name: RegressionDecisionTreeNode

synopsis:

gcc [flags ...] file ... -l /isip/tools/lib/$ISIP_BINARY/lib_search.a

#include <RegressionDecisionTreeNode.h>

~RegressionDecisionTreeNode();
RegressionDecisionTreeNode();
RegressionDecisionTreeNode(const RegressionDecisionTreeNode& copy_sym);

quick start:

#include <RegressionDecisionTreeNode.h>

RegressionDecisionTreeNode rdt_node;
Triple<Long, Long, Long> datapoint;
SingleLinkedList<datapoint > data;

rdt_node.createTransform(Vector& stat_models_a);
rdt_node.updateDistribution(Vector& stat_models_a);

description:

The RegressionDecisionTreeNode class is meant to store a collection of information for statistical models in the RegressionDecisionTree class.

dependencies:

public constants:

define the class name:
```
static const String CLASS_NAME;
```

define i/o related constants:

static const String DEF_PARAM;

static const String PARAM_DATAPOINTS;

static const String PARAM_CLUSTER_SCORE;

static const String PARAM_CLUSTER_ACCUMULATE;

static const String PARAM_NUMBER_COMPONENTS;

static const String PARAM_NODE_INDEX;

static const String PARAM_AVERAGE_COVARIANCE;

static const String PARAM_SPEECH_FLAG;

static const String PARAM_SPLIT_FLAG;

static const String PARAM_PARENT_NODE_INDEX;

static const String PARAM_TRANSFORM_FLAG;

static const String PARAM_STAT_MODELS;

static const String PARAM_W_TRANSFORM;

static const String PARAM_DBGL;

define default values and arguments:

static const long DEF_TYPICAL_INDEX = -1;

static const long DEF_ACTUAL_INDEX = -1;

static const bool DEF_FLAG_EXISTS = true;

other important constants:

typedef Triple<Long, Long, Long<String, String> > RDataPoint;

typedef SingleLinkedList<DataPoint> RData;

error codes:

error code indicating RegressionDecisionTreeNode class general error:

static const long ERR = 00100600;

static const long ERR_ADAPT_NO_GAUSSIAN = 00100610;

protected data:

define structures:

typedef Triple<Long, Long, Long<String, String> > RDataPoint;

typedef SingleLinkedList<DataPoint> RData;

define the list of gaussian components for this node

RData gaussian_models_d;

define variables to represent information for this node

VectorFloat average_mean_d;

Float cluster_score_d;

Float cluster_accumulate_d;

Long number_components_d;

Long node_index_d;

MatrixFloat average_covariance_d;

define a flag indicates if this node is speech/non-speech node

Boolean speech_flag_d;

define a flag to indicator if this node is splittable

Boolean split_flag_d;

define parent node index, use to search transformation index

Long parent_node_index_d;

transformation data enough?

Boolean transform_flag_d;

local copy of the statistical model;

Vector stat_models_d;

define transformation matrix W (one row for each Gaussian dimension)

Vector w_transform_d;

define Z matrix used to create transformation matrix

MatrixFloat z_glob_d;

define G matrix used to create transformation matrix

Vector g_glob_d;

define a debug level

DebugLevel debug_level_d;

define the memory manager:
```
static MemoryManager mgr_d;
```

required public methods:

static methods:

static const String& name();

static boolean diagnose(Integral::DEBUG debug_level);

debug methods:

boolean debug(const unichar* message) const;

static boolean setDebug(Integral::DEBUG debug_level);

destructor/constructor(s):

~RegressionDecisionTreeNode();

RegressionDecisionTreeNode();

RegressionDecisionTreeNode(const RegressionDecisionTreeNode& arg);

assign methods:

boolean assign(const RegressionDecisionTreeNode& copy_node);

i/o methods:

long sofSize() const;

boolean read(Sof& sof, long tag, const String& name = CLASS_NAME);

boolean write(Sof& sof, long tag, const String& name = CLASS_NAME) const;

boolean readData(Sof& sof, const String& pname = DEF_PARAM, long size = SofParser::FULL_OBJECT, boolean param = true, boolean nested = false) const;

boolean writeData(Sof& sof, const String& pname = DEF_PARAM) const;

equality methods:

boolean eq(const RegressionDecisionTreeNode& compare_node) const;

memory management methods:

static void* operator new(size_t size);

static void* operator new[](size_t size);

static void operator delete(void* ptr);

static void operator delete[](void* ptr);

static boolean setGrowSize(long grow_size);

boolean clear(Integral::CMODE ctype = Integral::DEF_CMODE);

class-specific public methods:

methods to get/set DataPoints

boolean setDataPoints(Data arg);

Data getDataPoints();

methods to get/set average mean

boolean setAverageMean(VectorFloat&  arg);

String getAverageMean(VectorFloat&  arg);

methods to get/set average covariance

boolean setAverageCov(MatrixFloat&  arg);

String getAverageCov(MatrixFloat&  arg);

methods to get/set cluster score

boolean setClusterScore(Float  arg);

Float& getClusterScore();

methods to get/set number of components

boolean setNumComponents(long  arg);

Float& getNumComponents();

methods to get/set speech flag

boolean setSpeechFlag(Boolean  arg);

boolean getSpeechFlag(Boolean arg);

methods to get/set speech flag

boolean setSplitFlag(Boolean  arg);

boolean getSplitFlag(Boolean arg);

methods to get/set transform flag

boolean setTransformFlag(Boolean  arg);

Boolean& getTransformFlag());

methods to get/set parent node index

boolean setParentNodeIndex(long  arg);

Long& getParentNodeIndex());

methods to get/set cluster accumulate

boolean setClusterAccumulate(Float  arg);

Float& getClusterAccumulate());

methods to get/set transformation

boolean setTransformation(Vector & arg);

Boolean& getTransformation(Vector & arg));

methods to get/set typical-index

boolean setTypicalIndex(Long arg);

Long getTypicalIndex();

methods to get/set node index

boolean setNodeIndex(long  arg);

Long& getNodeIndex());

other methods

boolean createTransform(Vector& stat_models_a);

boolean updateDistribution(Vector& stat_models_a);

boolean containModel(long sm_index_a, long gm_index_a);

private methods:

calculate cluster distribution

boolean calcClusterDistribution(Vector& stat_models_a);

compute sum of occupancy

boolean computeSumOccupancy(Vector& stat_models_a, float& sum_num_occ_a);

adapt part

boolean adaptPart(Vector& g_a, MatrixFloat& z_a, GaussianModel& gm_a);

Node score

boolean nodeScore(Vector& stat_models_a);

notes:

The RegressionDecisionTreeNode class is primarily used when using RegressionDecisionTree, i.e., it is used to store the information at a node in the tree.