Index (h2o-algos 3.46.0 API)

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

A

B

backwardSolve(double[][], double[], double[]) - Static method in class hex.util.LinearAlgebraUtils
balance_classes - Variable in class hex.schemas.ANOVAGLMV3.ANOVAGLMParametersV3: For imbalanced data, balance training data class counts via over/under-sampling.
balance_classes - Variable in class hex.schemas.DeepLearningV3.DeepLearningParametersV3: For imbalanced data, balance training data class counts via over/under-sampling.
balance_classes - Variable in class hex.schemas.GAMV3.GAMParametersV3: For imbalanced data, balance training data class counts via over/under-sampling.
balance_classes - Variable in class hex.schemas.GLMV3.GLMParametersV3: For imbalanced data, balance training data class counts via over/under-sampling.
balance_classes - Variable in class hex.schemas.ModelSelectionV3.ModelSelectionParametersV3: For imbalanced data, balance training data class counts via over/under-sampling.
balance_classes - Variable in class hex.schemas.NaiveBayesV3.NaiveBayesParametersV3: For imbalanced data, balance training data class counts via over/under-sampling.
balance_classes - Variable in class hex.schemas.SharedTreeV3.SharedTreeParametersV3: For imbalanced data, balance training data class counts via over/under-sampling.
base_models - Variable in class hex.schemas.StackedEnsembleV99.StackedEnsembleParametersV99
bestCol(DTree.UndecidedNode, DHistogram[], Constraints) - Method in class hex.tree.DTree.DecidedNode
bestSubmodel() - Method in class hex.glm.GLMModel.GLMOutput
bestSubmodelIndex() - Method in class hex.glm.GLMModel.GLMOutput
beta() - Method in class hex.glm.ComputationState
beta - Variable in class hex.glm.ComputationState.GramGrad
beta() - Method in class hex.glm.GLMModel
beta(double) - Method in class hex.glm.GLMModel
beta() - Method in class hex.glm.GLMModel.GLMOutput
beta - Variable in class hex.glm.GLMModel.Submodel
beta() - Method in class hex.modelselection.ModelSelectionModel.ModelSelectionModelOutput
beta - Variable in class hex.schemas.MakeGLMModelV3
beta_constraints - Variable in class hex.schemas.GAMV3.GAMParametersV3
beta_constraints - Variable in class hex.schemas.GLMV3.GLMParametersV3
beta_constraints - Variable in class hex.schemas.ModelSelectionV3.ModelSelectionParametersV3
beta_epsilon - Variable in class hex.schemas.GAMV3.GAMParametersV3
beta_epsilon - Variable in class hex.schemas.GLMV3.GLMParametersV3
beta_epsilon - Variable in class hex.schemas.ModelSelectionV3.ModelSelectionParametersV3
beta_internal() - Method in class hex.glm.GLMModel
beta_std(double) - Method in class hex.glm.GLMModel
BetaConstraint() - Constructor for class hex.glm.GLM.BetaConstraint
BetaConstraint(Frame) - Constructor for class hex.glm.GLM.BetaConstraint
BetaInfo(int, int) - Constructor for class hex.glm.GLM.BetaInfo
betaMultinomial() - Method in class hex.glm.ComputationState
betaMultinomial(int, double[]) - Method in class hex.glm.ComputationState
betaMultinomialFull(int, double[]) - Method in class hex.glm.ComputationState
bin(double) - Method in class hex.tree.DHistogram
bin() - Method in class hex.tree.DTree.Split
binaryEntropy() - Method in class hex.tree.dt.binning.SplitStatistics
binAt(int) - Method in class hex.tree.DHistogram
binIds - Variable in class hex.DataInfo.Row
BinningStrategy - Enum in hex.tree.dt.binning: Strategy for binning.
binomial_double_trees - Variable in class hex.schemas.DRFV3.DRFParametersV3
binomialOpt() - Method in class hex.tree.drf.DRFModel
binomialOpt() - Method in class hex.tree.SharedTreeModel
binomialOpt() - Method in class hex.tree.uplift.UpliftDRFModel
bins(int) - Method in class hex.tree.DHistogram
bits() - Method in class hex.deeplearning.Dropout
blending() - Method in class hex.ensemble.StackedEnsembleModel.StackedEnsembleParameters
blending_frame - Variable in class hex.schemas.StackedEnsembleV99.StackedEnsembleParametersV99
BMulInPlaceTask(DataInfo, double[][], int) - Constructor for class hex.util.LinearAlgebraUtils.BMulInPlaceTask
BMulInPlaceTask(DataInfo, double[][], int, boolean) - Constructor for class hex.util.LinearAlgebraUtils.BMulInPlaceTask
BMulTask(Key<Job>, DataInfo, double[][]) - Constructor for class hex.util.LinearAlgebraUtils.BMulTask
BMulTaskMatrices(Frame) - Constructor for class hex.util.LinearAlgebraUtils.BMulTaskMatrices
bprop(int) - Method in class hex.deeplearning.Neurons: Back propagation of error terms stored in _e (for non-final layers)
bprop(int) - Method in class hex.deeplearning.Neurons.ExpRectifier
bprop(int) - Method in class hex.deeplearning.Neurons.Input
bprop(int) - Method in class hex.deeplearning.Neurons.Maxout
bprop(int) - Method in class hex.deeplearning.Neurons.Output
bprop(int) - Method in class hex.deeplearning.Neurons.Rectifier
bprop(int) - Method in class hex.deeplearning.Neurons.Tanh
bpropMiniBatch(Neurons[], int) - Static method in class hex.deeplearning.DeepLearningTask: Helper to apply back-propagation without clearing out the gradients afterwards Used for gradient checking
bpropOutputLayer(int) - Method in class hex.deeplearning.Neurons: Back-propagate gradient in output layer
BranchInteractionConstraints - Class in hex.tree: Local branch interaction constraints class to save information about allowed interaction between columns indices
BranchInteractionConstraints(IcedHashSet<IcedInt>) - Constructor for class hex.tree.BranchInteractionConstraints
