How to compare 2 columns line by line between two matrix of different size?

Hello everyone, I am trying to collect data between 2 matrix, let me explain.

I have my first matrix1 of 128x26 --> in the future it will increase rows but not columns.

I have my second matrix2= 17x5 --> in the future it will increase rows but not columns.

I need to compare the first 2 columns of my matrix2 with the first 2 columns of my matrix1, if they match, store them in a new matrix and also add the value of row 1 column 5 of matrix2 to matrix1, it would now have 27 columns. for example.

matrix2 matrix1

looking at the figures of matrix1 and matrix2, the two values ​​of column 1 and 2 of the first row of matrix2 are equal to the values ​​of column 1 and 2 of the first row of matrix1?

If the answer is yes, as we see in the figures the condition is met, we must extract all that row from matrix1, also extracting the value of the first row of column 5 from matrix2 and store it in another matrix.

now next row the two values ​​of column 1 and 2 of the second row of matrix2 are equal to the values ​​of column 1 and 2 of the second row of matrix1?

As we see in this opportunity the condition is not met, then it would go to the next row of the matrix1.

we ask again the two values ​​of column 1 and 2 of the second row of matrix2 are equal to the values ​​of column 1 and 2 of the third row of matrix1?

we can see that this time the condition is not met either.

then he continues to go row by row through matrix1.

until it reaches row 13 of matrix where if the condition is met, the two values ​​of column 1 and 2 of the second row of matrix2 are equal to the values ​​of column 1 and 2 of the thirteenth row of matrix1?

s,i then extracts the entire row of matrix1, also extracting the value of the first column 5 row of matrix2 and storing it in another matrix. which would now be an array of ? x27.

any help will be of great help.

attached files datasetPDP.csv / M_reception.csv

matrix1 = readtable('datasetPDP.csv');
matrix2= readtable('M_reception.csv');
matrix1=table2array(matrix1);
matrix2=table2array(matrix2);


result =  zeros(128,27); %create empty array to store
for ii = 1:numel(result)
    if
%      result(ii) = matrix1(1,2)== matrix2(:,2);
    end
     
end

[dpb]

Look at ismember -- the 'rows' option will be of great help here to deal with the two columns.

This doesn't need loops; just logical indexing.

[KSSV]

@giancarlo maldonado cardenas say it loudly today your function is ismember.

[giancarlo maldonado cardenas]

try to use Lia = ismember(A,B) and i get this error,that's why my question is for 2 matrix of different size,and I think the best way is to loop through row by row

Error using ismember>ismemberR2012a (line 220)

Inputs A and B must be matrices with the same number of columns in the

'rows' case.

Error in ismember (line 154)

lia = ismemberR2012a(A,B,logical(flaginds(1)));

Answer 1

[Peter Perkins]

ismember will work, but ithis is a join operation. It's a one-liner:

