Can somebody explain to me the meaning of the # (function handle) operator and why to use it?
The function handle operator in MATLAB acts essentially like a pointer to a specific instance of a function. Some of the other answers have discussed a few of its uses, but I'll add another use here that I often have for it: maintaining access to functions that are no longer "in scope".
For example, the following function initializes a value count, and then returns a function handle to a nested function increment:
function fHandle = start_counting(count)
disp(count);
fHandle = #increment;
function increment
count = count+1;
disp(count);
end
end
Since the function increment is a nested function, it can only be used within the function start_counting (i.e. the workspace of start_counting is its "scope"). However, by returning a handle to the function increment, I can still use it outside of start_counting, and it still retains access to the variables in the workspace of start_counting! That allows me to do this:
>> fh = start_counting(3); % Initialize count to 3 and return handle
3
>> fh(); % Invoke increment function using its handle
4
>> fh();
5
Notice how we can keep incrementing count even though we are outside of the function start_counting. But you can do something even more interesting by calling start_counting again with a different number and storing the function handle in another variable:
>> fh2 = start_counting(-4);
-4
>> fh2();
-3
>> fh2();
-2
>> fh(); % Invoke the first handle to increment
6
>> fh2(); % Invoke the second handle to increment
-1
Notice that these two different counters operate independently. The function handles fh and fh2 point to different instances of the function increment with different workspaces containing unique values for count.
In addition to the above, using function handles in conjunction with nested functions can also help streamline GUI design, as I illustrate in this other SO post.
Function handles are an extremely powerful tool in matlab. A good start is to read the online help, which will give you far more than I can. At the command prompt, type
doc function_handle
A function handle is a simple way to create a function in one line. For example, suppose I wished to numerically integrate the function sin(k*x), where k has some fixed, external value. I could use an inline function, but a function handle is much neater. Define a function
k = 2;
fofx = #(x) sin(x*k);
See that I can now evaluate the function fofx at the command line. MATLAB knows what k is, so we can use fofx as a function now.
fofx(0.3)
ans =
0.564642473395035
In fact, we can pass fofx around, effectively as a variable. For example, lets call quad to do the numerical integration. I'll pick the interval [0,pi/2].
quad(fofx,0,pi/2)
ans =
0.999999998199215
As you can see, quad did the numerical integration. (By the way, an inline function would have been at least an order of magitude slower, and far less easy to work with.)
x = linspace(0,pi,1000);
tic,y = fofx(x);toc
Elapsed time is 0.000493 seconds.
By way of comparison, try an inline function.
finline = inline('sin(x*k)','x','k');
tic,y = finline(x,2);toc
Elapsed time is 0.002546 seconds.
A neat thing about a function handle is you can define it on the fly. Minimize the function cos(x), over the interval [0,2*pi]?
xmin = fminbnd(#(x) cos(x),0,2*pi)
xmin =
3.14159265358979
There are many, many other uses for function handles in MATLAB. I've only scratched the surface here.
Disclaimer: code not tested...
The function handle operator allows you to create a reference to a function and pass it around just like any other variable:
% function to add two numbers
function total = add(first, second)
total = first + second;
end
% this variable now points to the add function
operation = #add;
Once you've got a function handle, you can invoke it just like a regular function:
operation(10, 20); % returns 30
One nice thing about function handles is that you can pass them around just like any other data, so you can write functions that act on other functions. This often allows you to easily separate out business logic:
% prints hello
function sayHello
disp('hello world!');
end
% does something five times
function doFiveTimes(thingToDo)
for idx = 1 : 5
thingToDo();
end
end
% now I can say hello five times easily:
doFiveTimes(#sayHello);
% if there's something else I want to do five times, I don't have to write
% the five times logic again, only the operation itself:
function sayCheese
disp('Cheese');
end
doFiveTimes(#sayCheese);
% I don't even need to explicitly declare a function - this is an
% anonymous function:
doFiveTimes(#() disp('do something else'));
The Matlab documentation has a fuller description of the Matlab syntax, and describes some other uses for function handles like graphics callbacks.
