# Unique function not deleting duplicate rows.

37 views (last 30 days)
luc on 4 May 2015
Answered: Robert on 17 Oct 2018
attached my matrix "M" and here is my code.
[trash,idx] = unique(M,'rows');
pleb=M(idx,:)
gg=sort(pleb)
When inspecting gg we see that there are still duplicate rows.
I've also tried to do it in different ways, for example;
[~, III, ~] = unique(M,'first','rows'); %removing double points
III = sort(III);
pleb = M(III,:);
gg=sort(pleb);
But they either delete non duplicate data, or delete too few data.
What am I doing wrong?
##### 2 CommentsShowHide 1 older comment
luc on 4 May 2015
I attached it the right way now.

Stephen23 on 4 May 2015
Edited: Stephen23 on 4 May 2015
It is likely that the data are floating point and that they are not actually equal, which confuses many beginners and people not used to working with numeric data. Although what is displayed on the command window might look the same, floating point values can differ at the low end of their significand, so testing for equality (like unique does) does not work.
Alternatively, if the data are strings, trailing spaces are often overlooked by users...
Stephen23 on 5 May 2015
@luc: I'm glad to be able to help!

Titus Edelhofer on 4 May 2015
Hi Luc,
I don't see duplicate data, but the data change sign ...? Take last 4 rows of pleb and it's
19.4558 -4.1355 -2.0906
19.4558 -4.1355 2.0906
19.4558 4.1355 -2.0906
19.4558 4.1355 2.0906
Look similar but all 4 are completely different - as long as -2.0906 is different from 2.0906 ;-).
Similar for the other "4-row-blocks".
When you take the abs then the story is different,
Titus
Titus Edelhofer on 4 May 2015
Edited: Titus Edelhofer on 4 May 2015
Indeed. As I wrote as comment, if you would sort keeping rows as rows, i.e., using
sortrows(M)
then you would see, that there are no duplicate rows.