bs - Variable in class hex.schemas.GAMV3.GAMParametersV3
bsv_count - Variable in class hex.schemas.PSVMModelV3.PSVMModelOutputV3
BufStringDecisionPathTracker() - Constructor for class hex.tree.SharedTreeModel.BufStringDecisionPathTracker
build_glm_model - Variable in class hex.schemas.ModelSelectionV3.ModelSelectionParametersV3
build_null_model - Variable in class hex.schemas.GLMV3.GLMParametersV3
build_tree_one_node - Variable in class hex.schemas.SharedTreeV3.SharedTreeParametersV3
buildCalibrationModel(CalibrationHelper.ModelBuilderWithCalibration<M, P, O>, CalibrationHelper.ParamsWithCalibration, Job, M) - Static method in class hex.tree.CalibrationHelper
buildCVGamModels(GAMModel, GLMModel, GAMModel.GAMParameters, String) - Static method in class hex.gam.MatrixFrameUtils.GamUtils
builderVisibility() - Method in class hex.adaboost.AdaBoost
builderVisibility() - Method in class hex.aggregator.Aggregator
builderVisibility() - Method in class hex.coxph.CoxPH
builderVisibility() - Method in class hex.ensemble.StackedEnsemble
builderVisibility() - Method in class hex.gam.GAM
builderVisibility() - Method in class hex.generic.Generic
builderVisibility() - Method in class hex.grep.Grep
builderVisibility() - Method in class hex.isotonic.IsotonicRegression
builderVisibility() - Method in class hex.psvm.PSVM
builderVisibility() - Method in class hex.svd.SVD
builderVisibility() - Method in class hex.tree.dt.DT
builderVisibility() - Method in class hex.tree.isofor.IsolationForest
builderVisibility() - Method in class hex.word2vec.Word2Vec
buildExtractBestR2Model(Frame[], ModelSelectionModel.ModelSelectionParameters, int, String, Model.Parameters.FoldAssignmentScheme) - Static method in class hex.modelselection.ModelSelection: Given the training Frame array, build models for each training frame and return the GLMModel with the best R2 values.
buildGamFrame(GAMModel.GAMParameters, Frame, Key<Frame>[], String) - Static method in class hex.gam.MatrixFrameUtils.GamUtils
buildGammaGLM(Frame, Frame, int[], long, long, boolean) - Method in class hex.glm.GLM.GLMDriver: This method will generate a training frame according to HGLM doc, build a gamma GLM model with dispersion parameter set to 1 if enabled and calcluate the p-value if enabled.
buildGLMBuilders(GLMModel.GLMParameters[]) - Static method in class hex.anovaglm.ANOVAGLMUtils
buildGLMBuilders(GLMModel.GLMParameters[]) - Static method in class hex.modelselection.ModelSelectionUtils
buildGLMModel(List<Integer>) - Method in class hex.modelselection.ModelSelection.ModelSelectionDriver
buildGLMParameters(Frame[], ANOVAGLMModel.ANOVAGLMParameters) - Static method in class hex.anovaglm.ANOVAGLMUtils
buildLayer(Frame, int, DTree[], int[], DHistogram[][][], boolean) - Method in class hex.tree.SharedTree
buildLayer(Frame, int, DTree, int[], DHistogram[][][], boolean) - Method in class hex.tree.uplift.UpliftDRF
buildModel() - Method in class hex.deeplearning.DeepLearning.DeepLearningDriver: Train a Deep Learning model, assumes that all members are populated If checkpoint == null, then start training a new model, otherwise continue from a checkpoint
buildModel() - Method in class hex.modelselection.ModelSelection.ModelSelectionDriver
buildNextKTrees() - Method in class hex.tree.SharedTree.Driver
buildNextNode(Queue<DataFeaturesLimits>, int) - Method in class hex.tree.dt.DT: Build next node from the first limits in queue.
buildRandomCoefficients2DTable(double[], String[]) - Method in class hex.schemas.GLMModelV3.GLMModelOutputV3
buildRIDFrame(GLMModel.GLMParameters, Frame, Frame) - Static method in class hex.glm.GLMUtils
buildSpecificFrame(int[], Frame, String[][], ANOVAGLMModel.ANOVAGLMParameters) - Static method in class hex.anovaglm.ANOVAGLMUtils: This method is used to attach the weight/offset columns if they exist and the response columns, specific transformed columns to a training frames.
buildTable(String[], boolean) - Method in class hex.glrm.GLRM.Archetypes
buildTrainingFrames(Key<Frame>, int, String[][], ANOVAGLMModel.ANOVAGLMParameters) - Static method in class hex.anovaglm.ANOVAGLMUtils: This method will take the frame that contains transformed columns of predictor A, predictor B, interaction of predictor A and B and generate new training frames that contains the following columns: - transformed columns of predictor B, interaction of predictor A and B, response - transformed columns of predictor A, interaction of predictor A and B, response - transformed columns of predictor A, predictor B, response - transformed columns of predictor A, predictor B, interaction of predictor A and B, response The same logic applies if there are more than two individual predictors.
buildTree(double[][], long, int) - Method in class hex.tree.isoforextended.isolationtree.IsolationTree: Implementation of Algorithm 2 (iTree) from paper.
buildVariableInflationFactors(Frame, DataInfo) - Method in class hex.glm.GLMModel: This method will calculate the variable inflation factor of each numerical predictor using the following procedure: 1.
buildVariableInflationFactors(GLMModel.GLMParameters, String[], String[]) - Method in class hex.glm.GLMModel
bulkScore0(Chunk[]) - Method in interface hex.psvm.BulkSupportVectorScorer
BulkScorerFactory - Class in hex.psvm
BulkScorerFactory() - Constructor for class hex.psvm.BulkScorerFactory
BulkSupportVectorScorer - Interface in hex.psvm

