Matlab 当某个数字发生变化时，将一个向量拆分为多个向量

我们的目标是把它分解成其他载体。 拆分的标准是：

1-元素

V（i）

和

V（i+1）

至少有10e-1的变化

它将索引存储到新向量

W

，如果：

2-它们的指数差异大于30，即

W（j）-W（j+1）>30

如果这两个条件都满足，那么向量将被拆分，直到索引

i

。否则就会留下来。 例如，在给定向量中，输出指数应为：

    Let we have vector in Matlab such as

V = 
   0.000178395155017
   0.000213248167386
   0.000453830296775
   *0.000954267024225
   0.001985203786879
   0.002752603423106
   0.004236131760631
   0.005323800592906
   0.004742807895286
   0.005770068613768
   0.006502644472601
   0.007193873735489
   0.006799579314313
   0.007976014911026
   0.007799267839386
   0.008907651205854
   0.009190413742866
   0.008776108424259
   0.008578897868764
   0.009250162682559
   0.008646692914164
   0.008053809730819
   0.007451047998031
   0.006729120942615
   0.005979207722644
   0.007134289275269
   0.005186062958380
   0.005902382106719
   0.007762189959679
   0.008579238141806
   0.009485819832702
   0.009780236553648
   0.010538511758940
   0.012865379338644
   0.011390556215603
   0.010804154725939
   0.010290545066940
   0.009868360169125
   0.009357083973682
   0.008724843328595
   0.008560695909341
   0.008784388594649
   0.008739349797176
   0.008670604330509
   0.008753566233007
   0.008490628616888
   0.008003101497979
   0.008389140601319
   0.007282172470936
   0.006743970005005
   0.006452902620905
   0.005895958422668
   0.006326020573140
   0.005740103704853
   0.005142834616900
   0.004407465481546
   0.003835738957490
   0.004510955842245
   0.003693705366274
   0.002915993815608
   0.002889010498571
   0.003093844935832
   0.004282403818479
   0.003970929347537
   0.002819774971736
   0.003095369398698
   0.002920594268954
   0.002431265415582
   0.001949299679935
   0.001496954638334
   0.001096892239592
   0.001092281820070
   0.000790749599802
   0.000893827364410
   0.001334675844874
   0.001196368539888
   0.001160349994386
   0.001020290171146
   0.001112407234178
   0.001747377249787
   0.002765678114897
   0.003478849585666
   0.005355127628032
   0.004986389269550
   0.005947847852449
   0.006157789251547
   0.006181425650566
   0.006194151127049
   0.006269927746287
   0.006769754494212
   0.007712318414790
   0.008651586166082
   0.008790606282214
   0.009736113450469
   0.011185160343747
   0.012691072774672
   0.010504478607987
   0.014389683527318
   0.014831212205190
   0.028483974800043
   0.011080767592010
   0.011092374038746
   0.012487511417703
   0.013592769613900
   0.023109284793639
   0.011965905202027
   0.012215762791363
   0.013202467569807
   0.011692397524803
   0.011053425496442
   0.010772141978943
   0.010246937424879
   0.0099733483767118
   0.009945578183562
   0.010512885752603
   0.009531902580105
   0.008066298613627
   0.007407338182258
   0.007934227536212
   0.008839106853309
   0.007141630457420
   0.005890234651479
   0.005777682702918
   0.005975075824875
   0.007650395349746
   0.007857392419299
   0.005786229253901
   0.005200025773704
   0.004125852007666
   0.004066689824078
   0.003581520984740
   0.001457233291953
   0.001322814038819
   0.001253430603776
   0.002493349764335
   0.001702719375177
   0.001556075727362
   0.001200788268232
   0.000940090552841
   0.002078710051336
   0.001014764365464
   0.000954943700664
   0.001276066704394
   0.001359265944949
   0.001204978086943
   0.001593370287575
   0.001547390763560
   0.000930577956978
   0.000916566445246
   0.000546925307518
   0.013565059409590
   0.001282356857566
   0.015602976997836
   0.001499511920717
   0.001586946634746
   0.014156830819098
   0.000291360243454
   0.000429438521866
   0.000462155358170
   0.000362561191605
   0.000657801010925
   0.000593844882183
   0.000609582362382
   0.000310044384822
   0.000342618178403
   0.000282871074224
   0.000314326548349
   0.000204414827855
   0.000337206866696
   0.000532099888764
   0.001193470342198
   0.001296110099774
   0.000872141517716
   0.000700042872179
   0.000881365833141
   0.000373364170668
   0.000425611062469
   0.001421790419507
   0.000852308546181
   0.000815064422325
   0.000504599352342
   0.000700524140014
   0.000870732973155
   0.000714061606598
   0.001035552251181
   0.001149954823243
   0.001055911091689
   0.001199405112898
   0.001783769543970
   0.002409901173348
   0.003498367375372
   0.003703495953811
   0.006277776686302
   0.011652755634052
   0.010083799009042
   0.040863796019376
   0.013614001128299
   0.011042432683334
   0.015305185452370
   0.012711371813255
   0.016268067330822
   0.017370177368730
   0.019331483583318
   0.014673338103928
   0.017693568446981
   0.016892965150834
   0.014088831405978
   0.017200144763667
   0.014169123504048
   0.010078177083794
   0.014268458659556
   0.011690206086017
   0.006562739851251
   0.016466509298505
   0.015914670918691
   0.005624098695020
   0.017555623339083
   0.013692853335518
   0.004491863547507
   0.015326207984803
   0.014740709288865
   0.003199262301369
   0.013665227028721
   0.052745092682123
   0.029889776608225
   0.008659506247240
   0.008589034126194
   0.007785146195475
   0.007151058968527
   0.006980966929187
   0.005799220068642
   0.005386071463278
   0.004896307509138
   0.004818382921265
   0.004623210359464
   0.004383594039787
   0.003923666862792
   0.003524680860004
   0.003014782806900
   0.002558267907671
   0.002794987126812
   0.003018175443714
   0.003166769934104
   0.004138527203301
   0.004310037290568
   0.004610838173675
   0.004947713276471
   0.005083932336596
   0.005236700123762
   0.004687780786133
   0.004564827415894
   0.004302650724049
   0.004270824001320
   0.003937584195932
   0.003911947276786
   0.003620705359817
   0.003151576407325
   0.002751470077629
   0.002669908472863
   0.002232914262935
   0.002069299781104
   0.001979860796735
   0.001601476811607
   0.000301142580668
   0.000321068102647
   0.000390207925071
   0.000210753154473
   0.000387358708178
   0.000241971967384
   0.000366525972307
   0.000619750196280
   0.000412347959310
   0.000544608934404
   0.001311532428278
   0.000736536458620
   0.000523889409829
   0.000492076015774
   0.000297176544828
   0.000195452121510
   0.000292051729488
   0.000759425298714
   0.004267105657429
   0.000192598592911
   0.000235881092079
   0.000209623369819
   0.000160795350619
   0.000118611207140
   0.000253937140734
   0.000229140021426
   0.000132672582131
   0.000185947357453
   0.000336849580242
   0.000161741284909
   0.000212296295172
   0.000129317114703
   0.000078122221464
   0.000139606283922
   0.000074030291037
   0.000273018975900
   0.000084307182558

