R{hCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC C0000C C0000C  C0gPROGRAMIMSEGP.DET.AUTO0C C0pVERSION1.617-SEP-820/C C0000C \C0000C HCgPROGRAMIMSEGP.DET.AUTO(IMPAC:SEGPDETA)WASDEVELOPEDC qCfromProgramIMSEGP.DET.00jC CcTHE"IMSEGP"PROGRAMSAREFORSEGMENTINGMULTI-CHANNEL ᝦCDIGITALIMAGES.(FORSINGLE-CHANNELDATATHE"IMSEG1"PROGRAMS &CMAYBEUSED.) CIMSEGP.COMMONUSESDISTANCEINTHEMETRICOFTHEESTIMATED + CCOMMONCOVARIANCEMATRIX.IMSEG.DETUSESDIFFERENTCOVARIANCECMATRICES,WITHADJUSTMENTBYTHEDETERMINANTS, I.E.,ITUSESTHEC 0CtheestimatedloglikelihoodfortheGaussian modelwithC Q Cdifferentcovariancematrices.00/C){C0000~C/yCiAUTOMATIC MODE: TRIESA RANGEOFNUMBERSOFCLASSES,WITHC݌CAUTOMATICSETTINGOFINITIALMEANS,EQUALLYSPACEDTHROUGHTHECCRANGEOFEACHVARIABLE.00JC_-C0000~CC0000~CCPROGRAMMEDBY:000C`CDR.STANLEYL.SCLOVE0<312/996-2681C'CDEPARTMENTOFQUANTITATIVEMETHODS%312/996-2676CYCCOLLEGEOFBUSINESSADMINISTRATION00Cd}CUNIVERSITYOFILLINOISATCHICAGO00ChCBOX 4348,CHICAGO,IL 6068000aCC0000~Ch ChRESEARCHSUPPORTEDINPARTBY:00CmC0000~CqCONRCONTRACTN00014-80-C-0408,TASKNR042-4430/C CAROCONTRACTDAAG29-82-K-015500CC0000~CuCRESTRICTIONS(CANBEMODIFIED):00Cx8CNR,NUMBEROFROWSOFARRAY,ATMOST256;0~C\CNC,NUMBEROFCOLUMNSOFARRAY,ATMOST256;0CHCIP,NUMBEROFCHANNELS,ATMOST20;00CCK,NUMBEROFCLASSES,ATMOST29;00CqCITER,MAXIMUMNUMBEROFITERATIONS,20.0ACC0000~CĝCSUBROUTINE(S)CALLED:00CC&CMATEQ, WHICH CALLS MATDT00ACGC0000~CC0000~CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCK0COQC{CyDIMENSIONX(56,56,10),SUM(29,4)SDIMENSIOND(29),ICLUS(56,56)WC-DIMENSION IOTA(56),JOTA(29)C2DIMENSIONTITLE(18)6DIMENSIONNG(29),XMEAN(29,4)DIMENSIONFMT(18)Y DIMENSIONSS(29,4,4),SSD(29,4,4)}DIMENSION WGSS(4,4) DIMENSIONVARHAT(4,4),WGMS(4,4)DIMENSIONICLSOL(56,56) hDIMENSION XMIN(4),XMAX(4)CCHANGENO.OFCHANNELSFROM4TO20 CANDNO.OFROWSANDCOLUMNSFROM56TO256 CINDIMENSIONSTATEMENTSWHENALLOWABLEREGIONISINCREASED.DIMENSIONIV(20,20)DIMENSIONP(20,20)8C\DIMENSIONA(20,20)HCDIMENSIONET(29)qDIMENSIONPG(29,20,20)CDIMENSIONNT(29,29,29),IRSUM(29,29),TP(29,29,29) &DIMENSIONPROB(29)CC<DOUBLEPRECISIONSS,SUM|0DOUBLEPRECISIONWGSS,SSDQDOUBLEPRECISIONVARHAT{DOUBLEPRECISIONP$y DOUBLEPRECISIONDET^DOUBLEPRECISIONDDOUBLEPRECISION XMEAN-DOUBLEPRECISIONTEMPIV,TEMPJV,DOUBLEPRECISIONF?DOUBLEPRECISIONCFCDOUBLEPRECISIONA:YC=}DOUBLEPRECISIONETbDOUBLEPRECISIONPGfDOUBLEPRECISIONTP,PROBhCCIVISAWORK ARRAYFORSUBROUTINEMATEQ.kCo CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC~8C0000~Cv\C0000~CzHCCONTROLCARDS:000/C[C0000~Cq CDATASET TITLE000CCNUMBEROF ROWS,NR,INFORMAT(3X,I3)0CCNUMBEROFCOLUMNS,NC,INFORMAT(3X,I3)0C]&CNUMBEROFCHANNELS,IP,INFORMAT(3X,I2)0jCACFMT,INFORMAT(18A4), E.G.,(1X,F4.1)0ACECe"FMT"WILLALSOBEUSEDFOROUTPUT: ALLOWAT LEASTONE BLANKCICbATTHEBEGINNINGFORCARRIAGECONTROL.0jC0CDATA,ONENUMBERATA TIME,INFORMATSPECIFIEDBYFMTC}QCdDATAISINDEXEDBYCHANNEL,ROW,ANDCOLUMN.0CM{CfCOLUMNCHANGESFIRST,THENROW,THENCHANNEL.0CQyC0000~CCK,NUMBEROFCLASSES,INFORMAT(2X,I2)0C\CKINITIALMEANS,INFORMATSPECIFIEDBYFMT0CU-CgINITIAL MEANSAREINDEXEDBY CLASSANDCHANNEL.1CYCgCHANNELCHANGESFIRST,THENCLASS.0JCC0000~C0C0000~C V4CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC7YC}C READ(5,28000) TITLEChCWRITEPROGRAMINFORMATION. WRITE(6,17000)WRITE(6,53000) WRITE(6,35000) TITLECCREADNUMBERSOFROWSANDCOLUMNS,NRANDNC.8READ(5,16000)NR\READ(5,16000)NCHWRITE(6,33000)NRWRITE(6,34000)NCqCREADNUMBEROFCHANNELS(I.E.,VARIABLES),IP.READ(5,29000)IPWRITE(6,14000)IP&CCREADDATAFORMAT.READ(5,28000)FMTC0 CREAD DATA.