts - Variable in class gov.sandia.cognition.statistics.method.TukeyKramerConfidence.Statistic: Number of subjects in each treatment
subLearner - Variable in class gov.sandia.cognition.learning.algorithm.tree.RandomSubVectorThresholdLearner: The decider learner for the subspace.
SubsetIterator(int, int, ArrayList<ClassType>) - Constructor for class gov.sandia.cognition.math.Combinations.SubsetIterator: Creates a new instance of SubsetIterator
subtract(double, double) - Static method in class gov.sandia.cognition.math.LogMath: Subtracts two log-domain values.
subVector(int, int) - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
subVector(int, int) - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
subVector(int, int) - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVector: Gets a subvector of "this", specified by the inclusive indices
subVector(int, int) - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVector
subVector(int, int) - Method in interface gov.sandia.cognition.math.matrix.Vector: Gets a subvector of "this", specified by the inclusive indices
SubVectorEvaluator - Class in gov.sandia.cognition.learning.function.vector: Extracts the given set of indices from an input vector to create a new vector containing the input vector's elements at those indices.
SubVectorEvaluator() - Constructor for class gov.sandia.cognition.learning.function.vector.SubVectorEvaluator: Creates a new SubVectorEvaluator.
SubVectorEvaluator(int, int[]) - Constructor for class gov.sandia.cognition.learning.function.vector.SubVectorEvaluator: Creates a new SubVectorEvaluator with the given parameters.
SubVectorEvaluator(int, int[], VectorFactory<? extends Vector>) - Constructor for class gov.sandia.cognition.learning.function.vector.SubVectorEvaluator: Creates a new SubVectorEvaluator with the given parameters.
SuccessiveOverrelaxation<InputType> - Class in gov.sandia.cognition.learning.algorithm.svm: The SuccessiveOverrelaxation class implements the Successive Overrelaxation (SOR) algorithm for learning a Support Vector Machine (SVM).
SuccessiveOverrelaxation() - Constructor for class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation: Creates a new instance of SuccessiveOverrelaxation.
SuccessiveOverrelaxation(Kernel<? super InputType>) - Constructor for class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation: Creates a new instance of SuccessiveOverrelaxation.
SuccessiveOverrelaxation(Kernel<? super InputType>, double, double, double, int) - Constructor for class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation: Creates a new instance of SuccessiveOverrelaxation.
SuccessiveOverrelaxation.Entry - Class in gov.sandia.cognition.learning.algorithm.svm: The Entry class represents the data that the algorithm keeps about each training example.
successorIds(int) - Method in class gov.sandia.cognition.graph.GraphMetrics: Returns the id for all direct successors for the input node.
successorIds(NodeNameType) - Method in class gov.sandia.cognition.graph.GraphMetrics: Returns the ids of the direct successors for the input node.
successors(int) - Method in class gov.sandia.cognition.graph.GraphMetrics: Returns the node names for all direct successors to the input node.
successors(NodeNameType) - Method in class gov.sandia.cognition.graph.GraphMetrics: Returs the node names for all direct successors to the input node.
SufficientStatistic(MultivariateGaussian) - Constructor for class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression.IncrementalEstimator.SufficientStatistic: Creates a new instance of SufficientStatistic
SufficientStatistic(MultivariateGaussianInverseGammaDistribution) - Constructor for class gov.sandia.cognition.statistics.bayesian.BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic: Creates a new instance of SufficientStatistic
SufficientStatistic() - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatistic: Default constructor
SufficientStatistic(double) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatistic: Creates a new instance of SufficientStatistic
SufficientStatistic() - Constructor for class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.SufficientStatistic: Creates a new, empty SufficientStatistic.
SufficientStatistic(double) - Constructor for class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.SufficientStatistic: Creates a new SufficientStatistic with the given value to initialize the variance.
SufficientStatistic<DataType,DistributionType> - Interface in gov.sandia.cognition.statistics: Sufficient statistics are the data which are sufficient to store all information to create an underlying parameter, such as a Distribution.
SufficientStatisticCovarianceInverse() - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatisticCovarianceInverse: Default constructor
SufficientStatisticCovarianceInverse(double) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.SufficientStatisticCovarianceInverse: Creates a new instance of SufficientStatisticCovarianceInverse
sum() - Method in class gov.sandia.cognition.math.matrix.AbstractVectorSpace
sum() - Method in class gov.sandia.cognition.math.matrix.custom.DenseVector
sum() - Method in class gov.sandia.cognition.math.matrix.custom.SparseVector
sum() - Method in class gov.sandia.cognition.math.matrix.DefaultInfiniteVector
sum() - Method in class gov.sandia.cognition.math.matrix.mtj.DenseVector
sum() - Method in class gov.sandia.cognition.math.matrix.mtj.SparseVector
sum() - Method in interface gov.sandia.cognition.math.matrix.VectorSpace: Computes the sum of the elements in the vector.