C

D

E

F

G

H

h(Frame, String[], double, SharedTreeSubgraph[][]) - Static method in class hex.tree.FriedmanPopescusH
handlesSparseData() - Method in class hex.FrameTask2
handlesSparseData() - Method in class hex.glm.GLMTask.GLMIterationTask
hasBounds() - Method in class hex.glm.GLM.BetaConstraint
hasClosedForm(long) - Method in class hex.glrm.GLRM
hasGradient() - Method in class hex.glm.GLM.GramSolver
hasGradient() - Method in class hex.glm.GLM.ProximalGradientSolver
hasGradient() - Method in interface hex.optimization.ADMM.ProximalSolver
hash(byte[], int, int) - Method in class hex.deeplearning.MurmurHash
hashCode() - Method in class hex.rulefit.Condition
hashCode() - Method in class hex.rulefit.Rule
hashCode() - Method in class hex.tree.SharedTree.SharedTreeDebugParams
hasNABin() - Method in class hex.tree.DHistogram
hasNaNsOrInf() - Method in class hex.glm.GLMTask.GLMIterationTask
hasNaNsOrInfs() - Method in class hex.gram.Gram
hasProximalPenalty() - Method in class hex.glm.GLM.BetaConstraint
hasPValues() - Method in class hex.glm.GLMModel.GLMOutput
hasResponse() - Method in class hex.tree.isofor.IsolationForestModel.IsolationForestOutput
hasStartColumn() - Method in class hex.coxph.CoxPH
hasTreatment() - Method in class hex.generic.GenericModelOutput
hasVIF() - Method in class hex.glm.GLMModel.GLMOutput
hasZeroWeight() - Method in class hex.tree.CompressedTree
haveMojo() - Method in class hex.adaboost.AdaBoost
haveMojo() - Method in class hex.anovaglm.ANOVAGLM
haveMojo() - Method in class hex.coxph.CoxPH
haveMojo() - Method in class hex.deeplearning.DeepLearning
haveMojo() - Method in class hex.ensemble.StackedEnsemble
haveMojo() - Method in class hex.ensemble.StackedEnsembleModel
haveMojo() - Method in class hex.gam.GAM
haveMojo() - Method in class hex.generic.Generic
haveMojo() - Method in class hex.glm.GLM
haveMojo() - Method in class hex.glm.GLMModel
haveMojo() - Method in class hex.glrm.GLRM
haveMojo() - Method in class hex.isotonic.IsotonicRegression
haveMojo() - Method in class hex.isotonic.IsotonicRegressionModel
haveMojo() - Method in class hex.kmeans.KMeans
haveMojo() - Method in class hex.modelselection.ModelSelection
haveMojo() - Method in class hex.naivebayes.NaiveBayes
haveMojo() - Method in class hex.pca.PCA
haveMojo() - Method in class hex.rulefit.RuleFit
haveMojo() - Method in class hex.rulefit.RuleFitModel
haveMojo() - Method in class hex.svd.SVD
haveMojo() - Method in class hex.tree.isofor.IsolationForest
haveMojo() - Method in class hex.tree.isoforextended.ExtendedIsolationForest
haveMojo() - Method in class hex.tree.SharedTree
haveMojo() - Method in class hex.tree.uplift.UpliftDRF
haveMojo() - Method in class hex.word2vec.Word2Vec
havePojo() - Method in class hex.adaboost.AdaBoost
havePojo() - Method in class hex.anovaglm.ANOVAGLM
havePojo() - Method in class hex.deeplearning.DeepLearning
havePojo() - Method in class hex.gam.GAM
havePojo() - Method in class hex.generic.GenericModel
havePojo() - Method in class hex.glm.GLM
havePojo() - Method in class hex.glm.GLMModel
havePojo() - Method in class hex.glrm.GLRM
havePojo() - Method in class hex.kmeans.KMeans
havePojo() - Method in class hex.modelselection.ModelSelection
havePojo() - Method in class hex.naivebayes.NaiveBayes
havePojo() - Method in class hex.pca.PCA
havePojo() - Method in class hex.svd.SVD
havePojo() - Method in class hex.tree.isofor.IsolationForest
havePojo() - Method in class hex.tree.isoforextended.ExtendedIsolationForest
havePojo() - Method in class hex.tree.SharedTree
havePojo() - Method in class hex.tree.uplift.UpliftDRF
HelpercAIC(boolean, double) - Constructor for class hex.glm.GLMTask.HelpercAIC
hex - package hex
hex.adaboost - package hex.adaboost
hex.aggregator - package hex.aggregator
hex.anovaglm - package hex.anovaglm
hex.api - package hex.api
hex.coxph - package hex.coxph
hex.deeplearning - package hex.deeplearning
hex.ensemble - package hex.ensemble
hex.gam - package hex.gam
hex.gam.GamSplines - package hex.gam.GamSplines
hex.gam.MatrixFrameUtils - package hex.gam.MatrixFrameUtils
hex.generic - package hex.generic
hex.glm - package hex.glm
hex.glrm - package hex.glrm
hex.gram - package hex.gram
hex.grep - package hex.grep
hex.isotonic - package hex.isotonic
hex.kmeans - package hex.kmeans
hex.modelselection - package hex.modelselection
hex.naivebayes - package hex.naivebayes
hex.optimization - package hex.optimization
hex.pca - package hex.pca
hex.pca.jama - package hex.pca.jama
hex.pca.mtj - package hex.pca.mtj
hex.psvm - package hex.psvm
hex.psvm.psvm - package hex.psvm.psvm
hex.rulefit - package hex.rulefit
hex.schemas - package hex.schemas
hex.splitframe - package hex.splitframe
hex.svd - package hex.svd
hex.tree - package hex.tree
hex.tree.drf - package hex.tree.drf
hex.tree.dt - package hex.tree.dt
hex.tree.dt.binning - package hex.tree.dt.binning
hex.tree.dt.mrtasks - package hex.tree.dt.mrtasks
hex.tree.gbm - package hex.tree.gbm
hex.tree.isofor - package hex.tree.isofor
hex.tree.isoforextended - package hex.tree.isoforextended
hex.tree.isoforextended.isolationtree - package hex.tree.isoforextended.isolationtree
hex.tree.uplift - package hex.tree.uplift
hex.util - package hex.util
hex.word2vec - package hex.word2vec
HGLM - Variable in class hex.schemas.GLMV3.GLMParametersV3
hidden - Variable in class hex.schemas.DeepLearningV3.DeepLearningParametersV3: The number and size of each hidden layer in the model.
hidden_dropout_ratios - Variable in class hex.schemas.DeepLearningV3.DeepLearningParametersV3: A fraction of the inputs for each hidden layer to be omitted from training in order to improve generalization.
highest_interaction_term - Variable in class hex.schemas.ANOVAGLMV3.ANOVAGLMParametersV3
Histogram - Class in hex.tree.dt.binning
Histogram(Frame, DataFeaturesLimits, BinningStrategy) - Constructor for class hex.tree.dt.binning.Histogram
histogram_type - Variable in class hex.schemas.SharedTreeV3.SharedTreeParametersV3
History(int, int) - Constructor for class hex.optimization.L_BFGS.History
hyper_param - Variable in class hex.schemas.PSVMV3.PSVMParametersV3

