Hi for my code I would like to know how to best save my variable column. column is 733x1. Ideally I would like to have
column1(y)=column, but I obtain the error:
Conversion to cell from logical is not possible.
in the inner loop. I find it difficult to access these stored values in overlap.
for i = 1:7
for y = 1:ydim % ydim = 436
%execute code %code produces different 'column' on each iteration
column1{y} = column; %'column' size 733x1 %altogether 436 sets of 'column'
end
overlap{i} = column1; %iterates 7 times.
end
Ideally I want overlap to store 7 variables saved that are (733x436).
Thanks.
I'm assuming column is calculated using a procedure where each column is dependent on the latter. If not, then there are very likely improvements that can be made to this:
column = zeros(733, 1); % Might not need this. Depends on you code.
all_columns = zeros(xdim, ydim); % Pre-allocate memory (always do this)
% Note that the first dimension is usually called x,
% and the second called y in MATLAB
overlap = cell(7, 1);
overlap(:) = {zeros(xdim, ydim)}; % Pre-allocate memory
for ii = 1:numel(overlap) % numel is better than length
for jj = 1:ydim % ii and jj are better than i and j
% several_lines_of_code_to_calculate_column
column = something;
all_columns(:, jj) = column;
end
overlap{ii} = all_columns;
end
You can access the variables in overlap like this: overlap{1}(1,1);. This will get the first element in the first cell. overlap{2} will get the entire matrix in the second cell.
You specified that you wanted 7 variables. Your code implies that you know that cells are better than assigning it to different variables (var1, var2 ...). Good! The solution with different variables is bad bad bad.
Instead of using a cell array, you could instead use a 3D-array. This might make processing later on faster, if you can vectorize stuff for instance.
This will be:
column = zeros(733, 1); % Might not need this. Depends on you code.
overlap = zeros(xdim, ydim, 7) % Pre-allocate memory for 3D-matrix
for ii = 1:7
for jj = 1:ydim
% several_lines_of_code_to_calculate_column
column = something;
all_column(:, jj, ii) = column;
end
end
Related
This question already has answers here:
matlab iterative filenames for saving
(4 answers)
Closed 2 years ago.
I have a large data set (vector) I want to split up in to n smaller sets to look at later with other scripts. I.e.if n = 10 I want to turn one 1x80000000 double in to ten 1x8000000 doubles. My thoughts are turn the original in to a n by m matrix then save each row of the matrix in to it's own vector, as follows.
%data-n-splitter
n = 10 %number of sections
L = length(data);
Ls = L/n;
Ls = floor(Ls);
Counter = 1;
%converting vector to matrix
datamatrix = zeros(n,Ls);
for k = 1:n
datamatrix(k,:) = data(Counter:Counter+ Ls - 1);
Counter = Counter + Ls;
end
How do I make matlab loop this part of the code n times:
%save each row of matrix as seperate vector
P1 = datamatrix(1,:);
P2 = datamatrix(2,:);
P3 = datamatrix(3,:);
P4 = datamatrix(4,:);
P5 = datamatrix(5,:);
P6 = datamatrix(6,:);
P7 = datamatrix(7,:);
P8 = datamatrix(8,:);
P9 = datamatrix(9,:);
P10 = datamatrix(10,:);
Example answer that I'm hoping for:
for k = 1:n
P('n') = datamatrix(n,:);
end
I've seen some articles about using cell arrays but the scripts I'm passing the variables to aren't set up for this so I'd rather not go down that route if possible.
There are several options:
use a struct, which comes closest to what you are hoping for,
use a cell, more convenient looping but no access over meaningful names,
use a higher-dimension matrix (in your case it is only 2D, but the same applies for 3D or higher). This is the most memory-efficient option.
To round this off, you could also use a table, which is a hybrid of a struct and a cell as you can use both notations to access it. There is no other benefit.
