tesseract
3.04.00
|
#include "const.h"
#include "cluster.h"
#include "emalloc.h"
#include "genericheap.h"
#include "helpers.h"
#include "kdpair.h"
#include "matrix.h"
#include "tprintf.h"
#include "danerror.h"
#include "freelist.h"
#include <math.h>
Go to the source code of this file.
Classes | |
struct | TEMPCLUSTER |
struct | STATISTICS |
struct | BUCKETS |
struct | CHISTRUCT |
struct | ClusteringContext |
Macros | |
#define | HOTELLING 1 |
#define | FTABLE_X 10 |
#define | FTABLE_Y 100 |
#define | MINVARIANCE 0.0004 |
#define | MINSAMPLESPERBUCKET 5 |
#define | MINSAMPLES (MINBUCKETS * MINSAMPLESPERBUCKET) |
#define | MINSAMPLESNEEDED 1 |
#define | BUCKETTABLESIZE 1024 |
#define | NORMALEXTENT 3.0 |
#define | Odd(N) ((N)%2) |
#define | Mirror(N, R) ((R) - (N) - 1) |
#define | Abs(N) ( ( (N) < 0 ) ? ( -(N) ) : (N) ) |
#define | SqrtOf2Pi 2.506628275 |
#define | LOOKUPTABLESIZE 8 |
#define | MAXDEGREESOFFREEDOM MAXBUCKETS |
#define | MAXNEIGHBORS 2 |
#define | MAXDISTANCE MAX_FLOAT32 |
#define | CHIACCURACY 0.01 |
#define | MINALPHA (1e-200) |
#define | INITIALDELTA 0.1 |
#define | DELTARATIO 0.1 |
#define | ILLEGAL_CHAR 2 |
Typedefs | |
typedef tesseract::KDPairInc < float, TEMPCLUSTER * > | ClusterPair |
typedef tesseract::GenericHeap < ClusterPair > | ClusterHeap |
typedef FLOAT64(* | DENSITYFUNC )(inT32) |
typedef FLOAT64(* | SOLVEFUNC )(CHISTRUCT *, double) |
Variables | |
const double | FTable [FTABLE_Y][FTABLE_X] |
#define Abs | ( | N | ) | ( ( (N) < 0 ) ? ( -(N) ) : (N) ) |
Definition at line 208 of file cluster.cpp.
#define BUCKETTABLESIZE 1024 |
Definition at line 160 of file cluster.cpp.
#define CHIACCURACY 0.01 |
#define DELTARATIO 0.1 |
#define FTABLE_X 10 |
Definition at line 31 of file cluster.cpp.
#define FTABLE_Y 100 |
Definition at line 32 of file cluster.cpp.
#define HOTELLING 1 |
Definition at line 30 of file cluster.cpp.
#define ILLEGAL_CHAR 2 |
#define INITIALDELTA 0.1 |
#define LOOKUPTABLESIZE 8 |
Definition at line 228 of file cluster.cpp.
#define MAXDEGREESOFFREEDOM MAXBUCKETS |
Definition at line 229 of file cluster.cpp.
#define MAXDISTANCE MAX_FLOAT32 |
#define MAXNEIGHBORS 2 |
#define MINALPHA (1e-200) |
#define MINSAMPLES (MINBUCKETS * MINSAMPLESPERBUCKET) |
Definition at line 151 of file cluster.cpp.
#define MINSAMPLESNEEDED 1 |
Definition at line 152 of file cluster.cpp.
#define MINSAMPLESPERBUCKET 5 |
Definition at line 150 of file cluster.cpp.
#define MINVARIANCE 0.0004 |
Definition at line 142 of file cluster.cpp.
#define Mirror | ( | N, | |
R | |||
) | ((R) - (N) - 1) |
Definition at line 207 of file cluster.cpp.
#define NORMALEXTENT 3.0 |
Definition at line 161 of file cluster.cpp.
#define Odd | ( | N | ) | ((N)%2) |
Definition at line 206 of file cluster.cpp.