I

J

K

L

l - Variable in class hex.glm.GLMModel.GLMWeights
l1 - Variable in class hex.schemas.DeepLearningV3.DeepLearningParametersV3: A regularization method that constrains the absolute value of the weights and has the net effect of dropping some weights (setting them to zero) from a model to reduce complexity and avoid overfitting.
l1pen() - Method in class hex.glm.ComputationState
L1Solver(double, int, double[]) - Constructor for class hex.optimization.ADMM.L1Solver
L1Solver(double, int, double, double, double[]) - Constructor for class hex.optimization.ADMM.L1Solver
l2 - Variable in class hex.schemas.DeepLearningV3.DeepLearningParametersV3: A regularization method that constrains the sum of the squared weights.
l2pen() - Method in class hex.glm.ComputationState
L_BFGS - Class in hex.optimization: Created by tomasnykodym on 9/15/14.
L_BFGS() - Constructor for class hex.optimization.L_BFGS
L_BFGS.History - Class in hex.optimization: Keeps L-BFGS history ie curvature information recorded over the last m steps.
L_BFGS.ProgressMonitor - Interface in hex.optimization: Monitor progress and enable early termination.
L_BFGS.Result - Class in hex.optimization
lambda() - Method in class hex.glm.ComputationState
lambda - Variable in class hex.schemas.ANOVAGLMV3.ANOVAGLMParametersV3
lambda - Variable in class hex.schemas.GAMV3.GAMParametersV3
lambda - Variable in class hex.schemas.GLMV3.GLMParametersV3
lambda - Variable in class hex.schemas.ModelSelectionV3.ModelSelectionParametersV3
lambda - Variable in class hex.schemas.RuleFitV3.RuleFitParametersV3
lambda_1se() - Method in class hex.glm.GLMModel.GLMOutput
lambda_best() - Method in class hex.glm.GLMModel.GLMOutput
lambda_min_ratio - Variable in class hex.schemas.GAMV3.GAMParametersV3
lambda_min_ratio - Variable in class hex.schemas.GLMV3.GLMParametersV3
lambda_min_ratio - Variable in class hex.schemas.ModelSelectionV3.ModelSelectionParametersV3
lambda_search - Variable in class hex.schemas.ANOVAGLMV3.ANOVAGLMParametersV3
lambda_search - Variable in class hex.schemas.GAMV3.GAMParametersV3
lambda_search - Variable in class hex.schemas.GLMV3.GLMParametersV3
lambda_search - Variable in class hex.schemas.ModelSelectionV3.ModelSelectionParametersV3
lambda_selected() - Method in class hex.glm.GLMModel.GLMOutput
lambda_value - Variable in class hex.glm.GLMModel.Submodel
lambdas - Variable in class hex.schemas.GLMRegularizationPathV3
languageCatTreshold - Variable in class hex.rulefit.Condition
languageCondition - Variable in class hex.rulefit.Condition
laplace - Variable in class hex.schemas.NaiveBayesV3.NaiveBayesParametersV3
largestCat() - Method in class hex.DataInfo
last_scored() - Method in class hex.deeplearning.DeepLearningModel
lastSpecialColumnIdx() - Method in class hex.coxph.CoxPHModel.CoxPHOutput
leaf(float) - Method in class hex.tree.TreeVisitor
LeafNode(DTree, int) - Constructor for class hex.tree.DTree.LeafNode
LeafNode(DTree, int, int) - Constructor for class hex.tree.DTree.LeafNode
LeafNode(DTree.LeafNode, DTree) - Constructor for class hex.tree.DTree.LeafNode
learn_rate - Variable in class hex.schemas.AdaBoostV3.AdaBoostParametersV3
learn_rate - Variable in class hex.schemas.GBMV3.GBMParametersV3
learn_rate_annealing - Variable in class hex.schemas.GBMV3.GBMParametersV3
left_children - Variable in class hex.schemas.TreeV3
len() - Method in class hex.tree.DTree
levels - Variable in class hex.schemas.NaiveBayesModelV3.NaiveBayesModelOutputV3
levels - Variable in class hex.schemas.TreeV3
levels - Variable in class hex.tree.TreeHandler.TreeProperties
lfv_du_dv(GLMModel.GLMParameters.Family[], GLMModel.GLMParameters.Link[], double[], double[]) - Method in class hex.glm.GLM.GLMDriver
likelihood(double, double, double[]) - Method in class hex.generic.GenericModel
likelihood - Variable in class hex.glm.ComputationState.GramXY
likelihood() - Method in class hex.glm.ComputationState
likelihood(double, double) - Method in class hex.glm.GLMModel.GLMParameters
likelihood(double, double, double[]) - Method in class hex.glm.GLMModel.GLMParameters
likelihood(double, double, double) - Method in class hex.glm.GLMModel.GLMWeightsFun
likelihood(double, double) - Method in class hex.glm.GLMModel.GLMWeightsFun
likelihood(double, double, double[]) - Method in class hex.glm.GLMModel
likelihoodAndDeviance(double, GLMModel.GLMWeights, double) - Method in class hex.glm.GLMModel.GLMWeightsFun
LIMIT_MAX - Static variable in class hex.tree.dt.NumericFeatureLimits
LIMIT_MIN - Static variable in class hex.tree.dt.NumericFeatureLimits
Linear() - Constructor for class hex.deeplearning.Neurons.Linear
linear_constraints - Variable in class hex.schemas.GLMV3.GLMParametersV3
LinearAlgebraUtils - Class in hex.util
LinearAlgebraUtils() - Constructor for class hex.util.LinearAlgebraUtils
LinearAlgebraUtils.BMulInPlaceTask - Class in hex.util: Computes B = XY where X is n by k and Y is k by p, saving result in same frame Input: [X,B] (large frame) passed to doAll, where we write to B yt = Y' = transpose of Y (small matrix) ncolX = number of columns in X
LinearAlgebraUtils.BMulTask - Class in hex.util: Computes B = XY where X is n by k and Y is k by p, saving result in new vecs Input: dinfo = X (large frame) with dinfo._adaptedFrame passed to doAll yt = Y' = transpose of Y (small matrix) Output: XY (large frame) is n by p
LinearAlgebraUtils.BMulTaskMatrices - Class in hex.util: Compute B = XY where where X is n by k and Y is k by p and they are both stored as Frames.
LinearAlgebraUtils.CopyQtoQMatrix - Class in hex.util
LinearAlgebraUtils.FindMaxIndex - Class in hex.util
LinearAlgebraUtils.ForwardSolve - Class in hex.util: Given lower triangular L, solve for Q in QL' = A (LQ' = A') using forward substitution Dimensions: A is n by p, Q is n by p, R = L' is p by p Input: [A,Q] (large frame) passed to doAll, where we write to Q
LinearAlgebraUtils.ForwardSolveInPlace - Class in hex.util: Given lower triangular L, solve for Q in QL' = A (LQ' = A') using forward substitution Dimensions: A is n by p, Q is n by p, R = L' is p by p Input: A (large frame) passed to doAll, where we overwrite each row of A with its row of Q
LinearAlgebraUtils.SMulTask - Class in hex.util: Computes A'Q where A is n by p and Q is n by k Input: [A,Q] (large frame) passed to doAll Output: atq = A'Q (small matrix) is \tilde{p} by k where \tilde{p} = number of cols in A with categoricals expanded
LinearConstraintConditions(String[], String[], double[], String[], String[], boolean) - Constructor for class hex.glm.ConstrainedGLMUtils.LinearConstraintConditions
LinearConstraints() - Constructor for class hex.glm.ConstrainedGLMUtils.LinearConstraints
link(double) - Method in class hex.glm.GLMModel.GLMWeightsFun: Given the estimated model output x, we want to find the linear part which is transpose(beta)*p+intercept if beta does not contain the intercept.
link - Variable in class hex.schemas.ANOVAGLMV3.ANOVAGLMParametersV3
link - Variable in class hex.schemas.GAMV3.GAMParametersV3
link - Variable in class hex.schemas.GLMV3.GLMParametersV3
link - Variable in class hex.schemas.ModelSelectionV3.ModelSelectionParametersV3
linkDeriv(double) - Method in class hex.glm.GLMModel.GLMParameters
linkDeriv(double) - Method in class hex.glm.GLMModel.GLMWeightsFun
linkInv(double) - Method in class hex.gam.GAMModel.GAMParameters
linkInv(double) - Method in class hex.glm.GLMModel.GLMParameters
linkInv(double) - Method in class hex.glm.GLMModel.GLMWeightsFun: Given the linear combination transpose(beta)*p+intercept (if beta does not contain the intercept), this method will provide the estimated model output.
linkInvDeriv(double) - Method in class hex.glm.GLMModel.GLMWeightsFun
linkInvDeriv2(double) - Method in class hex.glm.GLMModel.GLMWeightsFun
lmulCatBlock(double[], int) - Method in class hex.glrm.GLRM.Archetypes
lmulNumCol(double[], int) - Method in class hex.glrm.GLRM.Archetypes
loadFrames() - Method in class hex.ContributionsWithBackgroundFrameTask
loading_name - Variable in class hex.schemas.GLRMV3.GLRMParametersV3
localModelInfoKey(H2ONode) - Method in class hex.deeplearning.DeepLearningModelInfo
logLikelihood(double, double) - Method in class hex.glm.TweedieEstimator
logLikelihood(double, double, double) - Method in class hex.glm.TweedieEstimator
logLikelihood(double, double, double, double) - Static method in class hex.glm.TweedieEstimator
logNodesHeight(Level) - Method in class hex.tree.isoforextended.isolationtree.IsolationTree: Helper method.
logNodesNumRows(Level) - Method in class hex.tree.isoforextended.isolationtree.IsolationTree: Helper method.
loss - Variable in class hex.schemas.DeepLearningV3.DeepLearningParametersV3: The loss (error) function to be minimized by the model.
loss - Variable in class hex.schemas.GLRMV3.GLRMParametersV3
loss_by_col - Variable in class hex.schemas.GLRMV3.GLRMParametersV3
loss_by_col_idx - Variable in class hex.schemas.GLRMV3.GLRMParametersV3
lre_min - Variable in class hex.schemas.CoxPHV3.CoxPHParametersV3
LSTask(H2O.H2OCountedCompleter, DataInfo, Key) - Constructor for class hex.glm.GLMTask.LSTask