(QDO300 ICHAN=1,IP{0DO300I=1,NRy0DO300J=1,NC0READ(5,FMT)X(I,J,ICHAN))0IF(I.EQ.1 .AND.J.EQ.1)GOTO100/- 0GOTO200ݫ1000CONTINUE0DO200IVAR=1,IP_0XMAX(IVAR)=X(1,1,IVAR)0XMIN(IVAR)=X(1,1,IVAR)Y2000CONTINUE}0DO300IVAR=1,IP@0IF(X(I,J,IVAR).LT.XMIN(IVAR))XMIN(IVAR)=X0X(I,J,IVAR)dh0IF(X(I,J,IVAR).GT.XMAX(IVAR))XMAX(IVAR)=hX0X(I,J,IVAR)3000CONTINUE WRITE(6,54000)mWRITE(6,FMT)(XMIN(IVAR),IVAR=1,IP)qWRITE(6,55000)8WRITE(6,FMT)(XMAX(IVAR),IVAR=1,IP)\CtHCREADKLANDKU,THEMINANDMAXNUMBEROFCLASSESTOBETRIED.xREAD(5,15000)KLqREAD(5,15000)KUWRITE(6,18000) KL,KUC&CSETCONSTANTS.CPI=3.1415927CCF0/N= NR*NCQXN=N{DO400INTEG=1,NCKyIOTA(INTEG)= INTEGO 400CONTINUEC-DO6500K= KL,KUSCWCCCOMPUTEINITIALMEANS:0DO500IG=1,K1Y0DO500IVAR=1,IP5}0>XMEAN(IG,IVAR)=XMIN(IVAR)+IG*(XMAX(IVAR)-9X0^XMIN(IVAR))/(K+1)5000CONTINUEhC WRITE(6,19000)KCWRITEINITIAL MEANS C0DO600IG=1,K 0 WRITE(6,20000)IG,(XMEAN(IG,IVAR),IVAR=1,IP)8 600CONTINUE\CH CSETCONSTANTSFORTHIS VALUEOFK.Cq0DO700J=1,K 0JOTA(J)=J 700CONTINUE&CC!PARAMETERSFOR MODELWITHCOMMONCOVARIANCEMATRIXARECKMEANVECTORSANDONECOVARIANCEMATRIX. NOPARM=K*IP+IP*(IP+1)/20C!PARAMETERSFOR MODELWITHDIFFERENTCOVARIANCEMATRICESAREQCKMEANVECTORSANDKCOVARIANCEMATRICES..{NPRMDF=K*IP+K*IP*(IP+1)/2|yCC^NON-DISTRIBUTIONALPARAMETERS:CK**2-BY-KTRANSITION PROB.MATRIX$-CGIVES(K**2)(K-1)FREETRANSITIONPROBABILITIES^CNOPARM=NOPARM+(K**2)*(K-1)NPRMDF=NPRMDF+(K**2)*(K-1),C>YC}CFOR FIRSTITERATION,MARGINALDISTRIBUTIONOFLABELS, PROB,CIS TAKENTOBEUNIFORM.:C=h0DO800IG=1,Kb 800PROB(IG)= 1.0/KfC CITER=1kC!ITERATIONS BEGINHEREn8Cr\ 900CONTINUEHIF (ITER.EQ.1)GOTO1100~0DO1000I=1,NRvq0DO1000J=1,NCz0,ICLSOL(I,J)=ICLUS(I,J)[10000CONTINUE&CSAVEPREVIOUS VALUEOF-2LOGMAXLIKELIHOOD:XMN20L=XMN2LL1100CONTINUE]C{0CCOMMENCEDISTANCECOMPUTATIONS.DQCH{0DO2400I=1,NRy0DO2400J=1,NC}C!INITIALIZEDISTANCES(TOBEACCUMULATED)AT ZERO.M0DO1200L=1,KQ- 0D(L)=0.012000CONTINUE\CFOR FIRSTITERATION,EUCLIDEANDISTANCEISUSEDBECAUSEUCNOCOVARIANCEMATRIXISYETAVAILABLE. AFTERTHE FIRSTYC!ITERATION,DISTANCEWILLBE TAKENINTHEMETRICOFTHEYCGROUPCOVARIANCEMATRIX.}C30IF (ITER.GT.1)GOTO140070DO1300 L=1,Kh00DO1300IVAR=1,IP00D(L)=D(L)+(XMEAN(L,IVAR)-X(I,J,X05IVAR))**2 13000CONTINUE  0GOTO170014000CONTINUE80DO1600 L=1,K\00DO1500IVAR=1,IPH00TEMPIV=XMEAN(L,IVAR)-X(I,J,IVAR)00DO1500JV=1,IPq00TEMPJV=XMEAN(L,JV)-X(I,J,JV)C00D(L)=D(L)+TEMPIV*PG(L,IVA R,JV)& X00*TEMPJVC150000CONTINUEC016000CONTINUEQ17000CONTINUE{Cy0DO2200L=1,K(CFOR FIRSTITERATION,CLASSIFICATIONISSIMPLYBYCMINIMUMDISTANCE.OTHERWISE,THETRANSITION-C)PROBABILITIESANDDETERMINANTSENTER:0IF (ITER.EQ.1)GOTO2200 7)CUPTONOW,D(L)IS(SQUARED)DISTANCE/CITMUSTNOWBEMODIFIEDTOPROBABILITY,CFORMULTIPLICATIONBYTHETRANSITIONPROBABILITIES.YCADDDETERMINANTOFCOVARIANCEMATRIXTODISTANCE:%} 0D(L)=D(L)+ ET(L)׼0ARG=-D(L)/2.00IF(ARG.LT.-180.2)GOTO1800h 0GOTO1900@18000D(L)=0.0 0GOTO2000d 19000CONTINUEhCCMOVEFROMLOGPROB SCALETOPROBSCALE:80D(L)=EXP(ARG)l\CD(L)ISNOLONGERADISTANCE:pHCITIS(PROPORTIONALTO)THEBELONGINGPROBABILITY20000CONTINUEqCt0IF(I.EQ.1.OR.J.EQ.1)GOTO2100x0IG1=ICLUS(I,J-1)&0IG2=ICLUS(I-1,J)0D(L)=-TP(IG1,IG2,L)*D(L) 0GOTO220021000CONTINUE0CCLASSIFYBOUNDARYOBSERVATIONSQ0D(L)=-D(L)*PROB(L)B{22000CONTINUEFyCˌ0/F=D(1)0ICLUS(I,J)=1K-0DO2400L=2,KO0IF(D(L)-F)2300,2400,240023000/F=D(L)0ICLUS(I,J)=LS24000CONTINUEVYWRITE(6,10000)Z}CWRITE(6,11000)(IOTA(J),J=1,NC)10DO2500I=1,NR5h0 WRITE(6,12000)I,(ICLUS(I,J),J=1,NC)92500CONTINUE0DO2600IG=1,K  0NG(IG)=0  0DO2600IVAR=1,IP 0%SUM(IG,IVAR)=0.0 80DO2600JV=1,IP \0SS(IG,IVAR,JV)=0.0 H0SSD(IG,IVAR,JV)=0.0 26000CONTINUE q0DO2700I=1,NR 0DO2700J=1,NC  0IGROUP=ICLUS(I,J) &0NG(IGROUP)=NG(IGROUP)+1  0DO2700IVAR=1,IP 0SUM(IGROUP,IVAR)=SUM(IGROUP,IVAR)+X(I,J, X0vIVAR) 000DO2700JV=1,IP Q00SS(IGROUP,IVAR,JV)=SS(IGROUP,IVAR,JV)+ {X00X(I,J,IVAR)*X(I,J,JV) y27000CONTINUE C .WRITE(6,27000)(NG(IG),IG=1,K) |-C 0DO2800IVAR=1,IP 0DO2800JV=1,IP $ 0_WGSS(IVAR,JV)=0.0 ^28000CONTINUE YC }C 0DO3100IG=1,K >0IF(NG(IG).EQ.0)GOTO2900 h 0GOTO3000 2900WRITE(6,60000)IG : 0GOTO6600 = 3000CONTINUE b 0DO3100IVAR=1,IP fCCOMPUTEMEANVECTORS i80>XMEAN(IG,IVAR)=SUM(IG,IVAR)/NG(IG) \CCOMPUTESUM-OF-PRODUCTSMATRICES