Serialized Form (Java Statistical Analysis Tool 0.0.8 API)

Package jsat.classifiers
- Class jsat.classifiers.BaseUpdateableClassifier extends Object implements Serializable
  
  serialVersionUID:
  
  3138493999362400767L
  - Serialized Fields
    - epochs
```
int epochs
```
- Class jsat.classifiers.CategoricalData extends Object implements Serializable
  
  serialVersionUID:
  
  5783467611963064930L
  - Serialized Fields
    - n
```
int n
```
    - catNames
```
List<E> catNames
```
    - categoryName
```
String categoryName
```
- Class jsat.classifiers.CategoricalResults extends Object implements Serializable
  - Serialized Fields
    - n
```
int n
```
    - probabilities
```
double[] probabilities
```
- Class jsat.classifiers.DataPoint extends Object implements Serializable
  
  serialVersionUID:
  
  -1363327591317639955L
  - Serialized Fields
    - weight
```
double weight
```
    - numericalValues
```
Vec numericalValues
```
    - categoricalValues
```
int[] categoricalValues
```
    - categoricalData
```
CategoricalData[] categoricalData
```
- Class jsat.classifiers.DataPointPair extends Object implements Serializable
  
  serialVersionUID:
  
  5091308998873225566L
  - Serialized Fields
    - dataPoint
```
DataPoint dataPoint
```
    - pair
```
Object pair
```
- Class jsat.classifiers.DDAG extends OneVSOne implements Serializable
  
  serialVersionUID:
  
  -9109002614319657144L
- Class jsat.classifiers.MajorityVote extends Object implements Serializable
  
  serialVersionUID:
  
  7945429768861275845L
  - Serialized Fields
    - voters
```
Classifier[] voters
```
- Class jsat.classifiers.MultinomialLogisticRegression extends Object implements Serializable
  
  serialVersionUID:
  
  -9168502043850569017L
  - Serialized Fields
    - classCoefficents
```
Vec[] classCoefficents
```
- Class jsat.classifiers.OneVSAll extends Object implements Serializable
  
  serialVersionUID:
  
  -326668337438092217L
  - Serialized Fields
    - oneVsAlls
```
Classifier[] oneVsAlls
```
    - baseClassifier
```
Classifier baseClassifier
```
    - predicting
```
CategoricalData predicting
```
    - concurrentTraining
```
boolean concurrentTraining
```
    - useScoreIfAvailable
```
boolean useScoreIfAvailable
```
- Class jsat.classifiers.OneVSOne extends Object implements Serializable
  
  serialVersionUID:
  
  733202830281869416L
  - Serialized Fields
    - baseClassifier
```
Classifier baseClassifier
```
      Main binary classifier
    - oneVone
```
Classifier[][] oneVone
```
      Uper-diagonal matrix of classifiers sans the first index since a classifier vs itself is useless. First index is the first dimensions is the first class, 2nd index + the value of the first + 1 is the opponent class
    - concurrentTrain
```
boolean concurrentTrain
```
    - predicting
```
CategoricalData predicting
```
- Class jsat.classifiers.PriorClassifier extends Object implements Serializable
  
  serialVersionUID:
  
  7763388716880766538L
  - Serialized Fields
    - cr
```
CategoricalResults cr
```
- Class jsat.classifiers.RegressorToClassifier extends Object implements Serializable
  
  serialVersionUID:
  
  -2607433019826385335L
  - Serialized Fields
    - regressor
```
Regressor regressor
```
- Class jsat.classifiers.Rocchio extends Object implements Serializable
  
  serialVersionUID:
  
  889524967453326517L
  - Serialized Fields
    - rocVecs
```
List<E> rocVecs
```
    - dm
```
DistanceMetric dm
```
    - dsdm
```
DenseSparseMetric dsdm
```
    - summaryConsts
```
double[] summaryConsts
```
Package jsat.classifiers.bayesian
- Class jsat.classifiers.bayesian.AODE extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  8386506277969540732L
  - Serialized Fields
    - predicting
```
CategoricalData predicting
```
    - odes
```
ODE[] odes
```
    - m
```
double m
```
      The minimum value to use a probability
- Class jsat.classifiers.bayesian.BestClassDistribution extends Object implements Serializable
  
  serialVersionUID:
  
  -1746145372146154228L
  - Serialized Fields
    - baseDist
```
MultivariateDistribution baseDist
```
    - dists
```
List<E> dists
```
    - priors
```
double[] priors
```
      The prior probabilities of each class
    - usePriors
```
boolean usePriors
```
      Controls whether or no the prior probability will be used when computing probabilities
- Class jsat.classifiers.bayesian.ConditionalProbabilityTable extends Object implements Serializable
  
  serialVersionUID:
  
  -287709075031023626L
  - Serialized Fields
    - predicting
```
CategoricalData predicting
```
      The predicting target class
    - countArray
```
double[] countArray
```
      The flat array that stores the n dimensional table
    - valid
```
Map<K,V> valid
```
      Set subset of the variables we will be using
    - realIndexToCatIndex
```
int[] realIndexToCatIndex
```
      Maps the index order for the CPT to the index values from the training data set
    - catIndexToRealIndex
```
int[] catIndexToRealIndex
```
      Maps the index values of the training set to the index values used by the CPT. A value of '-1' indicates that the feature is not in the CPT
    - dimSize
```
int[] dimSize
```
      the dimension size for each category, ie: the number of options for each category
    - predictingIndex
```
int predictingIndex
```
      The index of the predicting attribute, which is the number of categorical features in the training data set.
- Class jsat.classifiers.bayesian.MultinomialNaiveBayes extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  -469977945722725478L
  - Serialized Fields
    - apriori
```
double[][][] apriori
```
    - wordCounts
```
double[][] wordCounts
```
    - totalWords
```
double[] totalWords
```
    - priorSum
```
double priorSum
```
    - priors
```
double[] priors
```
    - smoothing
```
double smoothing
```
      Smoothing correction. Added in classification instead of addition
    - finalizeAfterTraining
```
boolean finalizeAfterTraining
```
    - finalized
```
boolean finalized
```
      No more training
- Class jsat.classifiers.bayesian.MultivariateNormals extends BestClassDistribution implements Serializable
  
  serialVersionUID:
  
  5977979334930517655L
- Class jsat.classifiers.bayesian.NaiveBayes extends Object implements Serializable
  
  serialVersionUID:
  
  -2437775653277531182L
  - Serialized Fields
    - apriori
```
double[][][] apriori
```
    - distributions
```
ContinuousDistribution[][] distributions
```
    - numericalHandling
```
NaiveBayes.NumericalHandeling numericalHandling
```
    - priors
```
double[] priors
```
    - sparceInput
```
boolean sparceInput
```
      Handles how vectors are handled. If true, it is assumed vectors are sparce - and zero values will be ignored when training and classifying.
- Class jsat.classifiers.bayesian.NaiveBayesUpdateable extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  1835073945715343486L
  - Serialized Fields
    - apriori
```
double[][][] apriori
```
      Counts for each option
    - valueStats
```
OnLineStatistics[][] valueStats
```
      Online stats about the variable values
    - priorSum
```
double priorSum
```
    - priors
```
double[] priors
```
    - sparseInput
```
boolean sparseInput
```
      Handles how vectors are handled. If true, it is assumed vectors are sparce - and zero values will be ignored when training and classifying.
- Class jsat.classifiers.bayesian.ODE extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  -7732070257669428977L
  - Serialized Fields
    - dependent
```
int dependent
```
      The attribute we will be dependent on
    - predTargets
```
int predTargets
```
      The number of possible values in the target class
    - depTargets
```
int depTargets
```
      The number of possible values for the dependent variable
    - counts
```
double[][][][] counts
```
      First index is the number of target values
      2nd index is the number of values for the dependent variable
      3rd is the number of categorical variables, including the dependent one
      4th is the count for the variable value
    - priors
```
double[][] priors
```
      The prior probability of each combination of target and dependent variable
    - priorSum
```
double priorSum
```
Package jsat.classifiers.bayesian.graphicalmodel
- Class jsat.classifiers.bayesian.graphicalmodel.DiscreteBayesNetwork extends Object implements Serializable
  
  serialVersionUID:
  
  2980734594356260141L
  - Serialized Fields
    - dag
```
DirectedGraph<N> dag
```
      The directed Graph that represents this BN
    - cpts
```
Map<K,V> cpts
```
      The Conditional probability tables for each variable
    - predicting
```
CategoricalData predicting
```
      The class we are predicting
    - priors
```
double[] priors
```
      The prior probabilities of each class value
    - usePriors
```
boolean usePriors
```
- Class jsat.classifiers.bayesian.graphicalmodel.K2NetworkLearner extends DiscreteBayesNetwork implements Serializable
  
  serialVersionUID:
  
  -9681177007308829L
  - Serialized Fields
    - ri
```
int[] ri
```
      list of all possible values of the attribute xi
    - maxParents
```
int maxParents
```
Package jsat.classifiers.boosting
- Class jsat.classifiers.boosting.AdaBoostM1 extends Object implements Serializable
  
  serialVersionUID:
  
  4205232097748332861L
  - Serialized Fields
    - weakLearner
```
Classifier weakLearner
```
    - maxIterations
```
int maxIterations
```
    - hypoths
```
List<E> hypoths
```
      The list of weak hypothesis
    - hypWeights
```
List<E> hypWeights
```
      The weights for each weak learner
    - predicting
```
CategoricalData predicting
```
- Class jsat.classifiers.boosting.AdaBoostM1PL extends AdaBoostM1 implements Serializable
  
  serialVersionUID:
  
  1027211688101553766L
- Class jsat.classifiers.boosting.ArcX4 extends Object implements Serializable
  
  serialVersionUID:
  
  3831448932874147550L
  - Serialized Fields
    - weakLearner
```
Classifier weakLearner
```
    - iterations
```
int iterations
```
    - coef
```
double coef
```
    - expo
```
double expo
```
    - predicing
```
CategoricalData predicing
```
    - hypoths
```
Classifier[] hypoths
```
- Class jsat.classifiers.boosting.Bagging extends Object implements Serializable
  
  serialVersionUID:
  
  -6566453570170428838L
  - Serialized Fields
    - baseClassifier
```
Classifier baseClassifier
```
    - baseRegressor
```
Regressor baseRegressor
```
    - predicting
```
CategoricalData predicting
```
    - extraSamples
```
int extraSamples
```
    - rounds
```
int rounds
```
    - simultaniousTraining
```
boolean simultaniousTraining
```
    - random
```
Random random
```
    - learners
```
List<E> learners
```
- Class jsat.classifiers.boosting.EmphasisBoost extends Object implements Serializable
  
  serialVersionUID:
  
  -6372897830449685891L
  - Serialized Fields
    - weakLearner
```
Classifier weakLearner
```
    - maxIterations
```
int maxIterations
```
    - hypoths
```
List<E> hypoths
```
      The list of weak hypothesis
    - hypWeights
```
List<E> hypWeights
```
      The weights for each weak learner
    - predicting
```
CategoricalData predicting
```
    - lambda
```
double lambda
```
- Class jsat.classifiers.boosting.LogitBoost extends Object implements Serializable
  
  serialVersionUID:
  
  1621062168467402062L
  - Serialized Fields
    - fScaleConstant
```
double fScaleConstant
```
      The constant factor that the sum of regressors is scaled by.
    - baseLearners
```
List<E> baseLearners
```
      Weak learners
    - baseLearner
```
Regressor baseLearner
```
      Weak learner to use, 'the oracle'
    - maxIterations
```
int maxIterations
```
    - zMax
```
double zMax
```
      Constant for stability and controls the maximum penalty
- Class jsat.classifiers.boosting.LogitBoostPL extends LogitBoost implements Serializable
  
  serialVersionUID:
  
  -7932049860430324903L
- Class jsat.classifiers.boosting.ModestAdaBoost extends Object implements Serializable
  
  serialVersionUID:
  
  8223388561185098909L
  - Serialized Fields
    - weakLearner
```
Classifier weakLearner
```
    - maxIterations
```
int maxIterations
```
    - hypoths
```
List<E> hypoths
```
      The list of weak hypothesis
    - hypWeights
```
List<E> hypWeights
```
      The weights for each weak learner
    - predicting
```
CategoricalData predicting
```
- Class jsat.classifiers.boosting.SAMME extends Object implements Serializable
  
  serialVersionUID:
  
  -3584203799253810599L
  - Serialized Fields
    - weakLearner
```
Classifier weakLearner
```
    - maxIterations
```
int maxIterations
```
    - hypoths
```
List<E> hypoths
```
      The list of weak hypothesis
    - hypWeights
```
List<E> hypWeights
```
      The weights for each weak learner
    - predicting
```
CategoricalData predicting
```
- Class jsat.classifiers.boosting.Stacking extends Object implements Serializable
  
  serialVersionUID:
  
  -6173323872903232074L
  - Serialized Fields
    - folds
```
int folds
```
    - weightsPerModel
```
int weightsPerModel
```
      The number of weights needed per model
    - aggregatingClassifier
```
Classifier aggregatingClassifier
```
    - baseClassifiers
```
List<E> baseClassifiers
```
    - aggregatingRegressor
```
Regressor aggregatingRegressor
```
    - baseRegressors
```
List<E> baseRegressors
```
- Class jsat.classifiers.boosting.UpdatableStacking extends Object implements Serializable
  
  serialVersionUID:
  
  -5111303510263114862L
  - Serialized Fields
    - weightsPerModel
```
int weightsPerModel
```
      The number of weights needed per model
    - aggregatingClassifier
```
UpdateableClassifier aggregatingClassifier
```
    - baseClassifiers
```
List<E> baseClassifiers
```
    - aggregatingRegressor
```
UpdateableRegressor aggregatingRegressor
```
    - baseRegressors
```
List<E> baseRegressors
```
- Class jsat.classifiers.boosting.Wagging extends Object implements Serializable
  
  serialVersionUID:
  
  4999034730848794619L
  - Serialized Fields
    - dist
```
ContinuousDistribution dist
```
    - iterations
```
int iterations
```
    - weakL
```
Classifier weakL
```
    - weakR
```
Regressor weakR
```
    - predicting
```
CategoricalData predicting
```
    - hypotsL
```
Classifier[] hypotsL
```
    - hypotsR
```
Regressor[] hypotsR
```
- Class jsat.classifiers.boosting.WaggingNormal extends Wagging implements Serializable
  
  serialVersionUID:
  
  -4149453672311329863L
Package jsat.classifiers.calibration
- Class jsat.classifiers.calibration.BinaryCalibration extends Object implements Serializable
  
  serialVersionUID:
  
  2356311701854978890L
  - Serialized Fields
    - base
```
BinaryScoreClassifier base
```
      The base classifier to train and calibrate the outputs of
    - folds
```
int folds
```
      The number of CV folds
    - holdOut
```
double holdOut
```
      The proportion of the data set to hold out for calibration
    - mode
```
BinaryCalibration.CalibrationMode mode
```
      The calibration mode to use
- Class jsat.classifiers.calibration.IsotonicCalibration extends BinaryCalibration implements Serializable
  
  serialVersionUID:
  
  -1295979238755262335L
  - Serialized Fields
    - outputs
```
double[] outputs
```
    - scores
```
double[] scores
```
- Class jsat.classifiers.calibration.PlattCalibration extends BinaryCalibration implements Serializable
  
  serialVersionUID:
  
  1099230240231262536L
  - Serialized Fields
    - A
```
double A
```
    - B
```
double B
```
    - maxIter
```
double maxIter
```
    - minStep
```
double minStep
```
    - sigma
```
double sigma
```
Package jsat.classifiers.evaluation
- Class jsat.classifiers.evaluation.Accuracy extends Object implements Serializable
  
  serialVersionUID:
  
  397690693205481128L
  - Serialized Fields
    - correct
```
double correct
```
    - total
```
double total
```
- Class jsat.classifiers.evaluation.AUC extends Object implements Serializable
  
  serialVersionUID:
  
  6882234590870560718L
  - Serialized Fields
    - scores
```
List<E> scores
```
- Class jsat.classifiers.evaluation.F1Score extends SimpleBinaryClassMetric implements Serializable
  
  serialVersionUID:
  
  -6192302685766444921L
- Class jsat.classifiers.evaluation.FbetaScore extends SimpleBinaryClassMetric implements Serializable
  
  serialVersionUID:
  
  -7530404462591303694L
  - Serialized Fields
    - beta
```
double beta
```
- Class jsat.classifiers.evaluation.Kappa extends Object implements Serializable
  
  serialVersionUID:
  
  -1684937057234736715L
  - Serialized Fields
    - errorMatrix
```
Matrix errorMatrix
```
- Class jsat.classifiers.evaluation.LogLoss extends Object implements Serializable
  
  serialVersionUID:
  
  3123851772991293430L
  - Serialized Fields
    - loss
```
double loss
```
    - weightSum
```
double weightSum
```
    - nudge
```
double nudge
```
- Class jsat.classifiers.evaluation.MatthewsCorrelationCoefficient extends SimpleBinaryClassMetric implements Serializable
  
  serialVersionUID:
  
  7102318546460007008L
- Class jsat.classifiers.evaluation.Precision extends SimpleBinaryClassMetric implements Serializable
  
  serialVersionUID:
  
  7046590252900909918L
- Class jsat.classifiers.evaluation.Recall extends SimpleBinaryClassMetric implements Serializable
  
  serialVersionUID:
  
  4832185425203972017L
- Class jsat.classifiers.evaluation.SimpleBinaryClassMetric extends Object implements Serializable
  
  serialVersionUID:
  
  -84479984342547212L
  - Serialized Fields
    - tp
```
double tp
```
      true positives
    - tn
```
double tn
```
      true negatives
    - fp
```
double fp
```
      false positives
    - fn
```
double fn
```
      false negatives
Package jsat.classifiers.knn
- Class jsat.classifiers.knn.DANN extends Object implements Serializable
  
  serialVersionUID:
  
  -272865942127664672L
  - Serialized Fields
    - kn
```
int kn
```
    - k
```
int k
```
    - maxIterations
```
int maxIterations
```
    - eps
```
double eps
```
    - vcf
```
VectorCollectionFactory<V extends Vec> vcf
```
    - predicting
```
CategoricalData predicting
```
    - vc
```
VectorCollection<V extends Vec> vc
```
      Vectors paired with their index in the original data set
    - vecList
```
List<E> vecList
```
- Class jsat.classifiers.knn.LWL extends Object implements Serializable
  
  serialVersionUID:
  