M

N

n0() - Method in class hex.tree.DTree.Split
n1() - Method in class hex.tree.DTree.Split
NaiveBayes - Class in hex.naivebayes: Naive Bayes This is an algorithm for computing the conditional a-posterior probabilities of a categorical response from independent predictors using Bayes rule.
NaiveBayes(NaiveBayesModel.NaiveBayesParameters) - Constructor for class hex.naivebayes.NaiveBayes
NaiveBayes(boolean) - Constructor for class hex.naivebayes.NaiveBayes
NaiveBayesModel - Class in hex.naivebayes
NaiveBayesModel(Key, NaiveBayesModel.NaiveBayesParameters, NaiveBayesModel.NaiveBayesOutput) - Constructor for class hex.naivebayes.NaiveBayesModel
NaiveBayesModel.NaiveBayesOutput - Class in hex.naivebayes
NaiveBayesModel.NaiveBayesParameters - Class in hex.naivebayes
NaiveBayesModelOutputV3() - Constructor for class hex.schemas.NaiveBayesModelV3.NaiveBayesModelOutputV3
NaiveBayesModelV3 - Class in hex.schemas
NaiveBayesModelV3() - Constructor for class hex.schemas.NaiveBayesModelV3
NaiveBayesModelV3.NaiveBayesModelOutputV3 - Class in hex.schemas
NaiveBayesOutput(NaiveBayes) - Constructor for class hex.naivebayes.NaiveBayesModel.NaiveBayesOutput
NaiveBayesParameters() - Constructor for class hex.naivebayes.NaiveBayesModel.NaiveBayesParameters
NaiveBayesParametersV3() - Constructor for class hex.schemas.NaiveBayesV3.NaiveBayesParametersV3
NaiveBayesV3 - Class in hex.schemas
NaiveBayesV3() - Constructor for class hex.schemas.NaiveBayesV3
NaiveBayesV3.NaiveBayesParametersV3 - Class in hex.schemas
names() - Method in class hex.glm.GLMModel
names - Variable in class hex.schemas.MakeGLMModelV3
nargs() - Method in class water.rapids.prims.AstPredictedVsActualByVar
nargs() - Method in class water.rapids.prims.AstSetCalibrationModel
nargs() - Method in class water.rapids.prims.isotonic.AstPoolAdjacentViolators
nargs() - Method in class water.rapids.prims.rulefit.AstPredictRule
nargs() - Method in class water.rapids.prims.tree.AstTreeUpdateWeights
nargs() - Method in class water.rapids.prims.word2vec.AstWord2VecToFrame
nas - Variable in class hex.schemas.TreeV3
NAsIncluded - Variable in class hex.rulefit.Condition
naSplitDir() - Method in class hex.tree.DTree.Split
nBins - Variable in class hex.DataInfo.Row
nbins - Variable in class hex.schemas.SharedTreeV3.SharedTreeParametersV3
nbins() - Method in class hex.tree.DHistogram
nbins_cats - Variable in class hex.schemas.SharedTreeV3.SharedTreeParametersV3
nbins_top_level - Variable in class hex.schemas.SharedTreeV3.SharedTreeParametersV3
NBSplinesTypeIDerivative - Class in hex.gam.GamSplines
NBSplinesTypeIDerivative(int, int, double[]) - Constructor for class hex.gam.GamSplines.NBSplinesTypeIDerivative
NBSplinesUtils - Class in hex.gam.GamSplines
NBSplinesUtils() - Constructor for class hex.gam.GamSplines.NBSplinesUtils
nclasses() - Method in class hex.coxph.CoxPHModel.CoxPHOutput
nclasses() - Method in class hex.ensemble.StackedEnsemble
nclasses() - Method in class hex.gam.GAMModel.GAMModelOutput
nclasses() - Method in class hex.glm.GLM
nclasses() - Method in class hex.glm.GLMModel.GLMOutput
nclasses() - Method in class hex.tree.gbm.GBMModel.GBMOutput
needsPostProcess() - Method in class hex.adaboost.AdaBoostModel
needsPostProcess() - Method in class hex.generic.GenericModel
needsPostProcess() - Method in class hex.glm.GLMModel
negative_weight - Variable in class hex.schemas.PSVMV3.PSVMParametersV3
nesterov_accelerated_gradient - Variable in class hex.schemas.DeepLearningV3.DeepLearningParametersV3: The Nesterov accelerated gradient descent method is a modification to traditional gradient descent for convex functions.
Neurons - Class in hex.deeplearning: This class implements the concept of a Neuron layer in a Neural Network During training, every MRTask F/J thread is expected to create these neurons for every map call (Cheap to make).
Neurons.ExpRectifier - Class in hex.deeplearning
Neurons.ExpRectifierDropout - Class in hex.deeplearning: Exponential Rectifier with dropout
Neurons.Input - Class in hex.deeplearning: Input layer of the Neural Network This layer is different from other layers as it has no incoming weights, but instead gets its activation values from the training points.
Neurons.Linear - Class in hex.deeplearning: Output neurons for regression - Linear units
Neurons.Maxout - Class in hex.deeplearning: Maxout neurons (picks the max out of the k activation_j = sum(A_ij*x_i) + b_j) Requires k times the model parameters (weights/biases) as a "normal" neuron
Neurons.MaxoutDropout - Class in hex.deeplearning: Maxout neurons with dropout
Neurons.Output - Class in hex.deeplearning: Abstract class for Output neurons
Neurons.Rectifier - Class in hex.deeplearning: Rectifier linear unit (ReLU) neurons
Neurons.RectifierDropout - Class in hex.deeplearning: Rectifier linear unit (ReLU) neurons with dropout
Neurons.Softmax - Class in hex.deeplearning: Output neurons for classification - Softmax
Neurons.Tanh - Class in hex.deeplearning: Tanh neurons - most common, most stable
Neurons.TanhDropout - Class in hex.deeplearning: Tanh neurons with dropout
newDenseRow() - Method in class hex.DataInfo
newDenseRow(double[], long) - Method in class hex.DataInfo
newInstance() - Method in interface hex.ensemble.MetalearnerProvider
newParametersSchemaInstance() - Method in interface hex.ensemble.MetalearnerProvider
nextLevelConstraints(Constraints, int, double, SharedTreeModel.SharedTreeParameters) - Method in class hex.tree.DTree.Split
nextLevelHistos(DHistogram[], int, double, SharedTreeModel.SharedTreeParameters, Constraints, BranchInteractionConstraints) - Method in class hex.tree.DTree.Split: Prepare children histograms, one per column.
nextLevelInteractionConstraints(GlobalInteractionConstraints, int) - Method in class hex.tree.BranchInteractionConstraints: Decide which column indices is allowed to be used for the next split in the next level of a tree.
nextNumericIdx(int) - Method in class hex.DataInfo: Get the next expanded number-column index.
nfeatures() - Method in class hex.aggregator.AggregatorModel.AggregatorOutput
