I'd like to declare first of all, that I'm a mathematician. This might be a stupid stupid question; but I've gone through all the matlab tutorials--they've gotten me nowhere. I imagine I could code this in C (it'd be exhausting); but I need matlab for this particular function. And I don't get exactly how to do it.
Here is the pasted Matlab code of where I'm running into trouble:
function y = TAU(z,n)
y=0;
for i =[1,n]
y(z) = log(beta(z+1,i) + y(z+1)) - beta(z,i);
end
end
(beta is an arbitrary "float" to "float" function with an index i.)
I'm having trouble declaring y as a function, in which we call the function at a different argument. I want to define y_n(z) with something something y_{n-1}(z+1). This is all done in a recursive process to create the function. I really feel like I'm missing something stupid.
As a default function it assigns y to be an array (or whatever you call the default index assignment). But I don't want an array. I want y to be assigned as a "function" class (i.e. takes "float" to "float"). And then I'm defining a sequence of y_n : "float" to "float". So that z to z+1 is a map on "float" to "float".
I don't know if I'm asking too much of matlab...
Help a poor mathematician who hasn't coded since the glory days of X-box mods.
...Please don't tell me I have to go back to Pari-GP/C drawing boards over something so stupid.
Please help!
EDIT: At rahnema1 & mimocha's request, I'll describe the math, and of what I am trying to do with my program. I can't see how to implement latex in here. So I'll write the latex code in a generator and upload a picture. I'm not so sure if there even is a work around to what I want to do.
As to the expected output. We'd want,
beta(z+1,i) + TAU(z+1,i) = exp(beta(z,i) + TAU(z,i+1))
And we want to grow i to a fixed value n. Again, I haven't programmed in forever, so I apologize if I'm speaking a little nonsensically.
EDIT2:
So, as #rahnema1 suggests; I should produce a reproducible example. In order to do this, I'll write the code for my beta function. It's surprisingly simple. This is for the case where the "multiplier" variable is set to log(2); but you don't need to worry about any of that.
function f = beta(z,n)
f=0;
for i = 0:n-1
f = exp(f)/(1+exp(log(2)*(n-i-z)));
end
end
This will work fine for z a float no greater than 4. Once you make z larger it'll start to overflow. So for example, if you put in,
beta(2,100)
1.4242
beta(3,100)
3.3235
beta(3,100) - exp(beta(2,100))/(1/4+1)
0
The significance of the 100, is simply how many iterations we perform; it converges fast so even setting this to 15 or so will still produce the same numerical accuracy. Now, the expected output I want for TAU is pretty straight forward,
TAU(z,1) = log(beta(z+1,1)) - beta(z,1)
TAU(z,2) = log(beta(z+1,2) + TAU(z+1,1)) - beta(z,2)
TAU(z,3) = log(beta(z+1,3) + TAU(z+1,2)) - beta(z,3)
...
TAU(z,n) = log(beta(z+1,n) + TAU(z+1,n-1)) -beta(z,n)
I hope this helps. I feel like there should be an easy way to program this sequence, and I must be missing something obvious; but maybe it's just not possible in Matlab.
At mimocha's suggestion, I'll look into tail-end recursion. I hope to god I don't have to go back to Pari-gp; but it looks like I may have to. Not looking forward to doing a deep dive on that language, lol.
Thanks, again!
Is this what you are looking for?
function out = tau(z,n)
% Ends recursion when n == 1
if n == 1
out = log(beta(z+1,1)) - beta(z,1);
return
end
out = log(beta(z+1,n) + tau(z+1,n-1)) - beta(z,n);
end
function f = beta(z,n)
f = 0;
for i = 0:n-1
f = exp(f) / (1 + exp(log(2)*(n-i-z)));
end
end
This is basically your code from the most recent edit, but I've added a simple catch in the tau function. I tried running your code and noticed that n gets decremented infinitely (no exit condition).
With the modification, the code runs successfully on my laptop for smaller integer values of n, where 1e5 > n >= 1; and for floating values of z, real and complex. So the code will unfortunately break for floating values of n, since I don't know what values to return for, say, tau(1,0) or tau(1,0.9). This should easily be fixable if you know the math though.
However, many of the values I get are NaNs or Infs. So I'm not sure if your original problem was Out of memory error (infinite recursion), or values blowing up to infinity / NaN (numerical stability issue).
Here is a quick 100x100 grid calculation I made with this code.
