I'm trying to plot a knn result from my data, which has 3 columns: x, y , label. There are 3 classes and for each of them I would like to used a different symbol. Here's the way I'm plotting now:
t1 = data(:,3) == 1;
t2 = data(:,3) == 2;
t3 = data(:,3) == 3;
train1 = data(t1,:);
train2 = data(t2,:);
train3 = data(t3,:);
figure(1);
plot(train1(:,1),train1(:,2),'#',train2(:,1),train2(:,2),'*',train3(:,1),train3(:,2),'o');
I want to know if there's a more concise way of doing this. Thanks
Here's the most concise (and working) way to plot your data:
figure(1);
hold all
plot(train1(:,1),train1(:,2),'o')
plot(train2(:,1),train2(:,2),'x')
plot(train3(:,1),train3(:,2),'s')
Here's an example that does what you want in a robust and modular manner. You can easily add classes or modify the figure output.
data = [0.53,0.17,2;0.78,0.60,3;0.93,0.26,1;0.13,0.65,2;0.57,0.69,1;...
0.47,0.75,3;0.010,0.45,1;0.34,0.080,3;0.16,0.23,3;0.79,0.91,3;...
0.31,0.15,1;0.53,0.83,2];
categories = [1,2,3];
symbols = {'s','<','o','d','v','+','x','*'};
figure;
hold all
for loopj = 1:length(categories)
t = data(:,3) == categories(loopj);
train = data(t,:);
label = strcat('Class ',num2str(categories(loopj)));
plot(train(:,1),train(:,2),symbols{loopj},'DisplayName',label,'LineWidth',1.3)
end
lg = legend('show');
lg.Location = 'best';
Use hold all to write on the figure without erasing the previous axi and let Matlab pick line colors.
In any case, you need to manually define the different symbols and each plot command comes with one unique type of line and markers.
Related
I have to plot some Venn diagrams to show intersections between arrays of strings. Let me explain: I have a category of names that includes three others that do not intersect each other. I want to understand which of these three occupies the largest percentage of the macro-category mentioned above. I would like to do it with MATLAB but I see that this development environment is somewhat devoid of such functions. If you have any ideas in mind I would be grateful.
Thanks in advance!
Venn Diagrams by Plotting Circles
Not the most elegant way but one method of plotting a Venn diagram can be plotting circles and filling the individual circles using a text() annotation/label. Unfortunately, here I manually placed where the labels are centred. Automating the label positioning may take several additional steps which I will leave out for simplicity. To find the intersections of the respective datasets I simply used the intersect() function.
A = {'A','B','C','D','E','F','G','H','I','L'};
B = {'A','B','C','D'};
C = {'E','F','G','H','I','L'};
%Finding the intersections of the arrays%
Intersection_AB = intersect(A,B);
fprintf("Intersection AB: ")
disp(Intersection_AB);
fprintf("\n");
Intersection_BC = intersect(B,C);
fprintf("Intersection BC: ")
disp(Intersection_BC);
fprintf("\n");
Intersection_AC = intersect(A,C);
fprintf("Intersection AC: ")
disp(Intersection_AC);
fprintf("\n");
Intersection_ABC = intersect(Intersection_AB,C);
fprintf("Intersection ABC: ")
disp(Intersection_ABC);
fprintf("\n");
clc;
clf;
Plotting_Interval = 0.01;
Angles_In_Radians = (0: Plotting_Interval: 2*pi);
Circle_Plot = #(X_Offset,Y_Offset,Radius) plot(X_Offset + Radius*cos(Angles_In_Radians),Y_Offset + Radius*sin(Angles_In_Radians));
hold on
%Plotting the 3 circles%
X_Offset_A = 0; Y_Offset_A = 2; Radius_A = 3;
Circle_A = Circle_Plot(X_Offset_A,Y_Offset_A,Radius_A);
fill(Circle_A.XData, Circle_A.YData,'r','FaceAlpha',0.2,'LineWidth',1);
X_Offset_B = -2; Y_Offset_B = -2; Radius_B = 3;
Circle_B = Circle_Plot(X_Offset_B,Y_Offset_B,Radius_B);
fill(Circle_B.XData, Circle_B.YData,'g','FaceAlpha',0.2,'LineWidth',1);
X_Offset_C = 2; Y_Offset_C = -2; Radius_C = 3;
Circle_Plot(X_Offset_C,Y_Offset_C,Radius_C);
Circle_C = Circle_Plot(X_Offset_C,Y_Offset_C,Radius_C);
fill(Circle_C.XData, Circle_C.YData,'b','FaceAlpha',0.2,'LineWidth',1);
title("Venn Diagram");
%Writing all the labels%
A_Label = strjoin(string(A));
text(X_Offset_A,Y_Offset_A,A_Label,'color','r');
B_Label = strjoin(string(B));
text(X_Offset_B,Y_Offset_B,B_Label,'color','g');
C_Label = strjoin(string(C));
text(X_Offset_C,Y_Offset_C,C_Label,'color','b');
AB_Label = strjoin(string(Intersection_AB));
text(-1.2,0,AB_Label);
BC_Label = strjoin(string(Intersection_BC));
text(0,-2,BC_Label);
AC_Label = strjoin(string(Intersection_AC));
text(1.2,0,AC_Label);
ABC_Label = strjoin(string(Intersection_ABC));
text(0,0,ABC_Label);
%Setting the labels to be relative to the centres%
set(findall(gcf,'type','text'),'HorizontalAlignment','center');
axis equal
axis off
Ran using MATLAB R2019b
I'm using a for loop to add more nodes and edges on my plot. However, when I add labels on new edges the old labels are removed. I don't know how to keep old edge-labels nor how to store the results of labeledge.