  6942465758987345997L
  - Serialized Fields
    - predicting
```
CategoricalData predicting
```
    - classifier
```
Classifier classifier
```
    - regressor
```
Regressor regressor
```
    - k
```
int k
```
    - dm
```
DistanceMetric dm
```
    - kf
```
KernelFunction kf
```
    - vcf
```
VectorCollectionFactory<V extends Vec> vcf
```
    - vc
```
VectorCollection<V extends Vec> vc
```
- Class jsat.classifiers.knn.NearestNeighbour extends Object implements Serializable
  
  serialVersionUID:
  
  4239569189624285932L
  - Serialized Fields
    - k
```
int k
```
    - weighted
```
boolean weighted
```
    - distanceMetric
```
DistanceMetric distanceMetric
```
    - predicting
```
CategoricalData predicting
```
    - vcf
```
VectorCollectionFactory<V extends Vec> vcf
```
    - vecCollection
```
VectorCollection<V extends Vec> vecCollection
```
    - mode
```
jsat.classifiers.knn.NearestNeighbour.Mode mode
```
      If we are in classification mode, the double is an integer that indicates class.
Package jsat.classifiers.linear
- Class jsat.classifiers.linear.ALMA2 extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  -4347891273721908507L
  - Serialized Fields
    - w
```
Vec w
```
    - alpha
```
double alpha
```
    - B
```
double B
```
    - C
```
double C
```
    - k
```
int k
```
    - useBias
```
boolean useBias
```
    - bias
```
double bias
```
- Class jsat.classifiers.linear.AROW extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  443803827811508204L
  - Serialized Fields
    - w
```
Vec w
```
    - sigmaM
```
Matrix sigmaM
```
      Full covariance matrix
    - sigmaV
```
Vec sigmaV
```
      Diagonal only covariance matrix
    - diagonalOnly
```
boolean diagonalOnly
```
    - r
```
double r
```
    - Sigma_xt
```
Vec Sigma_xt
```
      Temp vector used to store Sigma * x_t. Make sure the vector is zeroed out before returning from update
- Class jsat.classifiers.linear.BBR extends Object implements Serializable
  
  serialVersionUID:
  
  8297213093357011082L
  - Serialized Fields
    - w
```
Vec w
```
    - maxIterations
```
int maxIterations
```
    - regularization
```
double regularization
```
    - autoSetRegularization
```
boolean autoSetRegularization
```
    - bias
```
double bias
```
    - useBias
```
boolean useBias
```
    - tolerance
```
double tolerance
```
    - prior
```
BBR.Prior prior
```
- Class jsat.classifiers.linear.LinearBatch extends Object implements Serializable
  
  serialVersionUID:
  
  -446156124954287580L
  - Serialized Fields
    - ws
```
Vec[] ws
```
      Weight vectors
    - bs
```
double[] bs
```
      bias terms for each weight vector
    - loss
```
LossFunc loss
```
    - lambda0
```
double lambda0
```
    - optimizer
```
Optimizer2 optimizer
```
    - tolerance
```
double tolerance
```
    - useBiasTerm
```
boolean useBiasTerm
```
- Class jsat.classifiers.linear.LinearBatch.LossFunction extends Object implements Serializable
  
  serialVersionUID:
  
  -576682206943283356L
  - Serialized Fields
    - D
```
DataSet<Type extends DataSet> D
```
    - loss
```
LossFunc loss
```
- Class jsat.classifiers.linear.LinearBatch.LossMCFunction extends Object implements Serializable
  
  serialVersionUID:
  
  -861700500356609563L
  - Serialized Fields
    - D
```
ClassificationDataSet D
```
    - loss
```
LossMC loss
```
- Class jsat.classifiers.linear.LinearL1SCD extends StochasticSTLinearL1 implements Serializable
  
  serialVersionUID:
  
  3135562347568407186L
- Class jsat.classifiers.linear.LinearSGD extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  -59695592724956535L
  - Serialized Fields
    - loss
```
LossFunc loss
```
    - gradientUpdater
```
GradientUpdater gradientUpdater
```
    - eta
```
double eta
```
    - decay
```
DecayRate decay
```
    - ws
```
Vec[] ws
```
    - gus
```
GradientUpdater[] gus
```
    - bs
```
double[] bs
```
    - time
```
int time
```
    - lambda0
```
double lambda0
```
    - lambda1
```
double lambda1
```
    - l1U
```
double l1U
```
    - l1Q
```
double[][] l1Q
```
    - useBias
```
boolean useBias
```
- Class jsat.classifiers.linear.LogisticRegressionDCD extends Object implements Serializable
  
  serialVersionUID:
  
  -5813704270903243462L
  - Serialized Fields
    - w
```
Vec w
```
    - bias
```
double bias
```
    - useBias
```
boolean useBias
```
    - C
```
double C
```
    - maxIterations
```
int maxIterations
```
- Class jsat.classifiers.linear.NewGLMNET extends Object implements Serializable
  
  serialVersionUID:
  
  4133368677783573518L
  - Serialized Fields
    - w
```
Vec w
```
      Weight vector
    - b
```
double b
```
      Bias term
    - beta
```
double beta
```
    - v
```
double v
```
    - gamma
```
double gamma
```
    - sigma
```
double sigma
```
    - C
```
double C
```
    - alpha
```
double alpha
```
    - maxOuterIters
```
int maxOuterIters
```
    - e_out
```
double e_out
```
    - useBias
```
boolean useBias
```
    - maxLineSearchSteps
```
int maxLineSearchSteps
```
      The maximum allowed line-search steps
- Class jsat.classifiers.linear.NHERD extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  -1186002893766449917L
  - Serialized Fields
    - w
```
Vec w
```
    - sigmaM
```
Matrix sigmaM
```
      Full covariance matrix
    - sigmaV
```
Vec sigmaV
```
      Diagonal only covariance matrix
    - covMode
```
NHERD.CovMode covMode
```
    - C
```
double C
```
    - Sigma_xt
```
Vec Sigma_xt
```
      Temp vector used to store Sigma * x_t
- Class jsat.classifiers.linear.PassiveAggressive extends Object implements Serializable
  
  serialVersionUID:
  
  -7130964391528405832L
  - Serialized Fields
    - epochs
```
int epochs
```
    - C
```
double C
```
    - eps
```
double eps
```
    - w
```
Vec w
```
    - mode
```
PassiveAggressive.Mode mode
```
- Class jsat.classifiers.linear.ROMMA extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  8163937542627337711L
  - Serialized Fields
    - useBias
```
boolean useBias
```
    - aggressive
```
boolean aggressive
```
    - w
```
Vec w
```
    - bias
```
double bias
```
- Class jsat.classifiers.linear.SCD extends Object implements Serializable
  
  serialVersionUID:
  
  3576901723216525618L
  - Serialized Fields
    - w
```
Vec w
```
    - loss
```
LossFunc loss
```
    - reg
```
double reg
```
    - iterations
```
int iterations
```
- Class jsat.classifiers.linear.SCW extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  -6721377074407660742L
  - Serialized Fields
    - C
```
double C
```
    - eta
```
double eta
```
    - phi
```
double phi
```
    - phiSqrd
```
double phiSqrd
```
    - zeta
```
double zeta
```
    - psi
```
double psi
```
    - mode
```
SCW.Mode mode
```
    - w
```
Vec w
```
    - sigmaM
```
Matrix sigmaM
```
      Full covariance matrix
    - sigmaV
```
Vec sigmaV
```
      Diagonal only covariance matrix
    - Sigma_xt
```
Vec Sigma_xt
```
      Temp vector used to store Sigma * x_t. Make sure the vector is zeroed out before returning from update
    - diagonalOnly
```
boolean diagonalOnly
```
- Class jsat.classifiers.linear.SMIDAS extends StochasticSTLinearL1 implements Serializable
  
  serialVersionUID:
  
  -4888083541600164597L
  - Serialized Fields
    - eta
```
double eta
```
- Class jsat.classifiers.linear.SPA extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  3613279663279244169L
  - Serialized Fields
    - w
```
Vec[] w
```
    - bias
```
double[] bias
```
    - C
```
double C
```
    - useBias
```
boolean useBias
```
    - mode
```
PassiveAggressive.Mode mode
```
    - loss
```
double[] loss
```
    - it
```
IndexTable it
```
- Class jsat.classifiers.linear.STGD extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  5753298014967370769L
  - Serialized Fields
    - w
```
Vec w
```
    - K
```
int K
```
    - learningRate
```
double learningRate
```
    - threshold
```
double threshold
```
    - gravity
```
double gravity
```
    - time
```
int time
```
    - t
```
int[] t
```
- Class jsat.classifiers.linear.StochasticMultinomialLogisticRegression extends Object implements Serializable
  
  serialVersionUID:
  
  -492707881682847556L
  - Serialized Fields
    - epochs
```
int epochs
```
    - clipping
```
boolean clipping
```
    - regularization
```
double regularization
```
    - tolerance
```
double tolerance
```
    - initialLearningRate
```
double initialLearningRate
```
    - alpha
```
double alpha
```
    - learningRateDecay
```
DecayRate learningRateDecay
```
    - prior
```
StochasticMultinomialLogisticRegression.Prior prior
```
    - standardized
```
boolean standardized
```
    - useBias
```
boolean useBias
```
    - miniBatchSize
```
int miniBatchSize
```
    - B
```
Vec[] B
```
    - biases
```
double[] biases
```
- Class jsat.classifiers.linear.StochasticSTLinearL1 extends Object implements Serializable
  
  serialVersionUID:
  
  -6761456665014802608L
  - Serialized Fields
    - epochs
```
int epochs
```
      The number of training iterations
    - lambda
```
double lambda
```
      The regularization penalty
    - loss
```
StochasticSTLinearL1.Loss loss
```
      The loss function to use
    - w
```
Vec w
```
      The final weight vector
    - bias
```
double bias
```
      The bias term to add
    - obvMin
```
double[] obvMin
```
      The minimum observed value for each feature
    - obvMax
```
double[] obvMax
```
      The maximum observed value for each feature
    - reScale
```
boolean reScale
```
      Whether or not to perform feature rescaling
    - minScaled
```
double minScaled
```
      The scaled minimum
    - maxScaled
```
double maxScaled
```
      The scaled maximum
Package jsat.classifiers.linear.kernelized
- Class jsat.classifiers.linear.kernelized.ALMA2K extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  7247320234799227009L
  - Serialized Fields
    - alpha
```
double alpha
```
    - B
```
double B
```
    - C
```
double C
```
    - k
```
int k
```
    - curRounds
```
int curRounds
```
    - K
```
KernelTrick K
```
    - supports
```
List<E> supports
```
    - signedEtas
```
List<E> signedEtas
```
    - associatedScores
```
List<E> associatedScores
```
    - normalizers
```
List<E> normalizers
```
    - rounds
```
List<E> rounds
```
    - averaged
```
boolean averaged
```
- Class jsat.classifiers.linear.kernelized.BOGD extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  -3547832514098781996L
  - Serialized Fields
    - k
```
KernelTrick k
```
    - budget
```
int budget
```
    - eta
```
double eta
```
    - reg
```
double reg
```
    - maxCoeff
```
double maxCoeff
```
    - lossC
```
LossC lossC
```
    - uniformSampling
```
boolean uniformSampling
```
    - rand
```
Random rand
```
    - vecs
```
List<E> vecs
```
    - selfK
```
List<E> selfK
```
      Stores the sqrt of each support vector's kernel product with itself
    - alphas
```
DoubleList alphas
```
    - accelCache
```
List<E> accelCache
```
    - dist
```
double[] dist
```
      Cache of values used for BOGD++ sampling
- Class jsat.classifiers.linear.kernelized.CSKLR extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  2325605193408720811L
  - Serialized Fields
    - eta
```
double eta
```
    - alpha
```
DoubleList alpha
```
    - vecs
```
List<E> vecs
```
    - curNorm
```
double curNorm
```
    - k
```
KernelTrick k
```
    - R
```
double R
```
    - rand
```
Random rand
```
    - mode
```
CSKLR.UpdateMode mode
```
    - gamma
```
double gamma
```
    - accelCache
```
List<E> accelCache
```
- Class jsat.classifiers.linear.kernelized.CSKLRBatch extends SupportVectorLearner implements Serializable
  
  serialVersionUID:
  
  -2305532659182911285L
  - Serialized Fields
    - eta
```
double eta
```
    - curNorm
```
double curNorm
```
    - R
```
double R
```
    - T
```
int T
```
    - mode
```
CSKLR.UpdateMode mode
```
    - gamma
```
double gamma
```
    - epochs
```
int epochs
```
- Class jsat.classifiers.linear.kernelized.DUOL extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  -4751569462573287056L
  - Serialized Fields
    - k
```
KernelTrick k
```
      Kernel trick to use
    - S
```
List<E> S
```
      Set of support vectors
    - f_s
```
List<E> f_s
```
      Cached outputs of the current decision function on each support vector
    - alphas
```
List<E> alphas
```
      Signed weights for each support vector. Original paper uses the notation of gamma in the original paper, but this is weird and easily confused with the sign values y_i. The sign class values can be obtained from the signed alphas using Math.signum(double)
    - accelCache
```
List<E> accelCache
```
    - kTmp
```
DoubleList kTmp
```
      Stores the values of k(x_i, y) for reuse when observing a new example
    - rho
```
double rho
```
    - C
```
double C
```
- Class jsat.classifiers.linear.kernelized.Forgetron extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  -2631315082407427077L
  - Serialized Fields
    - K
```
KernelTrick K
```
    - I
```
Vec[] I
```
    - s
```
double[] s
```
      Stores the label times the weight. Getting the true weight is an abs operation. Getting the true label is a signum operation.
    - size
```
int size
```
    - curPos
```
int curPos
```
      Will always point to current insert position. Either empty, or the last value ever inserted
    - budget
```
int budget
```
    - U
```
double U
```
    - Bconst
```
double Bconst
```
    - Q
```
double Q
```
    - M
```
double M
```
    - selfTuned
```
boolean selfTuned
```
- Class jsat.classifiers.linear.kernelized.KernelSGD extends Object implements Serializable
  
  serialVersionUID:
  
  -4956596506787859023L
  - Serialized Fields
    - loss
```
LossFunc loss
```
    - kernel
```
KernelTrick kernel
```
    - lambda
```
double lambda
```
    - eta
```
double eta
```
    - budgetStrategy
```
KernelPoint.BudgetStrategy budgetStrategy
```
    - budgetSize
```
int budgetSize
```
    - errorTolerance
```
double errorTolerance
```
    - time
```
int time
```
    - kpoint
```
KernelPoint kpoint
```
    - kpoints
```
KernelPoints kpoints
```
    - epochs
```
int epochs
```
- Class jsat.classifiers.linear.kernelized.OSKL extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  4207594016856230134L
  - Serialized Fields
    - k
```
KernelTrick k
```
    - eta
```
double eta
```
    - R
```
double R
```
    - G
```
double G
```
    - curSqrdNorm
```
double curSqrdNorm
```
    - lossC
```
LossC lossC
```
    - useAverageModel
```
boolean useAverageModel
```
    - t
```
int t
```
    - last_t
```
int last_t
```
      Last time alphaAverage was updated
    - burnIn
```
int burnIn
```
    - alphaAveraged
```
DoubleList alphaAveraged
```
      Store the average of the weights over time
    - vecs
```
List<E> vecs
```
    - alphas
```
DoubleList alphas
```
    - inputKEvals
```
DoubleList inputKEvals
```
    - accelCache
```
List<E> accelCache
```
    - rand
```
Random rand
```
- Class jsat.classifiers.linear.kernelized.Projectron extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  -4025799790045954359L
  - Serialized Fields
    - k
```
KernelTrick k
```
    - eta
```
double eta
```
    - alpha
```
DoubleList alpha
```
      Marked as "d" in the original papers
    - S
```
List<E> S
```
    - cacheAccel
```
List<E> cacheAccel
```
    - InvK
```
Matrix InvK
```
    - InvKExpanded
```
Matrix InvKExpanded
```
    - k_raw
```
double[] k_raw
```
    - useMarginUpdates
```
boolean useMarginUpdates
```
Package jsat.classifiers.neuralnetwork
- Class jsat.classifiers.neuralnetwork.BackPropagationNet extends Object implements Serializable
  
  serialVersionUID:
  
  335438198218313862L
  - Serialized Fields
    - inputSize
```
int inputSize
```
    - outputSize
```
int outputSize
```
    - f
```
BackPropagationNet.ActivationFunction f
```
    - learningRateDecay
```
DecayRate learningRateDecay
```
    - momentum
```
double momentum
```
    - weightDecay
```
double weightDecay
```
    - epochs
```
int epochs
```
    - initialLearningRate
```
double initialLearningRate
```
    - weightInitialization
```
BackPropagationNet.WeightInitialization weightInitialization
```
    - targetBump
```
double targetBump
```
    - batchSize
```
int batchSize
```
    - npl
```
int[] npl
```
      Length of the array determines how many layers of hidden units. Value at each index determines how many neurons are in each hidden layer.
    - Ws
```
List<E> Ws
```
      Matrix of weights for each hidden layer and output layer
    - bs
```
List<E> bs
```
      Bias terms corresponding to each layer
    - targetMax
```
double targetMax
```
      Target min and max and scaling multiplier for regression problems to make the target into a range that the activation function can reach
    - targetMin
```
double targetMin
```
      Target min and max and scaling multiplier for regression problems to make the target into a range that the activation function can reach
    - targetMultiplier
```
double targetMultiplier
```
      Target min and max and scaling multiplier for regression problems to make the target into a range that the activation function can reach
- Class jsat.classifiers.neuralnetwork.BackPropagationNet.ActivationFunction extends Object implements Serializable
  
  serialVersionUID:
  
  8002040194215453918L
- Class jsat.classifiers.neuralnetwork.DReDNetSimple extends Object implements Serializable
  
  serialVersionUID:
  
  -342281027279571332L
  - Serialized Fields
    - network
```
SGDNetworkTrainer network
```
    - hiddenSizes
```
int[] hiddenSizes
```
    - batchSize
```
int batchSize
```
    - epochs
```
int epochs
```
- Class jsat.classifiers.neuralnetwork.LVQ extends Object implements Serializable
  
  serialVersionUID:
  