Now, how to do this? The simplest (and best) solution first: create a 2D matrix with reshape
Ary = 1:10; % I shrank your 1x80000000 array to 1x10 but you'll get the idea
%% create new structure
Mat = reshape(Ary,5,2);
%% access new structure (looping over columns)
for i = 1:size(Ary,2)
% access columns through slicing
ary_sct = Mat(:,i);
% do something
end
Pro: memory efficient (requires the same amount of memory as the initial array); easy looping
Con: only works if you can slice the initial array evenly
Next: create a cell
Ary = 1:10;
n = 2; % number of sections
L = floor(length(Ary)/n);
% allocate memory
C = cell(1,n);
%% create new structure
for i = 1:n
% access the content of a cell with {}
C{i} = Ary((i-1)*L+1:i*L);
end
%% access new structure (looping over entries)
for i = 1:length(C)
% access the content of a cell with {}
ary_sct = C{i};
% do something
end
Pro: You can store anything in a cell. Every data type and -- what is often more important -- of any dimension
Con: The accessing the content (through {}) or accessing the element (through ()) is a bit annoying if your are a beginner; each element require a memory overhead of about 60 bytes as those are pointers, which need to store the information where and on what they are pointing.
Next: use a struct
Ary = 1:10;
n = 2; % number of sections
L = floor(length(Ary)/n);
% create empty struct
S = struct();
%% create new structure
for i = 1:n
% create fieldname (must start with a character!)
fld = num2str(i,'F%d');
% write to field (note the brackets)
S.(fld) = Ary((i-1)*L+1:i*L);
end
%% access new structure (looping over fieldnames)
% get all field names
FlNms = fieldnames(S);
for i = 1:length(FldNames)
% access field names (this is a cell!)
fld = FldNms{i};
% access struct
ary_sct = S.(fld);
% do something
end
Pro: Field names are convenient to keep the overview of your data
Con: accessing field names in a loop is a bit tedious; each element require a memory overhead of about 60 bytes as those are pointers, which need to store the information where and on what they are pointing.
I have a data, which may be simulated in the following way:
N = 10^6;%10^8;
K = 10^4;%10^6;
subs = randi([1 K],N,1);
M = [randn(N,5) subs];
M(M<-1.2) = nan;
In other words, it is a matrix, where the last row is subscripts.
Now I want to calculate nanmean() for each subscript. Also I want to save number of rows for each subscript. I have a 'dummy' code for this:
uniqueSubs = unique(M(:,6));
avM = nan(numel(uniqueSubs),6);
for iSub = 1:numel(uniqueSubs)
tmpM = M(M(:,6)==uniqueSubs(iSub),1:5);
avM(iSub,:) = [nanmean(tmpM,1) size(tmpM,1)];
end
The problem is, that it is too slow. I want it to work for N = 10^8 and K = 10^6 (see commented part in the definition of these variables.
How can I find the mean of the data in a faster way?
This sounds like a perfect job for findgroups and splitapply.
% Find groups in the final column
G = findgroups(M(:,6));
% function to apply per group
fcn = #(group) [mean(group, 1, 'omitnan'), size(group, 1)];
% Use splitapply to apply fcn to each group in M(:,1:5)
result = splitapply(fcn, M(:, 1:5), G);
% Check
assert(isequaln(result, avM));
M = sortrows(M,6); % sort the data per subscript
IDX = diff(M(:,6)); % find where the subscript changes
tmp = find(IDX);
tmp = [0 ;tmp;size(M,1)]; % add start and end of data
for iSub= 2:numel(tmp)
% Calculate the mean over just a single subscript, store in iSub-1
avM2(iSub-1,:) = [nanmean(M(tmp(iSub-1)+1:tmp(iSub),1:5),1) tmp(iSub)-tmp(iSub-1)];tmp(iSub-1)];
end
This is some 60 times faster than your original code on my computer. The speed-up mainly comes from presorting the data and then finding all locations where the subscript changes. That way you do not have to traverse the full array each time to find the correct subscripts, but rather you only check what's necessary each iteration. You thus calculate the mean over ~100 rows, instead of first having to check in 1,000,000 rows whether each row is needed that iteration or not.