#define SqrtOf2Pi 2.506628275 |
Definition at line 218 of file cluster.cpp.
typedef tesseract::GenericHeap<ClusterPair> ClusterHeap |
Definition at line 169 of file cluster.cpp.
typedef tesseract::KDPairInc<float, TEMPCLUSTER*> ClusterPair |
Definition at line 168 of file cluster.cpp.
Definition at line 203 of file cluster.cpp.
Definition at line 204 of file cluster.cpp.
Definition at line 2361 of file cluster.cpp.
int AlphaMatch | ( | void * | arg1, |
void * | arg2 | ||
) |
Definition at line 2411 of file cluster.cpp.
Definition at line 2525 of file cluster.cpp.
LIST ClusterSamples | ( | CLUSTERER * | Clusterer, |
CLUSTERCONFIG * | Config | ||
) |
ClusterSamples *********************************************************** Parameters: Clusterer data struct containing samples to be clustered Config parameters which control clustering process Operation: This routine first checks to see if the samples in this clusterer have already been clustered before; if so, it does not bother to recreate the cluster tree. It simply recomputes the prototypes based on the new Config info. If the samples have not been clustered before, the samples in the KD tree are formed into a cluster tree and then the prototypes are computed from the cluster tree. In either case this routine returns a pointer to a list of prototypes that best represent the samples given the constraints specified in Config. Return: Pointer to a list of prototypes Exceptions: None History: 5/29/89, DSJ, Created.
Definition at line 508 of file cluster.cpp.
Definition at line 1884 of file cluster.cpp.
void ComputePrototypes | ( | CLUSTERER * | Clusterer, |
CLUSTERCONFIG * | Config | ||
) |
ComputePrototypes ******************************************************* Parameters: Clusterer data structure holding cluster tree Config parameters used to control prototype generation Operation: This routine decides which clusters in the cluster tree should be represented by prototypes, forms a list of these prototypes, and places the list in the Clusterer data structure. Return: None Exceptions: None History: 5/30/89, DSJ, Created.
Definition at line 931 of file cluster.cpp.
STATISTICS * ComputeStatistics | ( | inT16 | N, |
PARAM_DESC | ParamDesc[], | ||
CLUSTER * | Cluster | ||
) |
ComputeStatistics ********************************************************* Parameters: N number of dimensions ParamDesc array of dimension descriptions Cluster cluster whose stats are to be computed Operation: This routine searches the cluster tree for all leaf nodes which are samples in the specified cluster. It computes a full covariance matrix for these samples as well as keeping track of the ranges (min and max) for each dimension. A special data structure is allocated to return this information to the caller. An incremental algorithm for computing statistics is not used because it will not work with circular dimensions. Return: Pointer to new data structure containing statistics Exceptions: None History: 6/2/89, DSJ, Created.
Definition at line 1426 of file cluster.cpp.
void CreateClusterTree | ( | CLUSTERER * | Clusterer | ) |
CreateClusterTree ******************************************************* Parameters: Clusterer data structure holdings samples to be clustered Operation: This routine performs a bottoms-up clustering on the samples held in the kd-tree of the Clusterer data structure. The result is a cluster tree. Each node in the tree represents a cluster which conceptually contains a subset of the samples. More precisely, the cluster contains all of the samples which are contained in its two sub-clusters. The leaves of the tree are the individual samples themselves; they have no sub-clusters. The root node of the tree conceptually contains all of the samples. Return: None (the Clusterer data structure is changed) Exceptions: None History: 5/29/89, DSJ, Created.
Definition at line 698 of file cluster.cpp.
uinT16 DegreesOfFreedom | ( | DISTRIBUTION | Distribution, |
uinT16 | HistogramBuckets | ||
) |
Definition at line 2290 of file cluster.cpp.