SumKernel<InputType> - Class in gov.sandia.cognition.learning.function.kernel: The SumKernel class implements a kernel that adds together the result of applying multiple kernels to the same pair of inputs.
SumKernel() - Constructor for class gov.sandia.cognition.learning.function.kernel.SumKernel: Creates a new instance of SumKernel.
SumKernel(Collection<? extends Kernel<? super InputType>>) - Constructor for class gov.sandia.cognition.learning.function.kernel.SumKernel: Creates a new instance of SumKernel with the given collection of kernels.
summaries - Variable in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment: The summaries of performance.
summarize(Collection<? extends TargetEstimatePair<? extends TargetType, ? extends TargetType>>) - Method in class gov.sandia.cognition.learning.function.cost.AbstractSupervisedCostFunction
summarize(Collection<? extends DataType>) - Method in class gov.sandia.cognition.learning.function.summarizer.MostFrequentSummarizer: Summarizes the given data by returning the most frequent value.
summarize(Collection<? extends TargetEstimatePair<? extends TargetType, ? extends EstimateType>>) - Method in class gov.sandia.cognition.learning.performance.AbstractSupervisedPerformanceEvaluator
summarize(Collection<? extends Pair<? extends Boolean, ? extends Boolean>>) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix.ActualPredictedPairSummarizer
summarize(Collection<? extends ConfusionMatrix<Boolean>>) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrix.CombineSummarizer
summarize(Collection<? extends DefaultBinaryConfusionMatrix>) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrixConfidenceInterval.Summary
summarize(Collection<? extends Pair<? extends CategoryType, ? extends CategoryType>>) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultConfusionMatrix.ActualPredictedPairSummarizer
summarize(Collection<? extends ConfusionMatrix<CategoryType>>) - Method in class gov.sandia.cognition.learning.performance.categorization.DefaultConfusionMatrix.CombineSummarizer
summarize(Collection<? extends Number>) - Method in class gov.sandia.cognition.math.NumberAverager: Returns the average (arithmetic mean) of a Collection of Numbers, or null if the collection of Numbers are null
summarize(Collection<? extends RingType>) - Method in class gov.sandia.cognition.math.RingAverager
summarize(Collection<? extends WeightedValue<? extends Number>>) - Method in class gov.sandia.cognition.math.WeightedNumberAverager
summarize(Collection<? extends WeightedValue<RingType>>) - Method in class gov.sandia.cognition.math.WeightedRingAverager
summarize(Collection<? extends Number>) - Method in class gov.sandia.cognition.statistics.method.StudentTConfidence.Summary
summarize(Collection<? extends DataType>) - Method in interface gov.sandia.cognition.util.Summarizer: Creates a summary of the given collection of data.
summarizer - Variable in class gov.sandia.cognition.learning.experiment.LearnerComparisonExperiment: The summarizer for summarizing the result of the performance evaluator from all the folds.
summarizer - Variable in class gov.sandia.cognition.learning.experiment.LearnerRepeatExperiment: The summarizer for summarizing the result of the performance evaluator from all the folds.
summarizer - Variable in class gov.sandia.cognition.learning.experiment.LearnerValidationExperiment: The summarizer for summarizing the result of the performance evaluator from all the folds.
summarizer - Variable in class gov.sandia.cognition.learning.experiment.OnlineLearnerValidationExperiment: The summarizer for summarizing the result of the performance evaluator from all the folds.
Summarizer<DataType,SummaryType> - Interface in gov.sandia.cognition.util: The Summarizer interface defines the functionality of an object that can take a collection of some data and return a summary of that data.
summary - Variable in class gov.sandia.cognition.learning.experiment.LearnerRepeatExperiment: The summary of the performance evaluations made at the end of the experiment.
summary - Variable in class gov.sandia.cognition.learning.experiment.LearnerValidationExperiment: The summary of the performance evaluations made at the end of the experiment.
summary - Variable in class gov.sandia.cognition.learning.experiment.OnlineLearnerValidationExperiment: The summary of the performance evaluations made at the end of the experiment.
Summary(double) - Constructor for class gov.sandia.cognition.learning.performance.categorization.DefaultBinaryConfusionMatrixConfidenceInterval.Summary: Creates a new Summarizer.
