Package biz.k11i.xgboost.gbm

Class GBLinear

java.lang.Object

biz.k11i.xgboost.gbm.GBBase

biz.k11i.xgboost.gbm.GBLinear

All Implemented Interfaces:

GradBooster, java.io.Serializable

public class GBLinear
extends GBBase

Linear booster implementation

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
(package private) static class	GBLinear.ModelParam

Nested classes/interfaces inherited from interface biz.k11i.xgboost.gbm.GradBooster

GradBooster.Factory

Field Summary

Fields

Modifier and Type	Field	Description
private float[]	weights

Fields inherited from class biz.k11i.xgboost.gbm.GBBase

num_class, num_feature, num_output_group

Constructor Summary

Constructors

Constructor	Description
GBLinear()

Method Summary

Modifier and Type	Method	Description
float	bias(int gid)
int	getNumFeature()
int	getNumOutputGroup()
void	loadModel(PredictorConfiguration config, ModelReader reader, boolean ignored_with_pbuffer)	Loads model from stream.
(package private) float	pred(FVec feat, int gid, float base_score)
float[]	predict(FVec feat, int ntree_limit, float base_score)	Generates predictions for given feature vector.
int[]	predictLeaf(FVec feat, int ntree_limit)	Predicts the leaf index of each tree.
java.lang.String[]	predictLeafPath(FVec feat, int ntree_limit)	Predicts the path to leaf of each tree.
float	predictSingle(FVec feat, int ntree_limit, float base_score)	Generates a prediction for given feature vector.
float	weight(int fid, int gid)

Methods inherited from class biz.k11i.xgboost.gbm.GBBase

setNumClass, setNumFeature

Methods inherited from class java.lang.Object

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

Field Detail

weights

private float[] weights

Constructor Detail

GBLinear

public GBLinear()

Method Detail

loadModel

public void loadModel(PredictorConfiguration config,
                      ModelReader reader,
                      boolean ignored_with_pbuffer)
               throws java.io.IOException

Description copied from interface: GradBooster

Loads model from stream.

Parameters:

config - predictor configuration

reader - input stream

ignored_with_pbuffer - whether the incoming data contains pbuffer

Throws:

java.io.IOException - If an I/O error occurs

predict

public float[] predict(FVec feat,
                       int ntree_limit,
                       float base_score)

Description copied from interface: GradBooster

Generates predictions for given feature vector.

Parameters:

feat - feature vector

ntree_limit - limit the number of trees used in prediction

base_score - base score to initialize prediction

Returns:

prediction result

predictSingle

public float predictSingle(FVec feat,
                           int ntree_limit,
                           float base_score)

Description copied from interface: GradBooster

Generates a prediction for given feature vector.

This method only works when the model outputs single value.

Parameters:

feat - feature vector

ntree_limit - limit the number of trees used in prediction

base_score - base score to initialize prediction

Returns:

prediction result

pred

float pred(FVec feat,
           int gid,
           float base_score)

predictLeaf

public int[] predictLeaf(FVec feat,
                         int ntree_limit)

Description copied from interface: GradBooster

Predicts the leaf index of each tree. This is only valid in gbtree predictor.

Parameters:

feat - feature vector

ntree_limit - limit the number of trees used in prediction

Returns:

predicted leaf indexes

predictLeafPath

public java.lang.String[] predictLeafPath(FVec feat,
                                          int ntree_limit)

Description copied from interface: GradBooster

Predicts the path to leaf of each tree. This is only valid in gbtree predictor.

Parameters:

feat - feature vector

ntree_limit - limit the number of trees used in prediction

Returns:

predicted path to leaves

weight

public float weight(int fid,
                    int gid)

bias

public float bias(int gid)

getNumFeature

public int getNumFeature()

getNumOutputGroup

public int getNumOutputGroup()