MATLAB: Return many value in function.

function

hi, i have this main function that called calc_centroid(data) as folow:

[cc1,cc2,cc3,cc4] = calc_centroid(data) %is it correct syntax to accept the return value?

Meanwhile in the function define as follow:

function [c1,c2,c3,c5] = calc_centroid(data)

k1_1= sum(data{1:10,{'Var1'}})/10

k1_2= sum(data{1:10,{'Var2'}})/10

k1_3= sum(data{1:10,{'Var3'}})/10

k1_4= sum(data{1:10,{'Var4'}})/10

k1_5= sum(data{1:10,{'Var5'}})/10

k1_6= sum(data{1:10,{'Var6'}})/10

k1_7= sum(data{1:10,{'Var7'}})/10

k1_8= sum(data{1:10,{'Var8'}})/10

k1_9= sum(data{1:10,{'Var9'}})/10

k1_10= sum(data{1:10,{'Var10'}})/10

k2_1= sum(data{11:20,{'Var1'}})/10

k2_2= sum(data{11:20,{'Var2'}})/10

k2_3= sum(data{11:20,{'Var3'}})/10

k2_4= sum(data{11:20,{'Var4'}})/10

k2_5= sum(data{11:20,{'Var5'}})/10

k2_6= sum(data{11:20,{'Var6'}})/10

k2_7= sum(data{11:20,{'Var7'}})/10

k2_8= sum(data{11:20,{'Var8'}})/10

k2_9= sum(data{11:20,{'Var9'}})/10

k2_10= sum(data{11:20,{'Var10'}})/10

k3_1= sum(data{21:30,{'Var1'}})/10

k3_2= sum(data{21:30,{'Var2'}})/10

k3_3= sum(data{21:30,{'Var3'}})/10

k3_4= sum(data{21:30,{'Var4'}})/10

k3_5= sum(data{21:30,{'Var5'}})/10

k3_6= sum(data{21:30,{'Var6'}})/10

k3_7= sum(data{21:30,{'Var7'}})/10

k3_8= sum(data{21:30,{'Var8'}})/10

k3_9= sum(data{21:30,{'Var9'}})/10

k3_10= sum(data{21:30,{'Var10'}})/10

k4_1= sum(data{31:40,{'Var1'}})/10

k4_2= sum(data{31:40,{'Var2'}})/10

k4_3= sum(data{31:40,{'Var3'}})/10

k4_4= sum(data{31:40,{'Var4'}})/10

k4_5= sum(data{31:40,{'Var5'}})/10

k4_6= sum(data{31:40,{'Var6'}})/10

k4_7= sum(data{31:40,{'Var7'}})/10

k4_8= sum(data{31:40,{'Var8'}})/10

k4_9= sum(data{31:40,{'Var9'}})/10

k4_10= sum(data{31:40,{'Var10'}})/10

c1 = [k1_1 k1_2 k1_3 k1_4 k1_5 k1_6 k1_7 k1_8 k1_9 k1_10]

c2 = [k2_1 k2_2 k2_3 k2_4 k2_5 k2_6 k2_7 k2_8 k2_9 k2_10]

c3 = [k3_1 k3_2 k3_3 k3_4 k3_5 k1_6 k3_7 k3_8 k3_9 k3_10]

c4 = [k4_1 k4_2 k4_3 k4_4 k4_5 k4_6 k4_7 k4_8 k4_9 k4_10]

end

however, there is error said :

Output argument "c5" (and maybe others) not assigned during call to "calc_centroid".

Error in mainKmeans (line 26)

[cc1,cc2,cc3,cc4] = calc_centroid(data)

emm..i never use c5, howcome the error stated c5? and do i use the return value correcly. BTW iam not sure how to solve this error. TQIA for your attention.Please do help me 🙁

Best Answer

You do have c5 in your function line. It currently says:

function [c1,c2,c3,c5] = calc_centroid(data)

Perhaps, just swapping that for a c4 will do the trick?

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MATLAB: How to create a diagonal matrix with the semibandwidth of 4

Hi Lin Hongbin

not sure if your picture contains some errors, A(3,1) looks suspicious to me...

look at this and tell me if it fits your wishes :)

a=1:10;
b=11:20;
c=21:30;
d=31:40;
n=length(a);
A=zeros(n);
% set the rest
for i=1:n % rows
        A(i,i:i+3)=[a(i),b(i),c(i),d(i)];
end
>> A
A =
     1    11    21    31     0     0     0     0     0     0     0     0     0
     0     2    12    22    32     0     0     0     0     0     0     0     0
     0     0     3    13    23    33     0     0     0     0     0     0     0
     0     0     0     4    14    24    34     0     0     0     0     0     0
     0     0     0     0     5    15    25    35     0     0     0     0     0
     0     0     0     0     0     6    16    26    36     0     0     0     0
     0     0     0     0     0     0     7    17    27    37     0     0     0
     0     0     0     0     0     0     0     8    18    28    38     0     0
     0     0     0     0     0     0     0     0     9    19    29    39     0
     0     0     0     0     0     0     0     0     0    10    20    30    40

if u cut off the first and last column it looks more like your picture (beside A(3,1) and A(3,4) etc is not d3 ...)

B=A(1:n,2:n+2)
B =
  11    21    31     0     0     0     0     0     0     0     0
   2    12    22    32     0     0     0     0     0     0     0
   0     3    13    23    33     0     0     0     0     0     0
   0     0     4    14    24    34     0     0     0     0     0
   0     0     0     5    15    25    35     0     0     0     0
   0     0     0     0     6    16    26    36     0     0     0
   0     0     0     0     0     7    17    27    37     0     0
   0     0     0     0     0     0     8    18    28    38     0
   0     0     0     0     0     0     0     9    19    29    39
   0     0     0     0     0     0     0     0    10    20    30

so either matrix A or B might be the answer.

