Index (RF-SRC Spark Target 2.7.0 API)

C G H L M P R S T U

C

com.kogalur.randomforest - package com.kogalur.randomforest

G

get(int) - Static method in class com.kogalur.randomforest.Trace
get_blockSize() - Method in class com.kogalur.randomforest.ModelArg: Returns the value for the block size associated with the reporting of the error rate.
get_bootstrap() - Method in class com.kogalur.randomforest.ModelArg: Returns the type of bootstrap used in the model.
get_caseWeight() - Method in class com.kogalur.randomforest.ModelArg: Returns the case weight vector.
get_eventCount() - Method in class com.kogalur.randomforest.ModelArg: Returns the number of events in the data set, when survival or competing risk forests is in force.
get_eventWeight() - Method in class com.kogalur.randomforest.ModelArg: Returns the event weight vector, when survival or competing risk forests is in force.
get_family() - Method in class com.kogalur.randomforest.ModelArg: Returns the family of analysis intentioned by the ModelArg instance.
get_htry() - Method in class com.kogalur.randomforest.ModelArg: Returns the maximum hypercube dimension to be considered in Greedy Splitting.
get_mtry() - Method in class com.kogalur.randomforest.ModelArg: Returns the value for the number of x-variables to be randomly selected as candidates for splitting a node.
get_nImpute() - Method in class com.kogalur.randomforest.ModelArg: Returns the number of iterations used by the missing data algorithm.
get_nodeDepth() - Method in class com.kogalur.randomforest.ModelArg: Returns the maximum depth to which a tree should be grown.
get_nodeSize() - Method in class com.kogalur.randomforest.ModelArg: Returns the desired value for the average number of unique cases in a terminal node.
get_nSize() - Method in class com.kogalur.randomforest.ModelArg: Returns the number of records or rows (n) in the data set.
get_nSplit() - Method in class com.kogalur.randomforest.ModelArg: Returns the parameter specifying deterministic versus non-deterministic splitting.
get_ntree() - Method in class com.kogalur.randomforest.ModelArg: Returns the number of trees in the forest.
get_pruningCount() - Method in class com.kogalur.randomforest.RandomForestModel: Returns the value representing the number of terminal nodes to which each tree in the original model should be pruned back.
get_rfCores() - Method in class com.kogalur.randomforest.ModelArg: Returns the number of cores to be used by the algorithm when OpenMP parallel processing is in force.
get_rfCores() - Method in class com.kogalur.randomforest.RandomForestModel: Returns the number of cores to be used by the algorithm when OpenMP parallel processing is in force.
get_sample() - Method in class com.kogalur.randomforest.ModelArg: Returns the 2-D matrix explicitly specifying the bootstrap sample.
get_sampleSize() - Method in class com.kogalur.randomforest.ModelArg: Returns the size of sample used in generating the bootstrap.
get_sampleType() - Method in class com.kogalur.randomforest.ModelArg: Returns the type of sampling used in generating the bootstrap.
get_seed() - Method in class com.kogalur.randomforest.ModelArg: Returns the seed for the random number generator used by the algorithm.
get_seed() - Method in class com.kogalur.randomforest.RandomForestModel: Returns the seed for the random number generator used by the algorithm.
get_splitRule() - Method in class com.kogalur.randomforest.ModelArg: Returns the split rule used in generating the model.
get_timeInterest() - Method in class com.kogalur.randomforest.ModelArg: Returns the time interest vector used in the model, when survival or competing risk forests is in force.
get_timeInterestSize() - Method in class com.kogalur.randomforest.ModelArg: Returns the size of the time interest vector used in the model, when survival or competing risk forests is in force.
get_trace() - Method in class com.kogalur.randomforest.ModelArg: Returns the trace parameter indicating the specified update interval in seconds.
get_trace() - Method in class com.kogalur.randomforest.RandomForestModel: Returns the trace parameter indicating the specified update interval in seconds.
get_xData() - Method in class com.kogalur.randomforest.ModelArg: Returns a 2-D matrix of values representing the y-values.
get_xImportance() - Method in class com.kogalur.randomforest.RandomForestModel: Returns the x-variable(s) over which importance calculations should be conducted.
get_xLevel() - Method in class com.kogalur.randomforest.ModelArg: Returns a vector of length xSize (ModelArg.get_xSize()) containing the the number of levels found in each x-variable.
get_xSize() - Method in class com.kogalur.randomforest.ModelArg: Returns the number of x-variables in the data set.
get_xStatisticalWeight() - Method in class com.kogalur.randomforest.ModelArg: Returns the x-variable statistical weight vector.
get_xType() - Method in class com.kogalur.randomforest.ModelArg: Returns a vector of length xSize (ModelArg.get_xSize()) containing the x-variable types.
get_xWeight() - Method in class com.kogalur.randomforest.ModelArg: Returns the x-variable weight vector.
get_yData() - Method in class com.kogalur.randomforest.ModelArg: Returns a 2-D matrix of values representing the y-values.
get_yLevel() - Method in class com.kogalur.randomforest.ModelArg: Returns a vector of length ySize (ModelArg.get_ySize()) containing the the number of levels found in each y-variable.
get_ySize() - Method in class com.kogalur.randomforest.ModelArg: Returns the number of y-variables in the data set.
get_yTarget() - Method in class com.kogalur.randomforest.RandomForestModel: Returns the array of y-variable to be targeted when multivariate families are in force.
get_ytry() - Method in class com.kogalur.randomforest.ModelArg: Returns the value for the number of y-variables to be randomly selected as pseudo-responses when unsupervised forests is in force.
get_yType() - Method in class com.kogalur.randomforest.ModelArg: Returns a vector of length ySize (ModelArg.get_ySize()) containing the y-variables types.
get_yWeight() - Method in class com.kogalur.randomforest.ModelArg: Returns the y-variable weight vector.
getEnsemble(String) - Method in class com.kogalur.randomforest.RandomForestModel
getEnsembleArg(String) - Method in class com.kogalur.randomforest.ModelArg: Returns the current value for the specified ensemble argument.
getEnsembleArg(String) - Method in class com.kogalur.randomforest.RandomForestModel: Returns the current value for the specified ensemble argument.
getModelArg() - Method in class com.kogalur.randomforest.RandomForestModel: Returns the class containing the model arguments that produced the RandomForestModel object.
getModelType() - Method in class com.kogalur.randomforest.RandomForestModel