>> matrix1 = readtable('datasetPDP.csv');
>> matrix2 = readtable('M_reception.csv');
>> outerjoin(matrix1,matrix2,"Type","left","LeftKeys",["Rodada" "Preamble"],"RightKeys",["Var1" "Var2"],"RightVariables","Var5")
ans =
  128×27 table
    Rodada    Preamble      Signal      matrixpdp4    matrixpdp5    matrixpdp6    matrixpdp7    matrixpdp8    matrixpdp9    matrixpdp10    matrixpdp11    matrixpdp12    matrixpdp13    matrixpdp14    matrixpdp15    matrixpdp16    matrixpdp17    matrixpdp18    matrixpdp19    matrixpdp20    matrixpdp21    matrixpdp22    matrixpdp23    matrixpdp24    matrixpdp25    matrixpdp26    Var5
    ______    ________    __________    __________    __________    __________    __________    __________    __________    ___________    ___________    ___________    ___________    ___________    ___________    ___________    ___________    ___________    ___________    ___________    ___________    ___________    ___________    ___________    ___________    ___________    ____
      1           0        0.0010973     0.0014955     0.0021707      0.003359    0.00055829     0.0010751    0.00090908     0.0014945     0.00019773     0.00078146      0.0012317      0.0030336      0.0017859      0.0018387      0.0012646      0.0018806     0.00027404      0.0021579      0.0033589      0.0012098     0.00072156      0.0036446      0.0026105     0.00024817       1 
      1           1        0.0010774     0.0017781     0.0018294    0.00091074     0.0045575     0.0067504    0.00080268     0.0022846      0.0036092      0.0013504     0.00053713      0.0041816      0.0024775     0.00058104      0.0037931      0.0029195       0.000371     0.00095127      0.0006796     0.00056264      0.0025798      0.0010398       0.001775     0.00075008     NaN 
      1           2        0.0020478    0.00085579     0.0040942     0.0025075     0.0044309     0.0011097     0.0011937    0.00041591     0.00056487     0.00038507      0.0019512     0.00017701     0.00045695     0.00044913        0.00039      0.0010628      0.0036834      0.0035462      0.0028797      0.0035931      0.0023936       0.002308      0.0023255     0.00096789     NaN 
      1           3        0.0010441    0.00049841     0.0008076    0.00097527    0.00010521     0.0011686     0.0027039     0.0021738      0.0015503      0.0023266      0.0027327     0.00097983     0.00054617      0.0032336      0.0041698      0.0038683      0.0014237      0.0013809     0.00052897      0.0038032      0.0061047     0.00046472     0.00044809      0.0022013     NaN 
      1           4        0.0012156    0.00068193     0.0018693     0.0026049      0.002205     0.0034968     0.0035514     0.0031294      0.0024362      0.0017798      0.0010075      0.0014272      0.0015431       0.000693     0.00079977     0.00090793       0.000403      0.0010627     0.00071093      0.0012673      0.0020034      0.0017955      0.0012529     0.00011641     NaN 
      1           5        0.0017177    0.00069686    0.00053142     0.0010268      0.003255     0.0025269     0.0011327      0.001645      0.0018237      0.0022081      0.0063273       0.009835      0.0041595      0.0015196      0.0020088     0.00066475     0.00032383      0.0018303      0.0032275     0.00099837      0.0022101      0.0016684      0.0012008      0.0018507     NaN 
      1           6        0.0012676    0.00045186     0.0013013    0.00030612    0.00047142     0.0019475     0.0021581    0.00079162     0.00064098      0.0016653      0.0013672      0.0011073     0.00077234     0.00094002      0.0060251      0.0041629     0.00034144     0.00046791      0.0010455     0.00051369      0.0031551      0.0032793      0.0012046      0.0021144     NaN 
      1           7        0.0015077     0.0032757     0.0033144    0.00085496    0.00051226     0.0014641     0.0017391     0.0017929      0.0061214      0.0029792       0.002147      0.0017308      0.0025274      0.0036399       0.005072      0.0035843      0.0001834      0.0029567      0.0029361      0.0016222      0.0012737      0.0021096      0.0036728      0.0040209     NaN 
      1           8       0.00095541     0.0005468     0.0011631     0.0018271     0.0021716    0.00021437     0.0023052    0.00082586      0.0027262      0.0021262      0.0075821      0.0043669      0.0031274      0.0030417      0.0019816      0.0027805     0.00012739      0.0029001      0.0024738      0.0034005      0.0042034      0.0014699      0.0049535      0.0012263     NaN 