nfeatures() - Method in class hex.ensemble.StackedEnsembleModel.StackedEnsembleOutput
nfeatures() - Method in class hex.generic.GenericModelOutput
nfeatures() - Method in class hex.glrm.GLRM.Archetypes
nfeatures() - Method in class hex.glrm.GLRMModel.GLRMOutput: Override because base class implements ncols-1 for features with the last column as a response variable; for GLRM all the columns are features.
nfeatures() - Method in class hex.pca.PCAModel.PCAOutput: Override because base class implements ncols-1 for features with the last column as a response variable; for PCA all the columns are features.
nfeval() - Method in interface hex.optimization.OptimizationUtils.LineSearchSolver
nfeval() - Method in class hex.optimization.OptimizationUtils.MoreThuente
nfeval() - Method in class hex.optimization.OptimizationUtils.SimpleBacktrackingLS
nid() - Method in class hex.tree.DTree.Node
nid2Oob(int) - Static method in class hex.tree.ScoreBuildHistogram
nids0Index - Variable in class hex.tree.SharedTree.FrameMap
nlambdas - Variable in class hex.schemas.GAMV3.GAMParametersV3
nlambdas - Variable in class hex.schemas.GLMV3.GLMParametersV3
nlambdas - Variable in class hex.schemas.ModelSelectionV3.ModelSelectionParametersV3
nlearners - Variable in class hex.schemas.AdaBoostV3.AdaBoostParametersV3
nModelsInParallel(int) - Method in class hex.anovaglm.ANOVAGLM
nModelsInParallel(int) - Method in class hex.gam.GAM
nModelsInParallel(int) - Method in class hex.modelselection.ModelSelection
nModelsInParallel(int) - Method in class hex.tree.gbm.GBM
nNums - Variable in class hex.DataInfo.Row
NO_PARENT - Static variable in class hex.tree.DTree
nobs() - Method in class hex.glm.GLMTask.YMUTask
node(int) - Method in class hex.tree.DTree
Node(double[][], int, int) - Constructor for class hex.tree.isoforextended.isolationtree.IsolationTree.Node
node(double, double) - Method in class hex.tree.uplift.Divergence: Calculate distance metric between two probabilities in the node.
nomNA() - Method in class hex.tree.DHistogram
non_negative - Variable in class hex.schemas.ANOVAGLMV3.ANOVAGLMParametersV3
non_negative - Variable in class hex.schemas.GAMV3.GAMParametersV3
non_negative - Variable in class hex.schemas.GLMV3.GLMParametersV3
non_negative - Variable in class hex.schemas.ModelSelectionV3.ModelSelectionParametersV3
nonEmptyBins() - Method in class hex.tree.DHistogram
NonSPDMatrixException() - Constructor for exception hex.gram.Gram.NonSPDMatrixException
NonSPDMatrixException(String) - Constructor for exception hex.gram.Gram.NonSPDMatrixException
norm(double, double, double, double) - Method in class hex.tree.uplift.Divergence: Calculate normalization factor to normalize gain.
norm(double, double, double, double) - Method in class hex.tree.uplift.EuclideanDistance
norm(double, double, double, double) - Method in class hex.tree.uplift.KLDivergence
norm_model - Variable in class hex.schemas.Word2VecV3.Word2VecParametersV3
normalizeBeta(double[], boolean) - Method in class hex.DataInfo
normMul(int) - Method in class hex.DataInfo
normSub(int) - Method in class hex.DataInfo
notZeroLambdas(double[]) - Static method in class hex.glm.GLMUtils
nparallelism - Variable in class hex.schemas.ANOVAGLMV3.ANOVAGLMParametersV3
nparallelism - Variable in class hex.schemas.ModelSelectionV3.ModelSelectionParametersV3
npredictors() - Method in class hex.tree.drf.TreeMeasuresCollector.TreeMeasures: Returns number of voting predictors
nrows() - Method in class hex.tree.drf.TreeMeasuresCollector.TreeMeasures: Returns number of rows which were used during voting per individual tree.
nTreeEnsemblesInParallel(int) - Method in class hex.rulefit.RuleFit
ntrees - Variable in class hex.schemas.ExtendedIsolationForestV3.ExtendedIsolationForestParametersV3
ntrees - Variable in class hex.schemas.SharedTreeV3.SharedTreeParametersV3
ntrees() - Method in class hex.tree.CompressedForest
ntrees() - Method in class hex.tree.DTreeScorer
nullDOF() - Method in class hex.gam.MetricBuilderGAM
nullDOF() - Method in class hex.glm.GLMMetricBuilder
num_iteration_without_new_exemplar - Variable in class hex.schemas.AggregatorV99.AggregatorParametersV99
num_knots - Variable in class hex.schemas.GAMV3.GAMParametersV3
numCats() - Method in class hex.DataInfo
numcnt - Variable in class water.api.ModelMetricsGLRMV99
numColsExp(Frame, boolean) - Static method in class hex.util.LinearAlgebraUtils: Number of columns with categoricals expanded.
numControlNA() - Method in class hex.tree.DHistogram
NUMERICAL_FLAG - Static variable in class hex.tree.dt.mrtasks.CountBinsSamplesCountsMRTask
NUMERICAL_FLAG - Static variable in class hex.tree.dt.NumericFeatureLimits
NumericBin - Class in hex.tree.dt.binning: Single bin holding limits (min excluded), count of samples and count of class 0.
NumericBin(double, double, int, int) - Constructor for class hex.tree.dt.binning.NumericBin
NumericBin(double, double) - Constructor for class hex.tree.dt.binning.NumericBin
NumericBin(Pair<Double, Double>) - Constructor for class hex.tree.dt.binning.NumericBin
NumericFeatureLimits - Class in hex.tree.dt: Limits for one feature.
NumericFeatureLimits(double, double) - Constructor for class hex.tree.dt.NumericFeatureLimits
NumericSplittingRule - Class in hex.tree.dt
NumericSplittingRule(int, double, double) - Constructor for class hex.tree.dt.NumericSplittingRule
NumericSplittingRule(double) - Constructor for class hex.tree.dt.NumericSplittingRule
numerr - Variable in class water.api.ModelMetricsGLRMV99
numIds - Variable in class hex.DataInfo.Row
numNAFill() - Method in class hex.DataInfo
numNAFill(int) - Method in class hex.DataInfo
numNodes() - Method in class hex.tree.DTree.DecidedNode
numNodes() - Method in class hex.tree.DTree.LeafNode
numNodes() - Method in class hex.tree.DTree.Node
numNodes() - Method in class hex.tree.DTree.UndecidedNode
numNums() - Method in class hex.DataInfo
numStart() - Method in class hex.DataInfo
numTreatmentNA() - Method in class hex.tree.DHistogram
numTreshold - Variable in class hex.rulefit.Condition
numVals - Variable in class hex.DataInfo.Row
nv - Variable in class hex.schemas.SVDV99.SVDParametersV99