Definition at line 2191 of file cluster.cpp.
void FillBuckets | ( | BUCKETS * | Buckets, |
CLUSTER * | Cluster, | ||
uinT16 | Dim, | ||
PARAM_DESC * | ParamDesc, | ||
FLOAT32 | Mean, | ||
FLOAT32 | StdDev | ||
) |
Definition at line 2019 of file cluster.cpp.
FindNearestNeighbor ********************************************************* Parameters: Tree kd-tree to search in for nearest neighbor Cluster cluster whose nearest neighbor is to be found Distance ptr to variable to report distance found Operation: This routine searches the specified kd-tree for the nearest neighbor of the specified cluster. It actually uses the kd routines to find the 2 nearest neighbors since one of them will be the original cluster. A pointer to the nearest neighbor is returned, if it can be found, otherwise NULL is returned. The distance between the 2 nodes is placed in the specified variable. Return: Pointer to the nearest neighbor of Cluster, or NULL Exceptions: none History: 5/29/89, DSJ, Created. 7/13/89, DSJ, Removed visibility of kd-tree node data struct
Definition at line 802 of file cluster.cpp.
void FreeBuckets | ( | BUCKETS * | Buckets | ) |
Definition at line 2252 of file cluster.cpp.
void FreeCluster | ( | CLUSTER * | Cluster | ) |
Definition at line 2266 of file cluster.cpp.
void FreeClusterer | ( | CLUSTERER * | Clusterer | ) |
FreeClusterer ************************************************************* Parameters: Clusterer pointer to data structure to be freed Operation: This routine frees all of the memory allocated to the specified data structure. It will not, however, free the memory used by the prototype list. The pointers to the clusters for each prototype in the list will be set to NULL to indicate that the cluster data structures no longer exist. Any sample lists that have been obtained via calls to GetSamples are no longer valid. Return: None Exceptions: None History: 6/6/89, DSJ, Created.
Definition at line 536 of file cluster.cpp.
void FreeProtoList | ( | LIST * | ProtoList | ) |
FreeProtoList ************************************************************ Parameters: ProtoList pointer to list of prototypes to be freed Operation: This routine frees all of the memory allocated to the specified list of prototypes. The clusters which are pointed to by the prototypes are not freed. Return: None Exceptions: None History: 6/6/89, DSJ, Created.
Definition at line 564 of file cluster.cpp.
void FreePrototype | ( | void * | arg | ) |
FreePrototype ************************************************************ Parameters: Prototype prototype data structure to be deallocated Operation: This routine deallocates the memory consumed by the specified prototype and modifies the corresponding cluster so that it is no longer marked as a prototype. The cluster is NOT deallocated by this routine. Return: None Exceptions: None History: 5/30/89, DSJ, Created.
Definition at line 579 of file cluster.cpp.
void FreeStatistics | ( | STATISTICS * | Statistics | ) |
Definition at line 2230 of file cluster.cpp.
BUCKETS * GetBuckets | ( | CLUSTERER * | clusterer, |
DISTRIBUTION | Distribution, | ||
uinT32 | SampleCount, | ||
FLOAT64 | Confidence | ||
) |
GetBuckets ************************************************************** Parameters: Clusterer which keeps a bucket_cache for us. Distribution type of probability distribution to test for SampleCount number of samples that are available Confidence probability of a Type I error Operation: This routine returns a histogram data structure which can be used by other routines to place samples into histogram buckets, and then apply a goodness of fit test to the histogram data to determine if the samples belong to the specified probability distribution. The routine keeps a list of bucket data structures which have already been created so that it minimizes the computation time needed to create a new bucket. Return: Bucket data structure Exceptions: none History: Thu Aug 3 12:58:10 1989, DSJ, Created.
Definition at line 1709 of file cluster.cpp.