jH0DO3100JV=1,IP n0CF=SUM(IG,IVAR)*SUM(IG,JV)/NG(IG) rq0SSD(IG,IVAR,JV)=SS(IG,IVAR,JV)-CF 31000CONTINUE ~C v&C zC [CPOOL: C 00DO3200IG=1,K Q 0DO3200IVAR=1,IP {0DO3200JV=1,IP {y0]WGSS(IVAR,JV)=WGSS(IVAR,JV)+SSD(IG,IVAR, DX0tJV) H32000CONTINUE -C }C MCCOMPUTEVARHAT,MLEOFCOMMONCOVARIANCEMATRIX: Q 0DO3300IVAR=1,IP 0DO3300JV=1,IP Y0?VARHAT(IVAR,JV)=WGSS(IVAR,JV)/N T}33000CONTINUE XCCOMPUTEDET(VARHAT) IDET=1 hNRS1=0 30DO3400IVAR=1,IP 70DO3400JV=1,IP 0A(IVAR,JV)=VARHAT(IVAR,JV) 34000CONTINUECALLMATEQ(A,IP,20,JFLG,DET,IDET,IV ,NRS1,P,20)8C\CGENERALFORMOFCALLIS:HCCALLMATEQ(A,M,N,JFLG,DET,IDET,IV,NRS1,P,LL)CSEESUBROUTINELISTINGFORFULLEREXPLANATION.qC$DET(VARHAT)=DET*10.0**IDETC(ACTUALDET.=DET*10**IDET)C&IF (JFLG.GT.0) WRITE(6,43000)JFLGC XIDET=IDETXLGDET=DLOG(DET)+XIDET*ALOG(10.0)0XMN2LL=N*(IP*ALOG(2.0*PI)+IP+XLGDET)QC{CyIF (ITER.EQ.1)GOTO3600CCCOMPARENEWSEGMENTATIONWITHOLD-C(0DO3500I=1,NR0DO3500J=1,NC0IF(ICLUS(I,J).EQ.ICLSOL(I,J))GOTO3500 0GOTO3600*Y35000CONTINUE-} GOTO60003600CONTINUEC%hCIFNEWSEGMENTATIONDIFFERSFROMOLD,CCOMPUTEAND WRITENEWSTATISTICSC 0DO3700IG=1,K@0 WRITE(6,26000)IG,(XMEAN(IG,IVAR) ,IVAR=1,IP)3700CONTINUEc8Cg\CCOMPUTETRANSITIONPROBABILITYMATRIX:HCŵ0DO3800I1=1,Klq0DO3800I2=1,Kp0DO3800J=1,K0NT(I1,I2,J)=0&38000CONTINUEt0DO3900I=2,NRx0DO3900J=2,NC 0IM1=I-10 0JM1=J-1Q0INORTH=ICLUS(IM1,J){0IWEST=ICLUS(I,JM1)y0IY=ICLUS(I,J)،0?NT(IWEST,INORTH,IY)=NT(IWEST,INORTH,IY)+1B39000CONTINUEF-0DO4000I1=1,K˫0DO4000I2=1,K W0%IRSUM(I1,I2)=0K40000CONTINUEO0DO4100I1=1,KRY0DO4100I2=1,K}0DO4100J=1,K0IRSUM(I1,I2)=IRSUM(I1,I2)+NT(I1,I2,J)V41000CONTINUEZh0DO4400I1=1,K0DO4400I2=1,K10?XDENOM=IRSUM(I1,I2)5 0IF(XDENOM.EQ.0.0)GOTO42009 0GOTO430042000XDENOM=K843000CONTINUE\CH0DO4400J=1,K 0XNUM=NT(I1,I2,J)qCIF THEREARENOTRANSITIONSFROM(I1,I2),THENTP(I1,I2,J)CISSET EQUALTO ZERO,FORALLJ=1,2,...,K.C&44000,TP(I1,I2,J)=XNUM/XDENOM4500CONTINUECCCOMPUTEMARGINALDISTRIBUTIONOFLABELS:00DO4600IG=1,KQ0PROB(IG)=NG(IG){0PROB(IG)=PROB(IG)/Ny4600CONTINUEC TRANS=0.0-0DO4700I1=1,K0DO4700I2=1,K.0DO4700 J=1,K| 0ITEST=NT(I1,I2,J)0IF(ITEST.EQ.0)GOTO4700Y 0IF(TP(I1,I2,J).EQ.0.)GOTO4700!}0TRANS= TRANS+NT(I1,I2,J)*DLOG(TP(I1,I2,J));47000CONTINUEChTRANS=-2.0*TRANSC>CWRITETRANSITIONPROBABILITIES: WRITE(6,13000) WRITE(6,37000)(JOTA(JAY),JAY=1,K):0DO4800I1=1,K'80DO4800I2=1,Ka\0WRITE(6,38000)I1,I2,(NT(I1,I2,J),J=1,K)eH48000CONTINUEiWRITE(6,39000)q WRITE(6,37000)(JOTA(JAY),JAY=1,K)j0DO4900I1=1,Kn0DO4900I2=1,Kr&0WRITE(6,40000)I1,I2,(TP(I1,I2,J),J=1,K)49000CONTINUE~WRITE(6,63000)(PROB(IG),IG=1,K)vWRITE(6,32000) TRANSy0WRITE(6,50000)QWRITE(6,22000)XMN2LL{CyCACCOUNTFORLABELSOFBORDEROBSERVATIONS: FIRST=0.00DO5000J=1,NC{- 0LABEL1=ICLUS(1,J)D0PROBAB=PROB(LABEL1)H0 FIRST= FIRST+ALOG(PROBAB)5000CONTINUE}0DO5100I=2,NRLY 0LABEL1=ICLUS(I,1)P}0PROBAB=PROB(LABEL1)0 FIRST= FIRST+ALOG(PROBAB)5100CONTINUEThFIRST=-2.0*FIRSTXCC CCOMPUTE MODELSELECTIONCRITERIA:3CCOMPUTEVALUESCORRESPONDINGTONEWSEGMENTATIONANDOLD7C!COVARIANCEMATRIX (OMITONFOR FIRSTITERATION)8IF (ITER.EQ.1)GOTO5200\CFOR MODELWITHCOMMONCOVARIANCEMATRIXHC(NOTOPTIMIZEDINTHISPROGRAM;HOWEVER,ITIS CLEARCTHATONESHOULDUSETHECOMMON-COVARIANCE-MATRIX MODELIFqCTHEVALUESOFTHE MODELSELECTIONCRITERIAHEREFORTHAT MODELCARELESSTHAN THOSEFORTHE MODELWITHDIFFERENTCOVARIANCE C!MATRICES):&CAICOLD=XMN2OL+ TRANS+ FIRST+2.0*NOPARMSCHOLD=XMN2OL+ TRANS+ FIRST+ALOG(XN)*NOPARMWRITE(6,59000)NOPARM 0WRITE(6,46000)AICOLDQWRITE(6,47000)SCHOLD{5200CONTINUEyAIC=XMN2LL+ TRANS+ FIRST+2.0*NOPARMWRITE(6,48000)AICSCH=XMN2LL+ TRANS+ FIRST+ALOG(XN)*NOPARM-WRITE(6,49000)SCHCCCCOMPUTEPRECISIONMATRICESFORALLGROUPS.(C&YTERM=0.0}0DO5700 L=1,K0IF(NG(L).GT.IP)GOTO5300*0 WRITE(6,52000)L,NG(L)-h 0GOTO56005300CONTINUEC% 0DO5400IVAR=1,IP0DO5400JV=1,IP0A(IVAR,JV)=SSD(L,IVAR,JV)/ NG(L)854000CONTINUE#\0IDET=1"H0NRS1=0c0CALLMATEQ(A,IP,20,JFLG,DET,IDET,IV ,NRS1,P,20)gq0DO5500IVAR=1,IP0DO5500JV=1,IPŝ0]PG(L,IVAR,JV)=P(IVAR,JV)l&55000CONTINUEpC0 ET(L)=DLOG(DET*10**IDET)Cs05600CONTINUEwQC{0TERM=TERM+NG(L)*ET(L)y5700CONTINUECCOMPUTE MODELSELECTIONCRITERIAWITHDIFFERENTCOVARIANCE CMATRICES:-CثC$PARAMETERS:BCKMEANVECTORSOFDIMENSIONPANDKP-BY-PCOVARIANCEMATRICES,FCWHEREPISTHENUMBEROFCHANNELSXM2LLD=N*IP*ALOG(2*PI)+N*IP+TERMYXM2LLD=XM2LLD+ TRANS+ FIRSTJ}SCHD=XM2LLD+ALOG(XN)*NPRMDFNAICD=XM2LLD+2.0*NPRMDFRWRITE(6,23000)hWRITE(6,59000)NPRMDFWRITE(6,24000)XM2LLDV WRITE(6,57000)AICDZ WRITE(6,58000)SCHDC1C48 WRITE(6,25000)ITER8\0DO5800IG=1,KH0 WRITE(6,21000)IG,(XMEAN(IG,IVAR) ,IVAR=1,IP)5800CONTINUEqITER=ITER+1 ųIF (ITER.GE.21)GOTO5900 CUNLESS20ITERATIONS,HAVEALREADYBEENPERFORMED,GOBACK&CANDDOANOTHER.GOTO900 5900WRITE(6,44000) GOTO65000CQC{6000CONTINUEyCCOUTPUTTOBEWRITTENUPONCONVERGENCE:C- WRITE(6,41000)ITERWRITE(6,30000)CCOMPUTEAND WRITECOMMONCOVARIANCEMATRIXC 0DO6100IVAR=1,IPY0DO6100JV=1,IP+}0_WGMS(IVAR,JV)=WGSS(IVAR,JV)/(N-K)61000CONTINUE0DO6200IVAR=1,IP!h0 WRITE(6,31000)(WGMS(IVAR,JV),JV=1,IP);6200CONTINUEߨC WRITE(6,62000)CCOMPUTEAND WRITE CLASSCOVARIANCEMATRICES>C80DO6400L=1,K\0DO6300IVAR=1,IP`H0DO6300JV =1,IP'0]WGMS(IVAR,JV)=SSD(L,IVAR,JV)/(NG(L)-1)aq63000CONTINUEe0 WRITE(6,61000)Li 0DO6400IVAR=1,IP&0WRITE(6,31000)(WGMS(IVAR,JV),JV=1,IP)j64000CONTINUEnCrC0CQCu{CyyCڌWRITE(6,45000)KC-6500CONTINUECWRITE(6,42000){6600STOPDCGYC}10000 FORMAT(//1X,'SEGMENTATION:'/) 11000FORMAT(/,1X,'ROW:gCOLUMN:'/,4X,(40I3/))L12000 FORMAT(1X, I3, (40I3/) )Ph13000 FORMAT(//1X,'TRANSITIONS')14000 FORMAT(/1X,'NUMBER OF CHANNELS = ',I2/)15000 FORMAT(2X,I2)T 16000 FORMAT(3X,I3)X17000 FORMAT('1','###################################################',^X//,1X,'PROGRAMIMSEG.DET.AUTO'/8X,1X,'FOR IMAGESEGMENTATION'/2\$X,1X,'USINGDISTANCEINTHEMETRICSOFTHECOVARIANCEMATRICES'/6H;X,1X,'ADJUSTEDBYTHEDETERMINANTS0K'/^X//,1X,'DEVELOPEDANDPROGRAMMEDBY'//qX1X,'DR.STANLEYL.SCLOVE0312/996-2681'/X1X,'!DEPARTMENTOFQUANTITATIVEMETHODS;312/996-2676'/X1X,'!UNIVERSITYOFILLINOISATCHICAGO'/&X1X,'BOX 4348,CHICAGO,IL 60680'//^X//,1X,'VERSION1.617-SEP-82'//)18000 FORMAT('1',//,1X,'MIN AND MAX NUMBER OF CLASSES TO BE TRIED ARE ',XI2,'AND',I2//)019000 FORMAT(1H1,'K = ',I2,' CLASSES'//)Q20000 FORMAT(/1X,'INITIAL MEAN VECTOR FOR CLASS ',I2,': ',(4E14.5/)){ 21000FORMAT(1X,'MEANVECTORFOR CLASS',I2,':',(8E13.5/)) y22000 FORMAT(/,1X,'MINUS 2 LOG LIKELIHOOD FOR MODEL WITH COMMON',X'COVARIANCEMATRIX=',E13.5//)23000 FORMAT(//1X,'FOR MODEL WITH DIFFERENT COVARIANCE MATRICES:'//)-24000 FORMAT(/,1X,'MINUS 2 LOG LIKELIHOOD FOR MODEL WITH DIFFERENT ',*X'COVARIANCEMATRICES=',E13.5//)25000 FORMAT(///,1X,'ITERATION ', I2,//) 26000FORMAT(1X,'MEANVECTORFOR CLASS',I2,':',(8E13.5/))27000 FORMAT(/,1X,'NUMBERS IN CLASSES:'/,(9I12/)/)Y28000 FORMAT(18A4)}29000 FORMAT(3X,I2)<30000 FORMAT(///,1X,'COMMON COVARIANCE MATRIX (DIVISOR IS DF):',//)&31000 FORMAT(1X,(8E13.5/))h32000 FORMAT(//,' CONTRIBUTION OF TRANS. PROBS. TO LOG LIKELIHOOD =',XE15.5/)*33000 FORMAT(1X,'NUMBER OF ROWS = ',I4)- 34000 FORMAT(1X,'NUMBER OF COLUMNS = ',I3/) 35000 FORMAT(1X,18A4)36000 FORMAT(/9X,(9I7/)),837000 FORMAT(/9X,(9I7/))?\38000 FORMAT(1X, 2I4, (9I7/))H39000 FORMAT(//1X,'TRANSITION PROBABILITIES')ֵ40000 FORMAT(1X,2I4,3X,(9F7.4/))#q 41000 FORMAT(/1X,'CONVERGENCE: NO CASE CHANGED CLASSES AFTER ',"$X'ITERATION',I2,'.SOMEADDITIONALRESULTSAREPRINTEDBELOW.'//)c42000 FORMAT(/1X,'PROGRAM ENDED SUCCESSFULLY.')g&43000 FORMAT(/,1X,'JFLG = ',I2,'. IF JFLG=0, COMPUTATION OF DET',X'WENT WELL;OTHERWISE, THEREWASTROUBLEORMATRIXWAS',^X'ILL-CONDITIONED.'//)l44000 FORMAT(1X,'ROUTINE HAS NOT CONVERGED IN 20 ITERATIONS. STOP')o045000 FORMAT(/,1X,'PROGRAM ENDED SUCCESSFULLY FOR THE CASE ',QX'K=',I2,'.'/){46000 FORMAT(/1X,'AIC WITH NEW LABELS AND OLD DISTRIBUTIONAL ',sy)X'PARAMETERS:',E15.5)w47000 FORMAT(/1X,'SCHWARZ CRITERION WITH NEW LABELS AND OLD ',^X'DISTRIBUTIONALPARAMETERS:',E15.5)-48000 FORMAT(/1X,'AIC WITH NEW LABELS AND NEW DISTRIBUTIONAL ',)X'PARAMETERS:',E15.5)49000 FORMAT(/1X,'SCHWARZ CRITERION WITH NEW LABELS AND NEW ',^X'DISTRIBUTIONALPARAMETERS:',E15.5) Q50000 FORMAT(//1X,'FOR MODEL WITH COMMON COVARIANCE MATRIX ',AYX'(NOTOPTIMIZEDINTHISPROGRAM):'//)E}51000 FORMAT(1X,'AIC FOR MODEL WITH COMMON COVARIANCE MATRIX = ',E15.5/)I52000 FORMAT(/1X,'CLASS ',I2,' CONTAINS ONLY ',I4,' OBSERVATIONS: ', $X'PRECISIONMATRIXFROMPREVIOUSITERATIONIS BEINGRETAINED'/)Jh53000 FORMAT(/1X,'MODEL WITH COMMON COVARIANCE MATRIX IS NOT'/N;X1X,'OPTIMIZEDINTHISPROGRAM;HOWEVER,ITISCLEAR'/RX1X,'THATONESHOULDUSEITIFHERETHEVALUESOFTHEMODEL-'/ ;X1X,'SELECTIONCRITERIAFORTHAT MODELARELESSTHAN THOSE'/X1X,'FORTHE MODELWITHDIFFERENTCOVARIANCEMATRICES,'/V!X1X,'WHICHISOPTIMIZEDHERE.'//)Y854000 FORMAT(/1X,'MINIMUM FOR EACH CHANNEL: ',/)\55000 FORMAT(/1X,'MAXIMUM FOR EACH CHANNEL: ',/)0H56000 FORMAT(1X,'SCHWARZ CRITERION FOR MODEL WITH COMMON COVARIANCE ',4 X'MATRIX=',E15.5/)8q57000 FORMAT(1X,'AIC FOR MODEL WITH DIFFERENT COVARIANCE MATRICES = ',XE15.5/) 58000 FORMAT(1X,'SCHWARZ CRITERION FOR MODEL WITH DIFFERENT ', &*X'COVARIANCEMATRICES=',E15.5/) 59000 FORMAT(/,1X,'NUMBER OF PARAMETERS = ',I4//)  60000FORMAT(1X,'NOOBSERVATIONSIN GROUP',I3,'.STOP') 61000 FORMAT(//1X,'COVARIANCE MATRIX FOR CLASS ',I2/) 062000 FORMAT(///1X,'CLASS COVARIANCE MATRICES ', QX'(DIVISORSAREONELESSTHANNUMBERINGROUP):'/) {63000 FORMAT(/1X,'MARGINAL PROB.VECTOR:',(9F11.4/)) yEND SUBROUTINEMATEQ(A,M,N,JFLG,DET,IDET,IV,NRS1,P,LL) C!SUBROUTINE MATEQISDMATEQFROMTHEUICCSUBROUTINELIBRARY. -C  C0SUBROUTINEDMATEQ C0***************** CTHISROUTINEWILL SOLVEAREAL*8SYSTEMOFLINEAREQUATIONS,COMPUTE CTHEDETERMINANT,WITHOUTUNDERFLOWOROVERFLOW,OFAREAL*8MATRIX, YCAND/ORINVERTAREAL*8MATRIX. }CCALLINGSEQUENCE: CCALLDMATEQ(A,N,IA,JFLG,DET,IDET,IV,NRS,P,IP)WHERE; CjA(INPUT)-ISTHEREAL*8MATRIXON WHICHTHEROUTINEIS +hC0TO WORK.INTHEPROCESSOFCOMPUTATIONTHE C0CONTENTSOFTHISMATRIXAREDESTROYED. CjN(INPUT)-ISANINTEGER*4VARIABLE WHICHSPECIFIESTHE ! C0ORDEROFTHEAMATRIX. ;CkIA(INPUT)-ISANINTEGER*4VARIABLE WHICHSPECIFIESTHE C0ACTUALROWDIMENSIONOFAASDIMENSIONEDIN 8C0THECALLINGPROGRAM.IAMUSTBEGREATERTHAN \C0OR EQUALTON. _HCmJFLG(OUTPUT)-ISANINTEGER*4RETURNCODEVARIABLE.UPON C0RETURNFROMDMATEQIF; qC0JFLG=0,ALLWENT WELL. `C0JFLG=1,THEAMATRIXWASSINGULARORNEAR 'C0SINGULARANDTHECOMPUTATIONS COULDNOTBE a&C0COMPLETED.THECONTENTSOFTHEVARIABLES eC0A,DET,IDETANDPAREMEANINGLESS. iClDET(OUTPUT)-ISAREAL*8VARIABLE WHICHCONTAINSTHE C0DETERMINANTOFA.(SEE IDET) 0CmIDET(INPUT)-ISANINTEGER*4VARIABLE.ON INPUTIF; mQC0IDET=0,NODETERMINANTISCALCULATED. q{C0IDETNOT0,THEDETERMINANTOFAISCOMPUTED. yC0KONOUTPUTIDETCONTAINSTHE POWEROF10 ȌC0THATDETSHOULDBEMULTIPLIEDBYTOGIVETHE uC0CORRECT VALUEOFTHEDETERMINANT.I.E. y-C0DET(A)=DET*10.0D0**IDET. ګC0KIFDET(A)CANBECOMPUTEDWITHOUT UNDEROR C0OVERFLOW,THENIDET=0OTHERWISEIDETISSET C0TOTHEPROPER VALUESOTHATNO UNDEROR OVER- C0FLOWWILL OCCURINCOMPUTINGDET. YCkIV(INPUT)-ISANINTEGER*4WORK ARRAY WHICHSHOULDBE }C0DIMENSIONEDAT LEASTIV(N). CC GClNRS(INPUT)-ISANINTEGER*4VARIABLEWITHTHEFOLLOWING hC0INTERPRETATION: C0NRS>0, SOLVEASYSTEMOFLINEAREQUATIONS LC0WITHNRS RIGHTHANDSIDES. P C0NRS=0,INVERTTHEAMATRIX. C0NRS<0,ONLYCOMPUTETHEDETERMINANTOFA. C0INTHISCASEIDETMUSTBEDIFFERENTFROM0. S8CjP(INPUT)-ISAREAL*8 ARRAYWITHTHEFOLLOWINGINTER- W\C0PRETATION: HC0IFNRS>0,THENPCONTAINSTHENRS RIGHTHAND C0SIDESSTOREDBYCOLUMNS.INTHISCASEPMUST 2qC0BEDIMENSIONEDAT LEASTP(N,NRS).ONRETURN 6C0THECOLUMNSOFPAREREPLACEDBYTHERESPEC- C0TIVESOLUTIONS. &C0IFNRS=0,THENPMUSTBEDIMENSIONEDAT LEAST C0P(N,N).ONRETURNPWILLCONTAINTHEINVERSE C0OFA. C0IFNRS<0,THENPNEEDONLYBEA DUMMYVARIABLE 0C0INTHISCASEPIS NEVERACCESSEDBYDMATEQ. QCkIP(INPUT)-ISANINTEGER*4VARIABLE WHICHCONTAINSTHE {C0ACTUALROWDIMENSIONOFPASDIMENSIONEDIN yC0THECALLINGPROGRAM.IPMUSTBEGREATERTHAN C0OR EQUALTON. CNOTE:IMMEDIATELYONRETURNFROMDMATEQTHECONDITIONCODE FLAG, -CvJFLG,SHOULDBEINTERROGATED.IFJFLG=1,THENTHEROUTINE CvCOULDNOTCOMPUTEASOLUTION. CMETHOD-THEALGORITHMUSEDISGAUSSIANELIMINATIONWITHPARTIAL C0000-1 CrPIVOTING.INESSENCETHEROUTINEGENERATESAMATRIXLSUCH Y C2-1 } CmTHATL*A=U, WHEREUISAN UPPERTRIANGULARMATRIX.THENIT CoSOLVESTHESYSTEMA*X=PBY MEANSOFTHEEQUIVALENTSYSTEM C0-1b-1 hClU*X=L*A*X=L*PBYBACKSUBSTITUTION. < C2-1 &CtTHELMATRIXCANBEWRITTENASAPRODUCTOFTHEFORM Ck-1 CaL=L*P*....*L*P WHEREEACHPISAPERMUTATION *C3N-1N-1a11JK ^8CfMATRIXOBTAINEDBYINTERCHANGINGATMOSTTWOROWSOFTHE \ChIDENTITYMATRIX.(THISREPRESENTSTHEINTERCHANGINGOFTWO HCfROWS).THELMATRICESAREELIMINATIONMATRICES WHICHARE , C0K ?q CfCHOSENTOINTRODUCE ZEROSINTHELASTN-KENTRIESOFTHEK-TH CfCOLUMNOFTHEMATRIX. ֝C #&C02-1b-1 "ClTHECALCULATIONSOFL*AANDL*PAREDONEBYPERFORMING cCTHEPERMUTATIONSONAANDPRESPECTIVELY.THEACTUALLANDP gC0000K/K 0CARENOTCOMPUTED. QCSUBROUTINESCALLED:DMATDT k{ CREFERENCE: oyC G. W. STEWART, INTRODUCTION TO MATRIX COMPUTATIONS, C ACADEMIC PRESS, 1973. REAL*8A(N,1),DET,P(LL,1) s-REAL*8DNORM,DEN,DMULT ,DSUM,DISIGN wDIMENSION IV(1) NRS=NRS1  IF(NRS.EQ.0)IDET=1 >DISIGN=1.0D+00 Y ,DET=0.0D+00 }JFLG=0 ]C AȫC JFLG IS A TROUBLE FLAG.UPON EXIT IF JFLG=0 THEN THE MATRIX WAS PROCESS EhC WITHOUT TROUBLE.IF JFLG=1 EITHER THE MATRIX IS SINGULAR OR TROUBLE IC OCCURED.ISIGN=-ISIGN EVERY TIME A ROW IS INTERCHANGED.THIS IS USED TO C INSURE THAT THE DETERMINANT HAS THE PROPER SIGN. J C NM1=M-1 R DO100 I=1,M 8 100IV(I)=I \\ IF (NRS)500,200,500 UH200DO300 I=1,M Y0DO300 J=1,M q300P(I,J)=0.0D+00 0 DO400 I=1,M 4400>P(I,I)=1.0D+00 8&NRS=M C C INSTEAD OF ACTUALLY INTERCHANGING ROWS A POINTER ARRAY IS USED TO KEEP C TRACK OF THE ROW POSITIONS. 0C QC BEGIN ELIMINATION LOOP. {C y500DO1200K=1,M1 ICOL=K IPCOL=K -C C SEARCHING FOR LARGEST ELEMENT IN ABSOLUTE VALUE IN COLUMN K. C DNORM=A(IV(K),K) IFLG=0 YKK=K+1 }0DO600J=KK,M 0IF(DABS(A(IV(J),K)).LE.DABS(DNORM))GOTO600 0IFLG=1 h 0IPCOL=IV(J) 0DNORM=A(IPCOL,K) 600CONTINUE + C C IF IFLG=0 NO ROW INTERCHANGE TOOK PLACE.IF IFLG=1 A ROW INTERCHANGE C TOOK PLACE AND THE POINTER ARRAY IV MUST BE UPDATED. 8C )\IF(IFLG.EQ.0)GOTO800 /HISAVE=IV(ICOL) ݵIV(ICOL)=IPCOL q ICOL1=ICOL+1 _0DO700L=ICOL1,M 0IF(IV(L).EQ.IPCOL)IV(L)=ISAVE & 700CONTINUE `DISIGN=-DISIGN '800IF(DNORM.EQ.0.0D+00)GOTO1900 aC d0 C BEGIN ELIMINATION OF ROW BELOW IV(K).DEN IS THE PIVOT ELEMENT. hQC {K1=K+1 y0DO1100IM=K1,M mC qC BEFORE ACTUALLY ELIMINTING WE CHECK TO SEE IF A(IV(IM),K) HAS ALREADY -C BEEN ANIHALATED. ȫC u0IF(A(IV(IM),K).EQ.0.0D+00)GOTO1100 yC C CACULATE ELIMINATION FACTOR. YC } 0DMULT=-A(IV(IM),K) C C WE NOW CALCULATE VALUE OF OTHER ELEMENTS IN ROW IV(IM). hC C0DO900NN=K1,M G9000A(IV(IM),NN)=(DMULT*A(IV(K),NN))/DNORM+A(IV(IM),NN) 0IF(NRS.LE.0)GOTO1100 0DO1000IN=1,NRS L10000P(IV(IM),IN)=(DMULT*P(IV(K),IN))/DNORM+P(IV(IM),IN) O81100CONTINUE \1200CONTINUE HC SC CALCULATE VALUE OF DETERMINANT. WqC IF(A(IV(M),M).EQ.0.0D0)GOTO1900 DET=DISIGN 2&IF(IDET.NE.0)CALLDMATDT(A,N,M,DET,IV,IDET) 6IF(DET.EQ.0.0D+00)GOTO1900 IF(NRS.LE.0)GOTO2000 C0C WE START SOLVING RIGHT HAND SIDES.THE SOLUTION REPLACES THE RIGHT HAND QC VECTOR.{C y 1300N1=M-1DO1600JJ=1,NRS C-C BEGIN BACK SUBSTITUTION.C P(IV(M),JJ)=P(IV(M),JJ)/A(IV(M),M) 0DO1500I=1,N10DSUM=0.0D+00Y0DO1400 J=1,I}14000DSUM=DSUM-A(IV(M-I),M-J+1)*P(I V(M-J+1),JJ)1500P(IV(M-I),JJ)=(P(IV(M-I),JJ)+DSUM)/A(IV(M-I),M-I)1600CONTINUEhDO1800JJ=1,NRS 0DO1700IND=1,M1700A(IND,1)=P(IV(IND),JJ) 0DO1800IND=1,M<1800P(IND,JJ)=A(IND,1)&RETURN8 1900JFLG=1\IDET=0$H 2000RETURN^ENDqSUBROUTINEMATDT(A,IA,N,DET,IV,IDET)C!SUBROUTINE MATDTISDMATDTFROMTHEUICCSUBROUTINELIBRARY.,REAL*8A(IA,1),DET,B,LOG16?