  -3911765006048793222L
  - Serialized Fields
    - learningDecay
```
DecayRate learningDecay
```
    - iterations
```
int iterations
```
    - learningRate
```
double learningRate
```
    - dm
```
DistanceMetric dm
```
      The distance metric to use
    - lvqVersion
```
LVQ.LVQVersion lvqVersion
```
    - eps
```
double eps
```
    - mScale
```
double mScale
```
    - stoppingDist
```
double stoppingDist
```
    - representativesPerClass
```
int representativesPerClass
```
    - weights
```
Vec[] weights
```
      Array containing the learning vectors
    - weightClass
```
int[] weightClass
```
      Array of the class that each learning vector represents
    - wins
```
int[] wins
```
      Records the number of times each neuron won and was off the correct class during training. Neurons that end with a count of zero wins will be ignored
    - seedSelection
```
SeedSelectionMethods.SeedSelection seedSelection
```
    - vc
```
VectorCollection<V extends Vec> vc
```
      Contains the Learning vectors paired with their index in the weights array
    - vcf
```
VectorCollectionFactory<V extends Vec> vcf
```
- Class jsat.classifiers.neuralnetwork.LVQLLC extends LVQ implements Serializable
  
  serialVersionUID:
  
  3602640001545233744L
  - Serialized Fields
    - localClassifier
```
Classifier localClassifier
```
    - localClassifeirs
```
Classifier[] localClassifeirs
```
- Class jsat.classifiers.neuralnetwork.Perceptron extends Object implements Serializable
  
  serialVersionUID:
  
  -3605237847981632021L
  - Serialized Fields
    - learningRate
```
double learningRate
```
    - bias
```
double bias
```
    - weights
```
Vec weights
```
    - iteratinLimit
```
int iteratinLimit
```
- Class jsat.classifiers.neuralnetwork.RBFNet extends Object implements Serializable
  
  serialVersionUID:
  
  5418896646203518062L
  - Serialized Fields
    - numCentroids
```
int numCentroids
```
    - p1l
```
RBFNet.Phase1Learner p1l
```
    - p2l
```
RBFNet.Phase2Learner p2l
```
    - alpha
```
double alpha
```
    - p
```
int p
```
    - dm
```
DistanceMetric dm
```
    - normalize
```
boolean normalize
```
    - baseClassifier
```
Classifier baseClassifier
```
    - baseRegressor
```
Regressor baseRegressor
```
    - centroidDistCache
```
List<E> centroidDistCache
```
    - centroids
```
List<E> centroids
```
    - bandwidths
```
double[] bandwidths
```
- Class jsat.classifiers.neuralnetwork.SGDNetworkTrainer extends Object implements Serializable
  
  serialVersionUID:
  
  5753653181230693131L
  - Serialized Fields
    - layerSizes
```
int[] layerSizes
```
      An array where the length indicates the number of layers and the value of each index indicates the number of neurons in that layer. This includes both the input and output layers
    - eta
```
double eta
```
      The base learning rate to use
    - p_i
```
double p_i
```
      The dropout probability for the input layers
    - p_i_intThresh
```
int p_i_intThresh
```
      The integer threshold to used when sampling a value from Random.nextInt() to get the correct dropout probability
    - p_o
```
double p_o
```
      The dropout probability for the hidden layers.
    - p_o_intThresh
```
int p_o_intThresh
```
      The integer threshold to used when sampling a value from Random.nextInt() to get the correct dropout probability
    - updater
```
GradientUpdater updater
```
      The gradient updater to use for updating weights and biases
    - regularizer
```
WeightRegularizer regularizer
```
      The weight regularization method
    - weightInit
```
WeightInitializer weightInit
```
      The method to initialize all neuron connection weights from
    - biasInit
```
BiastInitializer biasInit
```
      The method to initialize all neuron bias values from
    - W
```
List<E> W
```
      This list contains the neuron weight connection matrix for each layer after the input layer
    - W_deltas
```
List<E> W_deltas
```
      This list contains the gradients to update the weight matrices by
    - W_updaters
```
List<E> W_updaters
```
      This list contains the gradient updaters used for each layer, where there is a list of each matrix and each matrix has a list for each row.
    - B
```
List<E> B
```
      This list contains the neuron bias connections for each layer after the input layer
    - B_deltas
```
List<E> B_deltas
```
      This list contains the gradients to update the weight biases by
    - B_updaters
```
List<E> B_updaters
```
      This list contains the gradient updaters used for each set of bias connections
    - layersActivation
```
List<E> layersActivation
```
      This list contains the activation method for each layer after the input layer
    - etaDecay
```
DecayRate etaDecay
```
      The decay rate to apply to the base learning rate
    - time
```
int time
```
      The time step, incremented after every mini batch
    - activations
```
Matrix[] activations
```
      Matrices for storing the activations of each layer
    - unactivated
```
Matrix[] unactivated
```
    - deltas
```
Matrix[] deltas
```
- Class jsat.classifiers.neuralnetwork.SOM extends Object implements Serializable
  
  serialVersionUID:
  
  -6444988770441043797L
  - Serialized Fields
    - somWidth
```
int somWidth
```
    - somHeight
```
int somHeight
```
    - maxIters
```
int maxIters
```
    - kf
```
KernelFunction kf
```
    - initialLearningRate
```
double initialLearningRate
```
    - learningDecay
```
DecayRate learningDecay
```
    - neighborDecay
```
DecayRate neighborDecay
```
    - dm
```
DistanceMetric dm
```
    - vcFactory
```
VectorCollectionFactory<V extends Vec> vcFactory
```
    - weights
```
Vec[][] weights
```
    - crWeightPairs
```
CategoricalResults[] crWeightPairs
```
    - vcCollection
```
VectorCollection<V extends Vec> vcCollection
```
    - weightUpdates
```
List<E> weightUpdates
```
      Contains the sum of all inputs that were the BMU for the given index. The final list of data point should be a synchronized list so that multiple threads can add to the list safely
Package jsat.classifiers.neuralnetwork.activations
- Class jsat.classifiers.neuralnetwork.activations.LinearLayer extends Object implements Serializable
  
  serialVersionUID:
  
  -4040058095010471379L
- Class jsat.classifiers.neuralnetwork.activations.ReLU extends Object implements Serializable
  
  serialVersionUID:
  
  -6691240473485759789L
- Class jsat.classifiers.neuralnetwork.activations.SigmoidLayer extends Object implements Serializable
  
  serialVersionUID:
  
  160273287445169627L
- Class jsat.classifiers.neuralnetwork.activations.SoftmaxLayer extends Object implements Serializable
  
  serialVersionUID:
  
  -6595701781466123463L
- Class jsat.classifiers.neuralnetwork.activations.SoftSignLayer extends Object implements Serializable
  
  serialVersionUID:
  
  9137125423044227288L
- Class jsat.classifiers.neuralnetwork.activations.TanhLayer extends Object implements Serializable
  
  serialVersionUID:
  
  -8369008344962638121L
Package jsat.classifiers.neuralnetwork.initializers
- Class jsat.classifiers.neuralnetwork.initializers.ConstantInit extends Object implements Serializable
  
  serialVersionUID:
  
  2638413936718283757L
  - Serialized Fields
    - c
```
double c
```
- Class jsat.classifiers.neuralnetwork.initializers.GaussianNormalInit extends Object implements Serializable
  
  serialVersionUID:
  
  -882418891606717433L
  - Serialized Fields
    - stndDev
```
double stndDev
```
- Class jsat.classifiers.neuralnetwork.initializers.TanhInitializer extends Object implements Serializable
  
  serialVersionUID:
  
  -4770682311082616208L
Package jsat.classifiers.neuralnetwork.regularizers
- Class jsat.classifiers.neuralnetwork.regularizers.Max2NormRegularizer extends Object implements Serializable
  
  serialVersionUID:
  
  1989826758516880355L
  - Serialized Fields
    - maxNorm
```
double maxNorm
```
Package jsat.classifiers.svm
- Class jsat.classifiers.svm.DCD extends Object implements Serializable
  
  serialVersionUID:
  
  -1489225034030922798L
  - Serialized Fields
    - maxIterations
```
int maxIterations
```
    - vecs
```
Vec[] vecs
```
    - alpha
```
double[] alpha
```
    - y
```
double[] y
```
    - bias
```
double bias
```
    - w
```
Vec w
```
    - C
```
double C
```
    - useL1
```
boolean useL1
```
    - onlineVersion
```
boolean onlineVersion
```
    - eps
```
double eps
```
    - useBias
```
boolean useBias
```
    - params
```
List<E> params
```
    - paramMap
```
Map<K,V> paramMap
```
- Class jsat.classifiers.svm.DCDs extends Object implements Serializable
  
  serialVersionUID:
  
  -1686294187234524696L
  - Serialized Fields
    - maxIterations
```
int maxIterations
```
    - tolerance
```
double tolerance
```
    - vecs
```
Vec[] vecs
```
    - alpha
```
double[] alpha
```
    - y
```
double[] y
```
    - bias
```
double bias
```
    - w
```
Vec w
```
    - C
```
double C
```
    - useL1
```
boolean useL1
```
    - eps
```
double eps
```
    - useBias
```
boolean useBias
```
    - params
```
List<E> params
```
    - paramMap
```
Map<K,V> paramMap
```
- Class jsat.classifiers.svm.DCSVM extends SupportVectorLearner implements Serializable
  - Serialized Fields
    - C
```
double C
```
    - tolerance
```
double tolerance
```
    - clusters
```
KernelKMeans clusters
```
    - m
```
int m
```
    - l_max
```
int l_max
```
    - l_early
```
int l_early
```
    - k
```
int k
```
    - early_models
```
Map<K,V> early_models
```
    - cache_size
```
long cache_size
```
- Class jsat.classifiers.svm.LSSVM extends SupportVectorLearner implements Serializable
  
  serialVersionUID:
  
  -7569924400631719451L
  - Serialized Fields
    - b
```
double b
```
    - b_low
```
double b_low
```
    - b_up
```
double b_up
```
    - C
```
double C
```
    - i_up
```
int i_up
```
    - i_low
```
int i_low
```
    - fcache
```
double[] fcache
```
    - dualObjective
```
double dualObjective
```
- Class jsat.classifiers.svm.Pegasos extends Object implements Serializable
  
  serialVersionUID:
  
  -2145631476467081171L
  - Serialized Fields
    - epochs
```
int epochs
```
    - reg
```
double reg
```
    - batchSize
```
int batchSize
```
    - projectionStep
```
boolean projectionStep
```
    - w
```
Vec w
```
    - bias
```
double bias
```
- Class jsat.classifiers.svm.PegasosK extends SupportVectorLearner implements Serializable
  
  serialVersionUID:
  
  5405460830472328107L
  - Serialized Fields
    - regularization
```
double regularization
```
    - iterations
```
int iterations
```
- Class jsat.classifiers.svm.PlattSMO extends SupportVectorLearner implements Serializable
  
  serialVersionUID:
  
  1533410993462673127L
  - Serialized Fields
    - b
```
double b
```
      Bias
    - b_low
```
double b_low
```
      Bias
    - b_up
```
double b_up
```
      Bias
    - C
```
double C
```
    - tolerance
```
double tolerance
```
    - eps
```
double eps
```
    - epsilon
```
double epsilon
```
    - maxIterations
```
int maxIterations
```
    - modificationOne
```
boolean modificationOne
```
    - fcache
```
double[] fcache
```
    - i_up
```
int i_up
```
    - i_low
```
int i_low
```
    - alpha_s
```
double[] alpha_s
```
    - I0
```
boolean[] I0
```
      i : 0 < a_i < C
      For regression this contains both of I0_a and I0_b
    - I0_a
```
boolean[] I0_a
```
      Indicates if the value that is currently in I0 is also in I0_a. If its not in I0, the value in this array is false
    - I0_b
```
boolean[] I0_b
```
      Indicates if the value that is currently in I0 is also in I0_b. If its not in I0, the value in this array is false
    - I1
```
boolean[] I1
```
      i: y_i = 1 AND a_i = 0
    - I2
```
boolean[] I2
```
      i: y_i = -1 AND a_i = C
    - I3
```
boolean[] I3
```
      i: y_i = 1 AND a_i = C
    - I4
```
boolean[] I4
```
      i: y_i = -1 AND a_i = 0
    - label
```
double[] label
```
      Stores the true value of the data point
    - weights
```
Vec weights
```
      Weight values to apply to each data point
- Class jsat.classifiers.svm.SBP extends SupportVectorLearner implements Serializable
  
  serialVersionUID:
  
  6112916782260792833L
  - Serialized Fields
    - nu
```
double nu
```
    - iterations
```
int iterations
```
    - burnIn
```
double burnIn
```
    - it
```
IndexTable it
```
- Class jsat.classifiers.svm.SupportVectorLearner extends Object implements Serializable
  
  serialVersionUID:
  
  210140232301130063L
  - Serialized Fields
    - kernel
```
KernelTrick kernel
```
    - vecs
```
List<E> vecs
```
      The array of vectors. In the training phase, this should be the set of all training vectors. After training, this should contain only the set of support vectors.
    - alphas
```
double[] alphas
```
      The array of coefficients associated with each support vector. This should be instantiated directly when training. When the set of alphas and support vectors is finalized, SupportVectorLearner.setAlphas(double[]) should be called with a reference to itself or the array where the final alphas are stored. This will initialized any accelerating structures so that SupportVectorLearner.kEvalSum(jsat.linear.Vec) can be called.
    - cacheMode
```
SupportVectorLearner.CacheMode cacheMode
```
    - accelCache
```
List<E> accelCache
```
      Kernel evaluation acceleration cache
    - fullCache
```
double[][] fullCache
```
    - partialCache
```
ConcurrentCacheLRU<K,V> partialCache
```
      Stores rows of a cache matrix.
    - specific_row_cache_values
```
double[] specific_row_cache_values
```
      We allow algorithms that know they are going to access a specific row to hint, and save that row in this object to avoid overhead of hitting the LRU. See SupportVectorLearner.accessingRow(int)
    - specific_row_cache_row
```
int specific_row_cache_row
```
      The row that has been explicitly cached
    - availableRow
```
double[] availableRow
```
      Holds an available row for inserting into the cache, null if not available. All values already set to Nan
    - cacheConst
```
int cacheConst
```
    - evalCount
```
int evalCount
```
    - cacheEvictions
```
int cacheEvictions
```
- Class jsat.classifiers.svm.SVMnoBias extends SupportVectorLearner implements Serializable
  - Serialized Fields
    - C
```
double C
```
    - tolerance
```
double tolerance
```
    - label
```
short[] label
```
      Stores the true label value (-1 or +1) of the data point
    - weights
```
Vec weights
```
      Weight values to apply to each data point
    - T_a
```
double T_a
```
    - S_a
```
double S_a
```
Package jsat.classifiers.svm.extended
- Class jsat.classifiers.svm.extended.AMM extends OnlineAMM implements Serializable
  
  serialVersionUID:
  
  -9198419566231617395L
  - Serialized Fields
    - subEpochs
```
int subEpochs
```
- Class jsat.classifiers.svm.extended.CPM extends Object implements Serializable
  
  serialVersionUID:
  
  3171068484917637037L
  - Serialized Fields
    - epochs
```
int epochs
```
    - lambda
```
double lambda
```
    - K
```
int K
```
    - entropyThreshold
```
double entropyThreshold
```
    - h
```
double h
```
    - Wp
```
Matrix Wp
```
    - Wn
```
Matrix Wn
```
    - bp
```
Vec bp
```
    - bn
```
Vec bn
```
- Class jsat.classifiers.svm.extended.OnlineAMM extends BaseUpdateableClassifier implements Serializable
  
  serialVersionUID:
  
  8291068484917637037L
  - Serialized Fields
    - weightMatrix
```
List<E> weightMatrix
```
    - nextID
```
int[] nextID
```
    - lambda
```
double lambda
```
    - k
```
int k
```
    - c
```
double c
```
    - time
```
int time
```
    - classBudget
```
int classBudget
```
Package jsat.classifiers.trees
- Class jsat.classifiers.trees.DecisionStump extends Object implements Serializable
  
  serialVersionUID:
  
  -2849268862089019514L
  - Serialized Fields
    - splittingAttribute
```
int splittingAttribute
```
      Indicates which attribute to split on
    - predicting
```
CategoricalData predicting
```
      Used only when trained for classification. Contains information about the class being predicted
    - catAttributes
```
CategoricalData[] catAttributes
```
      Contains the information about the attributes in the data set
    - numNumericFeatures
```
int numNumericFeatures
```
      The number of numeric features in the dataset that this Stump was trained from
    - boundries
```
List<E> boundries
```
      Used only in classification. Contains the numeric boundaries to split on
    - owners
```
List<E> owners
```
      Used only in classification. Contains the most likely class corresponding to each boundary split
    - results
```
CategoricalResults[] results
```
      Used only in classification. Contains the results for each of the split options
    - pathRatio
```
double[] pathRatio
```
      How much of the data went to each path
    - regressionResults
```
double[] regressionResults
```
      Only used during regression. Contains the averages for each branch in the first and 2nd index. 3rd index contains the split value. If no split could be done, the length is zero and it contains only the return value
    - gainMethod
```
ImpurityScore.ImpurityMeasure gainMethod
```
    - removeContinuousAttributes
```
boolean removeContinuousAttributes
```
    - minResultSplitSize
```
int minResultSplitSize
```
      The minimum number of points that must be inside the split result for a split to occur.
- Class jsat.classifiers.trees.DecisionTree extends Object implements Serializable
  
  serialVersionUID:
  
  9220980056440500214L
  - Serialized Fields
    - maxDepth
```
int maxDepth
```
    - minSamples
```
int minSamples
```
    - root
```
DecisionTree.Node root
```
    - predicting
```
CategoricalData predicting
```
    - pruningMethod
```
TreePruner.PruningMethod pruningMethod
```
    - testProportion
```
double testProportion
```
      What portion of the training data will be set aside for pruning.
    - baseStump
```
DecisionStump baseStump
```
      Base decision stump used to clone so that we can keep certain features inside the stump instead of duplicating them here.
- Class jsat.classifiers.trees.DecisionTree.Node extends TreeNodeVisitor implements Serializable
  
  serialVersionUID:
  
  -7507748424627088734L
  - Serialized Fields
    - stump
```
DecisionStump stump
```
    - paths
```
DecisionTree.Node[] paths
```
- Class jsat.classifiers.trees.ERTrees extends ExtraTree implements Serializable
  
  serialVersionUID:
  
  7139392253403373132L
  - Serialized Fields
    - baseTree
```
ExtraTree baseTree
```
    - useDefaultSelectionCount
```
boolean useDefaultSelectionCount
```
    - useDefaultStopSize
```
boolean useDefaultStopSize
```
    - predicting
```
CategoricalData predicting
```
    - forrest
```
ExtraTree[] forrest
```
    - forrestSize
```
int forrestSize
```
- Class jsat.classifiers.trees.ExtraTree extends Object implements Serializable
  
  serialVersionUID:
  
