java.lang.Object
- java.lang.Thread
- - gov.nih.mipav.model.algorithms.AlgorithmBase
  - - gov.nih.mipav.model.algorithms.AlgorithmMeanShiftClustering

All Implemented Interfaces:

java.awt.event.ActionListener, java.awt.event.WindowListener, java.lang.Runnable, java.util.EventListener
```
public class AlgorithmMeanShiftClustering
extends AlgorithmBase
```
The java code is ported from C++ code downloaded from http://coewww.rutgers.edu/riul/research/code.html. The relevant web page section says: Adaptive mean shift based clustering C++ code implementing an (approximate) mean shift procedure with variable bandwith (in high dimensions). The algorithm is described in Mean shift based clustering in high dimensions: A texture classification example. For comments, please contact Bogdan Georgescu or Ilan Shimshoni. The original paper was written by Bogdan Georgescu, Ilan Shimshoni, and P. Meer in the proceedings of ICCV 2003. The original files were fams.cpp and fams.h. This code was ported by William Gandler.

Nested Class Summary

Nested Classes
Modifier and Type	Class	Description
`private class`	`AlgorithmMeanShiftClustering.fams_cut`
`private class`	`AlgorithmMeanShiftClustering.fams_hash_entry`
`private class`	`AlgorithmMeanShiftClustering.fams_hash_entry2`
`private class`	`AlgorithmMeanShiftClustering.fams_res_cont`
`private class`	`AlgorithmMeanShiftClustering.famsPoint`

Nested classes/interfaces inherited from class java.lang.Thread
java.lang.Thread.State, java.lang.Thread.UncaughtExceptionHandler

Field Summary

Fields
Modifier and Type	Field	Description
`private int[]`	`array1`
`private static int`	`Bs`
`private static int`	`Bs2`
`private byte[]`	`byteFloatBuffer`	byte array for float *
`private byte[]`	`byteIntBuffer`	byte array for int *
`private int`	`choosePoints`
`private int[][]`	`data`
`private java.lang.String`	`data_file_name`
`private int`	`dataSize`
`private boolean`	`endianess`
`private float`	`epsilon`
`static int`	`EVERY_POINT`
`private static double`	`FAMS_ALPHA`
`private static int`	`FAMS_BLOCKSIZE`
`private static int`	`FAMS_BLOCKSIZE2`
`private boolean`	`FAMS_DO_SPEEDUP`
`private static int`	`FAMS_FKL_NEL`
`private static int`	`FAMS_FKL_TIMES`
`private static float`	`FAMS_FLOAT_SHIFT`
`private static int`	`FAMS_MAX_K`
`private static int`	`FAMS_MAX_L`
`private static int`	`FAMS_MAXITER`
`private static int`	`FAMS_PRUNE_HDIV`
`private static int`	`FAMS_PRUNE_MAXM`
`private static int`	`FAMS_PRUNE_MAXP`
`private static int`	`FAMS_PRUNE_MINN`
`private static int`	`FAMS_PRUNE_WINDOW`
`private boolean`	`findOptimalKL`
`private boolean`	`fixedWidth`
`private int[]`	`hashCoeffs`
`private int[][]`	`hmodes`
`private java.lang.String`	`input_directory`
`private int`	`jump`
`private int`	`K`
`private int`	`K_`
`private int`	`k_neigh`
`private int`	`Kjump`
`private int`	`Kmin`
`private int`	`L`
`private int`	`L_`
`private int`	`M`
`private int`	`M2`
`private float`	`maxVal`
`private float`	`minVal`
`private int[][]`	`modes`
`private ModelImage`	`modesImage`
`private int`	`nDims`
`private int`	`nnres1`
`private int`	`nnres2`
`private boolean`	`noLSH`
`private int`	`npm`
`private int`	`nPoints`
`private int[]`	`nprunedmodes`
`private int`	`nsel`
`private double`	`percent`
`private AlgorithmMeanShiftClustering.famsPoint[]`	`points`
`private int[][]`	`prunedmodes`
`private ModelImage`	`prunedModesImage`
`private int[]`	`psel`
`private float[]`	`pttemp`
`private java.io.RandomAccessFile`	`raFile`
`(package private) double[]`	`rr`
`static int`	`SELECT_ON_JUMP`
`static int`	`SELECT_PERCENT`
`private java.util.Random`	`srand`
`private int[][]`	`t_cut_res`
`private int[]`	`t_hjump`
`private int[]`	`t_m`
`private int[]`	`t_m2`
`private int[][]`	`t_old_cut_res`
`private int[]`	`t_old_m`
`private long`	`tt1`
`private float`	`width`

Fields inherited from class gov.nih.mipav.model.algorithms.AlgorithmBase
destFlag, destImage, image25D, mask, maxProgressValue, minProgressValue, multiThreadingEnabled, nthreads, progress, progressModulus, progressStep, runningInSeparateThread, separable, srcImage, threadStopped

Fields inherited from class java.lang.Thread
MAX_PRIORITY, MIN_PRIORITY, NORM_PRIORITY

Constructor Summary

Constructors
Constructor	Description
`AlgorithmMeanShiftClustering(ModelImage image, int K, int L, int k_neigh, java.lang.String data_file_name, java.lang.String input_directory, int choosePoints, int jump, double percent, boolean fixedWidth, float width, boolean findOptimalKL, float epsilon, int Kmin, int Kjump, boolean FAMS_DO_SPEEDUP, int nPoints, int nDims, float[] pttemp, ModelImage modesImage, ModelImage prunedModesImage)`	File formats: Files are all ASCII files.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`private void`	`addDataToHash(AlgorithmMeanShiftClustering.fams_hash_entry[][] HT, int[] hs, AlgorithmMeanShiftClustering.famsPoint pt, int where, int Bs, int M, int which, int which2, int hjump)`
`private void`	`addDataToRes(AlgorithmMeanShiftClustering.fams_hash_entry[][] HT, int[] hs, AlgorithmMeanShiftClustering.fams_res_cont res, int where, int Bs, int M, int which, int nnres, int which2, int hjump)`
`(package private) void`	`bgISort(int[] ra, int nVec, int[] ira)`
`private void`	`bgSort(long[] ra, int nVec)`
`private void`	`cleanPrunedModes()`
`private void`	`cleanSelected()`
`private boolean`	`compareCutRes(int[][] in_cr1, int[][] in_cr2)`
`private void`	`computePilot(AlgorithmMeanShiftClustering.fams_hash_entry[][] HT, int[] hs, AlgorithmMeanShiftClustering.fams_cut[][] cuts, java.lang.String pilot_file_name)`
`private void`	`computeRealBandwidths(int h)`
`private void`	`computeScores(AlgorithmMeanShiftClustering.fams_hash_entry[][] HT, int[] hs, AlgorithmMeanShiftClustering.fams_cut[][] cuts, float[] scores)`
`private int`	`distL1(AlgorithmMeanShiftClustering.famsPoint inPt1, AlgorithmMeanShiftClustering.famsPoint inPt2)`
`private boolean`	`distL1Data(int[] in_d1, AlgorithmMeanShiftClustering.famsPoint in_pt2, double in_dist, double[] in_res)`
`private void`	`doFAMS(AlgorithmMeanShiftClustering.fams_hash_entry[][] HT, int[] hs, AlgorithmMeanShiftClustering.fams_cut[][] cuts, AlgorithmMeanShiftClustering.fams_hash_entry2[][] HT2, int[] hs2)`
`private long`	`doFindKLIteration(int K, int L, float[] scores)`
`private int`	`doMeanShiftAdaptiveIteration(AlgorithmMeanShiftClustering.fams_res_cont res, int[] old, int[] ret)`
`private void`	`evalCutRes(int[] in_dat, AlgorithmMeanShiftClustering.fams_cut[] in_part, int[] in_cut_res)`
`private void`	`evalCutRes(AlgorithmMeanShiftClustering.famsPoint in_pt, AlgorithmMeanShiftClustering.fams_cut[] in_part, int[] in_cut_res)`
`private int[]`	`findInHash(AlgorithmMeanShiftClustering.fams_hash_entry2[][] HT2, int[] hs2, int where, int which, int M2, int hjump)`
`float`	`getFloat(boolean bigEndian)`	Reads four unsigned bytes from file.
`int`	`getInt(boolean bigEndian)`	Reads four signed bytes from file.
`private void`	`getNearestNeighbours(AlgorithmMeanShiftClustering.famsPoint who, AlgorithmMeanShiftClustering.fams_hash_entry[][] HT, int[] hs, AlgorithmMeanShiftClustering.fams_cut[][] cuts, AlgorithmMeanShiftClustering.fams_res_cont res, int print, int[] num_l)`
`private int[]`	`getNearestNeighbours2H(int[] who, AlgorithmMeanShiftClustering.fams_hash_entry[][] HT, int[] hs, AlgorithmMeanShiftClustering.fams_cut[][] cuts, AlgorithmMeanShiftClustering.fams_res_cont res, int[][] solution, AlgorithmMeanShiftClustering.fams_hash_entry2[][] HT2, int[] hs2)`
`private int`	`getPrime(int minp)`
`private int`	`hashFunction(int[] cutVals, int offset, int whichPartition, int kk, int M, int[] hjump, int hOffset)`
`private void`	`initHash(int nk)`
`private void`	`insertIntoHash(AlgorithmMeanShiftClustering.fams_hash_entry2[][] HT2, int[] hs2, int where, int which, int[][] solution, int M, int hjump)`
`private boolean`	`loadBandwidths(java.lang.String fn)`
`private void`	`makeCutL(AlgorithmMeanShiftClustering.fams_cut[] cut)`
`private void`	`makeCuts(AlgorithmMeanShiftClustering.fams_cut[][] cuts)`
`private boolean`	`notEq(int[] in_d1, int[] in_d2)`
`private int`	`pruneModes(int hprune, int npmin)`
`void`	`runAlgorithm()`	Actually runs the algorithm.
`private void`	`saveBandwidths(java.lang.String fn)`
`private void`	`saveModes(java.lang.String fn)`
`private void`	`savePrunedModes(java.lang.String fn)`
`private void`	`selectMSPoints(double percent, int jump)`
`private double`	`SQ(double x)`
`void`	`writeFloat(float data, boolean bigEndian)`	Writes a float as four bytes to a file.
`void`	`writeInt(int data, boolean bigEndian)`	Writes an int as four bytes to a file.

Methods inherited from class gov.nih.mipav.model.algorithms.AlgorithmBase
actionPerformed, addListener, addProgressChangeListener, calculateImageSize, calculatePrincipleAxis, computeElapsedTime, computeElapsedTime, convertIntoFloat, delinkProgressToAlgorithm, delinkProgressToAlgorithmMulti, displayError, errorCleanUp, finalize, fireProgressStateChanged, fireProgressStateChanged, fireProgressStateChanged, fireProgressStateChanged, fireProgressStateChanged, generateProgressValues, getDestImage, getElapsedTime, getMask, getMaxProgressValue, getMinProgressValue, getNumberOfThreads, getProgress, getProgressChangeListener, getProgressChangeListeners, getProgressModulus, getProgressStep, getProgressValues, getSrcImage, isCompleted, isImage25D, isMultiThreadingEnabled, isRunningInSeparateThread, isThreadStopped, linkProgressToAlgorithm, linkProgressToAlgorithm, makeProgress, notifyListeners, removeListener, removeProgressChangeListener, run, setCompleted, setImage25D, setMask, setMaxProgressValue, setMinProgressValue, setMultiThreadingEnabled, setNumberOfThreads, setProgress, setProgressModulus, setProgressStep, setProgressValues, setProgressValues, setRunningInSeparateThread, setSrcImage, setStartTime, setThreadStopped, startMethod, windowActivated, windowClosed, windowClosing, windowDeactivated, windowDeiconified, windowIconified, windowOpened

Methods inherited from class java.lang.Thread
activeCount, checkAccess, clone, countStackFrames, currentThread, dumpStack, enumerate, getAllStackTraces, getContextClassLoader, getDefaultUncaughtExceptionHandler, getId, getName, getPriority, getStackTrace, getState, getThreadGroup, getUncaughtExceptionHandler, holdsLock, interrupt, interrupted, isAlive, isDaemon, isInterrupted, join, join, join, onSpinWait, resume, setContextClassLoader, setDaemon, setDefaultUncaughtExceptionHandler, setName, setPriority, setUncaughtExceptionHandler, sleep, sleep, start, stop, suspend, toString, yield

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

EVERY_POINT
```
public static final int EVERY_POINT
```
See Also:

Constant Field Values

SELECT_ON_JUMP
```
public static final int SELECT_ON_JUMP
```
See Also:

Constant Field Values

SELECT_PERCENT
```
public static final int SELECT_PERCENT
```
See Also:

Constant Field Values

FAMS_FKL_NEL
```
private static final int FAMS_FKL_NEL
```
See Also:

Constant Field Values

FAMS_FKL_TIMES
```
private static final int FAMS_FKL_TIMES
```
See Also:

Constant Field Values

FAMS_MAX_K
```
private static final int FAMS_MAX_K
```
See Also:

Constant Field Values

FAMS_MAX_L
```
private static final int FAMS_MAX_L
```
See Also:

Constant Field Values

FAMS_BLOCKSIZE
```
private static final int FAMS_BLOCKSIZE
```
See Also:

Constant Field Values

FAMS_BLOCKSIZE2
```
private static final int FAMS_BLOCKSIZE2
```
See Also:

Constant Field Values

FAMS_MAXITER
```
private static final int FAMS_MAXITER
```
See Also:

Constant Field Values

FAMS_ALPHA
```
private static final double FAMS_ALPHA
```
See Also:

Constant Field Values

FAMS_PRUNE_WINDOW
```
private static final int FAMS_PRUNE_WINDOW
```
See Also:

Constant Field Values

FAMS_PRUNE_MINN
```
private static final int FAMS_PRUNE_MINN
```
See Also:

Constant Field Values

FAMS_PRUNE_MAXM
```
private static final int FAMS_PRUNE_MAXM
```
See Also:

Constant Field Values

FAMS_PRUNE_MAXP
```
private static final int FAMS_PRUNE_MAXP
```
See Also:

Constant Field Values

FAMS_PRUNE_HDIV
```
private static final int FAMS_PRUNE_HDIV
```
See Also:

Constant Field Values

Bs
```
private static final int Bs
```
See Also:

Constant Field Values

Bs2
```
private static final int Bs2
```
See Also:

Constant Field Values

FAMS_FLOAT_SHIFT
```
private static float FAMS_FLOAT_SHIFT
```

K
```
private int K
```

L
```
private int L
```

k_neigh
```
private int k_neigh
```

data_file_name

private java.lang.String data_file_name

input_directory

private java.lang.String input_directory

choosePoints
```
private int choosePoints
```

jump
```
private int jump
```

percent
```
private double percent
```

fixedWidth
```
private boolean fixedWidth
```

width
```
private float width
```

findOptimalKL
```
private boolean findOptimalKL
```

epsilon
```
private float epsilon
```

Kmin
```
private int Kmin
```

Kjump
```
private int Kjump
```

FAMS_DO_SPEEDUP
```
private boolean FAMS_DO_SPEEDUP
```

nPoints
```
private int nPoints
```

nDims
```
private int nDims
```

pttemp
```
private float[] pttemp
```

modesImage
```
private ModelImage modesImage
```

prunedModesImage
```
private ModelImage prunedModesImage
```

noLSH
```
private boolean noLSH
```

raFile

private java.io.RandomAccessFile raFile

byteIntBuffer
```
private final byte[] byteIntBuffer
```
byte array for int *

byteFloatBuffer
```
private final byte[] byteFloatBuffer
```
byte array for float *