&INTEGER*4IV(1),K,EQUIVALENCE (B,K) %NUM=16777216#?LOG16=.120411998265592457D+01=0IF(A(IV(N),N).EQ.0.0D+00)GOTO300bQL=0f{ DO100 I=1,NyB=DABS(A(IV(I),I)) K=K/NUM-64kL=L+Ko-100?DET=DET*(A(IV(I),I)/16.0D+00**K) ,B=DABS(DET) K=K/NUM-64sIW=L+KwIF((IW.LT.-64).OR .(IW.GT.63))GOTO200zY ?DET=DET*16.0D+00**L[}IDET=0GOTO400200DET=DET*16.0D+00**(-K)hIDET=L+K] %B=IDET*LOG16AIDET=BE ?B=B-DFLOAT(IDET)I?DET=DET*1.0D+01**BGOTO400}8300DET=0.0D+00M\IDET=0QH 400RETURNEND\q//GO.SYSIN DD *UFISHER IRIS DATAYNR=015&NC=0100 IP=044(1X,F3.1)8 5.1VARIABLE1A10010 4.9VARIABLE1A1002Q 4.7VARIABLE1A1003{ 4.6VARIABLE1A1004y 5.0VARIABLE1A1005 5.4VARIABLE1A1006 4.6VARIABLE1A1007- 5.0VARIABLE1A1008 4.4VARIABLE1A1009 4.9VARIABLE1A1010 5.4VARIABLE1A1011 4.8VARIABLE1A1012Y 4.8VARIABLE1A1013} 4.3VARIABLE1A1014 5.8VARIABLE1A1015 5.7VARIABLE1A1016h 5.4VARIABLE1A1017 5.1VARIABLE1A1018 5.7VARIABLE1A1019 5.1VARIABLE1A1020 5.4VARIABLE1A1021 5.1VARIABLE1A1022(8 4.6VARIABLE1A1023\ 5.1VARIABLE1A1024H 4.8VARIABLE1A1025 5.0VARIABLE1A1026)q 5.0VARIABLE1A1027/ 5.2VARIABLE1A1028ݝ 5.2VARIABLE1A1029& 4.7VARIABLE1A1030_ 4.8VARIABLE1A1031 5.4VARIABLE1A1032 5.2VARIABLE1A10330 5.5VARIABLE1A1034@Q 4.9VARIABLE1A1035{ 5.0VARIABLE1A1036dy 5.5VARIABLE1A1037h 4.9VARIABLE1A1038 4.4VARIABLE1A1039- 5.1VARIABLE1A1040m 5.0VARIABLE1A1041q 4.5VARIABLE1A1042 4.4VARIABLE1A1043 5.0VARIABLE1A1044tY 5.1VARIABLE1A1045x} 4.8VARIABLE1A1046 5.1VARIABLE1A1047 4.6VARIABLE1A1048h 5.3VARIABLE1A1049 5.0VARIABLE1A1050٨ 7.0VARIABLE1A2051 6.4VARIABLE1A2052C 6.9VARIABLE1A2053G 5.5VARIABLE1A20548 6.5VARIABLE1A2055\ 5.7VARIABLE1A2056KH 6.3VARIABLE1A2057O 4.9VARIABLE1A2058q 6.6VARIABLE1A2059 5.2VARIABLE1A2060S 5.0VARIABLE1A2061W& 5.9VARIABLE1A2062 6.0VARIABLE1A2063 6.1VARIABLE1A20642 5.6VARIABLE1A206550 6.7VARIABLE1A20669Q 5.6VARIABLE1A2067{ 5.8VARIABLE1A2068y 6.2VARIABLE1A2069 5.6VARIABLE1A2070 5.9VARIABLE1A2071 - 6.1VARIABLE1A2072 6.3VARIABLE1A2073  6.1VARIABLE1A2074 6.4VARIABLE1A2075 6.6VARIABLE1A2076Y 6.8VARIABLE1A2077} 6.7VARIABLE1A2078 6.0VARIABLE1A2079 5.7VARIABLE1A2080h 5.5VARIABLE1A2081 5.5VARIABLE1A2082 5.8VARIABLE1A2083 6.0VARIABLE1A2084  5.4VARIABLE1A2085 6.0VARIABLE1A20868 6.7VARIABLE1A2087.\ 6.3VARIABLE1A2088|H 5.6VARIABLE1A2089 5.5VARIABLE1A2090q 5.5VARIABLE1A2091$ 6.1VARIABLE1A2092^ 5.8VARIABLE1A2093& 5.0VARIABLE1A2094 5.6VARIABLE1A2095, 5.7VARIABLE1A2096? 5.7VARIABLE1A20970 6.2VARIABLE1A2098 4Q 5.1VARIABLE1A2099:{ 5.7VARIABLE1A2100=y 6.3VARIABLE1A3101b 5.8VARIABLE1A3102f 7.1VARIABLE1A3103- 6.3VARIABLE1A3104 6.5VARIABLE1A3105k 7.6VARIABLE1A3106o 4.9VARIABLE1A3107 7.3VARIABLE1A3108Y 6.7VARIABLE1A3109~} 7.2VARIABLE1A3110v 6.5VARIABLE1A3111z 6.4VARIABLE1A3112[h 6.8VARIABLE1A3113 5.7VARIABLE1A3114 5.8VARIABLE1A3115 6.4VARIABLE1A3116] 6.5VARIABLE1A3117A 7.7VARIABLE1A3118D8 7.7VARIABLE1A3119H\ 6.0VARIABLE1A3120H 6.9VARIABLE1A3121} 5.6VARIABLE1A3122Mq 7.7VARIABLE1A3123Q 6.3VARIABLE1A3124 6.7VARIABLE1A3125\& 7.2VARIABLE1A3126U 6.2VARIABLE1A3127Y 6.1VARIABLE1A3128 6.4VARIABLE1A31290 7.2VARIABLE1A31303Q 7.4VARIABLE1A31317{ 7.9VARIABLE1A3132y 6.4VARIABLE1A3133 6.3VARIABLE1A3134 6.1VARIABLE1A3135- 7.7VARIABLE1A3136 6.3VARIABLE1A3137 6.4VARIABLE1A3138 6.0VARIABLE1A3139 6.9VARIABLE1A3140Y 6.7VARIABLE1A3141} 6.9VARIABLE1A3142 5.8VARIABLE1A3143 6.8VARIABLE1A3144h 6.7VARIABLE1A3145 6.7VARIABLE1A3146 6.3VARIABLE1A3147 6.5VARIABLE1A3148 6.2VARIABLE1A3149 5.9VARIABLE1A31508 3.5VARIABLE21001\ 3.0VARIABLE21002H 3.2VARIABLE21003( 3.1VARIABLE21004q 3.6VARIABLE21005 3.9VARIABLE21006 3.4VARIABLE21007)& 3.4VARIABLE21008/ 2.9VARIABLE21009 3.1VARIABLE21010 