      1           9       0.00084515     0.0013416      0.003956     0.0035951    0.00065921     0.0035955       0.00579    0.00033185      0.0022705      0.0012696      0.0022217       0.033807       0.064108       0.016413      0.0029541      0.0065956      0.0021655      0.0015788       0.001384      0.0013451      0.0021701     0.00085581      0.0012541     0.00082248     NaN 
      1          10        0.0042198     0.0019744     0.0012101    0.00080912     0.0007666     0.0018416     0.0027759     0.0027681      0.0070458       0.010774      0.0047063      0.0003923      0.0031841      0.0025231      0.0018007     0.00034417      0.0015265       0.002909      0.0021168      0.0020538      0.0012833      0.0012541     0.00065291     0.00052467     NaN 
      1          11        0.0012448     0.0031821     0.0010628     0.0034886     0.0018811     0.0057215    0.00010455      0.003405      0.0060808      0.0098973       0.066043        0.39196        0.22502     0.00022782       0.044542      0.0031209      0.0094781      0.0021576      0.0039872     0.00046216      0.0019244      0.0014773     0.00066253      0.0028552     NaN 
      1          12        0.0051382     0.0056502     0.0027458     0.0014817    0.00070129     0.0022945     0.0017992     0.0042692      0.0019376       0.011582       0.013746        0.16584        0.14764      0.0093603      0.0078227      0.0031041      0.0078368     0.00040088      0.0035873      0.0050905      0.0052621      0.0031892     0.00084777       0.001533       1 
      1          13        0.0024913      0.003497     0.0042391     0.0016327    0.00061685       0.00463     0.0019077     0.0014411       0.005991       0.005015     0.00051202       0.001849      0.0010992      0.0028671      0.0014481       0.001406     0.00089814     0.00062381      0.0008219      0.0041691      0.0016705     0.00017857     0.00071384      0.0027307     NaN 
      1          14        0.0028523      0.004434     0.0025021    0.00056743     0.0012753     0.0012606    0.00055734     0.0077045      0.0083289      0.0028868      0.0010419       0.088706        0.17242       0.037488      0.0035278      0.0061256      0.0012858     0.00064601      0.0022378     0.00049119      0.0004179      0.0031359     0.00066957      0.0031972     NaN 
      1          15        0.0040321     0.0012932     0.0023014     0.0025301    0.00046779     0.0016559     0.0028288     0.0024749      0.0015211      0.0020137     0.00044966      0.0031585      0.0066212       0.004233      0.0023557     0.00084482      0.0015047     0.00089751      0.0019532      0.0031834      0.0020053      0.0021094      0.0010083     0.00072187     NaN 
      1          16         0.004941     0.0026842     0.0005658      0.002517     0.0003222    0.00079508    0.00079265     0.0026782     0.00067126     0.00088311     0.00090814      0.0019914      0.0018319      0.0016373      0.0012236     0.00049719      0.0012321      0.0012921     0.00098165     0.00056793       0.001041      0.0007905     0.00097527     0.00080592       0 
      1          17        0.0022534     0.0030143     0.0055441     0.0044463     0.0025262      0.017061     0.0088768     0.0027206      0.0032895       0.010912      0.0055377        0.12246        0.12625      0.0092601      0.0047975     0.00032622      0.0043357      0.0020372      0.0015425      0.0015002      0.0011104     0.00091825      0.0007817      0.0029933       1 
      1          18         0.001874     0.0012292    0.00064677     0.0038169     0.0035081    0.00074153    0.00060098     0.0015003      0.0034444      0.0017952      0.0018087      0.0014346      0.0011685      0.0018915       0.001687      0.0012081     0.00014338      0.0017354      0.0016956      0.0013922      0.0016273      0.0021601     0.00095762      0.0013722     NaN 
      1          19        0.0004795     0.0017168     0.0010648     0.0020244     0.0010584       0.00119     0.0024165     0.0021458     0.00099231      0.0010357      0.0010782      0.0044558      0.0043473      0.0043934       0.004216      0.0013792     0.00073113     0.00049909       0.001429      0.0010402     0.00027421      0.0015057      0.0020146      0.0019079     NaN 
      :          :            :             :             :             :             :             :             :              :              :              :              :              :              :              :              :              :              :              :              :              :              :              :              :              :          :  
      2          44        0.0018129    0.00089776     0.0023699     0.0010181     0.0017192    0.00076182     0.0012447     0.0010925       0.001366      0.0012242     0.00033044      0.0010379      0.0010969      0.0015877      0.0044794       0.001343      0.0010353      0.0015873      0.0014389      0.0018233     0.00049561        0.00327      0.0055067      0.0047465       1 
      2          45        0.0035632     0.0063304     0.0052247     0.0020673     0.0017972     0.0036182      0.002434    0.00034105     0.00085688      0.0022339      0.0026449      0.0014151     0.00054098     0.00064258      0.0026553       0.001397     0.00096313     0.00030289     0.00020618     0.00053574      0.0022085      0.0016364      0.0027255     0.00090731     NaN 
      2          46        0.0007001      0.001716    0.00077849     0.0006116     0.0012149    0.00038342     0.0007744    0.00044005      0.0054269      0.0053465     0.00063063      0.0040887      0.0013102     0.00027688      0.0015623       0.003641      0.0032719      0.0011169     0.00090554      0.0022397      0.0017888      0.0011034     0.00090255     0.00092326     NaN 
      2          47        0.0022405     0.0013799     0.0012279     0.0012122     0.0022226    0.00021536     0.0021071    0.00073198      0.0011624      0.0036028      0.0034959      0.0015374     0.00055692      0.0039569      0.0064082      0.0010124      0.0031754      0.0024986     0.00079648     0.00020623     0.00042075       0.001327     0.00081575      0.0010912     NaN 
      2          48       0.00024619     0.0012901     0.0017184     0.0023425     0.0033145    0.00044313    0.00018129      0.001065      0.0015592       0.002021      0.0041724      0.0043461      0.0033273      0.0018234      0.0040704      0.0043786      0.0031738      0.0058246      0.0036726      0.0033272      0.0023244       0.001504     0.00082242      0.0013057     NaN 
      2          49       0.00077536     0.0010171     0.0018011      0.001866     0.0020243     0.0010971    0.00038052    0.00099761     0.00075259     0.00091204     0.00099607      0.0014106      0.0010843       0.003391      0.0018399      0.0013811      0.0021575      0.0055965      0.0046285      0.0013592      0.0017625      0.0036897      0.0027101     0.00057446     NaN 
      2          50        0.0014045     0.0012965     0.0012251     0.0013734      0.002685    0.00075175     0.0023342     0.0018544      0.0056375      0.0037256      0.0051981     0.00086322     0.00086701      0.0013212     0.00090445     0.00065787     0.00059062      0.0038797      0.0021087      0.0019733      0.0017831     0.00080594       0.001747      0.0015876     NaN 
      2          51        0.0035614     0.0013334    0.00067066     0.0012252     0.0012402     0.0013522     0.0010245     0.0028272      0.0055757      0.0017266     0.00040195     0.00057075      0.0011545      0.0017927      0.0031428      0.0039066       0.007246        0.10684        0.27881        0.12934      0.0055709       0.010839      0.0015755      0.0028047       1 
      2          52        0.0005097     0.0012164     0.0013328    0.00078055      0.002145     0.0023428     0.0011545    0.00064442      0.0011088      0.0017516      0.0032288      0.0054092      0.0029596     0.00080221      0.0024652     0.00086444      0.0019534      0.0028125       0.002938      0.0014227     0.00068893       0.003827      0.0015591      0.0028916     NaN 
      2          53         0.002149    0.00037247     0.0019154     0.0019518     0.0032624      0.003956     0.0032743     0.0019048     0.00038879     0.00058397     0.00045235      0.0016106     0.00043441      0.0011746      0.0016053     0.00083189      0.0023806      0.0037916      0.0045566      0.0010793      0.0025769      0.0027969      0.0009914     0.00018068     NaN 