BOOL8 Independent | ( | PARAM_DESC | ParamDesc[], |
inT16 | N, | ||
FLOAT32 * | CoVariance, | ||
FLOAT32 | Independence | ||
) |
Independent *************************************************************** Parameters: ParamDesc descriptions of each feature space dimension N number of dimensions CoVariance ptr to a covariance matrix Independence max off-diagonal correlation coefficient Operation: This routine returns TRUE if the specified covariance matrix indicates that all N dimensions are independent of one another. One dimension is judged to be independent of another when the magnitude of the corresponding correlation coefficient is less than the specified Independence factor. The correlation coefficient is calculated as: (see Duda and Hart, pg. 247) coeff[ij] = stddev[ij] / sqrt (stddev[ii] * stddev[jj]) The covariance matrix is assumed to be symmetric (which should always be true). Return: TRUE if dimensions are independent, FALSE otherwise Exceptions: None History: 6/4/89, DSJ, Created.
Definition at line 1660 of file cluster.cpp.
void InitBuckets | ( | BUCKETS * | Buckets | ) |
Definition at line 2390 of file cluster.cpp.
Definition at line 1998 of file cluster.cpp.
double InvertMatrix | ( | const float * | input, |
int | size, | ||
float * | inv | ||
) |
Definition at line 2652 of file cluster.cpp.
int ListEntryMatch | ( | void * | arg1, |
void * | arg2 | ||
) |
Definition at line 2343 of file cluster.cpp.
BUCKETS * MakeBuckets | ( | DISTRIBUTION | Distribution, |
uinT32 | SampleCount, | ||
FLOAT64 | Confidence | ||
) |
Makebuckets ************************************************************* Parameters: Distribution type of probability distribution to test for SampleCount number of samples that are available Confidence probability of a Type I error Operation: This routine creates a histogram data structure which can be used by other routines to place samples into histogram buckets, and then apply a goodness of fit test to the histogram data to determine if the samples belong to the specified probability distribution. The buckets are allocated in such a way that the expected frequency of samples in each bucket is approximately the same. In order to make this possible, a mapping table is computed which maps "normalized" samples into the appropriate bucket. Return: Pointer to new histogram data structure Exceptions: None History: 6/4/89, DSJ, Created.
Definition at line 1759 of file cluster.cpp.
CLUSTERER* MakeClusterer | ( | inT16 | SampleSize, |
const PARAM_DESC | ParamDesc[] | ||
) |
MakeClusterer ********************************************************** Parameters: SampleSize number of dimensions in feature space ParamDesc description of each dimension Operation: This routine creates a new clusterer data structure, initializes it, and returns a pointer to it. Return: pointer to the new clusterer data structure Exceptions: None History: 5/29/89, DSJ, Created.
Definition at line 399 of file cluster.cpp.
PROTOTYPE * MakeDegenerateProto | ( | uinT16 | N, |
CLUSTER * | Cluster, | ||
STATISTICS * | Statistics, | ||
PROTOSTYLE | Style, | ||
inT32 | MinSamples | ||
) |
MakeDegenerateProto ****************************************************** Parameters: N number of dimensions Cluster cluster being analyzed Statistics statistical info about cluster Style type of prototype to be generated MinSamples minimum number of samples in a cluster Operation: This routine checks for clusters which are degenerate and therefore cannot be analyzed in a statistically valid way. A cluster is defined as degenerate if it does not have at least MINSAMPLESNEEDED samples in it. If the cluster is found to be degenerate, a prototype of the specified style is generated and marked as insignificant. A cluster is also degenerate if it does not have at least MinSamples samples in it. If the cluster is not degenerate, NULL is returned. Return: Pointer to degenerate prototype or NULL. Exceptions: None History: 6/20/89, DSJ, Created. 7/12/89, DSJ, Changed name and added check for 0 stddev. 8/8/89, DSJ, Removed check for 0 stddev (handled elsewhere).
Definition at line 1071 of file cluster.cpp.
void MakeDimRandom | ( | uinT16 | i, |
PROTOTYPE * | Proto, | ||
PARAM_DESC * | ParamDesc | ||
) |
Definition at line 1364 of file cluster.cpp.
void MakeDimUniform | ( | uinT16 | i, |
PROTOTYPE * | Proto, | ||
STATISTICS * | Statistics | ||
) |
MakeDimUniform *********************************************************** Parameters: i index of dimension to be changed Proto prototype whose dimension is to be altered Statistics statistical info about prototype Operation: This routine alters the ith dimension of the specified mixed prototype to be uniform. Return: None Exceptions: None History: 6/20/89, DSJ, Created.
