name: PhoneticDecisionTree : public DecisionTreeBase<PhoneticDecisionTreeNode>

synopsis:

g++ [flags ...] file ... -l /isip/tools/lib/$ISIP_BINARY/lib_pr.a

#include <PhoneticDecisionTree.h>

PhoneticDecisionTree(ALGORITHM algorithm = DEF_ALGORITHM, IMPLEMENTATION implementation = DEF_IMPLEMENTATION, float split_threshold, float merge_threshold, float num_occ_threshold);
boolean eq(const PhoneticDecisionTree& arg);
boolean setAlgorithm(ALGORITHM algorithm);
boolean setSplitThreshold(float split_threshold);
quick start:

PhoneticDecisionTree pdt;
pdt.setStopMode(THRESH);
pdt.setRunMode(TRAIN);
pdt.setAlgorithm(ML);
pdt.setImplementation(DEFAULT);
pdt.setSplitThreshold(100);
pdt.setMergeThreshold(1000);
pdt.setNumOccThreshold(600);
      
PhoneticDecisionTreeNode pdt_node;
Triple<Long, StatisticalModel, HashTable<String, String> > datapoint;
SingleLinkedList<Triple<Long, StatisticalModel, HashTable<String, String> > > data;
StatisticalModel sm;
GaussianModel gm;
MixtureModel mm;
mm.add(gm);
sm.assign(mm);
sm.setOccupancy((Double)0);
HashTable<String, String> hash;
datapoint.assign((Long)0, sm, hash);
data.insert(&datapoint);
pdt_node.setDataPoints(data);
pdt.load(attributes, pdt_node);
pdt.buildDecisionTree();

description:

The PhoneticDecisionTree class is used to solve the problem of sparse data by tying Statistical Models using the training data (Symbol Occupancies) and the phonetic questions (expert knowledge). A good reference of the algorithm is:
dependencies:

public constants:

error codes:

protected data:

required public methods:

class-specific public methods:

private methods: