In the code shown, I want to save one parameter (fval) per iteration in one variable, but not sure how to do it. Can someone advise?
clear;
close all;
clc;
for i = 0 : 100
ii = i * 0.01;
options = optimset('Display','iter-detailed', ...
'Algorithm','interior point', ...
'Diagnostics','on');
options.TolCon = 0;
options.TolFun = 0;
[X,faval,exitfag,output,lambda,grad,hessian]=fmincon(#myfun9,0,[],[],[],[],ii,1,#mycon,options);
end;
If you want to create 101 separate variables for storing each value, that's not recommended. Pre-allocate an array for fval for storing the values of faval in each iteration as shown below:
fval = zeros(101,1); %Pre-allocating memory for fval
for k = 0 : 100
ii = k * 0.01;
options = optimset('Display','iter-detailed', ...
'Algorithm','interior point', ...
'Diagnostics','on');
options.TolCon = 0;
options.TolFun = 0;
[X,faval,exitfag,output,lambda,grad,hessian]=fmincon(#myfun9,0,[],[],[],[],ii,1, ...
#mycon,options);
%Storing value of faval in fval(k+1). Note that indexing starts from 1 in MATLAB
fval(k+1) = faval;
end
By the way, it seems from your code that you're not interested in the values of all other parameters i.e. X,exitfag,output,lambda,grad,hessi, because these parameters will be overwritten in each iteration. If you're not interested in these values. You can skip storing them using tilde (~). So you might also want to use the following instead:
[~,faval]=fmincon(#myfun9,0,[],[],[],[],ii,1, #mycon,options);
% exitfag, output, lambda, grad, hessi are skipped automatically as per fmincon doc
Suggested reading:
1. Dynamic Variables
2. Preallocation
3. Why does MATLAB have 1 based indexing?
4. Tilde as an Argument Placeholder
Related
I want to assign one value to a list of objects in Matlab without using a for-loop (In order to increase efficiency)
Basically this works:
for i=1:Nr_of_Objects
Objectlist(i,1).weight=0.2
end
But I would like something like this:
Objectlist(:,1).weight=0.2
Which is not working. I get this error:
Expected one output from a curly brace or dot indexing expression, but there were 5 results.
Writing an array to the right hand side is also not working.
I`m not very familiar with object oriented programming in Matlab, so I would be happy if someone could help me.
Your looking for the deal function:
S(1,1).a = 1
S(2,1).a = 2
S(1,2).a = 3
[S(:,1).a] = deal(4)
Now S(1,1).a and S(2,1).a equal to 4.
In matlab you can concatenate several output in one array using []. And deal(X) copies the single input to all the requested outputs.
So in your case:
[Objectlist(:,1).weight] = deal(0.2)
Should work.
Noticed that I'm not sure that it will be faster than the for loop since I don't know how the deal function is implemented.
EDIT: Benchmark
n = 1000000;
[S(1:n,1).a] = deal(1);
tic
for ii=1:n
S(ii,1).a = 2;
end
toc
% Elapsed time is 3.481088 seconds
tic
[S(1:n,1).a] = deal(2);
toc
% Elapsed time is 0.472028 seconds
Or with timeit
n = 1000000;
[S(1:n,1).a] = deal(1);
g = #() func1(S,n);
h = #() func2(S,n);
timeit(g)
% ans = 3.67
timeit(h)
% ans = 0.41
function func1(S,n)
for ii=1:n
S(ii,1).a = 2;
end
end
function func2(S,n)
[S(1:n,1).a] = deal(2);
end
So it seems that using the deal function reduce the computational time.
I am using fseminf and fmincon functions in my alghoritm. I use it this way
[x,fval,exitflag,output,lambda] = fseminf(f,x0,1,#seminfcon1,A,b,Aeq,beq,lb,ub);
As an output I get some information:
iterations: 5
funcCount: 21
lssteplength: 1
stepsize: 4.9851e-06
algorithm: 'active-set'
firstorderopt: 2.9906e-08
constrviolation: 1.1283e-10
I am interested in funcCount, I want to save it. In my other alghoritm I use this in a loop and want to sum all the funcCount that my alghoritm does. In order to do that I need to save at each iteration this funcCount and add it. How can I do that? output(2) for instance does not work.
output.funcCoun gives you the current funcCoun
output.lssteplength gives you the current lssteplength, so on
% l is the length of the iteration
% Initialize an 1D array to store funcCoun
funcCoun_per_iteration = zeros(1,l);
for i = 1:l
[x,fval,exitflag,output,lambda] = fseminf(f,x0,1,#seminfcon1,A,b,Aeq,beq,lb,ub);
funcCoun_per_iteration(i) = output.funcCoun;
end
To sum them all just use sum()
Total_funcCoun = sum(funcCoun_per_iteration)
But if you really only want the sum no need to store them you can do it directly as follow
% l is the length of the iteration
% Initialize Total_funcCoun to accumulate funcCoun
Total_funcCoun = 0;
for i = 1:l
[x,fval,exitflag,output,lambda] = fseminf(f,x0,1,#seminfcon1,A,b,Aeq,beq,lb,ub);
Total_funcCoun = Total_funcCoun + output.funcCoun;
end
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 have the following code which eventually outputs a graph and a 'groups' value. The results are dependent on a random function so can provide different results every times.
function [t seqBeliefs] = extendedHK(n, tol, adj)
%extendedHK Summary of function goes here
%Detailed explanation goes here
beliefs = rand(n,1);
seqBeliefs = beliefs; %NxT matrix
converge = 0;
step = 0
t = step
while converge ~= 1
step = step+1;
t = [t step];
A = zeros (n,n);
for i=1:1:n
for j=i:1:n
if abs(beliefs(i) - beliefs(j)) < tol && adj(i,j)==1
A(j,i)=1;
A(i,j)=1;
end
end
end
beliefs = A*beliefs./ sum(A,2);
seqBeliefs = [seqBeliefs beliefs];
if sum(abs(beliefs - seqBeliefs(:,step)))<1e-12
converge = 1;
end
end
groups = length(uniquetol(seqBeliefs(:,step), 1e-10))
plot(t,seqBeliefs)
end
In command window type
adj=random_graph(n)
I usually use n as 100 then call extendedHK function with same n then tol value (I usually choose between 0.1 and 0.4) and 'adj'
e.g. adj = random_graph(100)
extendedHK(100, 0.2, adj)
What I now need help with is running this function say 100 times, and taking an average of how many 'groups' are formed.
First, include the parameter "groups" in your function output. to do so, try this instead of the first line of your code:
function [t seqBeliefs groups] = extendedHK(n, tol, adj)
then save this function in a extendedHK.m file.
open another .m file, say console.m and write this:
results = zeros(1,100);
for i = 1:100
% set n, tol and adj inputs here <=
[~,~,out] = extendedHK(n, tol, adj);
results(1,i) = out;
end
avg = mean(results)
don't forget to define "results" out of the loop. since parameters that change size every loop, will make your code slow
(suppressing unnecessary outputs with ~ added later to this reply)
It is not clear if you need the current outputs of extendedHK:[t seqBeliefs]
Anyhow, you can add "groups" to the output and then from the console
N=100;
groups_vec = zeros(1,N);
for i=1:N
adj = random_graph(100);
[~,~,groups_vec(i)] = extendedHK(100, 0.2, adj);
end
groups_avr = mean(groups_vec);
note that if you use this code you won't be able to "see" the graphs as they will be cleared every loop iteration. you can do one of the following (1) add "figure;" before the plot command and then you will have 100 figures. (2) add "pause" to wait for key press between each graph. (3) add "hold on" to print all graphs on the same figure.
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');