Definition at line 1389 of file cluster.cpp.
PROTOTYPE * MakeEllipticalProto | ( | CLUSTERER * | Clusterer, |
CLUSTER * | Cluster, | ||
STATISTICS * | Statistics, | ||
BUCKETS * | Buckets | ||
) |
MakeEllipticalProto **************************************************** Parameters: Clusterer data struct containing samples being clustered Cluster cluster to be made into an elliptical prototype Statistics statistical info about cluster Buckets histogram struct used to analyze distribution Operation: This routine tests the specified cluster to see if it can be approximated by an elliptical normal distribution. If it can be, then a new prototype is formed and returned to the caller. If it can't be, then NULL is returned to the caller. Return: Pointer to new elliptical prototype or NULL. Exceptions: None History: 6/12/89, DSJ, Created.
Definition at line 1258 of file cluster.cpp.
PROTOTYPE * MakeMixedProto | ( | CLUSTERER * | Clusterer, |
CLUSTER * | Cluster, | ||
STATISTICS * | Statistics, | ||
BUCKETS * | NormalBuckets, | ||
FLOAT64 | Confidence | ||
) |
MakeMixedProto *********************************************************** Parameters: Clusterer data struct containing samples being clustered Cluster cluster to be made into a prototype Statistics statistical info about cluster NormalBuckets histogram struct used to analyze distribution Confidence confidence level for alternate distributions Operation: This routine tests each dimension of the specified cluster to see what distribution would best approximate that dimension. Each dimension is compared to the following distributions in order: normal, random, uniform. If each dimension can be represented by one of these distributions, then a new prototype is formed and returned to the caller. If it can't be, then NULL is returned to the caller. Return: Pointer to new mixed prototype or NULL. Exceptions: None History: 6/12/89, DSJ, Created.
Definition at line 1302 of file cluster.cpp.
CLUSTER * MakeNewCluster | ( | CLUSTERER * | Clusterer, |
TEMPCLUSTER * | TempCluster | ||
) |
MakeNewCluster ************************************************************* Parameters: Clusterer current clustering environment TempCluster potential cluster to make permanent Operation: This routine creates a new permanent cluster from the clusters specified in TempCluster. The 2 clusters in TempCluster are marked as "clustered" and deleted from the kd-tree. The new cluster is then added to the kd-tree. Return: Pointer to the new permanent cluster Exceptions: none History: 5/29/89, DSJ, Created. 7/13/89, DSJ, Removed visibility of kd-tree node data struct
Definition at line 841 of file cluster.cpp.
void MakePotentialClusters | ( | ClusteringContext * | context, |
CLUSTER * | Cluster, | ||
inT32 | Level | ||
) |
MakePotentialClusters ************************************************** Parameters: context ClusteringContext (see definition above) Cluster current cluster being visited in kd-tree walk Level level of this cluster in the kd-tree Operation: This routine is designed to be used in concert with the KDWalk routine. It will create a potential cluster for each sample in the kd-tree that is being walked. This potential cluster will then be pushed on the heap.
Definition at line 768 of file cluster.cpp.
PROTOTYPE * MakePrototype | ( | CLUSTERER * | Clusterer, |
CLUSTERCONFIG * | Config, | ||
CLUSTER * | Cluster | ||
) |
MakePrototype *********************************************************** Parameters: Clusterer data structure holding cluster tree Config parameters used to control prototype generation Cluster cluster to be made into a prototype Operation: This routine attempts to create a prototype from the specified cluster that conforms to the distribution specified in Config. If there are too few samples in the cluster to perform a statistical analysis, then a prototype is generated but labelled as insignificant. If the dimensions of the cluster are not independent, no prototype is generated and NULL is returned. If a prototype can be found that matches the desired distribution then a pointer to it is returned, otherwise NULL is returned. Return: Pointer to new prototype or NULL Exceptions: None History: 6/19/89, DSJ, Created.