3.7VARIABLE21011%0 3.4VARIABLE21012Q 3.0VARIABLE21013{ 3.0VARIABLE21014y 4.0VARIABLE21015@ 4.4VARIABLE21016 3.9VARIABLE21017d- 3.5VARIABLE21018h 3.8VARIABLE21019 3.8VARIABLE21020 3.4VARIABLE21021m 3.7VARIABLE21022pY 3.6VARIABLE21023} 3.3VARIABLE21024 3.4VARIABLE21025t 3.0VARIABLE21026xh 3.4VARIABLE21027 3.5VARIABLE21028 3.4VARIABLE21029 3.2VARIABLE21030 3.1VARIABLE21031 3.4VARIABLE210328 4.1VARIABLE21033B\ 4.2VARIABLE21034FH 3.1VARIABLE21035˵ 3.2VARIABLE21036q 3.5VARIABLE21037K 3.6VARIABLE21038O 3.0VARIABLE21039& 3.4VARIABLE21040 3.5VARIABLE21041S 2.3VARIABLE21042W 3.2VARIABLE21043Z0 3.5VARIABLE21044Q 3.8VARIABLE210451{ 3.0VARIABLE210465y 3.8VARIABLE210479 3.2VARIABLE21048 3.7VARIABLE21049- 3.3VARIABLE21050 3.2VARIABLE22051 3.2VARIABLE22052  3.1VARIABLE22053 2.3VARIABLE22054Y 2.8VARIABLE22055} 2.8VARIABLE22056 3.3VARIABLE22057 2.4VARIABLE22058h 2.9VARIABLE22059 2.7VARIABLE22060 2.0VARIABLE22061 3.0VARIABLE22062 2.2VARIABLE22063 2.9VARIABLE220648 2.9VARIABLE22065\ 3.1VARIABLE22066H 3.0VARIABLE22067 2.7VARIABLE22068.q 2.2VARIABLE22069| 2.5VARIABLE22070 3.2VARIABLE22071& 2.8VARIABLE22072$ 2.5VARIABLE22073^ 2.8VARIABLE22074 2.9VARIABLE220750 3.0VARIABLE22076Q 2.8VARIABLE22077>{ 3.0VARIABLE22078y 2.9VARIABLE22079 2.6VARIABLE22080: 2.4VARIABLE22081=- 2.4VARIABLE22082b 2.7VARIABLE22083f 2.7VARIABLE22084 3.0VARIABLE22085 3.4VARIABLE22086jY 3.1VARIABLE22087n} 2.3VARIABLE22088r 3.0VARIABLE22089 2.5VARIABLE22090~h 2.6VARIABLE22091v 3.0VARIABLE22092z 2.6VARIABLE22093[ 2.3VARIABLE22094 2.7VARIABLE22095 3.0VARIABLE220968 2.9VARIABLE22097\ 2.9VARIABLE22098{H 2.5VARIABLE22099D 2.8VARIABLE22100Hq 3.3VARIABLE23101 2.7VARIABLE23102} 3.0VARIABLE23103M& 2.9VARIABLE23104Q 3.0VARIABLE23105 4 3.0VARIABLE23106\ 2.5VARIABLE23107T0 2.9VARIABLE23108XQ 2.5VARIABLE23109{ 3.6VARIABLE23110y 3.2VARIABLE231113 2.7VARIABLE231127 3.0VARIABLE23113- 2.5VARIABLE23114 2.8VARIABLE23115 3.2VARIABLE23116 3.0VARIABLE23117 3.8VARIABLE23118Y 2.6VARIABLE23119} 2.2VARIABLE23120 3.2VARIABLE23121 2.8VARIABLE23122h 2.8VARIABLE23123 2.7VARIABLE23124 3.3VARIABLE23125 3.2VARIABLE23126 2.8VARIABLE23127 3.0VARIABLE231288 2.8VARIABLE23129\ 3.0VARIABLE23130H 2.8VARIABLE23131 3.8VARIABLE23132q 2.8VARIABLE23133 2.8VARIABLE23134( 2.6VARIABLE23135& 3.0VARIABLE23136 3.4VARIABLE23137 3.1VARIABLE23138) 3.0VARIABLE23139-0 3.1VARIABLE23140Q 3.1VARIABLE23141{ 3.1VARIABLE23142%y 2.7VARIABLE23143׌ 3.2VARIABLE23144 3.3VARIABLE23145- 3.0VARIABLE23146@ 2.5VARIABLE23147 3.0VARIABLE23148d 3.4VARIABLE23149h 3.0VARIABLE23150Y 1.4VARIABLE3/1001} 1.4VARIABLE3/1002l 1.3VARIABLE3/1003p 1.5VARIABLE3/1004h 1.4VARIABLE3/1005 1.7VARIABLE3/1006t 1.4VARIABLE3/1007x 1.5VARIABLE3/1008 1.4VARIABLE3/1009 1.5VARIABLE3/10108 1.5VARIABLE3/1011\ 1.6VARIABLE3/1012H 1.4VARIABLE3/1013ص 1.1VARIABLE3/1014Bq 1.2VARIABLE3/1015F 1.5VARIABLE3/1016˝ 1.3VARIABLE3/1017& 1.4VARIABLE3/1018K 1.7VARIABLE3/1019O 1.5VARIABLE3/1020 1.7VARIABLE3/10210 1.5VARIABLE3/1022Q 1.0VARIABLE3/1023V{ 1.7VARIABLE3/1024Zy 1.9VARIABLE3/1025 1.6VARIABLE3/10261 1.6VARIABLE3/10275- 1.5VARIABLE3/10289 1.4VARIABLE3/1029 1.6VARIABLE3/1030 1.6VARIABLE3/1031 1.5VARIABLE3/1032Y 1.5VARIABLE3/1033 } 1.4VARIABLE3/1034 1.5VARIABLE3/1035 1.2VARIABLE3/1036h 1.3VARIABLE3/1037 1.4VARIABLE3/1038 1.3VARIABLE3/1039 1.5VARIABLE3/1040 1.3VARIABLE3/1041 1.3VARIABLE3/10428 1.3VARIABLE3/1043\ 1.6VARIABLE3/1044H 1.9VARIABLE3/1045 1.4VARIABLE3/1046q 1.6VARIABLE3/1047 1.4VARIABLE3/1048 1.5VARIABLE3/1049.& 1.4VARIABLE3/1050| 4.7VARIABLE3/2051 4.5VARIABLE3/2052 4.9VARIABLE3/2053!0 4.0VARIABLE3/2054;Q 4.6VARIABLE3/2055{ 4.5VARIABLE3/2056y 4.7VARIABLE3/2057 3.3VARIABLE3/2058> 4.6VARIABLE3/2059- 3.9VARIABLE3/2060 3.5VARIABLE3/2061: 4.2VARIABLE3/2062= 4.0VARIABLE3/2063b 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