This is what I have got so far.
for r = 1: 10
for j = 1:10
H = addnode(P,nodeName{r}{j});
P = addedge(H, s{r}{j}, t{r}{j}, w{r}{j});
figure;
hold on;
h = plot(P);
labeledge(h,s{r}{j},t{r}{j},labelText{r}{j})
end
end
Every time in the new plot, I can only see the newest cluster of labels while old labels are gone. Ideally, I'd love to hold on the results of labeledge but hold on can't do this. I need to show labels in each step in the loop, thus adding another overall labeledge is not my ideal solution. Any hint would be appreciated.
Edit: All my variables are multiple cells of difference sizes in cell arrays. I use for loop to help to pick up vectors from cells because I don't know how to insert all the levels of information from such cell arrays of cells etc. into addNode function.
The main problem in your code is that you keep plotting the graph again and again. This isn't necessary. Instead, use one loop to create the graph object (G), then plot it all at once, and then use another loop for labeling the graph:
P = graph;
for r = 1: 10
for j = 1:10
P = addedge(P, s{r}{j}, t{r}{j}, w{r}{j});
end
end
h = plot(P);
for r = 1: 10
for j = 1:10
labeledge(h,s{r}{j},t{r}{j},labelText{r}{j})
end
end
If you wish to plot your graph on every iteration, you can use subgraph for that:
for k = 1:height(P.Nodes)
H = subgraph(P,1:k);
figure;
h = plot(H);
c = 1;
out = false;
for r = 1: 10
if ~out
for j = 1:10
if c < k
labeledge(h,c,labelText{r}{j})
else
out = true;
break
end
c = c+1;
end
else
break
end
end
end
Besides that, you should know that (from Matlab documentation):
For the best performance, construct graphs all at once using a single call to graph. Adding nodes or edges in a loop can be slow for large graphs.
Also, regardless of the above recommendation, for an easier manipulation of your data, you should first convert your cells to an array. If your cell array contains a different number of elements in each cell, then it is better to collapse it all to one column:
C = [s{:}]; % and the same for t and w
while any(cellfun(#iscell,C))
C = vertcat(C{:});
end
C = cellfun(#(x) x(:),C,'UniformOutput', false);
S = vertcat(C{:});
Labels = [labelText{:}]; % and the same nodeName
while any(cellfun(#iscell,Labels))
Labels = vertcat(Labels{:});
end
Try to remove the 'figure;' command out of the FOR loop and try to see if this worked.
I have a plot with two intersecting parabolas. Is there a way that I can detect the two correctly and fit two parabolas, one through each of them? My current code fits only a single parabola:
x=-100:1:100;
y=(x.^2)/(4);
x1=-50:1:150;
y1=(x.^2)/(4);
x=[x,x1];
y=[y,y1];
f = fittype('((x)*(x))/4*p',...
'dependent',{'y'},'independent',{'x'},...
'coefficients',{'p'})
fit1= fit(x',y',f)
plot(fit1,x,y)
Following the suggestions I was able to write a code that runs ransac for curves
function [f1] = Ransac(x,y)
voting=[0,0,0,0];
for i=1:10000
[y1,idx] = datasample(y,5);
x1=x(idx);
p = polyfit(x1,y1,2);
f1 = polyval(p,x);
error=(((f1-y).^2));
error(error>100)=0;
indx =find(ismember(voting(:,1:3),'rows'),1);
if (isempty(indx)==1)
voting=[voting;[p,sum(error)]];
else
voting(indx,4) = voting(indx,4)+sum(error);
end
end
[s,t]=max(voting(:,4));
p=voting(t,1:3);
f1 = polyval(p,x);
end
I have 3 vectors: Y=rand(1000,1), X=Y-rand(1000,1) and ACTid=randi(6,1000,1).
I'd like to create boxplots by groups of Y and X corresponding to their group value 1:6 (from ACTid).
This is rather ad-hoc and looks nasty
for ii=
dummyY(ii)={Y(ACTid==ii)};
dummyX(ii)={X(ACTid==ii)}
end
Now I have the data in a cell but can't work out how to group it in a boxplot. Any thoughts?