Thus: in the original you check numel(uniqueSubs), 10,000 in this case, whether all N, 1,000,000 here, numbers belong to a certain category, which results in 10^12 checks. The proposed code sorts the rows (sorting is NlogN, thus 6,000,000 here), and then loop once over the full array without additional checks.
For completion, here is the original code, along with my version, and it shows the two are the same:
N = 10^6;%10^8;
K = 10^4;%10^6;
subs = randi([1 K],N,1);
M = [randn(N,5) subs];
M(M<-1.2) = nan;
uniqueSubs = unique(M(:,6));
%% zlon's original code
avM = nan(numel(uniqueSubs),7); % add the subscript for comparison later
tic
uniqueSubs = unique(M(:,6));
for iSub = 1:numel(uniqueSubs)
tmpM = M(M(:,6)==uniqueSubs(iSub),1:5);
avM(iSub,:) = [nanmean(tmpM,1) size(tmpM,1) uniqueSubs(iSub)];
end
toc
%%%%% End of zlon's code
avM = sortrows(avM,7); % Sort for comparison
%% Start of Adriaan's code
avM2 = nan(numel(uniqueSubs),6);
tic
M = sortrows(M,6);
IDX = diff(M(:,6));
tmp = find(IDX);
tmp = [0 ;tmp;size(M,1)];
for iSub = 2:numel(tmp)
avM2(iSub-1,:) = [nanmean(M(tmp(iSub-1)+1:tmp(iSub),1:5),1) tmp(iSub)-tmp(iSub-1)];
end
toc %tic/toc should not be used for accurate timing, this is just for order of magnitude
%%%% End of Adriaan's code
all(avM(:,1:6) == avM2) % Do the comparison
% End of script
% Output
Elapsed time is 58.561347 seconds.
Elapsed time is 0.843124 seconds. % ~70 times faster
ans =
1×6 logical array
1 1 1 1 1 1 % i.e. the matrices are equal to one another
I wrote this matlab code in order to concatenate the results of the integration of all the columns of a matrix extracted form a multi matrix array.
"datimf" is a matrix composed by 100 matrices, each of 224*640, vertically concatenated.
In the first loop i select every single matrix.
In the second loop i integrate every single column of the selected matrix
obtaining a row of 640 elements.
The third loop must concatenate vertically all the lines previously calculated.
Anyway i got always a problem with the third loop. Where is the error?
singleframe = zeros(224,640);
int_frame_all = zeros(1,640);
conc = zeros(100,640);
for i=0:224:(22400-224)
for j = 1:640
for k = 1:100
singleframe(:,:) = datimf([i+1:(i+223)+1],:);
int_frame_all(:,j) = trapz(singleframe(:,j));
conc(:,k) = vertcat(int_frame_all);
end
end
end
An alternate way to do this without using any explicit loops (edited in response to rayryeng's comment below. It's also worth noting that using cellfun may not be more efficient than explicitly looping.):
nmats = 100;
nrows = 224;
ncols = 640;
datimf = rand(nmats*nrows, ncols);
% convert to an nmats x 1 cell array containing each matrix
cellOfMats = mat2cell(datimf, ones(1, nmats)*nrows, ncols);
% Apply trapz to the contents of each cell
cellOfIntegrals = cellfun(#trapz, cellOfMats, 'UniformOutput', false);
% concatenate the results
conc = cat(1, cellOfIntegrals{:});
Taking inspiration from user2305193's answer, here's an even better "loop-free" solution, based on reshaping the matrix and applying trapz along the appropriate dimension:
datReshaped = reshape(datimf, nrows, nmats, ncols);
solution = squeeze(trapz(datReshaped, 1));
% verify solutions are equivalent:
all(solution(:) == conc(:)) % ans = true
I think I understand what you want. The third loop is unnecessary as both the inner and outer loops are 100 elements long. Also the way you have it you are assigning singleframe lots more times than necessary since it does not depend on the inner loops j or k. You were also trying to add int_frame_all to conc before int_frame_all was finished being populated.
