How can I set the colors in the legend if I show a number of legend elements which is lower thatn the number of plotted functions?

Dear, I'm trying to do a bar chart as the attached one (and shown below).

As you can see, the bar chart contains, for six different number of k (kNN imputation) the class separation for 22 features.

Then I have: group one with 22 features (for k=5), group two with 22 features (for k=4),... end so on.

You can notice that the color helps identifying features through different ks.

Now the problem is with the legend.

I don't want to repeat feat 1feat2 feat3 .... feat22 for six times, so I would like to have the legen showing feat1,..., feat22 just ones.

I can do it but the problem is that the legend color for feati (where i>1) does not match the bar color. In the legen the color are just blues.

How can I do that? I hope I have been clear

[Rik]

What code did you use to make the legend? And if you provide code that will reproduce the issue it is easier to help you.

[Elena Casiraghi]

Sorry! I forgot to attach it!

In the code the number of features is nFeat,

Y is a matrix with size (numel(kimpute), nFeat)

X = 1: numel(kimpute)
kimpute = [5 4 3 2 1 0] 

that is, each row of Y contains nFeat columns (one value for each feature: the golub test for assessing the separation between class c1 and class c2)

here is the code:

    figClassSep = figure('units','normalized','outerposition',[0 0 1 1]);
    title(['Class separation: ' titles{i}]);
    hold on; bb = bar(kimpute, Y, 'histc');
        cols = getColorValues(bb);
       xlabel('k value for KNN imputation');
        ylabel('inter-class separation');
        hleg = legend(featuresName,'Location','bestoutside');
        chleg = get(hleg,'children');
        ax = gca;
        ax.XTick = X-0.5; 
        ax.XTickLabels = kimpute;
        ax.XTickLabelRotation = 0;
        clear Y;
        saveas(figClassSep, [dirSave filesep 'GolubSep_' num2str(i) '.jpg']);
         savefig(figClassSep, [dirSave filesep  'GolubSep_' num2str(i) '.fig']);
        hold off; clear figClassSep;

[Elena Casiraghi]

results is this ( as you can see legends are blue)

Answer 1

[Bruno Luong]

h=bar(rand(10,3))
legend(h(1:3),'1','2','3') 

[Elena Casiraghi]

I don't know why this example works by with my data it doesn't

[Bruno Luong]

Then post your code.

[Elena Casiraghi]

Thanks Bruno,

You can find it above.

Anyway here you find it:

In the code the number of features is nFeat,

Y is a matrix with size (numel(kimpute), nFeat)

X = 1: numel(kimpute)
kimpute = [5 4 3 2 1 0] 

that is, each row of Y contains nFeat columns (one value for each feature: the golub test for assessing the separation between class c1 and class c2)

here is the code:

    figClassSep = figure('units','normalized','outerposition',[0 0 1 1]);
    title(['Class separation: ' titles{i}]);
    hold on; bb = bar(Y, 'histc');
       xlabel('k value for KNN imputation');
        ylabel('inter-class separation');
        hleg = legend(featuresName,'Location','bestoutside');
        ax = gca;
        ax.XTick = X-0.5; 
        ax.XTickLabels = kimpute;
        ax.XTickLabelRotation = 0;
        clear Y;
 
        hold off; clear figClassSep;

[Bruno Luong]

Your code is incompleted, I can't run. If you want to make our life easier, please post an independent code that anyone CAN run, and use the ">" button to format it so we simply copy and past and easily read it.

Here I change your code so that anyone can run, and it produce the expected result

kimpute = [5 4 3 2 1 0] ;
X = 1: numel(kimpute);
nFeat = 3;
Y = rand(numel(kimpute), nFeat);

figClassSep = figure('units','normalized','outerposition',[0 0 1 1]);
bb = bar(kimpute, Y, 'histc');
xlabel('k value for KNN imputation');
ylabel('inter-class separation');
featuresName = {'1' '2' '3'};
hleg = legend(bb(1:3), featuresName,'Location','bestoutside');

[Elena Casiraghi]

Sorry, I was not aware of that option.

This works, and I'm getting mad to understand why mione does't work

[Elena Casiraghi]