I've found aboxplot function that looks like this but I don't want that, I'd like the builtin boxplot function because i'm converting it to matlab2tikz and this one doesn't do it well.
EDIT
Thanks to Oleg: we now have a grouped boxplot... but the labels are all skew-whiff.
xylabel = repmat({'Bleh','Blah'},1000,1); % need a legend instead, but doesn't appear possible
boxplot([Y(:,end); cfu], {repmat(ACTid,2,1), xylabel(:)} ,'factorgap',10,'color','rk')
set(gca,'xtick',1.5:3.2:50)
set(gca,'xticklabel',{'Direct care','Housekeeping','Mealtimes','Medication','Miscellaneous','Personal care'})
>> ylabel('Raw CFU counts (Y)')
How to add a legend?
I had the same problem with grouping data in a box plot. A further constraint of mine was that different groups have different amounts of data points. Based on a tutorial I found, this seems to be a nice solution I wanted to share with you:
x = [1,2,3,4,5,1,2,3,4,6];
group = [1,1,2,2,2,3,3,3,4,4];
positions = [1 1.25 2 2.25];
boxplot(x,group, 'positions', positions);
set(gca,'xtick',[mean(positions(1:2)) mean(positions(3:4)) ])
set(gca,'xticklabel',{'Direct care','Housekeeping'})
color = ['c', 'y', 'c', 'y'];
h = findobj(gca,'Tag','Box');
for j=1:length(h)
patch(get(h(j),'XData'),get(h(j),'YData'),color(j),'FaceAlpha',.5);
end
c = get(gca, 'Children');
hleg1 = legend(c(1:2), 'Feature1', 'Feature2' );
Here is a link to the tutorial.
A two-line approach (although if you want to retain two-line xlables and center those in the first line, it's gonna be hackish):
Y = rand(1000,1);
X = Y-rand(1000,1);
ACTid = randi(6,1000,1);
xylabel = repmat('xy',1000,1);
boxplot([X; Y], {repmat(ACTid,2,1), xylabel(:)} ,'factorgap',10)
The result:
EDIT
To center labels...
% Retrieve handles to text labels
h = allchild(findall(gca,'type','hggroup'));
% Delete x, y labels
throw = findobj(h,'string','x','-or','string','y');
h = setdiff(h,throw);
delete(throw);
% Center labels
mylbl = {'this','is','a','pain','in...','guess!'};
hlbl = findall(h,'type','text');
pos = cell2mat(get(hlbl,'pos'));
% New centered position for first intra-group label
newPos = num2cell([mean(reshape(pos(:,1),2,[]))' pos(1:2:end,2:end)],2);
set(hlbl(1:2:end),{'pos'},newPos,{'string'},mylbl')
% delete second intra-group label
delete(hlbl(2:2:end))
Exporting as .png will cause problems...
I am on a project thumb recognition system on matlab. I implemented Kmean Algorithm and I got results as well. Actually now I want to plot the results like here they done. I am trying but couldn't be able to do so. I am using the following code.
load training.mat; % loaded just to get trainingData variable
labelData = zeros(200,1);
labelData(1:100,:) = 0;
labelData(101:200,:) = 1;
k=2;
[trainCtr, traina] = kmeans(trainingData,k);
trainingResult1=[];
for i=1:k
trainingResult1 = [trainingResult1 sum(trainCtr(1:100)==i)];
end
trainingResult2=[];
for i=1:k
trainingResult2 = [trainingResult2 sum(trainCtr(101:200)==i)];
end
load testing.mat; % loaded just to get testingData variable
c1 = zeros(k,1054);
c1 = traina;
cluster = zeros(200,1);
for j=1:200
testTemp = repmat(testingData(j,1:1054),k,1);
difference = sum((c1 - testTemp).^2, 2);
[value index] = min(difference);
cluster(j,1) = index;
end
testingResult1 = [];
for i=1:k
testingResult1 = [testingResult1 sum(cluster(1:100)==i)];
end
testingResult2 = [];
for i=1:k
testingResult2 = [testingResult2 sum(cluster(101:200)==i)];
end
in above code trainingData is matrix of 200 X 1054 in which 200 are images of thumbs and 1054 are columns. actually each image is of 25 X 42. I reshaped each image in to row matrix (1 X 1050) and 4 other (some features) columns so total of 1054 columns are in each image. Similarly testingData I made it in the similar manner as I made testingData It is also the order of 200 X 1054. Now my Problem is just to plot the results as they did in here.
After selecting 2 features, you can just follow the example. Start a figure, use hold on, and use plot or scatter to plot the centroids and the data points. E.g.
selectedFeatures = [42,43];
plot(trainingData(trainCtr==1,selectedFeatures(1)),
trainingData(trainCtr==1,selectedFeatures(2)),
'r.','MarkerSize',12)
Would plot the selected feature values of the data points in cluster 1.