  7433728970041876327L
  - Serialized Fields
    - stopSize
```
int stopSize
```
    - selectionCount
```
int selectionCount
```
    - predicting
```
CategoricalData predicting
```
    - binaryCategoricalSplitting
```
boolean binaryCategoricalSplitting
```
    - numNumericFeatures
```
int numNumericFeatures
```
      Just stores the number of numeric features that were in the dataset for that the getFeatures method can be implemented correctly for categorical variables.
    - impMeasure
```
ImpurityScore.ImpurityMeasure impMeasure
```
    - root
```
TreeNodeVisitor root
```
- Class jsat.classifiers.trees.ID3 extends Object implements Serializable
  
  serialVersionUID:
  
  -8473683139353205898L
  - Serialized Fields
    - predicting
```
CategoricalData predicting
```
    - attributes
```
CategoricalData[] attributes
```
    - root
```
jsat.classifiers.trees.ID3.ID3Node root
```
    - latch
```
ModifiableCountDownLatch latch
```
- Class jsat.classifiers.trees.ImportanceByUses extends Object implements Serializable
  - Serialized Fields
    - weightByDepth
```
boolean weightByDepth
```
- Class jsat.classifiers.trees.MDA extends Object implements Serializable
  - Serialized Fields
    - cs_base
```
ClassificationScore cs_base
```
    - rs_base
```
RegressionScore rs_base
```
- Class jsat.classifiers.trees.MDI extends Object implements Serializable
  - Serialized Fields
    - im
```
ImpurityScore.ImpurityMeasure im
```
- Class jsat.classifiers.trees.RandomDecisionTree extends DecisionTree implements Serializable
  
  serialVersionUID:
  
  -809244056947507494L
  - Serialized Fields
    - numFeatures
```
int numFeatures
```
- Class jsat.classifiers.trees.RandomForest extends Object implements Serializable
  
  serialVersionUID:
  
  2725020584282958141L
  - Serialized Fields
    - predicting
```
CategoricalData predicting
```
      Only used when training for a classification problem
    - extraSamples
```
int extraSamples
```
    - featureSamples
```
int featureSamples
```
      Setting the number of features to use. Default value is -1, indicating the heuristic of sqrt(N) or N/3 should be used for classification and regression respectively. This value should be set away from -1 before training work begins, and set back if it was not set explicitly by the used
    - maxForestSize
```
int maxForestSize
```
    - useOutOfBagError
```
boolean useOutOfBagError
```
    - useOutOfBagImportance
```
boolean useOutOfBagImportance
```
    - importanceMeasure
```
TreeFeatureImportanceInference importanceMeasure
```
    - feature_importance
```
OnLineStatistics[] feature_importance
```
    - outOfBagError
```
double outOfBagError
```
    - baseLearner
```
RandomDecisionTree baseLearner
```
    - forest
```
List<E> forest
```
- Class jsat.classifiers.trees.TreeNodeVisitor extends Object implements Serializable
  
  serialVersionUID:
  
  4026847401940409114L
Package jsat.clustering
- Class jsat.clustering.CLARA extends PAM implements Serializable
  
  serialVersionUID:
  
  174392533688953706L
  - Serialized Fields
    - sampleSize
```
int sampleSize
```
      The number of samples to take
    - sampleCount
```
int sampleCount
```
      The number of times to do sampling
    - autoSampleSize
```
boolean autoSampleSize
```
- Class jsat.clustering.ClustererBase extends Object implements Serializable
  
  serialVersionUID:
  
  4359554809306681680L
- Class jsat.clustering.ClusterFailureException extends RuntimeException implements Serializable
  
  serialVersionUID:
  
  -8084320940762402095L
- Class jsat.clustering.DBSCAN extends ClustererBase implements Serializable
  
  serialVersionUID:
  
  1627963360642560455L
  - Serialized Fields
    - vecFactory
```
VectorCollectionFactory<V extends Vec> vecFactory
```
      Factory used to create a vector space of the inputs. The paired Integer is the vector's index in the original dataset
    - dm
```
DistanceMetric dm
```
    - stndDevs
```
double stndDevs
```
- Class jsat.clustering.EMGaussianMixture extends KClustererBase implements Serializable
  
  serialVersionUID:
  
  2606159815670221662L
  - Serialized Fields
    - seedSelection
```
SeedSelectionMethods.SeedSelection seedSelection
```
    - gaussians
```
List<E> gaussians
```
    - a_k
```
double[] a_k
```
      The coefficients for the gaussians
    - tolerance
```
double tolerance
```
    - MaxIterLimit
```
int MaxIterLimit
```
      Control the maximum number of iterations to perform.
- Class jsat.clustering.FLAME extends ClustererBase implements Serializable
  
  serialVersionUID:
  
  2393091020100706517L
  - Serialized Fields
    - dm
```
DistanceMetric dm
```
    - k
```
int k
```
    - maxIterations
```
int maxIterations
```
    - vectorCollectionFactory
```
VectorCollectionFactory<V extends Vec> vectorCollectionFactory
```
    - stndDevs
```
double stndDevs
```
    - eps
```
double eps
```
- Class jsat.clustering.GapStatistic extends KClustererBase implements Serializable
  
  serialVersionUID:
  
  8893929177942856618L
  - Serialized Fields
    - base
```
KClusterer base
```
    - B
```
int B
```
    - dm
```
DistanceMetric dm
```
    - PCSampling
```
boolean PCSampling
```
    - ElogW
```
double[] ElogW
```
    - logW
```
double[] logW
```
    - gap
```
double[] gap
```
    - s_k
```
double[] s_k
```
- Class jsat.clustering.HDBSCAN extends ClustererBase implements Serializable
  - Serialized Fields
    - dm
```
DistanceMetric dm
```
    - m_pts
```
int m_pts
```
      minimium number of points
    - m_clSize
```
int m_clSize
```
    - vcf
```
VectorCollectionFactory<V extends Vec> vcf
```
- Class jsat.clustering.KClustererBase extends ClustererBase implements Serializable
  
  serialVersionUID:
  
  2542432122353325407L
- Class jsat.clustering.LSDBC extends ClustererBase implements Serializable
  
  serialVersionUID:
  
  6626217924334267681L
  - Serialized Fields
    - dm
```
DistanceMetric dm
```
    - vectorCollectionFactory
```
VectorCollectionFactory<V extends Vec> vectorCollectionFactory
```
    - alpha
```
double alpha
```
      The weight parameter for forming new clusters
    - k
```
int k
```
      The number of neighbors to use
- Class jsat.clustering.MeanShift extends ClustererBase implements Serializable
  
  serialVersionUID:
  
  4061491342362690455L
  - Serialized Fields
    - mkde
```
MultivariateKDE mkde
```
    - maxIterations
```
int maxIterations
```
    - scaleBandwidthFactor
```
double scaleBandwidthFactor
```
- Class jsat.clustering.OPTICS extends ClustererBase implements Serializable
  
  serialVersionUID:
  
  -1093772096278544211L
  - Serialized Fields
    - dm
```
DistanceMetric dm
```
    - vcf
```
VectorCollectionFactory<V extends Vec> vcf
```
    - vc
```
VectorCollection<V extends Vec> vc
```
    - radius
```
double radius
```
    - minPts
```
int minPts
```
    - core_distance
```
double[] core_distance
```
    - reach_d
```
double[] reach_d
```
      Stores the reachability distance of each point in the order they were first observed in the data set. After clustering is finished, it is altered to be in the reachability order used in clustering
    - processed
```
boolean[] processed
```
      Whether or not the given data point has been processed
    - allVecs
```
Vec[] allVecs
```
    - xi
```
double xi
```
    - one_min_xi
```
double one_min_xi
```
    - extractionMethod
```
OPTICS.ExtractionMethod extractionMethod
```
    - orderdSeeds
```
PriorityQueue<E> orderdSeeds
```
      The objects contained in OrderSeeds are sorted by their reachability-distance to the closest core object from which they have been directly density reachable. The paired double is their distance, the paired integer the the vector's index in the data set This is only used during building. We should probably refactor this out
- Class jsat.clustering.PAM extends KClustererBase implements Serializable
  
  serialVersionUID:
  
  4787649180692115514L
  - Serialized Fields
    - dm
```
DistanceMetric dm
```
    - rand
```
Random rand
```
    - seedSelection
```
SeedSelectionMethods.SeedSelection seedSelection
```
    - repeats
```
int repeats
```
    - iterLimit
```
int iterLimit
```
    - medoids
```
int[] medoids
```
    - storeMedoids
```
boolean storeMedoids
```
Package jsat.clustering.hierarchical
- Class jsat.clustering.hierarchical.DivisiveGlobalClusterer extends KClustererBase implements Serializable
  
  serialVersionUID:
  
  -9117751530105155090L
  - Serialized Fields
    - baseClusterer
```
KClusterer baseClusterer
```
    - clusterEvaluation
```
ClusterEvaluation clusterEvaluation
```
    - splitList
```
int[] splitList
```
    - fullDesignations
```
int[] fullDesignations
```
    - originalDataSet
```
DataSet<Type extends DataSet> originalDataSet
```
- Class jsat.clustering.hierarchical.DivisiveLocalClusterer extends KClustererBase implements Serializable
  
  serialVersionUID:
  
  8616401472810067778L
  - Serialized Fields
    - baseClusterer
```
KClusterer baseClusterer
```
    - clusterEvaluation
```
ClusterEvaluation clusterEvaluation
```
- Class jsat.clustering.hierarchical.NNChainHAC extends KClustererBase implements Serializable
  - Serialized Fields
    - distMeasure
```
LanceWilliamsDissimilarity distMeasure
```
    - dm
```
DistanceMetric dm
```
    - merges
```
int[] merges
```
      Stores the merge list, each merge is in a pair of 2 values. The final merge list should contain the last merged pairs at the front of the array (index 0, 1), and the first merges at the end of the array. The left value in each pair is the index of the data point that the clusters were merged under, while the right value is the index that was merged in and treated as no longer its own cluster.
- Class jsat.clustering.hierarchical.PriorityHAC extends KClustererBase implements Serializable
  
  serialVersionUID:
  
  -702489462117567542L
  - Serialized Fields
    - distMeasure
```
UpdatableClusterDissimilarity distMeasure
```
    - merges
```
int[] merges
```
      Stores the merge list, each merge is in a pair of 2 values. The final merge list should contain the last merged pairs at the front of the array (index 0, 1), and the first merges at the end of the array. The left value in each pair is the index of the data point that the clusters were merged under, while the right value is the index that was merged in and treated as no longer its own cluster.
    - curDataSet
```
DataSet<Type extends DataSet> curDataSet
```
- Class jsat.clustering.hierarchical.SimpleHAC extends KClustererBase implements Serializable
  
  serialVersionUID:
  
  7138073766768205530L
  - Serialized Fields
    - stndDevs
```
double stndDevs
```
      notion behind the large stnd devs is that as the clustering progresses, the min value is (usually) monotonically rising, so we would like a bigger jump
    - dissMeasure
```
ClusterDissimilarity dissMeasure
```
Package jsat.clustering.kmeans
- Class jsat.clustering.kmeans.ElkanKernelKMeans extends KernelKMeans implements Serializable
  
  serialVersionUID:
  
  4998832201379993827L
  - Serialized Fields
    - centroidSelfDistances
```
double[][] centroidSelfDistances
```
      Distances between centroid i and all other centroids
    - centroidPairDots
```
double[][] centroidPairDots
```
      un-normalized dot product between centroid i and all other centroids.
- Class jsat.clustering.kmeans.ElkanKMeans extends KMeans implements Serializable
  
  serialVersionUID:
  
  -1629432283103273051L
  - Serialized Fields
    - dmds
```
DenseSparseMetric dmds
```
    - useDenseSparse
```
boolean useDenseSparse
```
- Class jsat.clustering.kmeans.GMeans extends KMeans implements Serializable
  
  serialVersionUID:
  
  7306976407786792661L
  - Serialized Fields
    - trustH0
```
boolean trustH0
```
    - iterativeRefine
```
boolean iterativeRefine
```
    - minClusterSize
```
int minClusterSize
```
    - kmeans
```
KMeans kmeans
```
- Class jsat.clustering.kmeans.HamerlyKMeans extends KMeans implements Serializable
  
  serialVersionUID:
  
  -4960453870335145091L
- Class jsat.clustering.kmeans.KernelKMeans extends KClustererBase implements Serializable
  
  serialVersionUID:
  
  -5294680202634779440L
  - Serialized Fields
    - kernel
```
KernelTrick kernel
```
      The kernel trick to use
    - X
```
List<E> X
```
      The list of data points that this was trained on
    - W
```
Vec W
```
      The weight of each data point
    - accel
```
List<E> accel
```
      THe acceleration cache for the kernel
    - selfK
```
double[] selfK
```
      The value of k(x,x) for every point in KernelKMeans.X
    - meanSqrdNorms
```
double[] meanSqrdNorms
```
      The value of the un-normalized squared norm for each mean
    - normConsts
```
double[] normConsts
```
      The normalizing constant for each mean. General this would be 1/owned[k]²
    - ownes
```
double[] ownes
```
      The weighted number of dataums owned by each mean
    - newDesignations
```
int[] newDesignations
```
      A temporary space for updating ownership designations for each datapoint. When done, this will store the final designations for each point
    - maximumIterations
```
int maximumIterations
```
- Class jsat.clustering.kmeans.KMeans extends KClustererBase implements Serializable
  
  serialVersionUID:
  
  8730927112084289722L
  - Serialized Fields
    - dm
```
DistanceMetric dm
```
    - seedSelection
```
SeedSelectionMethods.SeedSelection seedSelection
```
    - rand
```
Random rand
```
    - storeMeans
```
boolean storeMeans
```
      Indicates whether or not the means from the clustering should be saved
    - saveCentroidDistance
```
boolean saveCentroidDistance
```
      Indicates whether or not the distance between a datapoint and its nearest centroid should be saved after clustering. This only applies when the error of the model is requested
    - nearestCentroidDist
```
double[] nearestCentroidDist
```
      Distance from a datapoint to its nearest centroid. May be an approximate distance
    - means
```
List<E> means
```
      The list of means
    - MaxIterLimit
```
int MaxIterLimit
```
      Control the maximum number of iterations to perform.
- Class jsat.clustering.kmeans.KMeansPDN extends KMeans implements Serializable
  
  serialVersionUID:
  
  -2358377567814606959L
  - Serialized Fields
    - kmeans
```
KMeans kmeans
```
    - fKs
```
double[] fKs
```
- Class jsat.clustering.kmeans.LloydKernelKMeans extends KernelKMeans implements Serializable
  
  serialVersionUID:
  
  1280985811243830450L
- Class jsat.clustering.kmeans.MiniBatchKMeans extends KClustererBase implements Serializable
  
  serialVersionUID:
  
  412553399508594014L
  - Serialized Fields
    - batchSize
```
int batchSize
```
    - iterations
```
int iterations
```
    - dm
```
DistanceMetric dm
```
    - seedSelection
```
SeedSelectionMethods.SeedSelection seedSelection
```
    - storeMeans
```
boolean storeMeans
```
    - means
```
List<E> means
```
- Class jsat.clustering.kmeans.NaiveKMeans extends KMeans implements Serializable
  
  serialVersionUID:
  
  6164910874898843069L
- Class jsat.clustering.kmeans.XMeans extends KMeans implements Serializable
  
  serialVersionUID:
  
  -2577160317892141870L
  - Serialized Fields
    - stopAfterFail
```
boolean stopAfterFail
```
    - iterativeRefine
```
boolean iterativeRefine
```
    - minClusterSize
```
int minClusterSize
```
    - kmeans
```
KMeans kmeans
```
Package jsat.datatransform
- Class jsat.datatransform.AutoDeskewTransform extends Object implements Serializable
  
  serialVersionUID:
  
  -4894242802345656448L
  - Serialized Fields
    - finalLambdas
```
double[] finalLambdas
```
    - mins
```
double[] mins
```
    - transform
```
IndexFunction transform
```
    - lambdas
```
List<E> lambdas
```
    - ignorZeros
```
boolean ignorZeros
```
- Class jsat.datatransform.DataModelPipeline extends Object implements Serializable
  
  serialVersionUID:
  
  -2300996837897094414L
  - Serialized Fields
    - baseDtp
```
DataTransformProcess baseDtp
```
    - baseClassifier
```
Classifier baseClassifier
```
    - baseRegressor
```
Regressor baseRegressor
```
    - learnedDtp
```
DataTransformProcess learnedDtp
```
    - learnedClassifier
```
Classifier learnedClassifier
```
    - learnedRegressor
```
Regressor learnedRegressor
```
- Class jsat.datatransform.DataTransformBase extends Object implements Serializable
- Class jsat.datatransform.DataTransformProcess extends Object implements Serializable
  
  serialVersionUID:
  
  -2844495690944305885L
  - Serialized Fields
    - transformSource
```
List<E> transformSource
```
    - learnedTransforms
```
List<E> learnedTransforms
```
- Class jsat.datatransform.DenseSparceTransform extends Object implements Serializable
  
  serialVersionUID:
  
  -1177913691660616290L
  - Serialized Fields
    - factor
```
double factor
```
- Class jsat.datatransform.FastICA extends Object implements Serializable
  
  serialVersionUID:
  
  -8644025740457515563L
  - Serialized Fields
    - C
```
int C
```
      the number of base components to assume and try to discover
    - G
```
FastICA.NegEntropyFunc G
```
      the Negative Entropy function to use
    - preWhitened
```
boolean preWhitened
```
      true to assume the data has already been
    - zeroMean
```
ZeroMeanTransform zeroMean
```
    - unmixing
```
Matrix unmixing
```
      Un-mixes the observed data into the raw components we learned
    - mixing
```
Matrix mixing
```
      The estimated mixing matrix to go from raw components to the observed data
- Class jsat.datatransform.Imputer extends Object implements Serializable
  - Serialized Fields
    - mode
```
Imputer.NumericImputionMode mode
```
    - cat_imputs
```
int[] cat_imputs
```
      The values to impute for missing numeric columns
    - numeric_imputs
```
double[] numeric_imputs
```
      The values to impute for missing numeric columns
- Class jsat.datatransform.InsertMissingValuesTransform extends Object implements Serializable
  - Serialized Fields
    - prob
```
double prob
```
    - rand
```
Random rand
```
- Class jsat.datatransform.InverseOfTransform extends Object implements Serializable
  
  serialVersionUID:
  
  2565737661260748018L
  - Serialized Fields
    - transform
```
InvertibleTransform transform
```
- Class jsat.datatransform.JLTransform extends DataTransformBase implements Serializable
  
  serialVersionUID:
  