 figClassSep = figure('units','normalized','outerposition',[0 0 1 1]);
        title(['Class separation: PROVA']);
Y=[0,00352773139157628	0,0245105237469620	0,0684189167502798	0,0457174215185948	0,0755463136415411	0,0333541328273327	0,00939606658675425	0,0438152432923879	0,111147405019837	0,0121358817668315	0,0381070519629694	0,00596488008511738	0,0437950326456825	0,0151064013039443	0,110690313757324	0,0101314966289956	0,0554477383830247	0,0272154231381807	0,0275179174147810	0,0731668732528484	0,00791367722049643	0,00705768117231613	0,113743041973737	0,128195949067710	0,0690734349741947	0,0906776040784420	0,0755181470463421	0,0756497274057818	0,0478624771933210	0,0910619814686025	0,0332545403945191	0,00486259082808798	0,0408987579331380	0,0394638988901118	0,0776017696435297	0,00502768931095035	0,0584086625164292	0,0349108874419341	0,00954971649784230	0,0826866229911194	0,0615203826455133	0,0284067401142204	0,0976821834090579	0,0951423642685848	0,129722964553467	0,135285240168524	0,111935700288519	0,0635832874095593	0,0379274748101937	0,0395976026110539	0,0465500861764497	0,0338226920001485	0,0978024063308301	0,0508686623300447	0,0574396281212244	4,67177132573323e-05	0,103867466738371	0,0720476834444672	0,0322515676986906	0,0166980268891537	0,00534970992115033	0,104529059613631	0,0777918619680008	0,0525941305544478	0,0781643895781336	0,155850960925222
0,00352773139157628	0,0245105237469620	0,0684189167502798	0,0457174215185948	0,0757650602002998	0,0333541328273327	0,00939606658675425	0,0438152432923879	0,111147405019837	0,0120095272980320	0,0379753999342189	0,00584605494759287	0,0437950326456825	0,0153648350132776	0,109432116850526	0,0101417437733446	0,0554062651015562	0,0272154231381807	0,0275179174147810	0,0731668732528484	0,00791367722049643	0,00705768117231613	0,113743041973737	0,128195949067710	0,0690734349741947	0,0906776040784420	0,0751876430985700	0,0756581842345854	0,0478624771933210	0,0915002878582255	0,0332545403945191	0,00486259082808798	0,0408869910903416	0,0392500516839558	0,0776017696435297	0,00502768931095035	0,0586503621899914	0,0358798084939208	0,00954971649784230	0,0826866229911194	0,0615203826455133	0,0284067401142204	0,0976821834090579	0,0951423642685848	0,129722964553467	0,135285240168524	0,111830023720803	0,0635832874095593	0,0375780960415576	0,0395976026110539	0,0467092561714405	0,0338226920001485	0,0978024063308301	0,0508686623300447	0,0574396281212244	4,67177132573323e-05	0,103867466738371	0,0720476834444672	0,0316480639072955	0,0183405917039119	0,00534970992115033	0,104529059613631	0,0777918619680008	0,0529259274555511	0,0781643895781336	0,155850960925222;
0,00352773139157628	0,0245105237469620	0,0684189167502798	0,0457174215185948	0,0752794583502872	0,0333541328273327	0,00939606658675425	0,0438152432923879	0,111147405019837	0,0121457666214951	0,0380738249941672	0,00590530258735806	0,0437950326456825	0,0156915779481442	0,109604624175827	0,0103181866601694	0,0555456967708953	0,0272154231381807	0,0275179174147810	0,0731668732528484	0,00791367722049643	0,00705768117231613	0,113743041973737	0,128195949067710	0,0690734349741947	0,0906776040784420	0,0755290631257140	0,0753984214856610	0,0478624771933210	0,0914649851141716	0,0332545403945191	0,00486259082808798	0,0408056552352384	0,0389902138672609	0,0776017696435297	0,00502768931095035	0,0598165994281133	0,0352511808965016	0,00954971649784230	0,0826866229911194	0,0615203826455133	0,0284067401142204	0,0976821834090579	0,0951423642685848	0,129722964553467	0,135285240168524	0,112122174103431	0,0635832874095593	0,0387946846303367	0,0395976026110539	0,0468800230111397	0,0338226920001485	0,0978024063308301	0,0508686623300447	0,0574396281212244	4,67177132573323e-05	0,103867466738371	0,0720476834444672	0,0321836421277401	0,0168228702195491	0,00534970992115033	0,104529059613631	0,0777918619680008	0,0532763358896435	0,0781643895781336	0,155850960925222;
0,00352773139157628	0,0245105237469620	0,0684189167502798	0,0457174215185948	0,0748618368597702	0,0333541328273327	0,00939606658675425	0,0438152432923879	0,111147405019837	0,0121979942659151	0,0380896195111711	0,00548462797988354	0,0437950326456825	0,0154000039373007	0,108973639621171	0,0113698523172448	0,0554364721026194	0,0272154231381807	0,0275179174147810	0,0731668732528484	0,00791367722049643	0,00705768117231613	0,113743041973737	0,128195949067710	0,0690734349741947	0,0906776040784420	0,0752354915568102	0,0752902682689790	0,0478624771933210	0,0843097385447768	0,0332545403945191	0,00486259082808798	0,0404528319988782	0,0386597594160090	0,0776017696435297	0,00502768931095035	0,0599740753965689	0,0367603368543403	0,00954971649784230	0,0826866229911194	0,0615203826455133	0,0284067401142204	0,0976821834090579	0,0951423642685848	0,129722964553467	0,135285240168524	0,112356277459076	0,0635832874095593	0,0379847823996197	0,0395976026110539	0,0475708079868249	0,0338226920001485	0,0978024063308301	0,0508686623300447	0,0574396281212244	4,67177132573323e-05	0,103867466738371	0,0720476834444672	0,0314018068351122	0,0186604568795201	0,00534970992115033	0,104529059613631	0,0777918619680008	0,0532763358896435	0,0781643895781336	0,155850960925222;
0,00352773139157628	0,0245105237469620	0,0684189167502798	0,0457174215185948	0,0735222958755212	0,0333541328273327	0,00939606658675425	0,0438152432923879	0,111147405019837	0,0123035739430327	0,0383757237189697	0,00575697361820347	0,0437950326456825	0,0148469793497561	0,109406484693211	0,0102761706162695	0,0553209213104503	0,0272154231381807	0,0275179174147810	0,0731668732528484	0,00791367722049643	0,00705768117231613	0,113743041973737	0,128195949067710	0,0690734349741947	0,0906776040784420	0,0761588872516961	0,0750211815321989	0,0478624771933210	0,0729668537249305	0,0332545403945191	0,00486259082808798	0,0402283467111668	0,0391962065721608	0,0776017696435297	0,00502768931095035	0,0618058524887792	0,0395498720866417	0,00954971649784230	0,0826866229911194	0,0615203826455133	0,0284067401142204	0,0976821834090579	0,0951423642685848	0,129722964553467	0,135285240168524	0,112356277459076	0,0635832874095593	0,0386832477087707	0,0395976026110539	0,0380719225866054	0,0338226920001485	0,0978024063308301	0,0508686623300447	0,0574396281212244	4,67177132573323e-05	0,103867466738371	0,0720476834444672	0,0313231052451139	0,0212263333566937	0,00534970992115033	0,104529059613631	0,0777918619680008	0,0461687288163433	0,0781643895781336	0,155850960925222;
0,00352773139157628	0,0245105237469620	0,0684189167502798	0,0457174215185948	0,0768025334637058	0,0333541328273327	0,00939606658675425	0,0438152432923879	0,111147405019837	0,0109157927084161	0,0366705078598375	0,00468694270539886	0,0437950326456825	0,0138969476117289	0,118153699997786	0,0109709626427021	0,0538715399082256	0,0272154231381807	0,0275179174147810	0,0731668732528484	0,00791367722049643	0,00705768117231613	0,113743041973737	0,128195949067710	0,0690734349741947	0,0906776040784420	0,0787428048518134	0,0774460600090258	0,0478624771933210	0,0831861408792214	0,0332545403945191	0,00486259082808798	0,0401279234591900	0,0389764756558760	0,0776017696435297	0,00502768931095035	0,0597046533159442	0,0347281242322993	0,00954971649784230	0,0826866229911194	0,0615203826455133	0,0284067401142204	0,0976821834090579	0,0951423642685848	0,129722964553467	0,135285240168524	0,102652134234181	0,0635832874095593	0,0416599187596793	0,0395976026110539	0,0266979976554437	0,0338226920001485	0,0978024063308301	0,0508686623300447	0,0574396281212244	4,67177132573323e-05	0,103867466738371	0,0720476834444672	0,0314850608405637	0,0130248620394017	0,00534970992115033	0,104529059613631	0,0777918619680008	0,0289308316724715	0,0781643895781336	0,155850960925222]
hold on; bb = bar(Y, 'histc');
       