endianess
```
private boolean endianess
```

array1
```
private int[] array1
```

minVal
```
private float minVal
```

maxVal
```
private float maxVal
```

points

private AlgorithmMeanShiftClustering.famsPoint[] points

data
```
private int[][] data
```

dataSize
```
private int dataSize
```

rr
```
double[] rr
```

psel
```
private int[] psel
```

nsel
```
private int nsel
```

modes
```
private int[][] modes
```

hmodes
```
private int[][] hmodes
```

npm
```
private int npm
```

prunedmodes
```
private int[][] prunedmodes
```

nprunedmodes
```
private int[] nprunedmodes
```

M
```
private int M
```

M2
```
private int M2
```

hashCoeffs
```
private int[] hashCoeffs
```

K_
```
private int K_
```

L_
```
private int L_
```

t_cut_res
```
private int[][] t_cut_res
```

t_old_cut_res
```
private int[][] t_old_cut_res
```

t_old_m
```
private int[] t_old_m
```

t_m
```
private int[] t_m
```

t_m2
```
private int[] t_m2
```

t_hjump
```
private int[] t_hjump
```

nnres1
```
private int nnres1
```

nnres2
```
private int nnres2
```

tt1
```
private long tt1
```

srand
```
private java.util.Random srand
```

Constructor Detail
- AlgorithmMeanShiftClustering
```
public AlgorithmMeanShiftClustering(ModelImage image,
                                    int K,
                                    int L,
                                    int k_neigh,
                                    java.lang.String data_file_name,
                                    java.lang.String input_directory,
                                    int choosePoints,
                                    int jump,
                                    double percent,
                                    boolean fixedWidth,
                                    float width,
                                    boolean findOptimalKL,
                                    float epsilon,
                                    int Kmin,
                                    int Kjump,
                                    boolean FAMS_DO_SPEEDUP,
                                    int nPoints,
                                    int nDims,
                                    float[] pttemp,
                                    ModelImage modesImage,
                                    ModelImage prunedModesImage)
```
  File formats: Files are all ASCII files. The input file has a one line header with two number_of_points dimension Then the input points are given as lines including d numbers. The pilot process produces the neighborhood size for each point If the input file is for example d040_1S.txt and the number of neighbors is k the pilot file will be pilot_d040_1S_k.txt If the file exists the program will assume that it was created by a previous run and use the information in it and will not rerun the pilot creation procedure. This enables the users to provide their own neighborhood sizes. The output files are for example: The result of MS on the selected points is in out_data_file_name.txt The result of joined modes is in modes_data_file_name.txt Example: We are given a file d040_1S.txt fams 30 46 200 d040_1S ./ -f 0.05 10 2 Finds K,L and optionally runs mean shift on all points. To run mean shift on same data with K=24 and L=35 fams 24 35 200 d040_1S ./ NOTES: - Internally the program scales the data between 0 and 2^16-1, therefore the precision is limited by range_of_data/2^16 - Running mean shift on all the points is not necessary when only the modes are needed - In the file fams.h there are several constants that influence the speed and accuracy of the program. For example you can set the bandwidth on which two modes are joined or the number of trials on which the test is run when finding K and L. Permission to port granted by Ilan Shimshoni: No problem. Good luck with that. I'll be happy to put your code also on my website if you like. Ilan Sent from my Cyanogen phone On Nov 23, 2016 9:02 PM, "Gandler, William (NIH/CIT) [E]" wrote: Dear Ilan Shimshoni, I am a Java programmer for the MIPAV imaging package at NIH working on mean shift. Could I have permission to port your Adaptive mean shift based clustering C++ code to Java for use in MIPAV? You will receive full acknowledgement for all code ported. Sincerely, William Gandler

Method Detail

runAlgorithm
```
public void runAlgorithm()
```
Description copied from class: AlgorithmBase

Actually runs the algorithm. Implemented by inheriting algorithms.

