Speeding up ''ismember'' in Matlab

Question

I'm using the Matlab build in command ''ismember'' to see whether a certain data set is within a larger dataset. The purpose of the piece of code is to remove any multiples of the [0 0] row from the larger data set shown below:

To do achieve this, I am using the following piece of code:

[Lia,locB] = ismember([0 0; 0 0],AFdata,'rows');
if sum(Lia) > 1 
AFdata(locB(1):locB(end-1),:) = [];

end

AFdata = [
1.0000   -0.0114
0.9975   -0.0098
0.9951   -0.0084
0.9928   -0.0074
0.9903   -0.0066
0.9804   -0.0042
0.9705   -0.0018
0.9606    0.0004
0.9507    0.0025
0.9408    0.0045
0.9309    0.0063
0.9210    0.0082
0.9111    0.0100
0.9012    0.0118
0.8913    0.0135
0.8814    0.0152
0.8715    0.0167
0.8616    0.0183
0.8517    0.0199
0.8418    0.0214
0.8318    0.0229
0.8219    0.0243
0.8120    0.0256
0.8021    0.0269
0.7922    0.0282
0.7823    0.0294
0.7724    0.0306
0.7625    0.0318
0.7526    0.0329
0.7427    0.0340
0.7328    0.0350
0.7229    0.0359
0.7130    0.0368
0.7031    0.0377
0.6932    0.0385
0.6833    0.0393
0.6734    0.0401
0.6635    0.0408
0.6536    0.0415
0.6437    0.0422
0.6338    0.0428
0.6239    0.0434
0.6140    0.0439
0.6041    0.0444
0.5942    0.0449
0.5843    0.0454
0.5744    0.0458
0.5645    0.0461
0.5546    0.0465
0.5447    0.0469
0.5348    0.0472
0.5249    0.0475
0.5150    0.0478
0.5051    0.0481
0.4951    0.0483
0.4852    0.0485
0.4753    0.0487
0.4654    0.0489
0.4555    0.0491
0.4456    0.0492
0.4357    0.0493
0.4258    0.0494
0.4159    0.0495
0.4060    0.0495
0.3961    0.0495
0.3862    0.0495
0.3763    0.0495
0.3664    0.0494
0.3565    0.0493
0.3466    0.0492
0.3367    0.0491
0.3268    0.0490
0.3169    0.0488
0.3070    0.0486
0.2971    0.0484
0.2872    0.0482
0.2773    0.0479
0.2674    0.0476
0.2575    0.0473
0.2476    0.0469
0.2377    0.0465
0.2278    0.0461
0.2179    0.0457
0.2080    0.0452
0.1981    0.0447
0.1882    0.0441
0.1783    0.0435
0.1684    0.0428
0.1584    0.0421
0.1485    0.0413
0.1386    0.0404
0.1287    0.0395
0.1188    0.0385
0.1089    0.0374
0.0990    0.0363
0.0891    0.0352
0.0792    0.0338
0.0693    0.0323
0.0594    0.0306
0.0495    0.0287
0.0396    0.0265
0.0297    0.0239
0.0198    0.0204
0.0099    0.0153
0.0050    0.0115
0.0020    0.0075
     0         0
     0         0
0.0020   -0.0075
0.0050   -0.0115
0.0099   -0.0153
0.0198   -0.0204
0.0297   -0.0239
0.0396   -0.0265
0.0495   -0.0287
0.0594   -0.0306
0.0693   -0.0323
0.0792   -0.0338
0.0891   -0.0352
0.0990   -0.0363
0.1089   -0.0375
0.1188   -0.0386
0.1287   -0.0396
0.1386   -0.0405
0.1485   -0.0414
0.1584   -0.0422
0.1684   -0.0429
0.1783   -0.0436
0.1882   -0.0442
0.1981   -0.0448
0.2080   -0.0454
0.2179   -0.0459
0.2278   -0.0463
0.2377   -0.0467
0.2476   -0.0471
0.2575   -0.0475
0.2674   -0.0478
0.2773   -0.0481
0.2872   -0.0484
0.2971   -0.0486
0.3070   -0.0488
0.3169   -0.0490
0.3268   -0.0491
0.3367   -0.0492
0.3466   -0.0493
0.3565   -0.0493
0.3664   -0.0493
0.3763   -0.0493
0.3862   -0.0492
0.3961   -0.0491
0.4060   -0.0490
0.4159   -0.0488
0.4258   -0.0486
0.4357   -0.0484
0.4456   -0.0481
0.4555   -0.0478
0.4654   -0.0474
0.4753   -0.0470
0.4852   -0.0465
0.4951   -0.0460
0.5051   -0.0455
0.5150   -0.0449
0.5249   -0.0442
0.5348   -0.0435
0.5447   -0.0427
0.5546   -0.0418
0.5645   -0.0408
0.5744   -0.0397
0.5843   -0.0386
0.5942   -0.0374
0.6041   -0.0362
0.6140   -0.0350
0.6239   -0.0337
0.6338   -0.0324
0.6437   -0.0310
0.6536   -0.0296
0.6635   -0.0281
0.6734   -0.0266
0.6833   -0.0252
0.6932   -0.0236
0.7031   -0.0220
0.7130   -0.0204
0.7229   -0.0188
0.7328   -0.0172
0.7427   -0.0156
0.7526   -0.0141
0.7625   -0.0125
0.7724   -0.0110
0.7823   -0.0095
0.7922   -0.0080
0.8021   -0.0067
0.8120   -0.0055
0.8219   -0.0045
0.8318   -0.0035
0.8418   -0.0026
0.8517   -0.0018
0.8616   -0.0012
0.8715   -0.0007
0.8814   -0.0004
0.8913   -0.0003
0.9012   -0.0004
0.9111   -0.0007
0.9210   -0.0012
0.9309   -0.0020
0.9408   -0.0030
0.9507   -0.0042
0.9606   -0.0055
0.9705   -0.0072
0.9804   -0.0092
0.9903   -0.0115
0.9928   -0.0119
0.9951   -0.0121
0.9975   -0.0119
1.0000   -0.0114]

However, this piece of code is executed for multiple datasets and numerous iterations which makes this a slows piece of code.

Is there any alternative to using ''ismember''? Or a quicker way to do this. Unfortunately I am not good with programing.

Answer 1

If you use logical indexing it will be faster.

%create index
index = sum(AFdata' == 0)==2;
%clean AFdata
AFdata(index,:) = [];

Answer 2

This is a minor improvement of obchardon's answer. There is no need to transpose the data set, instead use the dimension argument for sum or all. find is unnecessary, use logical indexing instead. Using all instead of sum makes the comparison unnecessary.

index =all(AFdata==0,2);
AFdata(index,:) = [];