Definition at line 978 of file cluster.cpp.
MakeSample *********************************************************** Parameters: Clusterer clusterer data structure to add sample to Feature feature to be added to clusterer CharID unique ident. of char that sample came from Operation: This routine creates a new sample data structure to hold the specified feature. This sample is added to the clusterer data structure (so that it knows which samples are to be clustered later), and a pointer to the sample is returned to the caller. Return: Pointer to the new sample data structure Exceptions: ALREADYCLUSTERED MakeSample can't be called after ClusterSamples has been called History: 5/29/89, DSJ, Created.
Definition at line 454 of file cluster.cpp.
PROTOTYPE * MakeSphericalProto | ( | CLUSTERER * | Clusterer, |
CLUSTER * | Cluster, | ||
STATISTICS * | Statistics, | ||
BUCKETS * | Buckets | ||
) |
Definition at line 1220 of file cluster.cpp.
Mean *********************************************************** Parameters: Proto prototype to return mean of Dimension dimension whose mean is to be returned Operation: This routine returns the mean of the specified prototype in the indicated dimension. Return: Mean of Prototype in Dimension Exceptions: none History: 7/6/89, DSJ, Created.
Definition at line 643 of file cluster.cpp.
inT32 MergeClusters | ( | inT16 | N, |
register PARAM_DESC | ParamDesc[], | ||
register inT32 | n1, | ||
register inT32 | n2, | ||
register FLOAT32 | m[], | ||
register FLOAT32 | m1[], | ||
register FLOAT32 | m2[] | ||
) |
inT32 MergeClusters | ( | inT16 | N, |
PARAM_DESC | ParamDesc[], | ||
inT32 | n1, | ||
inT32 | n2, | ||
FLOAT32 | m[], | ||
FLOAT32 | m1[], | ||
FLOAT32 | m2[] | ||
) |
MergeClusters ************************************************************ Parameters: N # of dimensions (size of arrays) ParamDesc array of dimension descriptions n1, n2 number of samples in each old cluster m array to hold mean of new cluster m1, m2 arrays containing means of old clusters Operation: This routine merges two clusters into one larger cluster. To do this it computes the number of samples in the new cluster and the mean of the new cluster. The ParamDesc information is used to ensure that circular dimensions are handled correctly. Return: The number of samples in the new cluster. Exceptions: None History: 5/31/89, DSJ, Created.
Definition at line 886 of file cluster.cpp.
Definition at line 2569 of file cluster.cpp.
Definition at line 2436 of file cluster.cpp.
PROTOTYPE * NewEllipticalProto | ( | inT16 | N, |
CLUSTER * | Cluster, | ||
STATISTICS * | Statistics | ||
) |
NewEllipticalProto ******************************************************* Parameters: N number of dimensions Cluster cluster to be made into an elliptical prototype Statistics statistical info about samples in cluster Operation: This routine creates an elliptical prototype data structure to approximate the samples in the specified cluster. Elliptical prototypes have a variance for each dimension. All dimensions are normally distributed and independent. Return: Pointer to a new elliptical prototype data structure Exceptions: None History: 6/19/89, DSJ, Created.
Definition at line 1548 of file cluster.cpp.
PROTOTYPE * NewMixedProto | ( | inT16 | N, |
CLUSTER * | Cluster, | ||
STATISTICS * | Statistics | ||
) |
MewMixedProto ************************************************************ Parameters: N number of dimensions Cluster cluster to be made into a mixed prototype Statistics statistical info about samples in cluster Operation: This routine creates a mixed prototype data structure to approximate the samples in the specified cluster. Mixed prototypes can have different distributions for each dimension. All dimensions are independent. The structure is initially filled in as though it were an elliptical prototype. The actual distributions of the dimensions can be altered by other routines. Return: Pointer to a new mixed prototype data structure Exceptions: None History: 6/19/89, DSJ, Created.