H

HGH - Static variable in class com.kogalur.randomforest.Trace

L

log(Level, String, Throwable) - Static method in class com.kogalur.randomforest.RFLogger
log(Level, String) - Static method in class com.kogalur.randomforest.RFLogger
LOW - Static variable in class com.kogalur.randomforest.Trace

M

MED - Static variable in class com.kogalur.randomforest.Trace
ModelArg - Class in com.kogalur.randomforest: Class containing the user-defined model arguments that produce the RandomForestModel object.
ModelArg(String, Dataset) - Constructor for class com.kogalur.randomforest.ModelArg: Sets default values for the training parameters and ensemble outputs for the forest, given the formula and dataset.

P

predict(RandomForestModel) - Static method in class com.kogalur.randomforest.RandomForest: Restores a random forest given the user-defined model arugments.
predict(ModelArg, Dataset) - Static method in class com.kogalur.randomforest.RandomForest
printEnsemble(Object) - Method in class com.kogalur.randomforest.RandomForestModel
printEnsembleList() - Method in class com.kogalur.randomforest.RandomForestModel

R

RandomForest - Class in com.kogalur.randomforest: Class that provides the static methods to train a random forest, to restore a previouly created random forest, and to predict using new test data with a previously created random forest.
RandomForestModel - Class in com.kogalur.randomforest: Class that represents a random forest model as a result of growing, restoring, or predicting using new data.
RFLogger - Class in com.kogalur.randomforest