  -8621368067861343912L
  - Serialized Fields
    - mode
```
JLTransform.TransformMode mode
```
    - R
```
Matrix R
```
    - k
```
int k
```
    - inMemory
```
boolean inMemory
```
- Class jsat.datatransform.LinearTransform extends Object implements Serializable
  
  serialVersionUID:
  
  5580283565080452022L
  - Serialized Fields
    - A
```
double A
```
      The max value
    - B
```
double B
```
      The min value
    - mins
```
Vec mins
```
      The minimum observed value for each attribute
    - mutliplyConstants
```
Vec mutliplyConstants
```
      Represents A - B ----------- max - min
- Class jsat.datatransform.NominalToNumeric extends Object implements Serializable
  
  serialVersionUID:
  
  -7765605678836464143L
  - Serialized Fields
    - origNumericalCount
```
int origNumericalCount
```
    - categoricalData
```
CategoricalData[] categoricalData
```
    - addedNumers
```
int addedNumers
```
- Class jsat.datatransform.NumericalToHistogram extends Object implements Serializable
  
  serialVersionUID:
  
  -2318706869393636074L
  - Serialized Fields
    - n
```
int n
```
    - conversionArray
```
double[][] conversionArray
```
    - newDataArray
```
CategoricalData[] newDataArray
```
- Class jsat.datatransform.PCA extends Object implements Serializable
  
  serialVersionUID:
  
  8736609877239941617L
  - Serialized Fields
    - P
```
Matrix P
```
      The transposed matrix of the Principal Components
    - maxPCs
```
int maxPCs
```
    - threshold
```
double threshold
```
- Class jsat.datatransform.PNormNormalization extends Object implements Serializable
  
  serialVersionUID:
  
  2934569881395909607L
  - Serialized Fields
    - p
```
double p
```
- Class jsat.datatransform.PolynomialTransform extends Object implements Serializable
  
  serialVersionUID:
  
  -5332216444253168283L
  - Serialized Fields
    - degree
```
int degree
```
- Class jsat.datatransform.RemoveAttributeTransform extends Object implements Serializable
  
  serialVersionUID:
  
  2803223213862922734L
  - Serialized Fields
    - catIndexMap
```
int[] catIndexMap
```
    - numIndexMap
```
int[] numIndexMap
```
    - categoricalToRemove
```
Set<E> categoricalToRemove
```
    - numericalToRemove
```
Set<E> numericalToRemove
```
- Class jsat.datatransform.StandardizeTransform extends Object implements Serializable
  
  serialVersionUID:
  
  -2349721113741805955L
  - Serialized Fields
    - means
```
Vec means
```
    - stdDevs
```
Vec stdDevs
```
- Class jsat.datatransform.UnitVarianceTransform extends Object implements Serializable
  
  serialVersionUID:
  
  3645532503475641917L
  - Serialized Fields
    - stndDevs
```
Vec stndDevs
```
- Class jsat.datatransform.WhitenedPCA extends DataTransformBase implements Serializable
  
  serialVersionUID:
  
  6134243673037330608L
  - Serialized Fields
    - regularization
```
double regularization
```
      Regularization parameter
    - dimensions
```
int dimensions
```
      The number of dimensions to project down to
    - transform
```
Matrix transform
```
      The final transformation matrix, that will create new points y = transform * x
- Class jsat.datatransform.WhitenedZCA extends WhitenedPCA implements Serializable
  
  serialVersionUID:
  
  7546033727733619587L
  - Serialized Fields
    - tempVecs
```
ThreadLocal<T> tempVecs
```
- Class jsat.datatransform.ZeroMeanTransform extends Object implements Serializable
  
  serialVersionUID:
  
  -7411115746918116163L
  - Serialized Fields
    - shiftVector
```
Vec shiftVector
```
      Shift vector stores the mean value of each variable in the original data set.
Package jsat.datatransform.featureselection
- Class jsat.datatransform.featureselection.BDS extends Object implements Serializable
  
  serialVersionUID:
  
  8633823674617843754L
  - Serialized Fields
    - finalTransform
```
RemoveAttributeTransform finalTransform
```
    - catSelected
```
Set<E> catSelected
```
    - numSelected
```
Set<E> numSelected
```
    - featureCount
```
int featureCount
```
    - folds
```
int folds
```
    - evaluator
```
Object evaluator
```
- Class jsat.datatransform.featureselection.LRS extends Object implements Serializable
  
  serialVersionUID:
  
  3065300352046535656L
  - Serialized Fields
    - finalTransform
```
RemoveAttributeTransform finalTransform
```
    - catSelected
```
Set<E> catSelected
```
    - numSelected
```
Set<E> numSelected
```
    - L
```
int L
```
    - R
```
int R
```
    - evaluater
```
Object evaluater
```
    - folds
```
int folds
```
- Class jsat.datatransform.featureselection.MutualInfoFS extends RemoveAttributeTransform implements Serializable
  
  serialVersionUID:
  
  -4394620220403363542L
  - Serialized Fields
    - featureCount
```
int featureCount
```
    - numericHandling
```
MutualInfoFS.NumericalHandeling numericHandling
```
- Class jsat.datatransform.featureselection.ReliefF extends RemoveAttributeTransform implements Serializable
  
  serialVersionUID:
  
  -3336500245613075520L
  - Serialized Fields
    - w
```
double[] w
```
    - featureCount
```
int featureCount
```
    - iterations
```
int iterations
```
    - neighbors
```
int neighbors
```
    - dm
```
DistanceMetric dm
```
    - vcf
```
VectorCollectionFactory<V extends Vec> vcf
```
- Class jsat.datatransform.featureselection.SBS extends RemoveAttributeTransform implements Serializable
  
  serialVersionUID:
  
  -2516121100148559742L
  - Serialized Fields
    - maxDecrease
```
double maxDecrease
```
    - folds
```
int folds
```
    - minFeatures
```
int minFeatures
```
    - maxFeatures
```
int maxFeatures
```
    - evaluator
```
Object evaluator
```
- Class jsat.datatransform.featureselection.SFS extends Object implements Serializable
  
  serialVersionUID:
  
  140187978708131002L
  - Serialized Fields
    - finalTransform
```
RemoveAttributeTransform finalTransform
```
    - catSelected
```
Set<E> catSelected
```
    - numSelected
```
Set<E> numSelected
```
    - maxIncrease
```
double maxIncrease
```
    - classifier
```
Classifier classifier
```
    - regressor
```
Regressor regressor
```
    - minFeatures
```
int minFeatures
```
    - maxFeatures
```
int maxFeatures
```
    - folds
```
int folds
```
    - evaluator
```
Object evaluator
```
Package jsat.datatransform.kernel
- Class jsat.datatransform.kernel.KernelPCA extends DataTransformBase implements Serializable
  
  serialVersionUID:
  
  5676602024560381023L
  - Serialized Fields
    - dimensions
```
int dimensions
```
      The dimension to project down to
    - k
```
KernelTrick k
```
    - basisSize
```
int basisSize
```
    - samplingMethod
```
Nystrom.SamplingMethod samplingMethod
```
    - eigenVals
```
double[] eigenVals
```
    - eigenVecs
```
Matrix eigenVecs
```
      The matrix of transformed eigen vectors
    - vecs
```
Vec[] vecs
```
      The vecs used for the transform
    - rowAvg
```
double[] rowAvg
```
    - allAvg
```
double allAvg
```
- Class jsat.datatransform.kernel.Nystrom extends DataTransformBase implements Serializable
  
  serialVersionUID:
  
  -3227844260130709773L
  - Serialized Fields
    - ridge
```
double ridge
```
    - k
```
KernelTrick k
```
    - dimension
```
int dimension
```
    - method
```
Nystrom.SamplingMethod method
```
    - basisSize
```
int basisSize
```
    - sampleWithReplacment
```
boolean sampleWithReplacment
```
    - basisVecs
```
List<E> basisVecs
```
    - accelCache
```
List<E> accelCache
```
    - transform
```
Matrix transform
```
- Class jsat.datatransform.kernel.RFF_RBF extends DataTransformBase implements Serializable
  
  serialVersionUID:
  
  -3478216020648280477L
  - Serialized Fields
    - transform
```
Matrix transform
```
    - offsets
```
Vec offsets
```
    - sigma
```
double sigma
```
    - dim
```
int dim
```
    - inMemory
```
boolean inMemory
```
Package jsat.datatransform.visualization
- Class jsat.datatransform.visualization.Isomap extends Object implements Serializable
  - Serialized Fields
    - dm
```
DistanceMetric dm
```
    - vcf
```
VectorCollectionFactory<V extends Vec> vcf
```
    - searchNeighbors
```
int searchNeighbors
```
    - mds
```
MDS mds
```
    - c_isomap
```
boolean c_isomap
```
- Class jsat.datatransform.visualization.LargeViz extends Object implements Serializable
  - Serialized Fields
    - dm_source
```
DistanceMetric dm_source
```
    - dm_embed
```
DistanceMetric dm_embed
```
    - perplexity
```
double perplexity
```
    - dt
```
int dt
```
    - M
```
int M
```
      This is the number of negative samples to take for each vertex
      "number of negative samples is set as 5"
    - gamma
```
double gamma
```
      "γ is set as 7 by default"
- Class jsat.datatransform.visualization.MDS extends Object implements Serializable
  - Serialized Fields
    - dm
```
DistanceMetric dm
```
    - tolerance
```
double tolerance
```
    - maxIterations
```
int maxIterations
```
    - targetSize
```
int targetSize
```
- Class jsat.datatransform.visualization.TSNE extends Object implements Serializable
  - Serialized Fields
    - alpha
```
double alpha
```
    - exageratedPortion
```
double exageratedPortion
```
    - dm
```
DistanceMetric dm
```
    - T
```
int T
```
    - perplexity
```
double perplexity
```
    - theta
```
double theta
```
    - s
```
int s
```
      The target embedding dimension, hard coded to 2 for now
Package jsat.distributions
- Class jsat.distributions.Beta extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  8001402067928143972L
  - Serialized Fields
    - alpha
```
double alpha
```
    - beta
```
double beta
```
- Class jsat.distributions.Cauchy extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  -5083645002030551206L
  - Serialized Fields
    - location
```
double location
```
    - scale
```
double scale
```
- Class jsat.distributions.ChiSquared extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  2446232102260721666L
  - Serialized Fields
    - df
```
double df
```
- Class jsat.distributions.ContinuousDistribution extends Distribution implements Serializable
  
  serialVersionUID:
  
  -5079392926462355615L
- Class jsat.distributions.Distribution extends Object implements Serializable
- Class jsat.distributions.Exponential extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  1675502925386052588L
  - Serialized Fields
    - lambda
```
double lambda
```
- Class jsat.distributions.FisherSendor extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  7628304882101574242L
  - Serialized Fields
    - v1
```
double v1
```
    - v2
```
double v2
```
- Class jsat.distributions.Gamma extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  6380493734491674483L
  - Serialized Fields
    - k
```
double k
```
    - theta
```
double theta
```
- Class jsat.distributions.Kolmogorov extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  7319511918364286930L
  - Serialized Fields
    - fCDF
```
Function fCDF
```
- Class jsat.distributions.Laplace extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  -4799360517803678236L
  - Serialized Fields
    - mu
```
double mu
```
      location
    - b
```
double b
```
- Class jsat.distributions.Levy extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  3132169946527422816L
  - Serialized Fields
    - location
```
double location
```
    - scale
```
double scale
```
    - logScale
```
double logScale
```
- Class jsat.distributions.Logistic extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  -8720773286818833591L
  - Serialized Fields
    - mu
```
double mu
```
      Location
    - s
```
double s
```
      Scale
- Class jsat.distributions.LogNormal extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  -6938582328705527274L
  - Serialized Fields
    - mu
```
double mu
```
    - sig
```
double sig
```
- Class jsat.distributions.LogUniform extends ContinuousDistribution implements Serializable
  - Serialized Fields
    - min
```
double min
```
    - max
```
double max
```
    - logMin
```
double logMin
```
    - logMax
```
double logMax
```
    - logDiff
```
double logDiff
```
    - diff
```
double diff
```
- Class jsat.distributions.MaxwellBoltzmann extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  -8273087046831433430L
  - Serialized Fields
    - sigma
```
double sigma
```
      shape
- Class jsat.distributions.Normal extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  -5298346576152986165L
  - Serialized Fields
    - mean
```
double mean
```
    - stndDev
```
double stndDev
```
- Class jsat.distributions.Pareto extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  2055881279858330509L
  - Serialized Fields
    - xm
```
double xm
```
      scale
    - alpha
```
double alpha
```
      shape
- Class jsat.distributions.Rayleigh extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  1451949391703281531L
  - Serialized Fields
    - sig
```
double sig
```
      scale parameter
- Class jsat.distributions.StudentT extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  -3157525461647767831L
  - Serialized Fields
    - df
```
double df
```
    - mu
```
double mu
```
    - sig
```
double sig
```
- Class jsat.distributions.TruncatedDistribution extends ContinuousDistribution implements Serializable
  - Serialized Fields
    - base
```
ContinuousDistribution base
```
    - min
```
double min
```
    - max
```
double max
```
    - probInOrigRange
```
double probInOrigRange
```
      The probability of a given value coming from the specified range of the original distribution
    - old_min_p
```
double old_min_p
```
      the PDF(min) of the base distribution
    - old_max_p
```
double old_max_p
```
      The PDF(max) of the base distribution
- Class jsat.distributions.Uniform extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  2479606544724378610L
  - Serialized Fields
    - a
```
double a
```
    - b
```
double b
```
- Class jsat.distributions.Weibull extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  -4083186674624535562L
  - Serialized Fields
    - alpha
```
double alpha
```
      Shape parameter
    - beta
```
double beta
```
      Scale parameter
    - logAlpha
```
double logAlpha
```
    - logBeta
```
double logBeta
```
Package jsat.distributions.discrete
- Class jsat.distributions.discrete.Binomial extends DiscreteDistribution implements Serializable
  - Serialized Fields
    - trials
```
int trials
```
    - p
```
double p
```
- Class jsat.distributions.discrete.DiscreteDistribution extends Distribution implements Serializable
- Class jsat.distributions.discrete.Poisson extends DiscreteDistribution implements Serializable
  - Serialized Fields
    - lambda
```
double lambda
```
- Class jsat.distributions.discrete.UniformDiscrete extends DiscreteDistribution implements Serializable
  - Serialized Fields
    - min
```
int min
```
    - max
```
int max
```
Package jsat.distributions.empirical
- Class jsat.distributions.empirical.KernelDensityEstimator extends ContinuousDistribution implements Serializable
  
  serialVersionUID:
  
  7708020456632603947L
  - Serialized Fields
    - X
```
double[] X
```
      The various values
    - weights
```
double[] weights
```
      Weights corresponding to each value. If all the same, weights should have a length of 0
    - sumOFWeights
```
double sumOFWeights
```
      For unweighted data, this is equal to X.length
    - h
```
double h
```
      The bandwidth
    - Xmean
```
double Xmean
```
    - Xvar
```
double Xvar
```
    - Xskew
```
double Xskew
```
    - k
```
KernelFunction k
```
    - cdfFunc
```
Function cdfFunc
```
Package jsat.distributions.empirical.kernelfunc
- Class jsat.distributions.empirical.kernelfunc.BiweightKF extends Object implements Serializable
  
  serialVersionUID:
  
  -7199542934997154186L
- Class jsat.distributions.empirical.kernelfunc.EpanechnikovKF extends Object implements Serializable
  
  serialVersionUID:
  
  8688942176576932932L
- Class jsat.distributions.empirical.kernelfunc.GaussKF extends Object implements Serializable
  
  serialVersionUID:
  
  -6765390012694573184L
- Class jsat.distributions.empirical.kernelfunc.TriweightKF extends Object implements Serializable
  
  serialVersionUID:
  
  -9156392658970318676L
- Class jsat.distributions.empirical.kernelfunc.UniformKF extends Object implements Serializable
  
  serialVersionUID:
  
  -6413579643511350896L
Package jsat.distributions.kernels
- Class jsat.distributions.kernels.BaseKernelTrick extends Object implements Serializable
  
  serialVersionUID:
  
  7230585838672226751L
- Class jsat.distributions.kernels.BaseL2Kernel extends Object implements Serializable
  
  serialVersionUID:
  
  2917497058710848085L
- Class jsat.distributions.kernels.DistanceMetricBasedKernel extends Object implements Serializable
  
  serialVersionUID:
  
  8395066824809874527L
  - Serialized Fields
    - d
```
DistanceMetric d
```
      the distance metric to use for the Kernel
- Class jsat.distributions.kernels.GeneralRBFKernel extends DistanceMetricBasedKernel implements Serializable
  
  serialVersionUID:
  
  1368225926995372017L
  - Serialized Fields
    - sigma
```
double sigma
```
    - sigmaSqrd2Inv
```
double sigmaSqrd2Inv
```
- Class jsat.distributions.kernels.LinearKernel extends BaseKernelTrick implements Serializable
  
  serialVersionUID:
  
  -1870181048970135367L
  - Serialized Fields
    - c
```
double c
```
    - param
```
Parameter param
```
- Class jsat.distributions.kernels.NormalizedKernel extends Object implements Serializable
  - Serialized Fields
    - k
```
KernelTrick k
```
- Class jsat.distributions.kernels.PolynomialKernel extends BaseKernelTrick implements Serializable
  
  serialVersionUID:
  
  9123109691782934745L
  - Serialized Fields
    - degree
```
double degree
```
    - alpha
```
double alpha
```
    - c
```
double c
```
- Class jsat.distributions.kernels.PukKernel extends BaseL2Kernel implements Serializable
  
  serialVersionUID:
  
  8727097671803148320L
  - Serialized Fields
    - sigma
```
double sigma
```
    - omega
```
double omega
```
    - cnst
```
double cnst
```
- Class jsat.distributions.kernels.RationalQuadraticKernel extends BaseL2Kernel implements Serializable
  
  serialVersionUID:
  
  6773399185851115840L
  - Serialized Fields
    - c
```
double c
```
- Class jsat.distributions.kernels.RBFKernel extends BaseL2Kernel implements Serializable
  
  serialVersionUID:
  
  -6733691081172950067L
  - Serialized Fields
    - sigma
```
double sigma
```
    - sigmaSqrd2Inv
```
double sigmaSqrd2Inv
```
- Class jsat.distributions.kernels.SigmoidKernel extends BaseKernelTrick implements Serializable
  
  serialVersionUID:
  
