Package | Description |
---|---|
com.ibm.bluej.consistency | |
com.ibm.bluej.consistency.learning | |
com.ibm.bluej.consistency.learning.coordescent | |
com.ibm.bluej.consistency.learning.multilevel |
Modifier and Type | Field and Description |
---|---|
LearningState |
CRFState.learningState |
Modifier and Type | Method and Description |
---|---|
static LearningState |
SGSearch.getLearningState() |
Modifier and Type | Field and Description |
---|---|
LearningState |
VariationRank.learningState |
Modifier and Type | Method and Description |
---|---|
void |
WorldWeightParam.badDelta(LearningState learningState)
This SGWeight represents the difference between two states:
this = weight(s2) - weight(s1).
|
boolean |
SingleDelta.endDelta(LearningState learningState,
double goldScore,
double prevGoldScore) |
boolean |
IDeltaWeight.endDelta(LearningState learningState,
double goldScore,
double prevGoldScore) |
boolean |
DeltaOnly.endDelta(LearningState learningState,
double goldScore,
double prevGoldScore) |
boolean |
FixedDeltaWeight.endDelta(LearningState learningState,
double goldScore,
double prevGoldScore) |
void |
Learnable.pushDelta(double delta,
LearningState learningState) |
void |
ParamWeight.pushDelta(double delta,
LearningState learningState)
TODO: pass LearningState, but not timestep - since LearningState should hold timestep
|
Constructor and Description |
---|
VariationRank(LearningState learningState) |
Modifier and Type | Method and Description |
---|---|
static void |
RankLearn.learn(LearningState learningState,
java.util.ArrayList<com.ibm.bluej.util.common.Pair<WorldWeightFunction,java.lang.Double>> scoreToGain,
double margin) |
static void |
RankLearn.rerank(LearningState learningState,
WorldWeightFunction lower,
WorldWeightFunction higher) |
Modifier and Type | Method and Description |
---|---|
void |
LearningNode.pushDelta(double delta,
LearningState learningState) |
void |
LearningNode.updateWeights(LearningState learningState) |