Package biz.k11i.xgboost.gbm
Class GBLinear
java.lang.Object
biz.k11i.xgboost.gbm.GBBase
biz.k11i.xgboost.gbm.GBLinear
- All Implemented Interfaces:
GradBooster
,Serializable
Linear booster implementation
- See Also:
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Nested Class Summary
Nested ClassesNested classes/interfaces inherited from interface biz.k11i.xgboost.gbm.GradBooster
GradBooster.Factory
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Field Summary
Fields -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescription(package private) float
bias
(int gid) void
loadModel
(ModelReader reader, boolean ignored_with_pbuffer) Loads model from stream.(package private) float
float[]
Generates predictions for given feature vector.int[]
predictLeaf
(FVec feat, int ntree_limit) Predicts the leaf index of each tree.float
predictSingle
(FVec feat, int ntree_limit) Generates a prediction for given feature vector.(package private) float
weight
(int fid, int gid) Methods inherited from class biz.k11i.xgboost.gbm.GBBase
setNumClass
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Field Details
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mparam
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weights
private float[] weights
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Constructor Details
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GBLinear
GBLinear()
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Method Details
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loadModel
Description copied from interface:GradBooster
Loads model from stream.- Parameters:
reader
- input streamignored_with_pbuffer
- whether the incoming data contains pbuffer- Throws:
IOException
- If an I/O error occurs
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predict
Description copied from interface:GradBooster
Generates predictions for given feature vector.- Parameters:
feat
- feature vectorntree_limit
- limit the number of trees used in prediction- Returns:
- prediction result
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predictSingle
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 vectorntree_limit
- limit the number of trees used in prediction- Returns:
- prediction result
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pred
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predictLeaf
Description copied from interface:GradBooster
Predicts the leaf index of each tree. This is only valid in gbtree predictor.- Parameters:
feat
- feature vectorntree_limit
- limit the number of trees used in prediction- Returns:
- predicted leaf indexes
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weight
float weight(int fid, int gid) -
bias
float bias(int gid)
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