Specified by:

runAlgorithm in class AlgorithmBase

savePrunedModes

private void savePrunedModes(java.lang.String fn)

saveModes

private void saveModes(java.lang.String fn)

pruneModes

private int pruneModes(int hprune,
                       int npmin)

cleanPrunedModes
```
private void cleanPrunedModes()
```

bgISort

void bgISort(int[] ra,
             int nVec,
             int[] ira)

doFAMS

private void doFAMS(AlgorithmMeanShiftClustering.fams_hash_entry[][] HT,
                    int[] hs,
                    AlgorithmMeanShiftClustering.fams_cut[][] cuts,
                    AlgorithmMeanShiftClustering.fams_hash_entry2[][] HT2,
                    int[] hs2)

doMeanShiftAdaptiveIteration

private int doMeanShiftAdaptiveIteration(AlgorithmMeanShiftClustering.fams_res_cont res,
                                         int[] old,
                                         int[] ret)

SQ
```
private double SQ(double x)
```

distL1Data

private boolean distL1Data(int[] in_d1,
                           AlgorithmMeanShiftClustering.famsPoint in_pt2,
                           double in_dist,
                           double[] in_res)

getNearestNeighbours2H

private int[] getNearestNeighbours2H(int[] who,
                                     AlgorithmMeanShiftClustering.fams_hash_entry[][] HT,
                                     int[] hs,
                                     AlgorithmMeanShiftClustering.fams_cut[][] cuts,
                                     AlgorithmMeanShiftClustering.fams_res_cont res,
                                     int[][] solution,
                                     AlgorithmMeanShiftClustering.fams_hash_entry2[][] HT2,
                                     int[] hs2)

insertIntoHash

private void insertIntoHash(AlgorithmMeanShiftClustering.fams_hash_entry2[][] HT2,
                            int[] hs2,
                            int where,
                            int which,
                            int[][] solution,
                            int M,
                            int hjump)

findInHash

private int[] findInHash(AlgorithmMeanShiftClustering.fams_hash_entry2[][] HT2,
                         int[] hs2,
                         int where,
                         int which,
                         int M2,
                         int hjump)

notEq

private boolean notEq(int[] in_d1,
                      int[] in_d2)

computePilot

private void computePilot(AlgorithmMeanShiftClustering.fams_hash_entry[][] HT,
                          int[] hs,
                          AlgorithmMeanShiftClustering.fams_cut[][] cuts,
                          java.lang.String pilot_file_name)

loadBandwidths

private boolean loadBandwidths(java.lang.String fn)

saveBandwidths

private void saveBandwidths(java.lang.String fn)

getInt
```
public final int getInt(boolean bigEndian)
                 throws java.io.IOException
```
Reads four signed bytes from file.

Parameters:

bigEndian - true indicates big endian byte order, false indicates little endian.

Returns:

The value of the integer read from the file.

Throws:

java.io.IOException - if there is an error reading the file

getFloat
```
public final float getFloat(boolean bigEndian)
                     throws java.io.IOException
```
Reads four unsigned bytes from file.

Parameters:

bigEndian - true indicates big endian byte order, false indicates little endian.

Returns:

The value of the float read from the file.

Throws:

java.io.IOException - if there is an error reading the file

writeInt
```
public final void writeInt(int data,
                           boolean bigEndian)
                    throws java.io.IOException
```
Writes an int as four bytes to a file.

Parameters:

data - Data to be written to file.

bigEndian - true indicates big endian byte order, false indicates little endian.

Throws:

java.io.IOException - if there is an error writing the file

writeFloat
```
public final void writeFloat(float data,
                             boolean bigEndian)
                      throws java.io.IOException
```
Writes a float as four bytes to a file.

Parameters:

data - Data to be written to file.

bigEndian - true indicates big endian byte order, false indicates little endian.