其中，将有4个向量：

 [4, 75, 185]

您对选择公差或制定标准有何看法？

---

任何想法都值得赞赏。

我想你想要的是：

V1 = [1:4]
V2 = [4:75]
v3 = [75:185]
V4 = [185:300]

给你一个单元格数组

V = [0.0003,0.0008,0.0019,0.0027,0.0034,0.0103,0.0110,0.0106,0.0113,0.0134,0.0143,0.0148,0.0152,0.0156,0.00166,0.00172,0.0174,0.0195,0.0193,0.000195,0.00021]

digit = 2;

output = accumarray(cumsum(logical([0; diff(floor(10^digit*V(:)))]))+1,V,[],@(x) {x})

一些解释：

output = 

    [5x1 double]
    [9x1 double]
    [2x1 double]
    [3x1 double]
    [2x1 double]

---

请为您的输入发布所需的输出。请相应地编辑您的问题，不要将其发布在评论中-否则会变得混乱；）问题是，你不知道你会得到多少个新向量，所以把结果存储在一个单元格数组中不是更好吗？

output={[0.0003,0.0008,0.0019,0.0027,0.0034]；…；…}

@顺便说一句，你对单元格数组的建议很好。你是说你希望差别有所不同吗？因此，条目5和条目6之间的差值应为10e-1，19条目20=10e-2？您也没有提到差异应该是精确的还是>=的，或者在

V=V.

中转置

V

有什么好处。？@kkuilla您需要accumarray的列向量。但是我做了一点小小的改变来避免这一行。非常感谢。这是一个敏锐的回答，不客气。你应该把你的问题说得更清楚一点，我这边有很多猜测。你为什么要把它转换成逻辑推理呢<代码>总和（[0；diff（floor（10^位*V（：）））]）与

cumsum（logical（[0；diff（floor（10^位*V（：））]））不同。

。两者都应该总结，但第一个不是。只有带有

逻辑的

。

%// get the important digit 
digs = floor(10^digit*V(:));
%// see where it changes
d = [0; diff(digs)];
%// convert to 0s and 1s and cumsum them up
subs = cumsum(logical(d))+1;
%// to get the subs for accumarray
output = accumarray(subs,V,[],@(x) {x})