xlabel('k value for KNN imputation');
ylabel('inter-class separation');
featuresNameMean={'mean01' 'mean02' 'mean03' 'mean04' 'mean05' 'mean06' 'mean07' 'mean08' 'mean09' 'mean10' 'mean11' 'mean12' 'mean13' 'mean14' 'mean15' 'mean16'  'mean17' 'mean18' 'mean19' 'mean20' 'mean21' 'mean22'};
nFeatMean = numel(featuresNameMean);
legend(bb(1:nFeatMean),featuresNameMean,'Location','bestoutside');
       
        ax = gca;
        ax.XTick = X-0.5; 
        ax.XTickLabels = kimpute;
        ax.XTickLabelRotation = 0;
        clear Y;
        hold off; clear figClassSep;

This is the working example

[Bruno Luong]

Your Y array is of size (6 x 132) and not (6 x 22) as in you description in the first post. So when you put in legend of the first 22 data it's just 1/6th of 132, it's normal they are almost blue, since they are at the begining of the spectrum.

I think the problem is on your data representation, and it seems it is not clear if you know them well, at least in the question the dimensions are somehow mixed up.

[Elena Casiraghi]

Dear Bruno,

sorry but copying generated the mess.

Indeed, my data is 6x66 and feature and featureNameMean is a cell array 1x66.

but anyhow, it's correct now, thanks a lot!

Answer 2

[Walter Roberson]

The trick for custom legends is to use

for k = 1:num2show
  bh(k) = bar(nan, 'color', BarColors(k, :) ;
end
legend(bh, BarLegends)

Where BarColors is an nx3 array of colors to use and BarLegends is a cell array of character vectors giving the corresponding text.

The way this works is that the nan data points tell bar() not to draw anything, but a graphics handle is created and its properties can be manipulated. You legend() those handles and it puts up the corresponding entry even though there are no visible data points for it.

[Elena Casiraghi]

Thanks Walter!