O

P

p_values - Variable in class hex.schemas.GLMRegularizationPathV3
p_values_threshold - Variable in class hex.schemas.ModelSelectionV3.ModelSelectionParametersV3
parameters - Variable in class hex.deeplearning.DeepLearningModelInfo
params - Variable in class hex.deeplearning.Neurons: Parameters (deep-cloned() from the user input, can be modified here, e.g.
parentPred() - Method in class hex.tree.DTree.DecidedNode
Parms() - Constructor for class hex.psvm.psvm.PrimalDualIPM.Parms
Parms(double, double) - Constructor for class hex.psvm.psvm.PrimalDualIPM.Parms
path - Variable in class hex.schemas.GenericV3.GenericParametersV3
PCA - Class in hex.pca: Principal Components Analysis It computes the principal components from the singular value decomposition using the power method.
PCA(PCAModel.PCAParameters) - Constructor for class hex.pca.PCA
PCA(boolean) - Constructor for class hex.pca.PCA
pca_impl - Variable in class hex.schemas.PCAV3.PCAParametersV3
pca_method - Variable in class hex.schemas.AggregatorV99.AggregatorParametersV99
pca_method - Variable in class hex.schemas.PCAV3.PCAParametersV3
PCA_MTJ_EVD_DenseMatrix - Class in hex.pca.mtj
PCA_MTJ_EVD_DenseMatrix(double[][]) - Constructor for class hex.pca.mtj.PCA_MTJ_EVD_DenseMatrix
PCA_MTJ_EVD_SymmMatrix - Class in hex.pca.mtj
PCA_MTJ_EVD_SymmMatrix(double[][]) - Constructor for class hex.pca.mtj.PCA_MTJ_EVD_SymmMatrix
PCA_MTJ_SVD_DenseMatrix - Class in hex.pca.mtj
PCA_MTJ_SVD_DenseMatrix(double[][]) - Constructor for class hex.pca.mtj.PCA_MTJ_SVD_DenseMatrix
PCAImplementation - Enum in hex.pca
PCAInterface - Interface in hex.pca
PCAJama - Class in hex.pca.jama
PCAJama(double[][]) - Constructor for class hex.pca.jama.PCAJama
PCAModel - Class in hex.pca
PCAModel(Key, PCAModel.PCAParameters, PCAModel.PCAOutput) - Constructor for class hex.pca.PCAModel
PCAModel.PCAOutput - Class in hex.pca
PCAModel.PCAParameters - Class in hex.pca
PCAModel.PCAParameters.Method - Enum in hex.pca
PCAModelMetrics(int) - Constructor for class hex.pca.ModelMetricsPCA.PCAModelMetrics
PCAModelOutputV3() - Constructor for class hex.schemas.PCAModelV3.PCAModelOutputV3
PCAModelV3 - Class in hex.schemas
PCAModelV3() - Constructor for class hex.schemas.PCAModelV3
PCAModelV3.PCAModelOutputV3 - Class in hex.schemas
PCAMojoWriter - Class in hex.pca
PCAMojoWriter() - Constructor for class hex.pca.PCAMojoWriter
PCAMojoWriter(PCAModel) - Constructor for class hex.pca.PCAMojoWriter
PCAOutput(PCA) - Constructor for class hex.pca.PCAModel.PCAOutput
PCAParameters() - Constructor for class hex.pca.PCAModel.PCAParameters
PCAParametersV3() - Constructor for class hex.schemas.PCAV3.PCAParametersV3
PCAV3 - Class in hex.schemas
PCAV3() - Constructor for class hex.schemas.PCAV3
PCAV3.PCAParametersV3 - Class in hex.schemas
pcond - Variable in class hex.schemas.NaiveBayesModelV3.NaiveBayesModelOutputV3
performOneSweep(double[][], ModelSelectionUtils.SweepVector[], int, boolean) - Static method in class hex.modelselection.ModelSelectionUtils: Perform one sweep according to section II of doc and generate sweep vector according to section V.II of doc.
period - Variable in class hex.schemas.GLRMV3.GLRMParametersV3
perRow(double[], float[], Model) - Method in class hex.aggregator.ModelMetricsAggregator.AggregatorModelMetrics
perRow(double[], float[], double, double, Model) - Method in class hex.gam.MetricBuilderGAM
perRow(double[], float[], Model) - Method in class hex.gam.MetricBuilderGAM
perRow(double[], float[], Model) - Method in class hex.glm.GLMMetricBuilder
perRow(double[], float[], double, double, Model) - Method in class hex.glm.GLMMetricBuilder
perRow(double[], float[], Model) - Method in class hex.glrm.ModelMetricsGLRM.GlrmModelMetricsBuilder
perRow(double[], float[], Model) - Method in class hex.pca.ModelMetricsPCA.PCAModelMetrics
perRow(double[], float[], Model) - Method in class hex.psvm.MetricBuilderPSVM
perRow(double[], float[], double, double, Model) - Method in class hex.psvm.MetricBuilderPSVM
perRow(double[], float[], Model) - Method in class hex.svd.SVDModel.ModelMetricsSVD.SVDModelMetrics
perRow(double[], float[], double, double, Model) - Method in class hex.tree.isofor.MetricBuilderAnomalySupervised
perRow(double[], float[], Model) - Method in class hex.tree.isofor.ModelMetricsAnomaly.MetricBuilderAnomaly
pickBestModel(GLMModel.GLMParameters) - Method in class hex.glm.GLMModel.GLMOutput
pid() - Method in class hex.tree.DTree.Node
plain_language_rules - Variable in class hex.schemas.TreeV3
plug_values - Variable in class hex.schemas.ANOVAGLMV3.ANOVAGLMParametersV3
plug_values - Variable in class hex.schemas.GAMV3.GAMParametersV3
plug_values - Variable in class hex.schemas.GLMV3.GLMParametersV3
plug_values - Variable in class hex.schemas.ModelSelectionV3.ModelSelectionParametersV3
PlugValuesImputer(Frame) - Constructor for class hex.glm.GLM.PlugValuesImputer
PoolAdjacentViolatorsDriver - Class in hex.isotonic: Distributed implementation of Pool Adjacent Violators algorithm for H2O Frames
PoolAdjacentViolatorsDriver() - Constructor for class hex.isotonic.PoolAdjacentViolatorsDriver
positive_weight - Variable in class hex.schemas.PSVMV3.PSVMParametersV3
post(int, float, int) - Method in class hex.tree.TreeVisitor
postGlobal() - Method in class hex.ContributionsMeanAggregator
postGlobal() - Method in class hex.coxph.CoxPH.CoxPHTask
postGlobal() - Method in class hex.deeplearning.DeepLearningTask: After all reduces are done, the driver node calls this method to clean up This is only needed if we're not inside a DeepLearningTask2 (which will do the reduction between replicated data workers).