On top of that the j loop isn't required either since trapz can operate on the entire matrix at once anyway.
I think this is closer to what you intended:
datimf = rand(224*100,640);
singleframe = zeros(224,640);
int_frame_all = zeros(1,640);
conc = zeros(100,640);
for i=1:100
idx = (i-1)*224+1;
singleframe(:,:) = datimf(idx:idx+223,:);
% for j = 1:640
% int_frame_all(:,j) = trapz(singleframe(:,j));
% end
% The loop is uncessary as trapz can operate on the entire matrix at once.
int_frame_all = trapz(singleframe,1);
%I think this is what you really want...
conc(i,:) = int_frame_all;
end
It looks like you're processing frames in a video.
The most efficent approach in my experience would be to reshape datimf to be 3-dimensional. This can easily be achieved with the reshape command.
something along the line of vid=reshape(datimf,224,640,[]); should get you far in this regard, where the 3rd dimension is time. vid(:,:,1) then would display the first frame of the video.
I am trying to concatenate several structs. What I take from each struct depends on a function that requires a for loop. Here is my simplified array:
t = 1;
for t = 1:5 %this isn't the for loop I am asking about
a(t).data = t^2; %it just creates a simple struct with 5 data entries
end
Here I am doing concatenation manually:
A = [a(1:2).data a(1:3).data a(1:4).data a(1:5).data] %concatenation function
As you can see, the range (1:2), (1:3), (1:4), and (1:5) can be looped, which I attempt to do like this:
t = 2;
A = [for t = 2:5
a(1:t).data
end]
This results in an error "Illegal use of reserved keyword "for"."
How can I do a for loop within the concatenate function? Can I do loops within other functions in Matlab? Is there another way to do it, other than copy/pasting the line and changing 1 number manually?
You were close to getting it right! This will do what you want.
A = []; %% note: no need to initialize t, the for-loop takes care of that
for t = 2:5
A = [A a(1:t).data]
end
This seems strange though...you are concatenating the same elements over and over...in this example, you get the result:
A =
1 4 1 4 9 1 4 9 16 1 4 9 16 25
If what you really need is just the .data elements concatenated into a single array, then that is very simple:
A = [a.data]
A couple of notes about this: why are the brackets necessary? Because the expressions
a.data, a(1:t).data
don't return all the numbers in a single array, like many functions do. They return a separate answer for each element of the structure array. You can test this like so:
>> [b,c,d,e,f] = a.data
b =
1
c =
4
d =
9
e =
16
f =
25
Five different answers there. But MATLAB gives you a cheat -- the square brackets! Put an expression like a.data inside square brackets, and all of a sudden those separate answers are compressed into a single array. It's magic!
Another note: for very large arrays, the for-loop version here will be very slow. It would be better to allocate the memory for A ahead of time. In the for-loop here, MATLAB is dynamically resizing the array each time through, and that can be very slow if your for-loop has 1 million iterations. If it's less than 1000 or so, you won't notice it at all.
Finally, the reason that HBHB could not run your struct creating code at the top is that it doesn't work unless a is already defined in your workspace. If you initialize a like this:
%% t = 1; %% by the way, you don't need this, the t value is overwritten by the loop below
a = []; %% always initialize!
for t = 1:5 %this isn't the for loop I am asking about
a(t).data = t^2; %it just creates a simple struct with 5 data entries
end
then it runs for anyone the first time.
As an appendix to gariepy's answer:
The matrix concatenation
A = [A k];
as a way of appending to it is actually pretty slow. You end up reassigning N elements every time you concatenate to an N size vector. If all you're doing is adding elements to the end of it, it is better to use the following syntax
A(end+1) = k;
In MATLAB this is optimized such that on average you only need to reassign about 80% of the elements in a matrix. This might not seam much, but for 10k elements this adds up to ~ an order of magnitude of difference in time (at least for me).
