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luc
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Unique function not deleting duplicate rows.

Asked by luc
on 4 May 2015
Latest activity Answered by 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 Comments

"What am I doing wrong": not clicking on both buttons to attach the data: you need to first click Choose file and then Attach file. Please try attaching your data again.
I attached it the right way now.

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4 Answers

Answer by Stephen Cobeldick on 4 May 2015
Edited by Stephen Cobeldick on 4 May 2015
 Accepted Answer

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.
To understand more about this topic read these:
Alternatively, if the data are strings, trailing spaces are often overlooked by users...

  8 Comments

@John D'Errico: Oh... but then what about:
1 == sqrt((+1)^2) == sqrt((-1)^2) == -1
I don't know that song, but I was humming the terms of Grandi's series in my head... still didn't reach 1/2 though... have to add a few more... will get there soon...
Thanks Stephen,
I think the sorting part had me confused.
nice explanation, I learned something.
:)
@luc: I'm glad to be able to help!

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Answer by 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

  3 Comments

Hey Titus,
I noticed that too, but that is because the points are coordinates spanning a sphere so some "mirror points" are to be expected.
If you use this code:
[~, III, ~] = unique(M,'first','rows'); %removing double points
III = sort(III);
M = M(III,:);
FF=sort(M)
FF(4,:)==FF(3,:)
U will see that even the rows that are equal are not removed.
@luc: There is no reason why those rows would be removed, as
  • all rows of M are already unique
  • sort(M) sorts each column independently, so there is no reason why these rows should be unique (or removed) either.
You need to actually describe what you are trying to achieve.
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.

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Answer by John D'Errico
on 4 May 2015
Edited by 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

  6 Comments

Thanks for the effort guys, but why am I seeing these double vallues then in my data?
Here's a screenshot as "proof".
First, your screenshot is too small to see.
Second, here's a good exercise to explain the small differences in floating point: Run this:
>> format hex
Then rerun the command. See! They're different, even if just by a little.
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!

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Answer by 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.

  0 Comments

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