Then I tested on negative values of z, and found the imaginary part of the output to looks kinda cool.
Not to mention I'm slightly geeking out over the fact that pi is showing up in the imaginary part as well :)
tau(-0.3,2) == -1.45179335740446147085 +3.14159265358979311600i
I have a function which does the following loop many, many times:
for cluster=1:max(bins), % bins is a list in the same format as kmeans() IDX output
select=bins==cluster; % find group of values
means(select,:)=repmat_fast_spec(meanOneIn(x(select,:)),sum(select),1);
% (*, above) for each point, write the mean of all points in x that
% share its label in bins to the equivalent row of means
delta_x(select,:)=x(select,:)-(means(select,:));
%subtract out the mean from each point
end
Noting that repmat_fast_spec and meanOneIn are stripped-down versions of repmat() and mean(), respectively, I'm wondering if there's a way to do the assignment in the line labeled (*) that avoids repmat entirely.
Any other thoughts on how to squeeze performance out of this thing would also be welcome.
Here is a possible improvement to avoid REPMAT:
x = rand(20,4);
bins = randi(3,[20 1]);
d = zeros(size(x));
for i=1:max(bins)
idx = (bins==i);
d(idx,:) = bsxfun(#minus, x(idx,:), mean(x(idx,:)));
end
Another possibility:
x = rand(20,4);
bins = randi(3,[20 1]);
m = zeros(max(bins),size(x,2));
for i=1:max(bins)
m(i,:) = mean( x(bins==i,:) );
end
dd = x - m(bins,:);
One obvious way to speed up calculation in MATLAB is to make a MEX file. You can compile C code and perform any operations you want. If you're searching for the fastest-possible performance, turning the operation into a custom MEX file would likely be the way to go.
You may be able to get some improvement by using ACCUMARRAY.
%# gather array sizes
[nPts,nDims] = size(x);
nBins = max(bins);
%# calculate means. Not sure whether it might be faster to loop over nDims
meansCell = accumarray(bins,1:nPts,[nBins,1],#(idx){mean(x(idx,:),1)},{NaN(1,nDims)});
means = cell2mat(meansCell);
%# subtract cluster means from x - this is how you can avoid repmat in your code, btw.
%# all you need is the array with cluster means.
delta_x = x - means(bins,:);
First of all: format your code properly, surround any operator or assignment by whitespace. I find your code very hard to comprehend as it looks like a big blob of characters.
Next of all, you could follow the other responses and convert the code to C (mex) or Java, automatically or manually, but in my humble opinion this is a last resort. You should only do such things when your performance is not there yet by a small margin. On the other hand, your algorithm doesn't show obvious flaws.
But the first thing you should do when trying to improve performance: profile. Use the MATLAB profiler to determine which part of your code is causing your problems. How much would you need to improve this to meet your expectations? If you don't know: first determine this boundary, otherwise you will be looking for a needle in a hay stack which might not even be in there in the first place. MATLAB will never be the fastest kid on the block with respect to runtime, but it might be the fastest with respect to development time for certain kinds of operations. In that respect, it might prove useful to sacrifice the clarity of MATLAB over the execution speed of other languages (C or even Java). But in the same respect, you might as well code everything in assembler to squeeze all of the performance out of the code.
Another obvious way to speed up calculation in MATLAB is to make a Java library (similar to #aardvarkk's answer) since MATLAB is built on Java and has very good integration with user Java libraries.
Java's easier to interface and compile than C. It might be slower than C in some cases, but the just-in-time (JIT) compiler in the Java virtual machine generally speeds things up very well.
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I think everyone would agree that the MATLAB language is not pretty, or particularly consistent. But nevermind! We still have to use it to get things done.
What are your favourite tricks for making things easier? Let's have one per answer so people can vote them up if they agree. Also, try to illustrate your answer with an example.
Using the built-in profiler to see where the hot parts of my code are:
profile on
% some lines of code
profile off
profile viewer
or just using the built in tic and toc to get quick timings:
tic;
% some lines of code
toc;
Directly extracting the elements of a matrix that satisfy a particular condition, using logical arrays:
x = rand(1,50) .* 100;
xpart = x( x > 20 & x < 35);
Now xpart contains only those elements of x which lie in the specified range.