  8066799016611439349L
  - Serialized Fields
    - alpha
```
double alpha
```
    - c
```
double c
```
Package jsat.distributions.multivariate
- Class jsat.distributions.multivariate.Dirichlet extends MultivariateDistributionSkeleton implements Serializable
  
  serialVersionUID:
  
  6229508050763067569L
  - Serialized Fields
    - alphas
```
Vec alphas
```
- Class jsat.distributions.multivariate.MetricKDE extends MultivariateKDE implements Serializable
  
  serialVersionUID:
  
  -2084039950938740815L
  - Serialized Fields
    - kf
```
KernelFunction kf
```
    - bandwidth
```
double bandwidth
```
    - distanceMetric
```
DistanceMetric distanceMetric
```
    - vcf
```
VectorCollectionFactory<V extends Vec> vcf
```
    - vecCollection
```
VectorCollection<V extends Vec> vecCollection
```
    - defaultK
```
int defaultK
```
    - defaultStndDev
```
double defaultStndDev
```
    - parameters
```
List<E> parameters
```
    - paramMap
```
Map<K,V> paramMap
```
- Class jsat.distributions.multivariate.MultivariateDistributionSkeleton extends Object implements Serializable
  
  serialVersionUID:
  
  4080753806798149915L
- Class jsat.distributions.multivariate.MultivariateKDE extends MultivariateDistributionSkeleton implements Serializable
  
  serialVersionUID:
  
  614136649331326270L
- Class jsat.distributions.multivariate.NormalM extends MultivariateDistributionSkeleton implements Serializable
  
  serialVersionUID:
  
  -7043369396743253382L
  - Serialized Fields
    - logPDFConst
```
double logPDFConst
```
      When computing the PDF of some x, part of the equation is only dependent on the covariance matrix. This part is
      -k -- -1 2 -- / __\ 2 \2 ||/ (|Sigma|)
      where k is the dimension, Sigma is the covariance matrix, and || denotes the determinant.
      Taking the negative log of this gives
      / __\ (-k) log\2 ||/ - log(|Sigma|) ----------------------------- 2
      This can then be added to the log of the x dependent part, which, when exponentiated, gives the correct result of dividing by this term.
    - invCovariance
```
Matrix invCovariance
```
      When we compute the constant NormalM.logPDFConst, we only need the inverse of the covariance matrix.
    - mean
```
Vec mean
```
    - L
```
Matrix L
```
      Lower triangular cholesky decomposition used for sampling such that L * L^T = Covariance Matrix
- Class jsat.distributions.multivariate.ProductKDE extends MultivariateKDE implements Serializable
  
  serialVersionUID:
  
  7298078759216991650L
  - Serialized Fields
    - k
```
KernelFunction k
```
    - sortedDimVals
```
double[][] sortedDimVals
```
    - bandwidth
```
double[] bandwidth
```
    - sortedIndexVals
```
int[][] sortedIndexVals
```
    - originalVecs
```
List<E> originalVecs
```
      The original list of vectors used to create the KDE, used to avoid an expensive reconstruction of the vectors
- Class jsat.distributions.multivariate.SymmetricDirichlet extends MultivariateDistributionSkeleton implements Serializable
  
  serialVersionUID:
  
  -1206894014440494142L
  - Serialized Fields
    - alpha
```
double alpha
```
    - dim
```
int dim
```
Package jsat.driftdetectors
- Class jsat.driftdetectors.ADWIN extends BaseDriftDetector<V> implements Serializable
  
  serialVersionUID:
  
  3287510845017257629L
  - Serialized Fields
    - delta
```
double delta
```
    - allStats
```
OnLineStatistics allStats
```
    - windows
```
LinkedList<E> windows
```
      List of all the stats summarizing the windows. New items are added to the tail end of the list so that we can iterate from the "head" down. This makes writing the logic somewhat easier.
      This means the head contains the oldest /largest items, and the tail contains the smallest / newest items. This is opposite of the ADWIN paper
    - M
```
int M
```
    - leftMean
```
double leftMean
```
    - leftVariance
```
double leftVariance
```
    - rightMean
```
double rightMean
```
    - rightVariance
```
double rightVariance
```
- Class jsat.driftdetectors.BaseDriftDetector extends Object implements Serializable
  
  serialVersionUID:
  
  -5857845807016446270L
  - Serialized Fields
    - time
```
int time
```
      Tracks the number of updates / trial scene. May be reset as needed, so long as it increases compared to BaseDriftDetector.driftStart
    - maxHistory
```
int maxHistory
```
      Controls the maximum amount of history to keep
    - warning
```
boolean warning
```
      Set to true to indicate that a warning mode in in effect.
    - drifting
```
boolean drifting
```
      Set to true to indicate that concept drift has occurred
    - driftStart
```
int driftStart
```
      Set this value to the time point where the drift is believed to have started from. Set to -1 to indicate no drift
    - history
```
Deque<E> history
```
      Holds the associated object history. The history is always FIFO, with the end (tail) of the queue containing the most recent object, and the front (head) containing the oldest object.
- Class jsat.driftdetectors.DDM extends BaseDriftDetector<V> implements Serializable
  
  serialVersionUID:
  
  3023405445609636195L
  - Serialized Fields
    - fails
```
int fails
```
      Number of times we won the trial
    - minSamples
```
int minSamples
```
    - p_min
```
double p_min
```
    - s_min
```
double s_min
```
    - warningThreshold
```
double warningThreshold
```
    - driftThreshold
```
double driftThreshold
```
- Class jsat.driftdetectors.UnhandledDriftException extends RuntimeException implements Serializable
  
  serialVersionUID:
  
  -5781626293819651067L
Package jsat.exceptions
- Class jsat.exceptions.FailedToFitException extends RuntimeException implements Serializable
  
  serialVersionUID:
  
  2982189541225068993L
  - Serialized Fields
    - faultException
```
Exception faultException
```
- Class jsat.exceptions.ModelMismatchException extends RuntimeException implements Serializable
  
  serialVersionUID:
  
  6962636868667470816L
- Class jsat.exceptions.UntrainedModelException extends RuntimeException implements Serializable
  
  serialVersionUID:
  
  3693546100471013277L
Package jsat.linear
- Class jsat.linear.CholeskyDecomposition extends Object implements Serializable
  
  serialVersionUID:
  
  8925094456733750112L
  - Serialized Fields
    - L
```
Matrix L
```
      Contains the matrix 'L', but instead of just keeping the lower triangular, we keep it in a symmetric copy so LUPDecomposition.forwardSub(jsat.linear.Matrix, jsat.linear.Vec) and backSub can be done without copying.
- Class jsat.linear.ConcatenatedVec extends Vec implements Serializable
  
  serialVersionUID:
  
  -1412322616974470550L
  - Serialized Fields
    - vecs
```
Vec[] vecs
```
    - lengthSums
```
int[] lengthSums
```
    - totalLength
```
int totalLength
```
- Class jsat.linear.ConstantVector extends Vec implements Serializable
  
  serialVersionUID:
  
  4840204242189111630L
  - Serialized Fields
    - constant
```
double constant
```
    - length
```
int length
```
- Class jsat.linear.DenseMatrix extends GenericMatrix implements Serializable
  
  serialVersionUID:
  
  -3112110093920307822L
  - Serialized Fields
    - matrix
```
double[][] matrix
```
- Class jsat.linear.DenseVector extends Vec implements Serializable
  
  serialVersionUID:
  
  -889493251793828934L
  - Serialized Fields
    - array
```
double[] array
```
    - sumCache
```
Double sumCache
```
    - varianceCache
```
Double varianceCache
```
    - minCache
```
Double minCache
```
    - maxCache
```
Double maxCache
```
    - startIndex
```
int startIndex
```
    - endIndex
```
int endIndex
```
- Class jsat.linear.EigenValueDecomposition extends Object implements Serializable
  
  serialVersionUID:
  
  -7169205761148043008L
  - Serialized Fields
    - n
```
int n
```
      Row and column dimension (square matrix).
      
      matrix dimension.
    - d
```
double[] d
```
      Arrays for internal storage of eigenvalues.
      
      internal storage of eigenvalues.
    - e
```
double[] e
```
      Arrays for internal storage of eigenvalues.
      
      internal storage of eigenvalues.
    - V
```
Matrix V
```
      Array for internal storage of eigenvectors.
      
      internal storage of eigenvectors.
    - H
```
Matrix H
```
      Array for internal storage of nonsymmetric Hessenberg form.
      
      internal storage of nonsymmetric Hessenberg form.
    - complexResult
```
boolean complexResult
```
      Used to indicate if the result contains complex eigen values
- Class jsat.linear.GenericMatrix extends Matrix implements Serializable
  
  serialVersionUID:
  
  -8173419025024676713L
- Class jsat.linear.HessenbergForm extends Object implements Serializable
  
  serialVersionUID:
  
  1411467026933172901L
- Class jsat.linear.LUPDecomposition extends Object implements Serializable
  
  serialVersionUID:
  
  -149659693838168048L
  - Serialized Fields
    - L
```
Matrix L
```
    - U
```
Matrix U
```
    - P
```
Matrix P
```
- Class jsat.linear.Matrix extends Object implements Serializable
  
  serialVersionUID:
  
  6888360415978051714L
- Class jsat.linear.MatrixOfVecs extends GenericMatrix implements Serializable
  
  serialVersionUID:
  
  6120353195388663462L
  - Serialized Fields
    - rows
```
List<E> rows
```
- Class jsat.linear.Poly2Vec extends Vec implements Serializable
  
  serialVersionUID:
  
  -5653680966558726340L
  - Serialized Fields
    - base
```
Vec base
```
    - reverseIndex
```
int[] reverseIndex
```
      This maps values pas the original coefficients (and bias term) shifted to start from zero, to the appropriate value for the fist coefficient.
      This will be created lazily as needed. Call Poly2Vec.getReverseIndex() to access this value
- Class jsat.linear.QRDecomposition extends Object implements Serializable
  
  serialVersionUID:
  
  7578073062361216223L
  - Serialized Fields
    - Q_T
```
Matrix Q_T
```
    - R
```
Matrix R
```
- Class jsat.linear.RandomMatrix extends GenericMatrix implements Serializable
  
  serialVersionUID:
  
  3514801206898749257L
  - Serialized Fields
    - rows
```
int rows
```
    - cols
```
int cols
```
    - seedMult
```
long seedMult
```
    - localRand
```
ThreadLocal<T> localRand
```
- Class jsat.linear.RandomVector extends Vec implements Serializable
  
  serialVersionUID:
  
  -1587968421978707875L
  - Serialized Fields
    - length
```
int length
```
    - seedMult
```
long seedMult
```
    - localRand
```
ThreadLocal<T> localRand
```
- Class jsat.linear.ScaledVector extends Vec implements Serializable
  
  serialVersionUID:
  
  7357893957632067299L
  - Serialized Fields
    - scale
```
double scale
```
    - base
```
Vec base
```
- Class jsat.linear.ShiftedVec extends Vec implements Serializable
  
  serialVersionUID:
  
  -8318033099234181766L
  - Serialized Fields
    - base
```
Vec base
```
    - shift
```
double shift
```
- Class jsat.linear.SingularValueDecomposition extends Object implements Serializable
  
  serialVersionUID:
  
  1711766946748622002L
  - Serialized Fields
    - U
```
Matrix U
```
    - V
```
Matrix V
```
    - s
```
double[] s
```
      Stores the diagonal values of the S matrix, and contains the bidiagonal values of A during initial steps.
- Class jsat.linear.SparseMatrix extends Matrix implements Serializable
  
  serialVersionUID:
  
  -4087445771022578544L
  - Serialized Fields
    - rows
```
SparseVector[] rows
```
- Class jsat.linear.SparseVector extends Vec implements Serializable
  
  serialVersionUID:
  
  8591745505666264662L
  - Serialized Fields
    - length
```
int length
```
      Length of the vector
    - used
```
int used
```
      number of indices used in this vector
    - indexes
```
int[] indexes
```
      The mapping to true index values
    - values
```
double[] values
```
      The Corresponding values for each index
    - sumCache
```
Double sumCache
```
    - varianceCache
```
Double varianceCache
```
    - minCache
```
Double minCache
```
    - maxCache
```
Double maxCache
```
- Class jsat.linear.SubMatrix extends GenericMatrix implements Serializable
  
  serialVersionUID:
  
  8842973175562587725L
  - Serialized Fields
    - baseMatrix
```
Matrix baseMatrix
```
    - firstRow
```
int firstRow
```
    - firstColumn
```
int firstColumn
```
    - toRow
```
int toRow
```
    - toCol
```
int toCol
```
- Class jsat.linear.SubVector extends Vec implements Serializable
  
  serialVersionUID:
  
  -873882618035700676L
  - Serialized Fields
    - startPosition
```
int startPosition
```
    - length
```
int length
```
    - vec
```
Vec vec
```
- Class jsat.linear.TransposeView extends GenericMatrix implements Serializable
  
  serialVersionUID:
  
  7762422292840392481L
  - Serialized Fields
    - base
```
Matrix base
```
- Class jsat.linear.Vec extends Object implements Serializable
  
  serialVersionUID:
  
  9035784536820782955L
- Class jsat.linear.VecPaired extends Vec implements Serializable
  
  serialVersionUID:
  
  8039272826439917423L
  - Serialized Fields
    - vector
```
Vec vector
```
    - pair
```
Object pair
```
- Class jsat.linear.VecPairedComparable extends VecPaired<V extends Vec,P extends Comparable<P>> implements Serializable
  
  serialVersionUID:
  
  -7061543870162459467L
- Class jsat.linear.VecWithNorm extends Vec implements Serializable
  
  serialVersionUID:
  
  3888178071694466561L
  - Serialized Fields
    - base
```
Vec base
```
    - normSqrd
```
double normSqrd
```
Package jsat.linear.distancemetrics
- Class jsat.linear.distancemetrics.ChebyshevDistance extends Object implements Serializable
  
  serialVersionUID:
  
  2528153647402824790L
- Class jsat.linear.distancemetrics.CosineDistance extends Object implements Serializable
  
  serialVersionUID:
  
  -6475546704095989078L
- Class jsat.linear.distancemetrics.CosineDistanceNormalized extends Object implements Serializable
  
  serialVersionUID:
  
  -4041803247001806577L
- Class jsat.linear.distancemetrics.DistanceCounter extends Object implements Serializable
  - Serialized Fields
    - base
```
DistanceMetric base
```
    - counter
```
AtomicLong counter
```
- Class jsat.linear.distancemetrics.EuclideanDistance extends Object implements Serializable
  
  serialVersionUID:
  
  8155062933851345574L
- Class jsat.linear.distancemetrics.KernelDistance extends Object implements Serializable
  
  serialVersionUID:
  
  -1553315486668768024L
  - Serialized Fields
    - kf
```
KernelTrick kf
```
- Class jsat.linear.distancemetrics.MahalanobisDistance extends TrainableDistanceMetric implements Serializable
  
  serialVersionUID:
  
  7878528119699276817L
  - Serialized Fields
    - reTrain
```
boolean reTrain
```
    - S
```
Matrix S
```
      The inverse of the covariance matrix
- Class jsat.linear.distancemetrics.ManhattanDistance extends Object implements Serializable
  
  serialVersionUID:
  
  3028834823742743351L
- Class jsat.linear.distancemetrics.MinkowskiDistance extends Object implements Serializable
  
  serialVersionUID:
  
  8976696315441171045L
  - Serialized Fields
    - p
```
double p
```
- Class jsat.linear.distancemetrics.NormalizedEuclideanDistance extends TrainableDistanceMetric implements Serializable
  
  serialVersionUID:
  
  210109457671623688L
  - Serialized Fields
    - invStndDevs
```
Vec invStndDevs
```
- Class jsat.linear.distancemetrics.PearsonDistance extends Object implements Serializable
  
  serialVersionUID:
  
  1090726755301934198L
  - Serialized Fields
    - bothNonZero
```
boolean bothNonZero
```
    - absoluteDistance
```
boolean absoluteDistance
```
- Class jsat.linear.distancemetrics.SquaredEuclideanDistance extends Object implements Serializable
  
  serialVersionUID:
  
  2966818558802484702L
- Class jsat.linear.distancemetrics.TrainableDistanceMetric extends Object implements Serializable
  
  serialVersionUID:
  
  6356276953152869105L
- Class jsat.linear.distancemetrics.WeightedEuclideanDistance extends Object implements Serializable
  
  serialVersionUID:
  
  2959997330647828673L
  - Serialized Fields
    - w
```
Vec w
```
Package jsat.linear.vectorcollection
- Class jsat.linear.vectorcollection.CoverTree extends Object implements Serializable
  - Serialized Fields
    - dm
```
DistanceMetric dm
```
    - vecs
```
List<E> vecs
```
    - accell_cache
```
List<E> accell_cache
```
    - root
```
jsat.linear.vectorcollection.CoverTree.TreeNode root
```
    - maxDistDirty
```
boolean maxDistDirty
```
    - looseBounds
```
boolean looseBounds
```
- Class jsat.linear.vectorcollection.CoverTree.CoverTreeFactory extends Object implements Serializable
  
  serialVersionUID:
  
  2707446304590604519L
- Class jsat.linear.vectorcollection.DefaultVectorCollectionFactory extends Object implements Serializable
  
  serialVersionUID:
  
  -7442543159507721642L
- Class jsat.linear.vectorcollection.EuclideanCollection extends Object implements Serializable
  
  serialVersionUID:
  
  3544832051605265927L
  - Serialized Fields
    - source
```
List<E> source
```
    - dotCache
```
double[] dotCache
```
      Cache of y dot y values
- Class jsat.linear.vectorcollection.EuclideanCollection.EuclideanCollectionFactory extends Object implements Serializable
  
  serialVersionUID:
  
  4838578403165658320L
- Class jsat.linear.vectorcollection.KDTree extends Object implements Serializable
  
  serialVersionUID:
  
  -7401342201406776463L
  - Serialized Fields
    - distanceMetric
```
DistanceMetric distanceMetric
```
    - root
```
jsat.linear.vectorcollection.KDTree.KDNode root
```
    - pvSelection
```
KDTree.PivotSelection pvSelection
```
    - size
```
int size
```
    - leaf_node_size
```
int leaf_node_size
```
    - allVecs
```
List<E> allVecs
```
    - distCache
```
List<E> distCache
```
    - hr_hi
```
double[] hr_hi
```
    - hr_low
```
double[] hr_low
```
- Class jsat.linear.vectorcollection.KDTree.KDTreeFactory extends Object implements Serializable
  
  serialVersionUID:
  