postGlobal() - Method in class hex.deeplearning.DeepLearningTask2: Finish up the work after all nodes have reduced their models via the above reduce() method.
postGlobal() - Method in class hex.gam.GamSplines.ThinPlateRegressionUtils.ScaleTPPenalty
postGlobal() - Method in class hex.gam.MatrixFrameUtils.GenCSSplineGamOneColumn
postGlobal() - Method in class hex.gam.MatrixFrameUtils.GenISplineGamOneColumn
postGlobal() - Method in class hex.gam.MatrixFrameUtils.GenMSplineGamOneColumn
postGlobal() - Method in class hex.glm.DispersionTask.ComputeMaxSumSeriesTsk
postGlobal() - Method in class hex.glm.GLMScore
postGlobal() - Method in class hex.glm.GLMTask.GLMMultinomialGradientBaseTask
postGlobal() - Method in class hex.glm.GLMTask.LSTask
postGlobal() - Method in class hex.glm.GLMTask.YMUTask
postGlobal() - Method in class hex.tree.ExactSplitPoints
postGlobal() - Method in class hex.tree.Score
postGlobal() - Method in class hex.tree.ScoreBuildHistogram2
postProcessPredictions(Frame, Job, CalibrationHelper.OutputWithCalibration) - Static method in class hex.tree.CalibrationHelper
postProcessPredictions(Frame, Frame, Job) - Method in class hex.tree.SharedTreeModel
pre(int, float, IcedBitSet, int, int) - Method in class hex.tree.TreeVisitor
pre_split_se() - Method in class hex.tree.DTree.Split
pre_trained - Variable in class hex.schemas.Word2VecV3.Word2VecParametersV3
pred(int) - Method in class hex.tree.DTree.DecidedNode
pred() - Method in class hex.tree.DTree.LeafNode
pred_noise_bandwidth - Variable in class hex.schemas.GBMV3.GBMParametersV3
predCombo(String[], int[]) - Static method in class hex.anovaglm.ANOVAGLMUtils
predControl(int) - Method in class hex.tree.DTree.DecidedNode
predictions - Variable in class hex.schemas.TreeV3
predictorMeans() - Method in class hex.glm.GLMTask.YMUTask
predictors_bad - Variable in class hex.DataInfo.Row
predictorSDs() - Method in class hex.glm.GLMTask.YMUTask
predictRowStartingFromNode(double[], int, String) - Method in class hex.tree.dt.CompressedDT: Makes prediction by recursively evaluating the data through the tree.
predictRules(Frame, String[]) - Method in class hex.rulefit.RuleFitModel
predictScoreImpl(Frame, Frame, String, Job, boolean, CFuncRef) - Method in class hex.aggregator.AggregatorModel
predictScoreImpl(Frame, Frame, String, Job, boolean, CFuncRef) - Method in class hex.coxph.CoxPHModel
predictScoreImpl(Frame, Frame, String, Job, boolean, CFuncRef) - Method in class hex.deeplearning.DeepLearningModel: Make either a prediction or a reconstruction.
predictScoreImpl(Frame, Frame, String, Job, boolean, CFuncRef) - Method in class hex.ensemble.StackedEnsembleModel: For StackedEnsemble we call score on all the base_models and then combine the results with the metalearner to create the final predictions frame.
predictScoreImpl(Frame, Frame, String, Job, boolean, CFuncRef) - Method in class hex.gam.GAMModel
predictScoreImpl(Frame, Frame, String, Job, boolean, CFuncRef) - Method in class hex.generic.GenericModel
predictScoreImpl(Frame, Frame, String, Job, boolean, CFuncRef) - Method in class hex.glm.GLMModel: Score an already adapted frame.
predictScoreImpl(Frame, Frame, String, Job, boolean, CFuncRef) - Method in class hex.glrm.GLRMModel
predictScoreImpl(Frame, Frame, String, Job, boolean, CFuncRef) - Method in class hex.kmeans.KMeansModel
predictScoreImpl(Frame, Frame, String, Job, boolean, CFuncRef) - Method in class hex.pca.PCAModel
predictScoreImpl(Frame, Frame, String, Job, boolean, CFuncRef) - Method in class hex.svd.SVDModel
predTreatment(int) - Method in class hex.tree.DTree.DecidedNode
prepareGamVec(int, GAMModel.GAMParameters, Frame) - Static method in class hex.gam.MatrixFrameUtils.GamUtils
preSplitUpliftGain() - Method in class hex.tree.DTree.Split
pretrained_autoencoder - Variable in class hex.schemas.DeepLearningV3.DeepLearningParametersV3
PrimalDualIPM - Class in hex.psvm.psvm: Implementation of Primal-Dual Interior Point Method based on https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/34638.pdf This implementation is based on and takes clues from the reference PSVM implementation in C++: https://code.google.com/archive/p/psvm/source/default/source original code: Copyright 2007 Google Inc., Apache License, Version 2.0
PrimalDualIPM() - Constructor for class hex.psvm.psvm.PrimalDualIPM
PrimalDualIPM.Parms - Class in hex.psvm.psvm
PrimalDualIPM.ProgressObserver - Interface in hex.psvm.psvm
printConstraintSummary(GLMModel, ComputationState, String[]) - Static method in class hex.glm.ConstrainedGLMUtils
printConstraintSummary(ComputationState, String[]) - Static method in class hex.glm.ConstrainedGLMUtils
prior - Variable in class hex.schemas.ANOVAGLMV3.ANOVAGLMParametersV3
prior - Variable in class hex.schemas.GAMV3.GAMParametersV3
prior - Variable in class hex.schemas.GLMV3.GLMParametersV3
prior - Variable in class hex.schemas.ModelSelectionV3.ModelSelectionParametersV3
probability - Variable in class hex.tree.dt.DTPrediction
process(ModelSelectionUtils.SweepElement, List<ModelSelectionUtils.SweepElement>) - Static method in class hex.modelselection.ModelSelectionUtils: This method will generate all the elements that are needed to perform sweeping on the currEle.
processMiniBatch(long, double[], double[], int) - Method in class hex.deeplearning.DeepLearningTask: Apply the gradient to update the weights
processMiniBatch(long, double[], double[], int) - Method in class hex.FrameTask: Mini-Batch update of model parameters