Provide quick access to other function documentation by adding a "SEE ALSO" line to the help comments. First, you must include the name of the function in all caps as the first comment line. Do your usual comment header stuff, then put SEE ALSO with a comma separated list of other related functions.
function y = transmog(x)
%TRANSMOG Transmogrifies a matrix X using reverse orthogonal eigenvectors
%
% Usage:
% y = transmog(x)
%
% SEE ALSO
% UNTRANSMOG, TRANSMOG2
When you type "help transmog" at the command line, you will see all the comments in this comment header, with hyperlinks to the comment headers for the other functions listed.
Turn a matrix into a vector using a single colon.
x = rand(4,4);
x(:)
Vectorizing loops. There are lots of ways to do this, and it is entertaining to look for loops in your code and see how they can be vectorized. The performance is astonishingly faster with vector operations!
Anonymous functions, for a few reasons:
to make a quick function for one-off uses, like 3x^2+2x+7. (see listing below) This is useful for functions like quad and fminbnd that take functions as arguments. It's also convenient in scripts (.m files that don't start with a function header) since unlike true functions you can't include subfunctions.
for closures -- although anonymous functions are a little limiting as there doesn't seem to be a way to have assignment within them to mutate state.
.
% quick functions
f = #(x) 3*x.^2 + 2*x + 7;
t = (0:0.001:1);
plot(t,f(t),t,f(2*t),t,f(3*t));
% closures (linfunc below is a function that returns a function,
% and the outer functions arguments are held for the lifetime
% of the returned function.
linfunc = #(m,b) #(x) m*x+b;
C2F = linfunc(9/5, 32);
F2C = linfunc(5/9, -32*5/9);
Matlab's bsxfun, arrayfun, cellfun, and structfun are quite interesting and often save a loop.
M = rand(1000, 1000);
v = rand(1000, 1);
c = bsxfun(#plus, M, v);
This code, for instance, adds column-vector v to each column of matrix M.
Though, in performance critical parts of your application you should benchmark these functions versus the trivial for-loop because often loops are still faster.
LaTeX mode for formulas in graphs: In one of the recent releases (R2006?) you add the additional arguments ,'Interpreter','latex' at the end of a function call and it will use LaTeX rendering. Here's an example:
t=(0:0.001:1);
plot(t,sin(2*pi*[t ; t+0.25]));
xlabel('t');
ylabel('$\hat{y}_k=sin 2\pi (t+{k \over 4})$','Interpreter','latex');
legend({'$\hat{y}_0$','$\hat{y}_1$'},'Interpreter','latex');
Not sure when they added it, but it works with R2006b in the text(), title(), xlabel(), ylabel(), zlabel(), and even legend() functions. Just make sure the syntax you are using is not ambiguous (so with legend() you need to specify the strings as a cell array).
Using xlim and ylim to draw vertical and horizontal lines. Examples:
Draw a horizontal line at y=10:
line(xlim, [10 10])
Draw vertical line at x=5:
line([5 5], ylim)
Here's a quick example:
I find the comma separated list syntax quite useful for building function calls:
% Build a list of args, like so:
args = {'a', 1, 'b', 2};
% Then expand this into arguments:
output = func(args{:})
Here's a bunch of nonobvious functions that are useful from time to time:
mfilename (returns the name of the currently running MATLAB script)
dbstack (gives you access to the names & line numbers of the matlab function stack)
keyboard (stops execution and yields control to the debugging prompt; this is why there's a K in the debug prompt K>>
dbstop error (automatically puts you in debug mode stopped at the line that triggers an error)
I like using function handles for lots of reasons. For one, they are the closest thing I've found in MATLAB to pointers, so you can create reference-like behavior for objects. There are a few neat (and simpler) things you can do with them, too. For example, replacing a switch statement:
switch number,
case 1,
outargs = fcn1(inargs);
case 2,
outargs = fcn2(inargs);
...
end
%
%can be turned into
%
fcnArray = {#fcn1, #fcn2, ...};
outargs = fcnArray{number}(inargs);
I just think little things like that are cool.
Using nargin to set default values for optional arguments and using nargout to set optional output arguments. Quick example
function hLine=myplot(x,y,plotColor,markerType)
% set defaults for optional paramters
if nargin<4, markerType='none'; end
if nargin<3, plotColor='k'; end
hL = plot(x,y,'linetype','-', ...
'color',plotColor, ...
'marker',markerType, ...
'markerFaceColor',plotColor,'markerEdgeColor',plotColor);
% return handle of plot object if required
if nargout>0, hLine = hL; end
Invoking Java code from Matlab
cellfun and arrayfun for automated for loops.