  3508731608962277804L
  - Serialized Fields
    - pivotSelectionMethod
```
KDTree.PivotSelection pivotSelectionMethod
```
- Class jsat.linear.vectorcollection.RandomBallCover extends Object implements Serializable
  
  serialVersionUID:
  
  2437771973228849200L
  - Serialized Fields
    - dm
```
DistanceMetric dm
```
    - ownedVecs
```
List<E> ownedVecs
```
      The indices match with their representatives in R
    - ownedRDists
```
List<E> ownedRDists
```
      The indices match with their representatives in R and in ownedVecs. Each value indicates the distance of the point to its owner. They are not in any order
    - R
```
List<E> R
```
      The list of representatives
    - size
```
int size
```
    - allVecs
```
List<E> allVecs
```
    - distCache
```
List<E> distCache
```
    - repRadius
```
double[] repRadius
```
      Distance from representative i to its farthest neighbor it owns
- Class jsat.linear.vectorcollection.RandomBallCover.RandomBallCoverFactory extends Object implements Serializable
  
  serialVersionUID:
  
  2707446304590604519L
- Class jsat.linear.vectorcollection.RandomBallCoverOneShot extends Object implements Serializable
  
  serialVersionUID:
  
  -2562499883847452797L
  - Serialized Fields
    - dm
```
DistanceMetric dm
```
    - ownedVecs
```
List<E> ownedVecs
```
    - R
```
List<E> R
```
    - allVecs
```
List<E> allVecs
```
    - distCache
```
List<E> distCache
```
    - s
```
int s
```
      The number of points each representative will consider
    - repRadius
```
double[] repRadius
```
      Distance from representative i to its farthest neighbor it owns
- Class jsat.linear.vectorcollection.RandomBallCoverOneShot.RandomBallCoverOneShotFactory extends Object implements Serializable
  
  serialVersionUID:
  
  7658115337969827371L
- Class jsat.linear.vectorcollection.RTree extends Object implements Serializable
  
  serialVersionUID:
  
  -7067110612346062800L
  - Serialized Fields
    - size
```
int size
```
    - root
```
jsat.linear.vectorcollection.RTree.RNode<V extends Vec> root
```
    - M
```
int M
```
      Maximum number of entries per node
    - m
```
int m
```
      Minimum number of entries per node
    - dim
```
int dim
```
      The dimension of vectors stored
    - dcScratch
```
DenseVector dcScratch
```
      Scratch space for distance calculations
    - dm
```
DistanceMetric dm
```
- Class jsat.linear.vectorcollection.RTree.RTreeFactory extends Object implements Serializable
  
  serialVersionUID:
  
  5690819734453191098L
- Class jsat.linear.vectorcollection.VectorArray extends ArrayList<V extends Vec> implements Serializable
  
  serialVersionUID:
  
  5365949686370986234L
  - Serialized Fields
    - distanceMetric
```
DistanceMetric distanceMetric
```
    - distCache
```
List<E> distCache
```
- Class jsat.linear.vectorcollection.VectorArray.VectorArrayFactory extends Object implements Serializable
  
  serialVersionUID:
  
  -7470849503958877157L
- Class jsat.linear.vectorcollection.VPTree extends Object implements Serializable
  
  serialVersionUID:
  
  -7271540108746353762L
  - Serialized Fields
    - dm
```
DistanceMetric dm
```
    - distCache
```
List<E> distCache
```
    - allVecs
```
List<E> allVecs
```
    - rand
```
Random rand
```
    - sampleSize
```
int sampleSize
```
    - searchIterations
```
int searchIterations
```
    - root
```
jsat.linear.vectorcollection.VPTree.TreeNode root
```
    - vpSelection
```
VPTree.VPSelection vpSelection
```
    - size
```
int size
```
    - maxLeafSize
```
int maxLeafSize
```
- Class jsat.linear.vectorcollection.VPTree.VPTreeFactory extends Object implements Serializable
  
  serialVersionUID:
  
  -2739851193676265510L
  - Serialized Fields
    - vpSelectionMethod
```
VPTree.VPSelection vpSelectionMethod
```
- Class jsat.linear.vectorcollection.VPTreeMV extends VPTree<V extends Vec> implements Serializable
  
  serialVersionUID:
  
  6668184445206226077L
- Class jsat.linear.vectorcollection.VPTreeMV.VPTreeMVFactory extends Object implements Serializable
  
  serialVersionUID:
  
  4265451324896792148L
  - Serialized Fields
    - vpSelectionMethod
```
VPTree.VPSelection vpSelectionMethod
```
Package jsat.linear.vectorcollection.lsh
- Class jsat.linear.vectorcollection.lsh.RandomProjectionLSH extends Object implements Serializable
  
  serialVersionUID:
  
  -2042964665052386855L
  - Serialized Fields
    - randProjMatrix
```
Matrix randProjMatrix
```
    - projections
```
int[] projections
```
    - slotsPerEntry
```
int slotsPerEntry
```
    - vecs
```
List<E> vecs
```
    - tempVecs
```
ThreadLocal<T> tempVecs
```
- Class jsat.linear.vectorcollection.lsh.RandomProjectionLSH.RandomProjectionLSHFactory extends Object implements Serializable
  
  serialVersionUID:
  
  1805047681811290699L
  - Serialized Fields
    - intsToUse
```
int intsToUse
```
    - inMemory
```
boolean inMemory
```
    - poolSize
```
int poolSize
```
Package jsat.lossfunctions
- Class jsat.lossfunctions.AbsoluteLoss extends Object implements Serializable
  
  serialVersionUID:
  
  -3398199227407867808L
- Class jsat.lossfunctions.EpsilonInsensitiveLoss extends Object implements Serializable
  
  serialVersionUID:
  
  -8735274561429676350L
  - Serialized Fields
    - eps
```
double eps
```
- Class jsat.lossfunctions.HingeLoss extends Object implements Serializable
  
  serialVersionUID:
  
  -7001702646530236153L
- Class jsat.lossfunctions.HuberLoss extends Object implements Serializable
  
  serialVersionUID:
  
  -4463269746356262940L
  - Serialized Fields
    - c
```
double c
```
- Class jsat.lossfunctions.LogisticLoss extends Object implements Serializable
  
  serialVersionUID:
  
  -3929171604513497068L
- Class jsat.lossfunctions.SoftmaxLoss extends LogisticLoss implements Serializable
  
  serialVersionUID:
  
  3936898932252996024L
- Class jsat.lossfunctions.SquaredLoss extends Object implements Serializable
  
  serialVersionUID:
  
  130786305325167077L
Package jsat.math
- Class jsat.math.Complex extends Object implements Serializable
  
  serialVersionUID:
  
  -2219274170047061708L
  - Serialized Fields
    - real
```
double real
```
    - imag
```
double imag
```
- Class jsat.math.ExponentialMovingStatistics extends Object implements Serializable
  - Serialized Fields
    - mean
```
double mean
```
    - variance
```
double variance
```
    - smoothing
```
double smoothing
```
- Class jsat.math.FunctionBase extends Object implements Serializable
  
  serialVersionUID:
  
  424945317407895409L
- Class jsat.math.IndexFunction extends Object implements Serializable
  
  serialVersionUID:
  
  -7306754195712805257L
- Class jsat.math.OnLineStatistics extends Object implements Serializable
  
  serialVersionUID:
  
  -4286295481362462983L
  - Serialized Fields
    - mean
```
double mean
```
      The current mean
    - n
```
double n
```
      The current number of samples seen
    - m2
```
double m2
```
    - m3
```
double m3
```
    - m4
```
double m4
```
    - min
```
Double min
```
    - max
```
Double max
```
Package jsat.math.decayrates
- Class jsat.math.decayrates.ExponetialDecay extends Object implements Serializable
  
  serialVersionUID:
  
  4762109043998381095L
  - Serialized Fields
    - min
```
double min
```
    - maxTime
```
double maxTime
```
- Class jsat.math.decayrates.InverseDecay extends Object implements Serializable
  
  serialVersionUID:
  
  2756825625752543664L
  - Serialized Fields
    - tau
```
double tau
```
    - alpha
```
double alpha
```
- Class jsat.math.decayrates.LinearDecay extends Object implements Serializable
  
  serialVersionUID:
  
  4934146018742844875L
  - Serialized Fields
    - min
```
double min
```
    - maxTime
```
double maxTime
```
- Class jsat.math.decayrates.NoDecay extends Object implements Serializable
  
  serialVersionUID:
  
  -4502356199281880268L
- Class jsat.math.decayrates.PowerDecay extends Object implements Serializable
  
  serialVersionUID:
  
  6075066391550611699L
  - Serialized Fields
    - tau
```
double tau
```
    - alpha
```
double alpha
```
Package jsat.math.optimization
- Class jsat.math.optimization.IterativelyReweightedLeastSquares extends Object implements Serializable
  
  serialVersionUID:
  
  -6872953184371630318L
  - Serialized Fields
    - hessian
```
DenseMatrix hessian
```
      The hessian matrix
    - coefficentMatrix
```
DenseMatrix coefficentMatrix
```
      Contains the values of the coefficients for each data point
    - derivatives
```
DenseVector derivatives
```
    - errors
```
DenseVector errors
```
    - gradiant
```
DenseVector gradiant
```
- Class jsat.math.optimization.NelderMead extends Object implements Serializable
  
  serialVersionUID:
  
  -2930235371787386607L
  - Serialized Fields
    - reflection
```
double reflection
```
      Reflection constant
    - expansion
```
double expansion
```
      Expansion constant
    - contraction
```
double contraction
```
      Contraction constant
    - shrink
```
double shrink
```
      Shrink constant
- Class jsat.math.optimization.RosenbrockFunction extends Object implements Serializable
  
  serialVersionUID:
  
  -5573482950045304948L
Package jsat.math.optimization.stochastic
- Class jsat.math.optimization.stochastic.AdaDelta extends Object implements Serializable
  
  serialVersionUID:
  
  5855631993426837618L
  - Serialized Fields
    - rho
```
double rho
```
    - gSqrd
```
Vec gSqrd
```
    - deltaXSqrt
```
Vec deltaXSqrt
```
    - biasGSqrd
```
double biasGSqrd
```
    - deltaBiasSqrt
```
double deltaBiasSqrt
```
    - eps
```
double eps
```
- Class jsat.math.optimization.stochastic.AdaGrad extends Object implements Serializable
  
  serialVersionUID:
  
  5138474612999751777L
  - Serialized Fields
    - daigG
```
Vec daigG
```
    - biasG
```
double biasG
```
- Class jsat.math.optimization.stochastic.Adam extends Object implements Serializable
  
  serialVersionUID:
  
  5352504067435579553L
  - Serialized Fields
    - m
```
Vec m
```
      1st moment vector
    - v
```
Vec v
```
      2nd moment vector
    - t
```
long t
```
      time step
    - alpha
```
double alpha
```
    - beta_1
```
double beta_1
```
    - beta_2
```
double beta_2
```
    - eps
```
double eps
```
    - lambda
```
double lambda
```
    - vBias
```
double vBias
```
    - mBias
```
double mBias
```
- Class jsat.math.optimization.stochastic.NAdaGrad extends Object implements Serializable
  
  serialVersionUID:
  
  5138675613579751777L
  - Serialized Fields
    - G
```
double[] G
```
    - S
```
double[] S
```
    - N
```
double N
```
    - biasG
```
double biasG
```
    - t
```
long t
```
- Class jsat.math.optimization.stochastic.RMSProp extends Object implements Serializable
  
  serialVersionUID:
  
  3512851084092042727L
  - Serialized Fields
    - rho
```
double rho
```
    - daigG
```
Vec daigG
```
    - biasG
```
double biasG
```
- Class jsat.math.optimization.stochastic.Rprop extends Object implements Serializable
  - Serialized Fields
    - eta_pos
```
double eta_pos
```
    - eta_neg
```
double eta_neg
```
    - eta_start
```
double eta_start
```
    - eta_max
```
double eta_max
```
    - eta_min
```
double eta_min
```
    - prev_w
```
double[] prev_w
```
      holds what would be w^(t-1)
    - prev_grad
```
double[] prev_grad
```
    - cur_eta
```
double[] cur_eta
```
    - prev_grad_bias
```
double prev_grad_bias
```
    - cur_eta_bias
```
double cur_eta_bias
```
    - prev_bias
```
double prev_bias
```
- Class jsat.math.optimization.stochastic.SGDMomentum extends Object implements Serializable
  
  serialVersionUID:
  
  -3837883539010356899L
  - Serialized Fields
    - momentum
```
double momentum
```
    - nestrov
```
boolean nestrov
```
    - velocity
```
Vec velocity
```
    - biasVelocity
```
double biasVelocity
```
- Class jsat.math.optimization.stochastic.SimpleSGD extends Object implements Serializable
  
  serialVersionUID:
  
  4022442467298319553L
Package jsat.math.rootfinding
- Class jsat.math.rootfinding.Bisection extends Object implements Serializable
  
  serialVersionUID:
  
  -8107160048637997385L
- Class jsat.math.rootfinding.RiddersMethod extends Object implements Serializable
  
  serialVersionUID:
  
  8154909945080099018L
- Class jsat.math.rootfinding.Secant extends Object implements Serializable
  
  serialVersionUID:
  
  -5175113107084930582L
- Class jsat.math.rootfinding.Zeroin extends Object implements Serializable
  
  serialVersionUID:
  
  -8359510619103768778L
Package jsat.parameters
- Class jsat.parameters.BooleanParameter extends Parameter implements Serializable
  
  serialVersionUID:
  
  4961692453234546675L
- Class jsat.parameters.DecayRateParameter extends ObjectParameter<DecayRate> implements Serializable
  
  serialVersionUID:
  
  -3751128637789053385L
- Class jsat.parameters.DoubleParameter extends Parameter implements Serializable
  
  serialVersionUID:
  
  4132422231433472554L
- Class jsat.parameters.GridSearch extends ModelSearch implements Serializable
  
  serialVersionUID:
  
  -1987196172499143753L
  - Serialized Fields
    - searchValues
```
List<E> searchValues
```
      The matching list of values we will test. This includes the integer parameters, which will have to be cast back and forth from doubles.
    - useWarmStarts
```
boolean useWarmStarts
```
      Use warm starts when possible
- Class jsat.parameters.IntParameter extends Parameter implements Serializable
  
  serialVersionUID:
  
  -8467918069240345315L
- Class jsat.parameters.KernelFunctionParameter extends ObjectParameter<KernelFunction> implements Serializable
  
  serialVersionUID:
  
  2100826688956817533L
- Class jsat.parameters.MetricParameter extends Parameter implements Serializable
  
  serialVersionUID:
  
  -8525270531723322719L
- Class jsat.parameters.ModelSearch extends Object implements Serializable
  - Serialized Fields
    - baseClassifier
```
Classifier baseClassifier
```
    - trainedClassifier
```
Classifier trainedClassifier
```
    - classificationTargetScore
```
ClassificationScore classificationTargetScore
```
    - regressionTargetScore
```
RegressionScore regressionTargetScore
```
    - baseRegressor
```
Regressor baseRegressor
```
    - trainedRegressor
```
Regressor trainedRegressor
```
    - searchParams
```
List<E> searchParams
```
      The list of parameters we will search for, currently only Int and Double params should be used
    - folds
```
int folds
```
      The number of CV folds
    - trainModelsInParallel
```
boolean trainModelsInParallel
```
      If true, parallelism will be obtained by training the models in parallel. If false, parallelism is obtained from the model itself.
    - trainFinalModel
```
boolean trainFinalModel
```
      If true, trains the final model on the parameters used
    - reuseSameCVFolds
```
boolean reuseSameCVFolds
```
      If true, create the CV splits once and re-use them for all parameters
- Class jsat.parameters.ObjectParameter extends Parameter implements Serializable
  
  serialVersionUID:
  
  7639067170001873762L
- Class jsat.parameters.Parameter extends Object implements Serializable
  
  serialVersionUID:
  
  -6903844587637472657L
- Class jsat.parameters.RandomSearch extends ModelSearch implements Serializable
  - Serialized Fields
    - trials
```
int trials
```
    - searchValues
```
List<E> searchValues
```
      The matching list of distributions we will test.
Package jsat.regression
- Class jsat.regression.AveragedRegressor extends Object implements Serializable
  
  serialVersionUID:
  
  8870461208829349608L
  - Serialized Fields
    - voters
```
Regressor[] voters
```
      The array of voting regressors
- Class jsat.regression.BaseUpdateableRegressor extends Object implements Serializable
  
  serialVersionUID:
  
  -679467882721432240L
  - Serialized Fields
    - epochs
```
int epochs
```
- Class jsat.regression.KernelRidgeRegression extends Object implements Serializable
  
  serialVersionUID:
  
  6275333785663250072L
  - Serialized Fields
    - lambda
```
double lambda
```
    - k
```
KernelTrick k
```
    - vecs
```
List<E> vecs
```
    - alphas
```
double[] alphas
```
- Class jsat.regression.KernelRLS extends Object implements Serializable
  
  serialVersionUID:
  
  -7292074388953854317L
  - Serialized Fields
    - k
```
KernelTrick k
```
    - errorTolerance
```
double errorTolerance
```
    - vecs
```
List<E> vecs
```
    - kernelAccel
```
List<E> kernelAccel
```
    - K
```
Matrix K
```
    - InvK
```
Matrix InvK
```
    - P
```
Matrix P
```
    - KExpanded
```
Matrix KExpanded
```
    - InvKExpanded
```
Matrix InvKExpanded
```
    - PExpanded
```
Matrix PExpanded
```
    - alphaExpanded
```
double[] alphaExpanded
```
- Class jsat.regression.LogisticRegression extends Object implements Serializable
  
  serialVersionUID:
  
  -5115807516729861730L
  - Serialized Fields
    - coefficents
```
Vec coefficents
```
    - shift
```
double shift
```
      Logistic regression needs values on the range [0, 1]. The shift makes sure that values are on the range [0, x], for some x
    - scale
```
double scale
```
      Logistic regression needs values on the range [0, 1]. The scale makes sure that values of the form [0, x] are converted to [0, 1]
    - logitFun
```
Function logitFun
```
    - logitFunD
```
Function logitFunD
```
- Class jsat.regression.MultipleLinearRegression extends Object implements Serializable
  
  serialVersionUID:
  
  7694194181910565061L
  - Serialized Fields
    - B
```
Vec B
```
      The vector B such that Y = X * B is the least squares solution. Will be stored as Y = X * B + a
    - a
```
double a
```
      The offset value that is not multiplied by any variable
    - useWeights
```
boolean useWeights
```
- Class jsat.regression.NadarayaWatson extends Object implements Serializable
  
  serialVersionUID:
  