Definition at line 1593 of file cluster.cpp.
NewSimpleProto *********************************************************** Parameters: N number of dimensions Cluster cluster to be made into a prototype Operation: This routine allocates memory to hold a simple prototype data structure, i.e. one without independent distributions and variances for each dimension. Return: Pointer to new simple prototype Exceptions: None History: 6/19/89, DSJ, Created.
Definition at line 1618 of file cluster.cpp.
PROTOTYPE * NewSphericalProto | ( | uinT16 | N, |
CLUSTER * | Cluster, | ||
STATISTICS * | Statistics | ||
) |
NewSpericalProto ********************************************************* Parameters: N number of dimensions Cluster cluster to be made into a spherical prototype Statistics statistical info about samples in cluster Operation: This routine creates a spherical prototype data structure to approximate the samples in the specified cluster. Spherical prototypes have a single variance which is common across all dimensions. All dimensions are normally distributed and independent. Return: Pointer to a new spherical prototype data structure Exceptions: None History: 6/19/89, DSJ, Created.
Definition at line 1514 of file cluster.cpp.
NextSample ************************************************************ Parameters: SearchState ptr to list containing clusters to be searched Operation: This routine is used to find all of the samples which belong to a cluster. It starts by removing the top cluster on the cluster list (SearchState). If this cluster is a leaf it is returned. Otherwise, the right subcluster is pushed on the list and we continue the search in the left subcluster. This continues until a leaf is found. If all samples have been found, NULL is returned. InitSampleSearch() must be called before NextSample() to initialize the search. Return: Pointer to the next leaf cluster (sample) or NULL. Exceptions: None History: 6/16/89, DSJ, Created.
Definition at line 618 of file cluster.cpp.
uinT16 NormalBucket | ( | PARAM_DESC * | ParamDesc, |
FLOAT32 | x, | ||
FLOAT32 | Mean, | ||
FLOAT32 | StdDev | ||
) |
Definition at line 2107 of file cluster.cpp.
Definition at line 1944 of file cluster.cpp.
int NumBucketsMatch | ( | void * | arg1, |
void * | arg2 | ||
) |
Definition at line 2320 of file cluster.cpp.
Definition at line 1843 of file cluster.cpp.
Definition at line 2461 of file cluster.cpp.
StandardDeviation ************************************************* Parameters: Proto prototype to return standard deviation of Dimension dimension whose stddev is to be returned Operation: This routine returns the standard deviation of the prototype in the indicated dimension. Return: Standard deviation of Prototype in Dimension Exceptions: none History: 7/6/89, DSJ, Created.
Definition at line 657 of file cluster.cpp.
PROTOTYPE * TestEllipticalProto | ( | CLUSTERER * | Clusterer, |
CLUSTERCONFIG * | Config, | ||
CLUSTER * | Cluster, | ||
STATISTICS * | Statistics | ||
) |
TestEllipticalProto **************************************************** Parameters: Clusterer data struct containing samples being clustered Config provides the magic number of samples that make a good cluster Cluster cluster to be made into an elliptical prototype Statistics statistical info about cluster Operation: This routine tests the specified cluster to see if ** there is a statistically significant difference between the sub-clusters that would be made if the cluster were to be split. If not, then a new prototype is formed and returned to the caller. If there is, then NULL is returned to the caller. Return: Pointer to new elliptical prototype or NULL.
Definition at line 1113 of file cluster.cpp.
uinT16 UniformBucket | ( | PARAM_DESC * | ParamDesc, |
FLOAT32 | x, | ||
FLOAT32 | Mean, | ||
FLOAT32 | StdDev | ||
) |
Definition at line 2149 of file cluster.cpp.
Definition at line 1973 of file cluster.cpp.
Definition at line 35 of file cluster.cpp.