Related
The following code won't work, but this is the idea I'm trying to get at.
c = #(x)constraints;
%this is where I would initialize sum as 0 but not sure how...
for i = 1:length(c)
sum = #(x)(sum(x) + (min(c(x)(i),0))^2);
end
penFunc = #(x)(funcHandle(x) + sig*sum(x));
where constraints and funcHandle are functions of x. This entire code would iterate for a sequence of sig's.
Obviously c(x)(i) isn't functional. I'm trying to write the function where the minimum of c(x) at i (c(x) is a vector) or 0 is taken and then squared.
I know I could calculate c(x) and then analyze it at each i, but I eventually want to pass penFunc as a handle to another function which calculates the minimum of penFunc, so I need to keep it as a function.
I confess I don't understand entirely what you're trying to achieve, but it appears you're trying to create a function handle of an anonymous function with a changing value sum that you precompute. MATLAB anonymous functions do allow you to do this.
It appears there might be some confusion with anonymous functions here. To start with, the line:
c = #(x)constraints;
is probably supposed to be something else, unless you really want c to be a function handle. The # at the start of the line declares a new anonymous function, when I think you just want to call the existing function constraints. It appears you really want c to be an array of constraints coming from the constraints function, in which case I think you mean to say
c = constraints(x);
Then we get to the sum, which I can't tell if you want as a vector or as a single sum. To start with, let's not name it 'sum', since that's the name of a built-in MATLAB function. Let's call it 'sumval'. If it's just a single value, then it's easy (it's easy both ways, but let's do this.) Start before the for loop with sumval=0; to initialize it, then the loop would be:
sumval = 0;
for i = 1:length(c)
sumval = sumval + (min(c(i),0))^2);
end
All four lines could be vectorized if you like to:
c(c>0) = 0; %Replace all positive values with 0
sumval = sum(c.^2); % Use .^ to do a element by element square.
The last line is obviously where you make your actual function handle, and I'm still not quite sure what is desired here. If sig is a function, then perhaps you really meant to have:
penFunc = #(x)(funcHandle(x) + sig*sumval);
But I'm not sure. If you wanted sum to be a vector, then how we specified it here wouldn't work.
Notice that it is indeed fine to have penFunc be an anonymous function with a variable within it (namely sumval), but it will continue to use the value of sumval that existed at the time of the function handle declaration.
So really the issues are A) the creation of c, which I don't think you meant to be a function handle, and B) the initialization of sum, which should probably be sumval (to not interact with MATLAB's own function), and which probably shouldn't declare a new function handle.
I have a class with properties in it (let say the name of the class file is inputvar),
I use it as the input argument for two different functions, which have an exactly the same calculation, but a little bit different code, which I'll explain later.
For the first function (let say the name is myfun1), I wrote the input argument like this:
f = myfun1 (inputvar)
So every time I want to use variables from the class inside the function, I'll have to call inputvar.var1, inputvar.var2, and etc.
For the second function (myfun2), I wrote each variables from the class in the input argument, so it looks like this:
f = myfun2 (inputvar.var1, inputvar.var2, ... etc )
Inside the function, I just have to use var1, var2, and etc, without having to include the name of the class.
After running both functions, I found that myfun2 runs a lot faster than myfun1, about 60% (I used tic-toc).
Can someone explain to me exactly why is that ?
with the reference:
MATLAB uses a system commonly called "copy-on-write" to avoid making a
copy of the input argument inside the function workspace until or
unless you modify the input argument. If you do not modify the input
argument, MATLAB will avoid making a copy. For instance, in this code:
function y = functionOfLargeMatrix(x) y = x(1); MATLAB will not make a
copy of the input in the workspace of functionOfLargeMatrix, as x is
not being changed in that function. If on the other hand, you called
this function:
function y = functionOfLargeMatrix2(x) x(2) = 2; y = x(1);
then x is being modified inside the workspace of functionOfLargeMatrix2, and so a copy must be made.