Oh, and reverse an array
v = 1:10;
v_reverse = v(length(v):-1:1);
conditional arguments in the left-hand side of an assignment:
t = (0:0.005:10)';
x = sin(2*pi*t);
x(x>0.5 & t<5) = 0.5;
% This limits all values of x to a maximum of 0.5, where t<5
plot(t,x);
Know your axis properties! There are all sorts of things you can set to tweak the default plotting properties to do what you want:
set(gca,'fontsize',8,'linestyleorder','-','linewidth',0.3,'xtick',1:2:9);
(as an example, sets the fontsize to 8pt, linestyles of all new lines to all be solid and their width 0.3pt, and the xtick points to be [1 3 5 7 9])
Line and figure properties are also useful, but I find myself using axis properties the most.
Be strict with specifying dimensions when using aggregation functions like min, max, mean, diff, sum, any, all,...
For instance the line:
reldiff = diff(a) ./ a(1:end-1)
might work well to compute relative differences of elements in a vector, however in case the vector degenerates to just one element the computation fails:
>> a=rand(1,7);
>> diff(a) ./ a(1:end-1)
ans =
-0.5822 -0.9935 224.2015 0.2708 -0.3328 0.0458
>> a=1;
>> diff(a) ./ a(1:end-1)
??? Error using ==> rdivide
Matrix dimensions must agree.
If you specify the correct dimensions to your functions, this line returns an empty 1-by-0 matrix, which is correct:
>> diff(a, [], 2) ./ a(1, 1:end-1)
ans =
Empty matrix: 1-by-0
>>
The same goes for a min-function which usually computes minimums over columns on a matrix, until the matrix only consists of one row. - Then it will return the minimum over the row unless the dimension parameter states otherwise, and probably break your application.
I can almost guarantee you that consequently setting the dimensions of these aggregation functions will save you quite some debugging work later on.
At least that would have been the case for me. :)
The colon operator for the manipulation of arrays.
#ScottieT812, mentions one: flattening an array, but there's all the other variants of selecting bits of an array:
x=rand(10,10);
flattened=x(:);
Acolumn=x(:,10);
Arow=x(10,:);
y=rand(100);
firstSix=y(1:6);
lastSix=y(end-5:end);
alternate=y(1:2:end);
In order to be able to quickly test a function, I use nargin like so:
function result = multiply(a, b)
if nargin == 0 %no inputs provided, run using defaults for a and b
clc;
disp('RUNNING IN TEST MODE')
a = 1;
b = 2;
end
result = a*b;
Later on, I add a unit test script to test the function for different input conditions.
Using ismember() to merge data organized by text identfiers. Useful when you are analyzing differing periods when entries, in my case company symbols, come and go.
%Merge B into A based on Text identifiers
UniverseA = {'A','B','C','D'};
UniverseB = {'A','C','D'};
DataA = [20 40 60 80];
DataB = [30 50 70];
MergeData = NaN(length(UniverseA),2);
MergeData(:,1) = DataA;
[tf, loc] = ismember(UniverseA, UniverseB);
MergeData(tf,2) = DataB(loc(tf));
MergeData =
20 30
40 NaN
60 50
80 70
Asking 'why' (useful for jarring me out of a Matlab runtime-fail debugging trance at 3am...)
Executing a Simulink model directly from a script (rather than interactively) using the sim command. You can do things like take parameters from a workspace variable, and repeatedly run sim in a loop to simulate something while varying the parameter to see how the behavior changes, and graph the results with whatever graphical commands you like. Much easier than trying to do this interactively, and it gives you much more flexibility than the Simulink "oscilloscope" blocks when visualizing the results. (although you can't use it to see what's going on in realtime while the simulation is running)
A really important thing to know is the DstWorkspace and SrcWorkspace options of the simset command. These control where the "To Workspace" and "From Workspace" blocks get and put their results. Dstworkspace defaults to the current workspace (e.g. if you call sim from inside a function the "To Workspace" blocks will show up as variables accessible from within that same function) but SrcWorkspace defaults to the base workspace and if you want to encapsulate your call to sim you'll want to set SrcWorkspace to current so there is a clean interface to providing/retrieving simulation input parameters and outputs. For example:
function Y=run_my_sim(t,input1,params)
% runs "my_sim.mdl"
% with a From Workspace block referencing I1 as an input signal
% and parameters referenced as fields of the "params" structure
% and output retrieved from a To Workspace block with name O1.
opt = simset('SrcWorkspace','current','DstWorkspace','current');
I1 = struct('time',t,'signals',struct('values',input1,'dimensions',1));
Y = struct;
Y.t = sim('my_sim',t,opt);
Y.output1 = O1.signals.values;
Contour plots with [c,h]=contour and clabel(c,h,'fontsize',fontsize). I usually use the fontsize parameter to reduce the font size so the numbers don't run into each other. This is great for viewing the value of 2-D functions without having to muck around with 3D graphs.