Summary(double) - Constructor for class gov.sandia.cognition.statistics.method.StudentTConfidence.Summary: Creates a new Summarizer.
sumOfColumns() - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
sumOfColumns() - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix
sumOfColumns() - Method in interface gov.sandia.cognition.math.matrix.Matrix: Returns a new vector containing the sum across the columns.
sumOfRows() - Method in class gov.sandia.cognition.math.matrix.AbstractMatrix
sumOfRows() - Method in class gov.sandia.cognition.math.matrix.custom.SparseMatrix
sumOfRows() - Method in interface gov.sandia.cognition.math.matrix.Matrix: Returns a new vector containing the sum across the rows.
SumProductBeliefPropagation<LabelType> - Class in gov.sandia.cognition.graph.inference: This class implements the sum-product belief propagation algorithm for arbitrary energy functions.
SumProductBeliefPropagation(int, double, int) - Constructor for class gov.sandia.cognition.graph.inference.SumProductBeliefPropagation: Creates a new BP solver with the specified settings.
SumProductBeliefPropagation(int) - Constructor for class gov.sandia.cognition.graph.inference.SumProductBeliefPropagation: Creates a new BP solver with the default settings excepting max number of iterations.
SumProductBeliefPropagation() - Constructor for class gov.sandia.cognition.graph.inference.SumProductBeliefPropagation: Creates a new BP solver with the default settings.
SumProductDirectedPropagation<LabelType> - Class in gov.sandia.cognition.graph.inference: This class implements a Bayes Net -- but only allowing for pairwise influence along edges.
SumProductDirectedPropagation(int, double, int) - Constructor for class gov.sandia.cognition.graph.inference.SumProductDirectedPropagation: Creates a new pairwise Bayes Net solver with the specified settings.
SumProductDirectedPropagation(int) - Constructor for class gov.sandia.cognition.graph.inference.SumProductDirectedPropagation: Creates a new pairwise Bayes Net solver with the default settings excepting max number of iterations.
SumProductDirectedPropagation() - Constructor for class gov.sandia.cognition.graph.inference.SumProductDirectedPropagation: Creates a new pairwise Bayes Net solver with the default settings.
SumProductInferencingAlgorithm<LabelType> - Class in gov.sandia.cognition.graph.inference: Base class for Sum-Product inferencing algorithms on graphs/energy functions
SumProductInferencingAlgorithm(int, double, int) - Constructor for class gov.sandia.cognition.graph.inference.SumProductInferencingAlgorithm: Creates a new solver with the specified settings.
SumProductInferencingAlgorithm(int) - Constructor for class gov.sandia.cognition.graph.inference.SumProductInferencingAlgorithm: Creates a new solver with the default settings excepting max number of iterations.
SumProductInferencingAlgorithm() - Constructor for class gov.sandia.cognition.graph.inference.SumProductInferencingAlgorithm: Creates a new solver with the default settings.
sumSquaredDifferences - Variable in class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.SufficientStatistic: This is the sum-squared differences
SumSquaredErrorCostFunction - Class in gov.sandia.cognition.learning.function.cost: This is the sum-squared error cost function
SumSquaredErrorCostFunction() - Constructor for class gov.sandia.cognition.learning.function.cost.SumSquaredErrorCostFunction: Creates a new instance of SumSquaredErrorCostFunction
SumSquaredErrorCostFunction(Collection<? extends InputOutputPair<? extends Vector, Vector>>) - Constructor for class gov.sandia.cognition.learning.function.cost.SumSquaredErrorCostFunction: Creates a new instance of MeanSquaredErrorCostFunction
SumSquaredErrorCostFunction.Cache - Class in gov.sandia.cognition.learning.function.cost: Caches often-used values for the Cost Function
SumSquaredErrorCostFunction.GradientPartialSSE - Class in gov.sandia.cognition.learning.function.cost: Partial result from the SSE gradient computation
sumWeights(Collection<? extends InputOutputPair<?, ?>>) - Static method in class gov.sandia.cognition.learning.data.DatasetUtil: Gets the sum of the weights of the weights of the elements of the dataset.
SupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>> - Interface in gov.sandia.cognition.learning.algorithm: Interface for a class that is a supervised learning algorithm that can be used both batch and incremental contexts.
SupervisedBatchLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>> - Interface in gov.sandia.cognition.learning.algorithm: The BatchSupervisedLearner interface is an extension of the BatchLearner interface that contains the typical generic definition conventions for a batch, supervised learning algorithm.