John D'Errico on 4 May 2015
Edited: John D'Errico on 4 May 2015
There are NO equal rows. I checked. They are different in sign. There are no rows that are even that close to each other, although the nearest neighbor is not uniformly close.
The check that I made was to find the point for each row that was closest in distance. I.e., the nearest neighbor. There ARE no essentially zero distances.
The overall closest pair of points are 1.7291 units apart.
Mu = unique(M,'rows');
D = ipdm(Mu,'subset','smallestfew','limit',1)
D =
(87,95) 1.7291
D = ipdm(Mu,'subset','nearest')
D =
(2,1) 4.1811
(1,2) 4.1811
(13,3) 4.1811
(14,4) 4.1811
(6,5) 4.1811
(5,6) 4.1811
(8,7) 4.1811
(7,8) 4.1811
(15,9) 4.1811
(16,10) 4.1811
(17,11) 4.1811
(18,12) 4.1811
(3,13) 4.1811
(4,14) 4.1811
(9,15) 4.1811
(10,16) 4.1811
(11,17) 4.1811
(12,18) 4.1811
(26,25) 4.1811
(25,26) 4.1811
(28,27) 4.1811
(27,28) 4.1811
(35,29) 4.1811
(36,30) 4.1811
(37,31) 4.1811
(38,32) 4.1811
(19,33) 4.1811
(20,34) 4.1811
(29,35) 4.1811
(30,36) 4.1811
(31,37) 4.1811
(32,38) 4.1811
(21,39) 4.1811
(22,40) 4.1811
(23,41) 4.1811
(24,42) 4.1811
(33,47) 4.1811
(53,47) 3.3826
(55,47) 3.3826
(34,48) 4.1811
(54,48) 3.3826
(56,48) 3.3826
(43,49) 4.1811
(44,50) 4.1811
(45,51) 4.1811
(46,52) 4.1811
(39,53) 4.1811
(47,53) 3.3826
(40,54) 4.1811
(48,54) 3.3826
(41,55) 4.1811
(42,56) 4.1811
(58,57) 4.1811
(57,58) 4.1811
(60,59) 4.1811
(59,60) 4.1811
(69,61) 4.1811
(70,62) 4.1811
(71,63) 4.1811
(72,64) 4.1811
(49,65) 4.1811
(91,65) 3.3826
(50,66) 4.1811
(92,66) 3.3826
(51,67) 4.1811
(93,67) 3.3826
(52,68) 4.1811
(94,68) 3.3826
(61,69) 4.1811
(62,70) 4.1811
(63,71) 4.1811
(64,72) 4.1811
(79,77) 1.7291
(81,77) 1.7291
(80,78) 1.7291
(82,78) 1.7291
(77,79) 1.7291
(78,80) 1.7291
(73,83) 4.1811
(74,84) 4.1811
(75,85) 4.1811
(76,86) 4.1811
(95,87) 1.7291
(96,88) 1.7291
(97,89) 1.7291
(98,90) 1.7291
(65,91) 3.3826
(83,91) 4.1811
(105,91) 3.3826
(66,92) 3.3826
(84,92) 4.1811
(106,92) 3.3826
(67,93) 3.3826
(85,93) 4.1811
(107,93) 3.3826
(68,94) 3.3826
(86,94) 4.1811
(108,94) 3.3826
(87,95) 1.7291
(101,95) 1.7291
(88,96) 1.7291
(102,96) 1.7291
(89,97) 1.7291
(103,97) 1.7291
(90,98) 1.7291
(104,98) 1.7291
(99,101) 4.1811
(100,102) 4.1811
(109,105) 4.1811
(110,106) 4.1811
(111,107) 4.1811
(112,108) 4.1811
(113,109) 4.1811
(114,110) 4.1811
(115,111) 4.1811
(116,112) 4.1811
(121,117) 4.1811
(122,118) 4.1811
(123,119) 4.1811
(124,120) 4.1811
(117,121) 4.1811
(118,122) 4.1811
(119,123) 4.1811
(120,124) 4.1811
(126,125) 4.1811
(125,126) 4.1811
(133,127) 1.7291
(134,128) 1.7291
(135,129) 1.7291
(136,130) 1.7291
(132,131) 4.1811
(131,132) 4.1811
(127,133) 1.7291
(128,134) 1.7291
(129,135) 1.7291
(130,136) 1.7291
(139,137) 1.7291
(140,138) 1.7291
(137,139) 1.7291
(138,140) 1.7291
(145,141) 4.1811
(149,141) 3.3826
(146,142) 4.1811
(150,142) 3.3826
(147,143) 4.1811
(151,143) 3.3826
(148,144) 4.1811
(152,144) 3.3826
(153,145) 4.1811
(154,146) 4.1811
(155,147) 4.1811
(156,148) 4.1811
(141,149) 3.3826
(165,149) 4.1811
(142,150) 3.3826
(166,150) 4.1811
(143,151) 3.3826
(167,151) 4.1811
(144,152) 3.3826
(168,152) 4.1811
(159,157) 3.3826
(177,157) 4.1811
(160,158) 3.3826
(178,158) 4.1811
(157,159) 3.3826
(179,159) 4.1811
(158,160) 3.3826
(180,160) 4.1811
(169,161) 4.1811
(170,162) 4.1811
(171,163) 4.1811
(172,164) 4.1811
(181,165) 4.1811
(182,166) 4.1811
(183,167) 4.1811
(184,168) 4.1811
(161,169) 4.1811
(162,170) 4.1811
(163,171) 4.1811
(164,172) 4.1811
(174,173) 4.1811
(173,174) 4.1811
(176,175) 4.1811
(175,176) 4.1811
(189,177) 4.1811
(190,178) 4.1811
(191,179) 4.1811
(192,180) 4.1811
(193,185) 4.1811
(194,186) 4.1811
(195,187) 4.1811
(196,188) 4.1811
(185,193) 4.1811
(186,194) 4.1811
(187,195) 4.1811
(188,196) 4.1811
(198,197) 4.1811
(197,198) 4.1811
(200,199) 4.1811
(199,200) 4.1811
(205,201) 4.1811
(206,202) 4.1811
(207,203) 4.1811
(208,204) 4.1811
(201,205) 4.1811
(202,206) 4.1811
(203,207) 4.1811
(204,208) 4.1811
(210,209) 4.1811
(209,210) 4.1811
(212,211) 4.1811
(211,212) 4.1811
luc on 4 May 2015
Hey Sean,
U can click on the screenshot to enlarge it.
But I think Stephen solved my problem. The sort functions grabs each colums independant, and not as a whole.
Thanks guys!

Robert on 17 Oct 2018
If anyone encounters truly duplicate rows in the output of unique like I did, this may be caused by NaN in your data being treated as distinct values. See this question for more info.