Vectorization:
function iNeedle = findClosest(hay,needle)
%FINDCLOSEST find the indicies of the closest elements in an array.
% Given two vectors [A,B], findClosest will find the indicies of the values
% in vector A closest to the values in vector B.
[hay iOrgHay] = sort(hay(:)'); %#ok must have row vector
% Use histogram to find indices of elements in hay closest to elements in
% needle. The bins are centered on values in hay, with the edges on the
% midpoint between elements.
[iNeedle iNeedle] = histc(needle,[-inf hay+[diff(hay)/2 inf]]); %#ok
% Reversing the sorting.
iNeedle = iOrgHay(iNeedle);
Using persistent (static) variables when running an online algorithm. It may speed up the code in areas like Bayesian machine learning where the model is trained iteratively for the new samples. For example, for computing the independent loglikelihoods, I compute the loglikelihood initially from scratch and update it by summing this previously computed loglikelihood and the additional loglikelihood.
Instead of giving a more specialized machine learning problem, let me give a general online averaging code which I took from here:
function av = runningAverage(x)
% The number of values entered so far - declared persistent.
persistent n;
% The sum of values entered so far - declared persistent.
persistent sumOfX;
if x == 'reset' % Initialise the persistent variables.
n = 0;
sumOfX = 0;
av = 0;
else % A data value has been added.
n = n + 1;
sumOfX = sumOfX + x;
av = sumOfX / n; % Update the running average.
end
Then, the calls will give the following results
runningAverage('reset')
ans = 0
>> runningAverage(5)
ans = 5
>> runningAverage(10)
ans = 7.5000
>> runningAverage(3)
ans = 6
>> runningAverage('reset')
ans = 0
>> runningAverage(8)
ans = 8
I'm surprised that while people mentioned the logical array approach of indexing an array, nobody mentioned the find command.
e.g. if x is an NxMxO array
x(x>20) works by generating an NxMxO logical array and using it to index x (which can be bad if you have large arrays and are looking for a small subset
x(find(x>20)) works by generating list (i.e. 1xwhatever) of indices of x that satisfy x>20, and indexing x by it. "find" should be used more than it is, in my experience.
More what I would call 'tricks'
you can grow/append to arrays and cell arrays if you don't know the size you'll need, by using end + 1 (works with higher dimensions too, so long as the dimensions of the slice match -- so you'll have to initialize x to something other than [] in that case). Not good for numerics but for small dynamic lists of things (or cell arrays), e.g. parsing files.
e.g.
>> x=[1,2,3]
x = 1 2 3
>> x(end+1)=4
x = 1 2 3 4
Another think many people don't know is that for works on any dim 1 array, so to continue the example
>> for n = x;disp(n);end
1
2
3
4
Which means if all you need is the members of x you don't need to index them.
This also works with cell arrays but it's a bit annoying because as it walks them the element is still wrapped in a cell:
>> for el = {1,2,3,4};disp(el);end
[1]
[2]
[3]
[4]
So to get at the elements you have to subscript them
>> for el = {1,2,3,4};disp(el{1});end
1
2
3
4
I can't remember if there is a nicer way around that.
-You can make a Matlab shortcut to an initialization file called startup.m. Here, I define formatting, precision of the output, and plot parameters for my Matlab session (for example, I use a larger plot axis/font size so that .fig's can be seen plainly when I put them in presentations.) See a good blog post from one of the developers about it http://blogs.mathworks.com/loren/2009/03/03/whats-in-your-startupm/ .
-You can load an entire numerical ascii file using the "load" function. This isn't particularly fast, but gets the job done quickly for prototyping (shouldn't that be the Matlab motto?)
-As mentioned, the colon operator and vectorization are lifesavers. Screw loops.
x=repmat([1:10],3,1); % say, x is an example array of data
l=x>=3; % l is a logical vector (1s/0s) to highlight those elements in the array that would meet a certain condition.
N=sum(sum(l));% N is the number of elements that meet that given condition.
cheers -- happy scripting!