SupervisedCostFunction<InputType,TargetType> - Interface in gov.sandia.cognition.learning.function.cost: A type of CostFunction normally used in supervised-learning applications.
SupervisedIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>> - Interface in gov.sandia.cognition.learning.algorithm: Interface for supervised incremental learning algorithms.
SupervisedLearnerComparisonExperiment<InputType,OutputType,StatisticType,SummaryType> - Class in gov.sandia.cognition.learning.experiment: A comparison experiment for supervised learners.
SupervisedLearnerComparisonExperiment() - Constructor for class gov.sandia.cognition.learning.experiment.SupervisedLearnerComparisonExperiment: Creates a new instance of SupervisedLearnerComparisonExperiment.
SupervisedLearnerComparisonExperiment(ValidationFoldCreator<InputOutputPair<InputType, OutputType>, InputOutputPair<InputType, OutputType>>, PerformanceEvaluator<? super Evaluator<? super InputType, OutputType>, ? super Collection<? extends InputOutputPair<InputType, OutputType>>, ? extends StatisticType>, NullHypothesisEvaluator<Collection<? extends StatisticType>>, Summarizer<? super StatisticType, ? extends SummaryType>) - Constructor for class gov.sandia.cognition.learning.experiment.SupervisedLearnerComparisonExperiment: Creates a new instance of SupervisedLearnerComparisonExperiment.
SupervisedLearnerValidationExperiment<InputType,OutputType,StatisticType,SummaryType> - Class in gov.sandia.cognition.learning.experiment: The SupervisedLearnerValidationExperiment class extends the LearnerValidationExperiment class to provide a easy way to create a learner validation experiment for supervised learning.
SupervisedLearnerValidationExperiment() - Constructor for class gov.sandia.cognition.learning.experiment.SupervisedLearnerValidationExperiment: Creates a new instance of SupervisedLearnerValidationExperiment.
SupervisedLearnerValidationExperiment(ValidationFoldCreator<InputOutputPair<InputType, OutputType>, InputOutputPair<InputType, OutputType>>, PerformanceEvaluator<? super Evaluator<? super InputType, ? extends OutputType>, ? super Collection<? extends InputOutputPair<InputType, OutputType>>, ? extends StatisticType>, Summarizer<? super StatisticType, ? extends SummaryType>) - Constructor for class gov.sandia.cognition.learning.experiment.SupervisedLearnerValidationExperiment: Creates a new instance of SupervisedLearnerValidationExperiment.
SupervisedPerformanceEvaluator<InputType,TargetType,EstimateType,ResultType> - Interface in gov.sandia.cognition.learning.performance: The SupervisedPerformanceEvaluator interface extends the PerformanceEvaluator interface for performance evaluations of supervised machine learning algorithms where the target type is evaluated against the estimated type produced by the evaluator.
supportInserted - Variable in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation.Entry: Indicates if the support vector has been inserted into the map of support vectors or not.
supportsMap - Variable in class gov.sandia.cognition.learning.algorithm.svm.SuccessiveOverrelaxation: The mapping of weight objects to non-zero weighted examples (support vectors).
svdDecompose() - Method in class gov.sandia.cognition.math.matrix.custom.DenseMatrix: Leverages LAPACK to compute the Singular Value Decomposition (SVD) of this.
swap(int, int) - Method in class gov.sandia.cognition.collection.DoubleArrayList: Swaps the values stored at p1 and p2.
swap(int, int) - Method in class gov.sandia.cognition.collection.IntArrayList: Swaps the values stored at p1 and p2.
swap(int, int) - Method in class gov.sandia.cognition.graph.DenseMemoryGraph: Helper that swaps the values at i and j in the edge list (handles the fact that the edge list is 2 values in a row for each edge).
swap(int, int) - Method in class gov.sandia.cognition.graph.WeightedDenseMemoryGraph: Overrides the parent's version to ensure the weights swap at the same time the edges do
SynonymFilter - Class in gov.sandia.cognition.text.term.filter: A term filter that uses a mapping of synonyms to replace a word with its synonym.
SynonymFilter() - Constructor for class gov.sandia.cognition.text.term.filter.SynonymFilter: Creates a new, empty SynonymFilter.
SynonymFilter(Map<Term, Term>) - Constructor for class gov.sandia.cognition.text.term.filter.SynonymFilter: Creates a new SynonymFilter with the given synonyms.
synonyms - Variable in class gov.sandia.cognition.text.term.filter.SynonymFilter: The mapping of terms to the synonym to replace them with.

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