According to the statement above, when you directly pass a class object and you change any member of this object, a whole copy operation for the class is applied.
On the other side, with giving the class members as separate arguments, copy operation is applied only for the related members modified in the function, resulting in a faster execution.
I found that accessing properties is very slow in Matlab. I have not found a way around it, but some basic ideas are found here: http://blogs.mathworks.com/loren/2012/03/26/considering-performance-in-object-oriented-matlab-code/
But this article talks only about avoiding horrible, abysmal performance. Even with the simplest properties, performance is mediocre at best.
Take the example class from the Mathworks article. I did small test script:
clear all
clc
n = 1e5;
%% OOP way - abysimal
result = zeros(1, n);
tic
for i = 1:n
cyl = SimpleCylinder();
cyl.R = i;
cyl.Height = 10;
result(i) = cyl.volume();
end
toc
%% OOP Vectorized - fair
clear result
tic
cyl = SimpleCylinder();
cyl.R = 1:n;
cyl.Height = 10;
result = cyl.volume();
toc
%% for loop without objects - good
result = zeros(1, n);
tic
for i = 1:n
result(i) = pi .* i.^2 .* 10;
end
toc
%% Vectorized without objects - excellent
clear result
tic
R = 1:n;
result = pi .* R.^2 .* 10;
toc
With these results:
Elapsed time is 6.141445 seconds.
Elapsed time is 0.006245 seconds.
Elapsed time is 0.002116 seconds.
Elapsed time is 0.000478 seconds.
As you can see, every property access is slowing down. Try to vectorize (as always) but even the simple for-loop outperforms the vectorized OOP solution for small n. (On my PC, they break even at 1e7)
Essential message: OOP in Matlab is slow! You pay the price for every property access.
To your question: When you call
myfun2 (inputvar.var1, inputvar.var2, ... etc )
the values are copied. Within the function, you are no longer dealing with classes. Access to variables is fast. However, if you pass the whole class, every access to a property is slow. You can circumvent this by caching all properties in local variables and use these.
If you modify the class to inherit from handle everything gets a bit faster, but the difference is negligible.
Is it possible to create a wrapper around a function that has the exact same name as the original function?
This would be very useful in circumstances where the user wants to do some additional checks on input variables before they are passed on to the built in function How to interrupt MATLAB IDE when it hangs on displaying very large array?
Actually alternatively to slayton's answer you don't need to use openvar. If you define a function with the same name as a matlab function, it will shadow that function (i.e. be called instead).
To then avoid recursively calling your own function, you can call the original function from within the wrapper by using builtin.
e.g.
outputs = builtin(funcname, inputs..);
Simple example, named rand.m and in the matlab path:
function out = main(varargin)
disp('Test wrapping rand... calling rand now...');
out = builtin('rand', varargin{:});
Note that this only works for functions that are found by builtin. For those that are not, slayton's approach is likely necessary.
Yes this is possible but it requires a bit of hacking. It requires that you copy around some function handles.
Using the example provided in the question I will show how to wrap the function openvar in a user defined function that checks the size of the input variable and then allows the user to cancel any open operation for variables that are too large.
Additionally, this should work when the user double clicks a variable in the Workspace pane of the Matlab IDE.
We need to do three things.
Get a handle to the original openvar function
Define the wrapper function that calls openvar
Redirect the original openvar name to our new function.