Throws:

java.io.IOException - if there is an error writing the file

bgSort

private void bgSort(long[] ra,
                    int nVec)

selectMSPoints

private void selectMSPoints(double percent,
                            int jump)

computeRealBandwidths

private void computeRealBandwidths(int h)

doFindKLIteration

private long doFindKLIteration(int K,
                               int L,
                               float[] scores)

computeScores

private void computeScores(AlgorithmMeanShiftClustering.fams_hash_entry[][] HT,
                           int[] hs,
                           AlgorithmMeanShiftClustering.fams_cut[][] cuts,
                           float[] scores)

getNearestNeighbours

private void getNearestNeighbours(AlgorithmMeanShiftClustering.famsPoint who,
                                  AlgorithmMeanShiftClustering.fams_hash_entry[][] HT,
                                  int[] hs,
                                  AlgorithmMeanShiftClustering.fams_cut[][] cuts,
                                  AlgorithmMeanShiftClustering.fams_res_cont res,
                                  int print,
                                  int[] num_l)

addDataToRes

private void addDataToRes(AlgorithmMeanShiftClustering.fams_hash_entry[][] HT,
                          int[] hs,
                          AlgorithmMeanShiftClustering.fams_res_cont res,
                          int where,
                          int Bs,
                          int M,
                          int which,
                          int nnres,
                          int which2,
                          int hjump)

compareCutRes

private boolean compareCutRes(int[][] in_cr1,
                              int[][] in_cr2)

addDataToHash

private void addDataToHash(AlgorithmMeanShiftClustering.fams_hash_entry[][] HT,
                           int[] hs,
                           AlgorithmMeanShiftClustering.famsPoint pt,
                           int where,
                           int Bs,
                           int M,
                           int which,
                           int which2,
                           int hjump)

hashFunction

private int hashFunction(int[] cutVals,
                         int offset,
                         int whichPartition,
                         int kk,
                         int M,
                         int[] hjump,
                         int hOffset)

evalCutRes

private void evalCutRes(AlgorithmMeanShiftClustering.famsPoint in_pt,
                        AlgorithmMeanShiftClustering.fams_cut[] in_part,
                        int[] in_cut_res)

evalCutRes

private void evalCutRes(int[] in_dat,
                        AlgorithmMeanShiftClustering.fams_cut[] in_part,
                        int[] in_cut_res)

makeCuts

private void makeCuts(AlgorithmMeanShiftClustering.fams_cut[][] cuts)

makeCutL

private void makeCutL(AlgorithmMeanShiftClustering.fams_cut[] cut)

initHash
```
private void initHash(int nk)
```

getPrime
```
private int getPrime(int minp)
```

distL1

private int distL1(AlgorithmMeanShiftClustering.famsPoint inPt1,
                   AlgorithmMeanShiftClustering.famsPoint inPt2)

cleanSelected
```
private void cleanSelected()
```

Class AlgorithmMeanShiftClustering

Nested Class Summary

Nested classes/interfaces inherited from class java.lang.Thread

Field Summary

Fields inherited from class gov.nih.mipav.model.algorithms.AlgorithmBase

Fields inherited from class java.lang.Thread

Constructor Summary

Method Summary

Methods inherited from class gov.nih.mipav.model.algorithms.AlgorithmBase

Methods inherited from class java.lang.Thread

Methods inherited from class java.lang.Object

Field Detail

EVERY_POINT

SELECT_ON_JUMP

SELECT_PERCENT

FAMS_FKL_NEL

FAMS_FKL_TIMES

FAMS_MAX_K

FAMS_MAX_L

FAMS_BLOCKSIZE

FAMS_BLOCKSIZE2

FAMS_MAXITER

FAMS_ALPHA

FAMS_PRUNE_WINDOW

FAMS_PRUNE_MINN

FAMS_PRUNE_MAXM

FAMS_PRUNE_MAXP

FAMS_PRUNE_HDIV

Bs

Bs2

FAMS_FLOAT_SHIFT

K

L

k_neigh

data_file_name

input_directory

choosePoints

jump

percent

fixedWidth

width

findOptimalKL

epsilon

Kmin

Kjump

FAMS_DO_SPEEDUP

nPoints

nDims

pttemp

modesImage

prunedModesImage

noLSH

raFile

byteIntBuffer

byteFloatBuffer

endianess

array1

minVal

maxVal

points

data

dataSize

rr

psel

nsel

modes

hmodes

npm

prunedmodes

nprunedmodes

M

M2

hashCoeffs

K_

L_

t_cut_res

t_old_cut_res

t_old_m

t_m

t_m2