      2          54        0.0017755     0.0029364     0.0010729     0.0027167     0.0044297     0.0015856     0.0015175     0.0015852     0.00074844      0.0024197      0.0024942      0.0019054      0.0020422      0.0010931     0.00091567     0.00069952     0.00025896     0.00091218     0.00097232      0.0012196     0.00060903     0.00089067     0.00067336     0.00092845     NaN 
      2          55        0.0030385     0.0019786    0.00043091     0.0019478      0.001599    0.00067014     0.0022767     0.0014854     0.00088575      0.0024318      0.0024133      0.0011435      0.0012668     0.00095854      0.0020945      0.0053136      0.0034482     0.00044109      0.0034679      0.0010682      0.0018075     0.00057173      0.0013796      0.0016545     NaN 
      2          56        0.0075449     0.0052677    0.00081658     0.0012355    0.00069084     0.0016768    0.00015458    0.00073926     0.00032303     0.00046185      0.0012235     0.00030528     0.00068002      0.0013886      0.0017564      0.0011536      0.0013455     0.00035626      0.0015029      0.0021757      0.0010432     0.00037814      0.0010076      0.0013477       1 
      2          57        0.0047445     0.0023403     0.0066734     0.0034636     0.0030496      0.001132     0.0045062     0.0018714       0.005971      0.0053931      0.0012656      0.0014415      0.0012442      0.0027818      0.0018774      0.0013538      0.0021553      0.0017431     0.00042992      0.0040285      0.0032781      0.0006399      0.0012157      0.0043137     NaN 
      2          58        0.0025177     0.0039624     0.0013396     0.0012327     0.0016084    0.00077188    0.00027701    0.00078746     0.00066841     0.00067663     0.00018197      0.0060773       0.005797     0.00016341       0.001688       0.004599      0.0017107       0.002735     0.00044471       0.024889       0.041071      0.0040108      0.0034443      0.0018994     NaN 
      2          59       8.1339e-05    0.00069285     0.0016601    0.00075471     0.0011265     0.0034647    0.00092708     0.0026734      0.0067707      0.0052703     0.00052984      0.0020042      0.0045039      0.0032346       0.004779      0.0035327      0.0025712      0.0053006      0.0089947        0.03699       0.014163     0.00074291      0.0014575     0.00083619     NaN 
      2          60         0.011836     0.0010351     0.0064164     0.0034499     0.0081156     0.0031529     0.0024829    0.00022536     0.00047512      0.0012795      0.0010406      0.0019063      0.0026321      0.0032091      0.0034932      0.0032668      0.0020201     0.00099025      0.0016848      0.0051396      0.0025274     0.00067372     0.00033823     0.00029294     NaN 
      2          61        0.0008977    0.00097358    8.4165e-05     0.0026509     0.0026732     0.0010886     0.0005029    0.00079601      0.0024851      0.0007122      0.0017964      0.0023497     0.00063573       0.003965      0.0007579      0.0056555     0.00085557      0.0028269      0.0033492      0.0092183       0.025056        0.20152        0.15012      0.0040343     NaN 
      2          62        0.0034501     0.0015632     0.0042426      0.002751    0.00080137    0.00039144     0.0018245      0.002492     0.00066393      0.0016724     0.00079415      0.0018821      0.0014331      0.0011794     0.00071549      0.0016982      0.0014695      0.0020075      0.0026873      0.0048475     0.00077319     0.00090235     0.00068204      0.0002192     NaN 
      2          63        0.0028523     0.0020075    0.00062044    0.00067918     0.0016999      0.004335     0.0015263      0.001882      0.0018724     0.00077001       0.001575      0.0033639      0.0018316     0.00081367     0.00025862     0.00032023     0.00045461     0.00069571      0.0016788     0.00096171      0.0015312      0.0009506     0.00042282      0.0027451     NaN 
	Display all 128 rows.

Probably the Type parameter is unnecessary, but it makes clear what you are doing.

[dpb]

I never could fathom join, Peter... :)

[giancarlo maldonado cardenas]

This solves the problem in part, maybe I didn't mention it clearly, but in the new array it only has to be stored if there is a match, if the condition is met, the rows that don't meet the condition should not be stored.

you can help me with that?

[Peter Perkins]

Giancarlo, look at the doc for innerjoin and outerjoin. It sounds like you want inner.