Bare in mind that this works only in MATLAB 2012b and higher as described in this thead: Octave/Matlab: Adding new elements to a vector
This is the code I used. tic/toc syntax is not the most accurate method for profiling in MATLAB, but it illustrates the point.
close all; clear all; clc;
t_cnc = []; t_app = [];
N = 1000;
for n = 1:N;
% Concatenate
tic;
A = [];
for k = 1:n;
A = [A k];
end
t_cnc(end+1) = toc;
% Append
tic;
A = [];
for k = 1:n;
A(end+1) = k;
end
t_app(end+1) = toc;
end
t_cnc = t_cnc*1000; t_app = t_app*1000; % Convert to ms
% Fit a straight line on a log scale
P1 = polyfit(log(1:N),log(t_cnc),1); P_cnc = #(x) exp(P1(2)).*x.^P1(1);
P2 = polyfit(log(1:N),log(t_app),1); P_app = #(x) exp(P2(2)).*x.^P2(1);
% Plot and save
loglog(1:N,t_cnc,'.',1:N,P_cnc(1:N),'k--',...
1:N,t_app,'.',1:N,P_app(1:N),'k--');
grid on;
xlabel('log(N)');
ylabel('log(Elapsed time / ms)');
title('Concatenate vs. Append in MATLAB 2014b');
legend('A = [A k]',['O(N^{',num2str(P1(1)),'})'],...
'A(end+1) = k',['O(N^{',num2str(P2(1)),'})'],...
'Location','northwest');
saveas(gcf,'Cnc_vs_App_test.png');
I am refering to an example like this
I have a function to analize the elements of a vector, 'input'. If these elements have a special property I store their values in a vector, 'output'.
The problem is that at the begging I don´t know the number of elements it will need to store in 'output'so I don´t know its size.
I have a loop, inside I go around the vector, 'input' through an index. When I consider special some element of this vector capture the values of 'input' and It be stored in a vector 'ouput' through a sentence like this:
For i=1:N %Where N denotes the number of elements of 'input'
...
output(j) = input(i);
...
end
The problem is that I get an Error if I don´t previously "declare" 'output'. I don´t like to "declare" 'output' before reach the loop as output = input, because it store values from input in which I am not interested and I should think some way to remove all values I stored it that don´t are relevant to me.
Does anyone illuminate me about this issue?
Thank you.
How complicated is the logic in the for loop?
If it's simple, something like this would work:
output = input ( logic==true )
Alternatively, if the logic is complicated and you're dealing with big vectors, I would preallocate a vector that stores whether to save an element or not. Here is some example code:
N = length(input); %Where N denotes the number of elements of 'input'
saveInput = zeros(1,N); % create a vector of 0s
for i=1:N
...
if (input meets criteria)
saveInput(i) = 1;
end
end
output = input( saveInput==1 ); %only save elements worth saving
The trivial solution is:
% if input(i) meets your conditions
output = [output; input(i)]
Though I don't know if this has good performance or not
If N is not too big so that it would cause you memory problems, you can pre-assign output to a vector of the same size as input, and remove all useless elements at the end of the loop.
output = NaN(N,1);
for i=1:N
...
output(i) = input(i);
...
end
output(isnan(output)) = [];
There are two alternatives
If output would be too big if it was assigned the size of N, or if you didn't know the upper limit of the size of output, you can do the following
lengthOutput = 100;
output = NaN(lengthOutput,1);
counter = 1;
for i=1:N
...
output(counter) = input(i);
counter = counter + 1;
if counter > lengthOutput
%# append output if necessary by doubling its size
output = [output;NaN(lengthOutput,1)];
lengthOutput = length(output);
end
end
%# remove unused entries
output(counter:end) = [];
Finally, if N is small, it is perfectly fine to call
output = [];
for i=1:N
...
output = [output;input(i)];
...
end
Note that performance degrades dramatically if N becomes large (say >1000).