But neither A nor B are quadratic. A has size n x n+3 and B: n x n+1

MATLAB: How to make average of set of data

[Edit to fix typo in image.]

This answer references your previous question, here.

I was able to extract the data from the curve.fig plot by renaming it .mat,

data = load('curve.mat');
x = data.hgS_070000.children(1).children.properties.XData;
y = data.hgS_070000.children(1).children.properties.YData;

Plotting the data shows that, over the first 10% of the x-data, there can be up to four duplicate y-values. However, because of the discreteness of the data and the fact that x isn't uniformly sampled, some of the "duplicate" values appear to missing.

Just for grins, I plotted the x and y data vs. their index, as well as diff(x) and diff(y): it appears that y comes from an analytical Gaussian curve, centered at 500, while x exhibits some odd discretization artifacts.

figure;
subplot(3, 1, 1);
hp1 = plot(x, y);
title('original data');
xlabel('x');
ylabel('y');
xlim([0, 2325]);
ylim([0, 12]);
subplot(3, 1, 2);
hp2 = plotyy(1:numel(x), x, 1:numel(y), y);
title('x and y vs. index')
xlabel('ind')
ylabel(hp2(1), 'x data');
ylabel(hp2(2), 'y data');
xlim(hp2(1), [0, 1000]);
xlim(hp2(2), [0, 1000]);
subplot(3, 1, 3);
hp3 = plotyy(2:numel(x), diff(x), 2:numel(y), diff(y));
title('diff(x) and diff(y) vs. index')
xlabel('ind')
ylabel(hp3(1), 'diff(x)');
ylabel(hp3(2), 'diff(y)');
xlim(hp3(1), [0, 1000]);
xlim(hp3(2), [0, 1000]);

In order to apply a summation or averaging filter to the y data, it's useful to fill in the gaps in x so that it is uniformly spaced, as addressed in your previous question. The difficulty lies in that x is non-monotonic, as evidenced by the 0 and negative values in the diff(x) plot. However, if we can get x to be uniformly spaced integers (with potentially repeated values), then we can convert x and y to parametric equations based on their common index, and interpolate that way, i.e. convert the ideal (non-Matlab form) y(x) to x(k) & y(k), and interpolate based on k, as in the previous question:

diffx = diff(x);
val = sign(diffx); 
len = abs(diffx) + 1; % add one to include diff==0 as new phase; need to subtract off cumsum in ind
ind = [0, cumsum(len) - cumsum(abs(val))] + 1; %  add one for 1-based indexing; note: x == X(ind);
n = ind(end); % note: numel(X) == sum(abs(diff(x))) + 1 == n;
mask = false(1, n-1); 
mask(ind(1:end-1)) = true; % ind(end) == numel(X), not start of new phase
diffX = val(cumsum(mask)); % cumsum(mask) gives the rle phase number, i.e. index into val
X = x(1) + cumsum([0, diffX]);
K = 1:numel(X);
Y = interp1(K(ind), y, K);

We're now free to play with a few different smoothing techniques. As an example, I'll show the results of summing, averaging, and then smoothing the averaged data using a moving-average filter:

subs = (X - min(X)) + 1;
Ysum = accumarray(subs(:), Y).';
Ymean = accumarray(subs(:), Y, [], @mean).';
windowSize = 10;
Yfilt = filter(ones(1, windowSize), windowSize, Ymean);

Plotting the results,

figure;
subplot(3, 1, 1);
hp1 = plot(x, y, '-', X, Y, '--');
title('original and interpolated data');
xlabel('x');
ylabel('y');
legend({'orig x-y', 'interp X-Y'});
xlim([0, 2325]);
ylim([0, 12]);
subplot(3, 1, 2);
hp2 = plot(x, y);
hold all;
plot(X, Y, '--');
plot(min(X):max(X), Ysum, '--');
plot(min(X):max(X), Ymean, '--');
plot(min(X):max(X), Yfilt, '--');
title('various Y schemes')
xlabel('X');
ylabel('{y, Y, Ysum, Ymean, Yfilt}');
xlim([0, 2325]);
ylim([0, 12]);
legend({'orig', 'interp', 'sum', 'mean', 'moving avg. (filt)'});
subplot(3, 1, 3);
hp3 = plotyy(2:numel(X), diff(X), 2:numel(Y), diff(Y));
% hp3 = plotyy(2:numel(x), diff(x), 2:numel(y), diff(y));
title('diff(X) and diff(Y) vs. index')
xlabel('ind')
ylabel(hp3(1), 'diff(X)');
ylim(hp3(1), [-10, 10]);
ylabel(hp3(2), 'diff(Y) ');

In the first subplot, we see that the interpolated data (dashed line) exactly matches the original data. In the second, we see that none of the smoothing schemes are terribly pretty, though which is "best" will depend on your application. Note that all have a jump where the data becomes monotonic. (You could use a larger running average filter to further smooth the mean data; using filtfilt might be wise to prevent a phase lag. However, since y seems to be analytical, I wonder if there is an analytical method you should be considering...) In the third subplot, we see that the step size in x is now exactly +1, 0, or -1, as desired in the previous question; because of the interpolation, however, the step size in y is no longer the analytical derivative of the Gaussian, but some discretely filtered variation of it.

I hope this helps you better analyze your problem. In general, once you've applied the interpolation scheme to get X and Y, you should find it trivial to apply any desired smoothing algorithm, though again, thinking about an analytical solution might be wise.

Please accept this answer if it helps, or let me know in the comments if I've missed something. (Note: I might not get back to you for a couple of days.)

Best Answer

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MATLAB: How to create a diagonal matrix with the semibandwidth of 4

MATLAB: How to make average of set of data

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