public class GLM extends hex.ModelBuilder<GLMModel,GLMModel.GLMParameters,GLMModel.GLMOutput>
Modifier and Type | Class and Description |
---|---|
class |
GLM.BetaConstraint |
class |
GLM.GLMDriver
Main loop of the glm algo.
|
static class |
GLM.GLMGradientInfo |
static class |
GLM.GLMGradientSolver
Gradient and line search computation for L_BFGS and also L_BFGS solver wrapper (for ADMM)
|
static class |
GLM.GramSolver
Created by tomasnykodym on 3/30/15.
|
static class |
GLM.ProximalGradientInfo |
static class |
GLM.ProximalGradientSolver
Simple wrapper around ginfo computation, adding proximal penalty
|
Modifier and Type | Field and Description |
---|---|
protected boolean |
_cv |
java.lang.String |
_generatedWeights |
static int |
SCORING_INTERVAL_MSEC |
protected static long |
WORK_TOTAL |
Constructor and Description |
---|
GLM(boolean startup_once) |
GLM(GLMModel.GLMParameters parms) |
GLM(GLMModel.GLMParameters parms,
water.Key dest) |
Modifier and Type | Method and Description |
---|---|
hex.ModelCategory[] |
can_build() |
protected void |
checkMemoryFootPrint(DataInfo activeData) |
double[] |
COD_solve(GLMTask.GLMIterationTask gt,
double alpha,
double lambda) |
void |
computeCrossValidation()
GLM implementation of N-fold cross-validation.
|
protected boolean |
computePriorClassDistribution() |
void |
cv_computeAndSetOptimalParameters(hex.ModelBuilder[] cvModelBuilders)
If run with lambda search, we need to take extra action performed after cross-val models are built.
|
boolean |
haveMojo() |
boolean |
havePojo() |
void |
init(boolean expensive) |
boolean |
isSupervised() |
int |
nclasses() |
protected static double |
sparseOffset(double[] beta,
DataInfo dinfo) |
protected GLM.GLMDriver |
trainModelImpl() |
algoName, algos, builderVisibility, checkDistributions, checkMemoryFootPrint, clearInitState, clearValidationErrors, cv_AssignFold, cv_buildModels, cv_mainModelScores, cv_makeFramesAndBuilders, cv_makeWeights, cv_scoreCVModels, defaultKey, desiredChunks, dest, error_count, error, get, getToEigenVec, hasFoldCol, hasOffsetCol, hasWeightCol, hide, ignoreBadColumns, ignoreConstColumns, ignoreInvalidColumns, ignoreStringColumns, info, init_adaptFrameToTrain, isClassifier, isStopped, javaName, logMe, make, message, nFoldCV, nFoldWork, nModelsInParallel, numSpecialCols, paramName, rebalance, response, schemaDirectory, separateFeatureVecs, setTrain, shouldReorder, specialColNames, stop_requested, timeout, train, trainModel, trainModelNested, valid, validationErrors, vresponse, warn
protected boolean _cv
public static final int SCORING_INTERVAL_MSEC
public java.lang.String _generatedWeights
protected static final long WORK_TOTAL
public GLM(boolean startup_once)
public GLM(GLMModel.GLMParameters parms)
public GLM(GLMModel.GLMParameters parms, water.Key dest)
public boolean isSupervised()
isSupervised
in class hex.ModelBuilder<GLMModel,GLMModel.GLMParameters,GLMModel.GLMOutput>
public hex.ModelCategory[] can_build()
can_build
in class hex.ModelBuilder<GLMModel,GLMModel.GLMParameters,GLMModel.GLMOutput>
public boolean havePojo()
havePojo
in class hex.ModelBuilder<GLMModel,GLMModel.GLMParameters,GLMModel.GLMOutput>
public boolean haveMojo()
haveMojo
in class hex.ModelBuilder<GLMModel,GLMModel.GLMParameters,GLMModel.GLMOutput>
public void computeCrossValidation()
computeCrossValidation
in class hex.ModelBuilder<GLMModel,GLMModel.GLMParameters,GLMModel.GLMOutput>
public void cv_computeAndSetOptimalParameters(hex.ModelBuilder[] cvModelBuilders)
cv_computeAndSetOptimalParameters
in class hex.ModelBuilder<GLMModel,GLMModel.GLMParameters,GLMModel.GLMOutput>
protected void checkMemoryFootPrint(DataInfo activeData)
public int nclasses()
nclasses
in class hex.ModelBuilder<GLMModel,GLMModel.GLMParameters,GLMModel.GLMOutput>
protected boolean computePriorClassDistribution()
computePriorClassDistribution
in class hex.ModelBuilder<GLMModel,GLMModel.GLMParameters,GLMModel.GLMOutput>
public void init(boolean expensive)
init
in class hex.ModelBuilder<GLMModel,GLMModel.GLMParameters,GLMModel.GLMOutput>
protected GLM.GLMDriver trainModelImpl()
trainModelImpl
in class hex.ModelBuilder<GLMModel,GLMModel.GLMParameters,GLMModel.GLMOutput>
public double[] COD_solve(GLMTask.GLMIterationTask gt, double alpha, double lambda)
protected static double sparseOffset(double[] beta, DataInfo dinfo)