S

set(int) - Static method in class com.kogalur.randomforest.Trace
set_blockSize() - Method in class com.kogalur.randomforest.ModelArg: Sets the default value for the block size associated with the reporting of the error rate.
set_blockSize(int) - Method in class com.kogalur.randomforest.ModelArg: Sets the specified value for the block size associated with the reporting of the error rate.
set_bootstrap(int, String, String, int, int[][], double[]) - Method in class com.kogalur.randomforest.ModelArg: Sets the bootstrap related parameters in the model.
set_bootstrap(int) - Method in class com.kogalur.randomforest.ModelArg: Sets the bootstrap related parameters in the model.
set_bootstrap(int, int[][]) - Method in class com.kogalur.randomforest.ModelArg: Sets the bootstrap related parameters in the model.
set_bootstrap() - Method in class com.kogalur.randomforest.ModelArg: Sets the bootstrap related parameters in the model.
set_eventWeight(double[]) - Method in class com.kogalur.randomforest.ModelArg: Sets the event weight vector, when survival or competing risk forests is in force.
set_eventWeight() - Method in class com.kogalur.randomforest.ModelArg: Sets the event weight vector, when survival or competing risk forests is in force, to uniform weights.
set_htry(int) - Method in class com.kogalur.randomforest.ModelArg: Sets the maximum hypercube dimension to be considered in Greedy Splitting.
set_mtry(int) - Method in class com.kogalur.randomforest.ModelArg: Sets the number of x-variables to be randomly selected as candidates for splitting a node.
set_mtry() - Method in class com.kogalur.randomforest.ModelArg: Sets the default value for the number of x-variables to be randomly selected as candidates for splitting a node.
set_nImpute(int) - Method in class com.kogalur.randomforest.ModelArg: Sets the number of iterations for the missing data algorithm.
set_nodeDepth(int) - Method in class com.kogalur.randomforest.ModelArg: Sets the maximum depth to which a tree should be grown.
set_nodeSize(int) - Method in class com.kogalur.randomforest.ModelArg: Sets the desired average number of unique cases in a terminal node.
set_nodeSize() - Method in class com.kogalur.randomforest.ModelArg: Sets the default value for the average number of unique cases in a terminal node.
set_nSplit(int) - Method in class com.kogalur.randomforest.ModelArg: Sets the parameter specifying deterministic versus non-deterministic splitting.
set_nSplit() - Method in class com.kogalur.randomforest.ModelArg: Sets the default value for the parameter specifying deterministic versus non-deterministic splitting.
set_pruningCount(int) - Method in class com.kogalur.randomforest.RandomForestModel: Sets the number of terminal nodes to which each tree in the original model should be pruned back.
set_rfCores(int) - Method in class com.kogalur.randomforest.ModelArg: Sets the number of cores to be used by the algorithm when OpenMP parallel processing is in force.
set_rfCores(int) - Method in class com.kogalur.randomforest.RandomForestModel: Sets the number of cores to be used by the algorithm when OpenMP parallel processing is in force.
set_seed(int) - Method in class com.kogalur.randomforest.ModelArg: Sets the seed for the random number generator used by the algorithm.
set_seed(int) - Method in class com.kogalur.randomforest.RandomForestModel: Sets the seed for the random number generator used by the algorithm.
set_splitRule(String) - Method in class com.kogalur.randomforest.ModelArg: Sets the split rule to be used in generating the model.
set_splitRule() - Method in class com.kogalur.randomforest.ModelArg: Sets the default split rule, based on the data set and formuala.
set_timeInterest(double[]) - Method in class com.kogalur.randomforest.ModelArg: Sets the time interest vector, when survival or competing risk forests is in force.
set_timeInterest() - Method in class com.kogalur.randomforest.ModelArg: Sets the time interest vector, when survival or competing risk forests is in force to the default value.
set_trace(int) - Method in class com.kogalur.randomforest.ModelArg: Sets the trace parameter indicating the specified update interval in seconds.
set_trace(int) - Method in class com.kogalur.randomforest.RandomForestModel: Sets the trace parameter indicating the specified update interval in seconds.
set_xImportance(String[]) - Method in class com.kogalur.randomforest.RandomForestModel: Sets the x-variable(s) over which importance calculations should be conducted.
set_xImportance() - Method in class com.kogalur.randomforest.RandomForestModel: Sets the x-variable(s), over which importance calculations should be conducted, to all x-variables.
set_xStatisticalWeight(double[]) - Method in class com.kogalur.randomforest.ModelArg: Sets the x-variable statistical weight vector.
set_xStatisticalWeight() - Method in class com.kogalur.randomforest.ModelArg: Set the x-variable statistical weight vector to uniform weights.
set_xWeight(double[]) - Method in class com.kogalur.randomforest.ModelArg: Sets the x-variable weight vector.
set_xWeight() - Method in class com.kogalur.randomforest.ModelArg: Set the x-variable weight vector to uniform weights.
set_yTarget(String[]) - Method in class com.kogalur.randomforest.RandomForestModel: Sets the y-variable(s) to be targeted when multivariate families are in force.
set_yTarget() - Method in class com.kogalur.randomforest.RandomForestModel: Sets the default action for the y-variable(s) to be targeted when multivariate families are in force.
set_ytry(int) - Method in class com.kogalur.randomforest.ModelArg: Sets the number of randomly selected pseudo-responses when unsupervised forests is in force.
set_yWeight(double[]) - Method in class com.kogalur.randomforest.ModelArg: Sets the y-variable weight vector.
set_yWeight() - Method in class com.kogalur.randomforest.ModelArg: Set the y-variable weight vector to uniform weights.
setEnsembleArg(String, String) - Method in class com.kogalur.randomforest.ModelArg: Sets the ensemble outputs desired from the model.
setEnsembleArg() - Method in class com.kogalur.randomforest.ModelArg: Sets default values for the ensemble outputs resulting from the model.
setEnsembleArg(String, String) - Method in class com.kogalur.randomforest.RandomForestModel: Sets the ensemble outputs desired when restoring the model or using it to predict with new data.
setEnsembleArg() - Method in class com.kogalur.randomforest.RandomForestModel: Sets default values for the ensemble outputs resulting from restoring the model or using it to predict with new data.

T

Trace - Class in com.kogalur.randomforest
train(ModelArg) - Static method in class com.kogalur.randomforest.RandomForest: Trains a random forest given the user-defined model arugments.

U

USR - Static variable in class com.kogalur.randomforest.Trace

C G H L M P R S T U