  8632599345930394763L
  - Serialized Fields
    - kde
```
MultivariateKDE kde
```
- Class jsat.regression.OrdinaryKriging extends Object implements Serializable
  
  serialVersionUID:
  
  -5774553215322383751L
  - Serialized Fields
    - vari
```
OrdinaryKriging.Variogram vari
```
    - X
```
Vec X
```
      The weight values for each data point
    - dataSet
```
RegressionDataSet dataSet
```
    - errorSqrd
```
double errorSqrd
```
    - nugget
```
double nugget
```
    - params
```
List<E> params
```
    - paramMap
```
Map<K,V> paramMap
```
- Class jsat.regression.RANSAC extends Object implements Serializable
  
  serialVersionUID:
  
  -5015748604828907703L
  - Serialized Fields
    - initialTrainSize
```
int initialTrainSize
```
      the minimum number of data required to fit the model
    - iterations
```
int iterations
```
      the number of iterations performed by the algorithm
    - maxPointError
```
double maxPointError
```
      a threshold value for determining when a datum fits a model
    - minResultSize
```
int minResultSize
```
      the number of close data values required to assert that a model fits well to data
    - baseRegressor
```
Regressor baseRegressor
```
    - consensusSet
```
boolean[] consensusSet
```
      True marks that the data point is part of the consensus set. False indicates it is not.
    - modelError
```
double modelError
```
- Class jsat.regression.RidgeRegression extends Object implements Serializable
  
  serialVersionUID:
  
  -4605757038780391895L
  - Serialized Fields
    - lambda
```
double lambda
```
    - w
```
Vec w
```
    - bias
```
double bias
```
    - mode
```
RidgeRegression.SolverMode mode
```
- Class jsat.regression.StochasticGradientBoosting extends Object implements Serializable
  
  serialVersionUID:
  
  -2855154397476855293L
  - Serialized Fields
    - trainingProportion
```
double trainingProportion
```
      The proportion of the data set to be used for each iteration of training. The points that make up the iteration are a random sampling without replacement.
    - weakLearner
```
Regressor weakLearner
```
    - strongLearner
```
Regressor strongLearner
```
    - F
```
List<E> F
```
      The ordered list of weak learners
    - coef
```
List<E> coef
```
      The list of learner coefficients for each weak learner.
    - learningRate
```
double learningRate
```
    - maxIterations
```
int maxIterations
```
- Class jsat.regression.StochasticRidgeRegression extends Object implements Serializable
  
  serialVersionUID:
  
  -3462783438115627128L
  - Serialized Fields
    - lambda
```
double lambda
```
    - epochs
```
int epochs
```
    - batchSize
```
int batchSize
```
    - learningRate
```
double learningRate
```
    - learningDecay
```
DecayRate learningDecay
```
    - w
```
Vec w
```
    - bias
```
double bias
```
Package jsat.regression.evaluation
- Class jsat.regression.evaluation.CoefficientOfDetermination extends TotalHistoryRegressionScore implements Serializable
  
  serialVersionUID:
  
  1215708502913888821L
- Class jsat.regression.evaluation.MeanAbsoluteError extends Object implements Serializable
  
  serialVersionUID:
  
  -637676526509989776L
  - Serialized Fields
    - absError
```
OnLineStatistics absError
```
- Class jsat.regression.evaluation.MeanSquaredError extends Object implements Serializable
  
  serialVersionUID:
  
  3655567184376550126L
  - Serialized Fields
    - meanError
```
OnLineStatistics meanError
```
    - rmse
```
boolean rmse
```
- Class jsat.regression.evaluation.RelativeAbsoluteError extends TotalHistoryRegressionScore implements Serializable
  
  serialVersionUID:
  
  -6152988968756871647L
- Class jsat.regression.evaluation.RelativeSquaredError extends TotalHistoryRegressionScore implements Serializable
  
  serialVersionUID:
  
  8377798320269626429L
- Class jsat.regression.evaluation.TotalHistoryRegressionScore extends Object implements Serializable
  
  serialVersionUID:
  
  -5262934560490160236L
  - Serialized Fields
    - truths
```
DoubleList truths
```
      List of the true target values
    - predictions
```
DoubleList predictions
```
      List of the predict values for each target
    - weights
```
DoubleList weights
```
      The weight of importance for each point
Package jsat.text
- Class jsat.text.BasicTextVectorCreator extends Object implements Serializable
  
  serialVersionUID:
  
  -8620485679300539556L
  - Serialized Fields
    - tokenizer
```
Tokenizer tokenizer
```
    - wordIndex
```
Map<K,V> wordIndex
```
    - weighting
```
WordWeighting weighting
```
- Class jsat.text.ClassificationHashedTextDataLoader extends HashedTextDataLoader implements Serializable
  
  serialVersionUID:
  
  -1350008848821058696L
  - Serialized Fields
    - classLabels
```
List<E> classLabels
```
      The list of the true class labels for the data that was loaded before HashedTextDataLoader.finishAdding() was called.
    - labelInfo
```
CategoricalData labelInfo
```
      The information about the class label that would be predicted for a classification data set.
- Class jsat.text.ClassificationTextDataLoader extends TextDataLoader implements Serializable
  
  serialVersionUID:
  
  -3826551504785236576L
  - Serialized Fields
    - classLabels
```
List<E> classLabels
```
      The list of the true class labels for the data that was loaded before TextDataLoader.finishAdding() was called.
    - labelInfo
```
CategoricalData labelInfo
```
      The information about the class label that would be predicted for a classification data set.
- Class jsat.text.HashedTextDataLoader extends Object implements Serializable
  
  serialVersionUID:
  
  8513621180409278670L
  - Serialized Fields
    - dimensionSize
```
int dimensionSize
```
    - tokenizer
```
Tokenizer tokenizer
```
      Tokenizer to apply to input strings
    - weighting
```
WordWeighting weighting
```
    - vectors
```
List<E> vectors
```
      List of original vectors
    - termDocumentFrequencys
```
AtomicIntegerArray termDocumentFrequencys
```
    - noMoreAdding
```
boolean noMoreAdding
```
    - documents
```
int documents
```
    - workSpace
```
ThreadLocal<T> workSpace
```
      Temporary work space to use for tokenization
    - storageSpace
```
ThreadLocal<T> storageSpace
```
      Temporary storage space to use for tokenization
    - wordCounts
```
ThreadLocal<T> wordCounts
```
      Temporary space to use when creating vectors
    - tvc
```
TextVectorCreator tvc
```
- Class jsat.text.HashedTextVectorCreator extends Object implements Serializable
  
  serialVersionUID:
  
  1081388790985568192L
  - Serialized Fields
    - dimensionSize
```
int dimensionSize
```
    - tokenizer
```
Tokenizer tokenizer
```
    - weighting
```
WordWeighting weighting
```
- Class jsat.text.TextDataLoader extends Object implements Serializable
  
  serialVersionUID:
  
  -657253682338792871L
  - Serialized Fields
    - vectors
```
List<E> vectors
```
      List of original vectors
    - tokenizer
```
Tokenizer tokenizer
```
      Tokenizer to apply to input strings
    - wordIndex
```
ConcurrentHashMap<K,V> wordIndex
```
      Maps words to their associated index in an array
    - allWords
```
List<E> allWords
```
      list of all word tokens encountered in order of first observation
    - termDocumentFrequencys
```
ConcurrentHashMap<K,V> termDocumentFrequencys
```
      The map of integer counts of how many times each word token was seen. Key is the index of the word, value is the number of times it was seen. Using a map instead of a list so that it can be updated in a efficient thread safe way
    - weighting
```
WordWeighting weighting
```
    - workSpace
```
ThreadLocal<T> workSpace
```
      Temporary work space to use for tokenization
    - storageSpace
```
ThreadLocal<T> storageSpace
```
      Temporary storage space to use for tokenization
    - wordCounts
```
ThreadLocal<T> wordCounts
```
      Temporary space to use when creating vectors
    - tvc
```
TextVectorCreator tvc
```
    - noMoreAdding
```
boolean noMoreAdding
```
      true when TextDataLoader.finishAdding() is called, and no new original documents can be inserted
    - currentLength
```
AtomicInteger currentLength
```
    - documents
```
int documents
```
Package jsat.text.stemming
- Class jsat.text.stemming.LovinsStemmer extends Stemmer implements Serializable
  
  serialVersionUID:
  
  -3229865664217642197L
- Class jsat.text.stemming.PaiceHuskStemmer extends Stemmer implements Serializable
  
  serialVersionUID:
  
  -5949389288166850651L
- Class jsat.text.stemming.PorterStemmer extends Stemmer implements Serializable
  
  serialVersionUID:
  
  -3809291457988435043L
- Class jsat.text.stemming.Stemmer extends Object implements Serializable
  
  serialVersionUID:
  
  1889842876393488149L
- Class jsat.text.stemming.VoidStemmer extends Stemmer implements Serializable
  
  serialVersionUID:
  
  -5059926028932641447L
Package jsat.text.tokenizer
- Class jsat.text.tokenizer.NaiveTokenizer extends Object implements Serializable
  
  serialVersionUID:
  
  -2112091783442076933L
  - Serialized Fields
    - useLowerCase
```
boolean useLowerCase
```
    - otherToWhiteSpace
```
boolean otherToWhiteSpace
```
    - noDigits
```
boolean noDigits
```
    - minTokenLength
```
int minTokenLength
```
    - maxTokenLength
```
int maxTokenLength
```
- Class jsat.text.tokenizer.NGramTokenizer extends Object implements Serializable
  
  serialVersionUID:
  
  7551087420391197139L
  - Serialized Fields
    - n
```
int n
```
      The number of n-grams to generate
    - base
```
Tokenizer base
```
      The base tokenizer
    - allSubN
```
boolean allSubN
```
      whether or not to generate all sub n-grams
- Class jsat.text.tokenizer.StemmingTokenizer extends Object implements Serializable
  
  serialVersionUID:
  
  2883247633791522390L
  - Serialized Fields
    - stemmer
```
Stemmer stemmer
```
    - baseTokenizer
```
Tokenizer baseTokenizer
```
- Class jsat.text.tokenizer.StopWordTokenizer extends Object implements Serializable
  
  serialVersionUID:
  
  445704970760705567L
  - Serialized Fields
    - base
```
Tokenizer base
```
    - stopWords
```
Set<E> stopWords
```
Package jsat.text.wordweighting
- Class jsat.text.wordweighting.BinaryWordPresent extends WordWeighting implements Serializable
  
  serialVersionUID:
  
  5633647387188363706L
- Class jsat.text.wordweighting.OkapiBM25 extends WordWeighting implements Serializable
  
  serialVersionUID:
  
  6456657674702490465L
  - Serialized Fields
    - k1
```
double k1
```
    - b
```
double b
```
    - N
```
double N
```
    - docAvg
```
double docAvg
```
    - df
```
int[] df
```
      Okapi document frequency is the number of documents that contain a term, not the number of times it occurs
- Class jsat.text.wordweighting.TfIdf extends WordWeighting implements Serializable
  
  serialVersionUID:
  
  5749882005002311735L
  - Serialized Fields
    - totalDocuments
```
double totalDocuments
```
    - df
```
List<E> df
```
    - docMax
```
double docMax
```
    - tfWeighting
```
TfIdf.TermFrequencyWeight tfWeighting
```
- Class jsat.text.wordweighting.WordCount extends WordWeighting implements Serializable
  
  serialVersionUID:
  
  4665749166722300326L
- Class jsat.text.wordweighting.WordWeighting extends IndexFunction implements Serializable
  
  serialVersionUID:
  
  2372760149718829334L
Package jsat.utils
- Class jsat.utils.BoundedSortedList extends ArrayList<E extends Comparable<E>> implements Serializable
  
  serialVersionUID:
  
  5503813399376102571L
  - Serialized Fields
    - maxSize
```
int maxSize
```
- Class jsat.utils.BoundedSortedSet extends TreeSet<V> implements Serializable
  
  serialVersionUID:
  
  -4774987058243433217L
  - Serialized Fields
    - maxSize
```
int maxSize
```
- Class jsat.utils.DoubleList extends AbstractList<Double> implements Serializable
  
  serialVersionUID:
  
  653930294509274337L
  - Serialized Fields
    - array
```
double[] array
```
    - end
```
int end
```
- Class jsat.utils.IndexTable extends Object implements Serializable
  
  serialVersionUID:
  
  -1917765351445664286L
  - Serialized Fields
    - index
```
IntList index
```
      We use an array of Integer objects instead of integers because we need the arrays.sort function that accepts comparators.
    - prevSize
```
int prevSize
```
      The size of the previously sorted array or list
- Class jsat.utils.IntList extends AbstractList<Integer> implements Serializable
  
  serialVersionUID:
  
  8189142393005394477L
  - Serialized Fields
    - array
```
int[] array
```
    - end
```
int end
```
- Class jsat.utils.IntPriorityQueue extends AbstractQueue<Integer> implements Serializable
  
  serialVersionUID:
  
  -310756323843109562L
  - Serialized Fields
    - heap
```
int[] heap
```
    - size
```
int size
```
    - comparator
```
Comparator<T> comparator
```
    - valueIndexMap
```
HashMap<K,V> valueIndexMap
```
    - valueIndexStore
```
int[] valueIndexStore
```
    - fastValueRemove
```
IntPriorityQueue.Mode fastValueRemove
```
- Class jsat.utils.IntSet extends AbstractSet<Integer> implements Serializable
  
  serialVersionUID:
  
  -2175363824037596497L
  - Serialized Fields
    - loadFactor
```
float loadFactor
```
    - used
```
int used
```
    - status
```
byte[] status
```
    - keys
```
int[] keys
```
- Class jsat.utils.IntSetFixedSize extends AbstractSet<Integer> implements Serializable
  
  serialVersionUID:
  
  7743166074116253587L
  - Serialized Fields
    - nnz
```
int nnz
```
    - first
```
int first
```
    - has
```
boolean[] has
```
    - prev
```
int[] prev
```
    - next
```
int[] next
```
- Class jsat.utils.IntSortedSet extends AbstractSet<Integer> implements Serializable
  
  serialVersionUID:
  
  -2675363824037596497L
  - Serialized Fields
    - store
```
int[] store
```
    - size
```
int size
```
- Class jsat.utils.LongList extends AbstractList<Long> implements Serializable
  
  serialVersionUID:
  
  3060216677615816178L
  - Serialized Fields
    - array
```
long[] array
```
    - end
```
int end
```
- Class jsat.utils.ProbailityMatch extends Object implements Serializable
  
  serialVersionUID:
  
  -1544116376166946986L
  - Serialized Fields
    - probability
```
double probability
```
    - match
```
Object match
```
- Class jsat.utils.SimpleList extends AbstractList<E> implements Serializable
  
  serialVersionUID:
  
  -1641584937585415217L
  - Serialized Fields
    - source
```
Object[] source
```
    - size
```
int size
```
- Class jsat.utils.SortedArrayList extends ArrayList<T extends Comparable<T>> implements Serializable
  
  serialVersionUID:
  
  -8728381865616791954L
Package jsat.utils.concurrent
- Class jsat.utils.concurrent.AtomicDoubleArray extends Object implements Serializable
  
  serialVersionUID:
  
  -8799170460903375842L
  - Serialized Fields
    - larray
```
AtomicLongArray larray
```
Package jsat.utils.random
- Class jsat.utils.random.CMWC4096 extends Random implements Serializable
  
  serialVersionUID:
  
  -5061963074440046713L
  - Serialized Fields
    - c
```
int c
```
    - i
```
int i
```
    - Q
```
int[] Q
```
- Class jsat.utils.random.XOR128 extends Random implements Serializable
  
  serialVersionUID:
  
  -5218902638864900490L
  - Serialized Fields
    - x
```
long x
```
    - y
```
long y
```
    - z
```
long z
```
    - w
```
long w
```
- Class jsat.utils.random.XOR96 extends Random implements Serializable
  
  serialVersionUID:
  
  1247900882148980639L
  - Serialized Fields
    - x
```
long x
```
    - y
```
long y
```
    - z
```
long z
```
- Class jsat.utils.random.XORWOW extends Random implements Serializable
  
  serialVersionUID:
  
  4516396552618366318L
  - Serialized Fields
    - x
```
long x
```
    - y
```
long y
```
    - z
```
long z
```
    - w
```
long w
```
    - v
```
long v
```
    - d
```
long d
```

Serialized Form

Package jsat.classifiers

Class jsat.classifiers.BaseUpdateableClassifier extends Object implements Serializable

Serialized Fields

epochs

Class jsat.classifiers.CategoricalData extends Object implements Serializable

Serialized Fields

n

catNames

categoryName

Class jsat.classifiers.CategoricalResults extends Object implements Serializable

Serialized Fields

n

probabilities

Class jsat.classifiers.DataPoint extends Object implements Serializable

Serialized Fields

weight

numericalValues

categoricalValues

categoricalData

Class jsat.classifiers.DataPointPair extends Object implements Serializable

Serialized Fields

dataPoint

pair

Class jsat.classifiers.DDAG extends OneVSOne implements Serializable

Class jsat.classifiers.MajorityVote extends Object implements Serializable

Serialized Fields

voters

Class jsat.classifiers.MultinomialLogisticRegression extends Object implements Serializable

Serialized Fields

classCoefficents

Class jsat.classifiers.OneVSAll extends Object implements Serializable

Serialized Fields

oneVsAlls

baseClassifier

predicting

concurrentTraining

useScoreIfAvailable

Class jsat.classifiers.OneVSOne extends Object implements Serializable

Serialized Fields

baseClassifier

oneVone

concurrentTrain

predicting

Class jsat.classifiers.PriorClassifier extends Object implements Serializable

Serialized Fields

cr

Class jsat.classifiers.RegressorToClassifier extends Object implements Serializable

Serialized Fields

regressor

Class jsat.classifiers.Rocchio extends Object implements Serializable

Serialized Fields

rocVecs

dm

dsdm

summaryConsts

Package jsat.classifiers.bayesian

Class jsat.classifiers.bayesian.AODE extends BaseUpdateableClassifier implements Serializable

Serialized Fields

predicting

odes

m

Class jsat.classifiers.bayesian.BestClassDistribution extends Object implements Serializable

Serialized Fields

baseDist

dists

priors

usePriors

Class jsat.classifiers.bayesian.ConditionalProbabilityTable extends Object implements Serializable

Serialized Fields

predicting

countArray

valid

realIndexToCatIndex

catIndexToRealIndex

dimSize

predictingIndex

Class jsat.classifiers.bayesian.MultinomialNaiveBayes extends BaseUpdateableClassifier implements Serializable

Serialized Fields

apriori