Functions>Generalappliedmath,StatisticsPerformsk-meansclusteringviatheHartiganandWongAS-136algorithm.Availableinversion6.3.0andlater.functionkmeans_as136(x:numeric,;floatordoubl" />

AS136模具鋼多少錢(qián)一公斤_材料成分

發(fā)布時(shí)間:2023-07-05 12:23:58
    AS136模具鋼多少錢(qián)一公斤_材料成分

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  Functions>

  Generalappliedmath,

  Statistics

  Performsk-meansclusteringviatheHartiganandWongAS-136algorithm.

  Availableinversion6.3.0andlater.

  functionkmeans_as136(

  x:numeric,;floatordouble

  k[1]:integer,

  opt[1]:logical

  return_val:floatordouble

  Thereturnarray(say),clcnter,willcontainthecluster

  centers.Itwillbedimensioned(k,N),whereN

  collectivelyrepresentsthe'variable'dimension(s).

  clcnterwillhavethefollowingattributesassociatedwithit:

  id-aone-dimensionalintegerarrayof

  sizeMindicatingtheclustertowhicheachobservationis

  assigned.

  npts-aone-dimensionalintegerarrayof

  sizekcontainingthenumberofpointsineachcluster.

  ss2-aone-dimensionaldoublearrayof

  sizekcontainingthewithin-clustersumofsquares.

  K-meansisacentroid-basedclustermethod.

  Theobservationsareallocatedtokclustersinsuchawaythatthe

  within-clustersumofsquaresisminimized.K-meansclusteringrequiresthat

  thenumberofclusterstobeextractedbespecifiedinadvance.

  Asnotedby

  "Thenumberofclustersshouldmatchthedata.Anincorrectchoiceofthenumberofclusterswillinvalidatethewholeprocess.AnempiricalwaytofindthebestnumberofclustersistotryK-meansclusteringwithdifferentnumberofclustersandmeasuretheresultingsumofsquares."

  Thek-meansalgorithmworksreasonablywellwhenthedatafitstheclustermodel:

  Thenumberofclustersis'consistent'withthedata.

  Thedatapointswithinaclusterarecenteredaroundthatcluster

  Thespread/varianceoftheclustersissimilar,ieeachdatapointbelongstotheclosestcluster

  Limitations:K-meansmayhaveproblemswhenclustersareofverydifferingsizes;

  outliersarepresent;oremptyclustersexist.

  TheoriginalcodeiscreatedforCartesiangrids.Iftheapplicationrequiresusinggridpoints

  orstationslocatedathighlatitudes,itissuggestedthat

  css2cbeused

  tointerpolatetoCartesiancoordinates.Thesebetterreflectthetruedistancesandshouldbeinputtothefunction.

  Themodifiedcodeusedbythisfunctionwasdownloadedfrom

  JohnBurkardt'swebsite.

  TheoriginalHartigan&WongFortrancodewasfrom:

  JohnHartigan,ManchekWong,

  AlgorithmAS136:

  AK-MeansClusteringAlgorithm,

  AppliedStatistics,

  Volume28,Number1,1979,pages100-108.

  Example1:Thesourceofthisexampleis

  Defaultoptionsareused.

  v0(/1.0,1.5,3.0,5.0,3.5,4.5,3.5/);1stvariable

  v1(/1.0,2.0,4.0,7.0AS136模具鋼多少錢(qián)一公斤,5.0,5.0,4.5/);2ndvariable

  mdimsizes(v1);#observations

  n2;#variables

  k2;#clusters(userspecified)

  xnew((/n,m/),typeof(v1),"No_FillValue")

  x(0,:)v0

  x(1,:)v1

  clcntrkmeans_as136(x,k,False);usedefaultoptions

  print(clcntr);(1.25,1.5)and(3.9,5.1)

  Aneditedversionoftheoutputfollows:

  Variable:clcntr

  Type:float

  TotalSize:16bytes

  4values

  NumberofDimensions:2

  Dimensionsandsizes:[2]x[2](kcX(:,{-30:30},:);x(time,lat,lon)

  ;reorderviaNCL'snameddimensionreordering

  xrx(lat|:,lon|:,time|:);make'time'(observations;M)therightmostdimension

  ;thelat,lonarethe'variables'(N)

  k3;#clusters(userspecified)

  optTrue

  opt@iseed1

  clcntrkmeans_as136(xr,k,opt);inputthereorderedarray

  :;clcntr(3,nlat,mlon)

  delete(xr);deleteifnolongerneeded(notnecessary)蘇州東锜公司自創(chuàng)立以來(lái)一直秉承“誠(chéng)信、務(wù)實(shí)、高效、創(chuàng)新”的企業(yè)精神,將這一精神融入到企業(yè)的經(jīng)營(yíng)管理和工作實(shí)踐當(dāng)中,并獲得了社會(huì)各界信任與尊重。我公司先后多次被評(píng)為“蘇州市鋼材營(yíng)銷(xiāo)企業(yè)五十強(qiáng)”,“全國(guó)百?gòu)?qiáng)鋼材營(yíng)銷(xiāo)企業(yè)”,被中國(guó)質(zhì)量檢驗(yàn)協(xié)會(huì)評(píng)為“全國(guó)質(zhì)量服務(wù)誠(chéng)信示范企業(yè)”及“全國(guó)行業(yè)質(zhì)量示范企業(yè)”。

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