hex.glm

Class GLM.GLMDriver

java.lang.Object

jsr166y.ForkJoinTask<java.lang.Void>

jsr166y.CountedCompleter

water.H2O.H2OCountedCompleter<hex.ModelBuilder.Driver>

hex.glm.GLM.GLMDriver

All Implemented Interfaces:

L_BFGS.ProgressMonitor, java.io.Serializable, java.lang.Cloneable, java.util.concurrent.Future<java.lang.Void>, water.Freezable<hex.ModelBuilder.Driver>

Enclosing class:

GLM

public final class GLM.GLMDriver
extends water.H2O.H2OCountedCompleter<hex.ModelBuilder.Driver>
implements L_BFGS.ProgressMonitor

Main loop of the glm algo.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
GLMDriver()

Method Summary

Modifier and Type	Method and Description
java.util.List<java.lang.String>	changeCoeffBetainfo(java.lang.String[] coefNames)
void	checkKKTConditions(double[] betaCnd, GLM.GLMGradientInfo gradientInfo, int iterCnt) We will check it the constraint stopping conditions are met.
void	computeImpl()
protected GLMModel.Submodel	computeSubmodel(int i, double lambda, double nullDevTrain, double nullDevValid)
void	genRID() Generate the regression influence diagnostic for gaussian and binomial families.
void	onCompletion(jsr166y.CountedCompleter caller)
boolean	onExceptionalCompletion(java.lang.Throwable t, jsr166y.CountedCompleter caller)
boolean	progress(double[] beta, double likelihood)
boolean	progress(double[] beta, OptimizationUtils.GradientInfo ginfo)
protected void	updateProgress(boolean canScore)

Methods inherited from class water.H2O.H2OCountedCompleter

asBytes, clone, compute, compute1, currThrPriority, frozenType, icer, priority, read, readJSON, reloadFromBytes, write, writeJSON

Methods inherited from class jsr166y.CountedCompleter

__tryComplete, addToPendingCount, compareAndSetPendingCount, complete, exec, getCompleter, getPendingCount, getRawResult, setCompleter, setPendingCount, setRawResult, tryComplete

Methods inherited from class jsr166y.ForkJoinTask

adapt, adapt, adapt, cancel, compareAndSetForkJoinTaskTag, completeExceptionally, fork, get, get, get, getException, getForkJoinTaskTag, getPool, getQueuedTaskCount, getSurplusQueuedTaskCount, helpQuiesce, inForkJoinPool, invoke, invokeAll, invokeAll, invokeAll, isCancelled, isCompletedAbnormally, isCompletedNormally, isDone, join, peekNextLocalTask, pollNextLocalTask, pollTask, quietlyComplete, quietlyInvoke, quietlyJoin, reinitialize, setForkJoinTaskTag, tryUnfork

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

GLMDriver

public GLMDriver()

Method Detail

checkKKTConditions

public void checkKKTConditions(double[] betaCnd,
                               GLM.GLMGradientInfo gradientInfo,
                               int iterCnt)

We will check it the constraint stopping conditions are met.

changeCoeffBetainfo

public java.util.List<java.lang.String> changeCoeffBetainfo(java.lang.String[] coefNames)

computeSubmodel

protected GLMModel.Submodel computeSubmodel(int i,
                                            double lambda,
                                            double nullDevTrain,
                                            double nullDevValid)

computeImpl

public void computeImpl()

genRID

public void genRID()

Generate the regression influence diagnostic for gaussian and binomial families. It takes the following steps: 1. generate the inverse of gram matrix; 2. generate sum of all columns of gram matrix; 3. task is called to generate the hat matrix equation 2 or equation 6 and the diagnostic in equation 3 or 7. Note that redundant predictors are excluded.

onCompletion

public void onCompletion(jsr166y.CountedCompleter caller)

onExceptionalCompletion

public boolean onExceptionalCompletion(java.lang.Throwable t,
                                       jsr166y.CountedCompleter caller)

progress

public boolean progress(double[] beta,
                        OptimizationUtils.GradientInfo ginfo)

Specified by:

progress in interface L_BFGS.ProgressMonitor

progress

public boolean progress(double[] beta,
                        double likelihood)

updateProgress

protected void updateProgress(boolean canScore)