Example Function
function openVarWrapper(x, vector)
maxVarSize = 10000;
%declare the global variable
persistent openVarHandle;
%if the variable is empty then make the link to the original openvar
if isempty(openVarHandle)
openVarHandle = #openvar;
end
%no variable name passed, call was to setup connection
if narargin==0
return;
end
%get a copy of the original variable to check its size
tmpVar = evalin('base', x);
%if the variable is big and the user doesn't click yes then return
if prod( size( tmpVar)) > maxVarSize
resp = questdlg(sprintf('Variable %s is very large, open anyway?', x));
if ~strcmp(resp, 'Yes')
return;
end
end
if ischar(x) && ~isempty(openVarHandle);
openVarHandle(x);
end
end
Once this function is defined then you simply need to execute a script that
Clears any variables named openvar
run the openVarWrapper script to setup the connection
point the original openVar to openVarWrapper
Example Script:
clear openvar;
openVarWrapper;
openvar = #openVarWrapper;
Finally when you want to clean everything up you can simply call:
clear openvar;
I prefer jmetz's approach using builtin() when it can be applied, because it is clean and to the point. Unfortunately, many many functions are not found by builtin().
I found that I was able to wrap a function using a combination of the which -all and cd commands. I suspect that this approach can be adapted to a wide variety of applications.
In my example case, I wanted to (temporarily) wrap the interp1 function so that I could check for NaN output values. (The interp1 function will, by default, return a NaN under some conditions, such as if a query point is larger than the largest sample point.) Here's what I came up with:
function Vq = interp1(varargin)
persistent interp1_builtin;
if (isempty(interp1_builtin)) % first call: handle not set
toolbox = 'polyfun';
% get a list of all known instances of the function, and then
% select the first such instance that contains the toolbox name
% in its path
which_list = which('interp1','-all');
for ii = 1:length(which_list)
if (strfind(which_list{ii}, [filesep, toolbox, filesep]))
base_path = fileparts(which_list{ii}); % path to the original function
current_path = pwd;
cd(base_path); % go to the original function's directory
interp1_builtin = #interp1; % create a function handle to the original function
cd(current_path); % go back to the working directory
break
end
end
end
Vq = interp1_builtin(varargin{:}); % call the original function
% test if the output was NaN, and print a message
if (any(isnan(Vq)))
dbstack;
disp('ERROR: interp1 returned a NaN');
keyboard
end
end
See also: How to use MATLAB toolbox function which has the same name of a user defined function
In short: is there an elegant way to restrict the scope of anonymous functions, or is Matlab broken in this example?
I have a function that creates a function handle to be used in a pipe network solver. It takes as input a Network state which includes information about the pipes and their connections (or edges and vertices if you must), constructs a large string which will return a large matrix when in function form and "evals" that string to create the handle.
function [Jv,...] = getPipeEquations(Network)
... %// some stuff happens here
Jv_str = ['[listConnected(~endNodes,:)',...
' .* areaPipes(~endNodes,:);\n',...
anotherLongString,']'];
Jv_str = sprintf(Jv_str); %// This makes debugging the string easier
eval(['Jv = #(v,f,rho)', Jv_str, ';']);
This function works as intended, but whenever I need to save later data structures that contain this function handle, it requires a ridiculous amount of memory (150MB) - coincidentally about as much as the entire Matlab workspace at the time of this function's creation (~150MB). The variables that this function handle requires from the getPipeEquations workspace are not particularly large, but what's even crazier is that when I examine the function handle:
>> f = functions(Network.jacobianFun)
f =
function: [1x8323 char]
type: 'anonymous'
file: '...\pkg\+adv\+pipe\getPipeEquations.m'
workspace: {2x1 cell}
...the workspace field contains everything that getPipeEquations had (which, incidentally is not the entire Matlab workspace).
If I instead move the eval statement to a sub-function in an attempt to force the scope, the handle will save much more compactly (~1MB):
function Jv = getJacobianHandle(Jv_str,listConnected,areaPipes,endNodes,D,L,g,dz)
eval(['Jv = #(v,f,rho)', Jv_str, ';']);
Is this expected behavior? Is there a more elegant way to restrict the scope of this anonymous function?
As an addendum, when I run the simulation that includes this function several times, clearing workspaces becomes painfully slow, which may or may not be related to Matlab's handling of the function and its workspace.
I can reproduce: anonymous functions for me are capturing copies of all variables in the enclosing workspace, not just those referenced in the expression of the anonymous function.
Here's a minimal repro.
function fcn = so_many_variables()
a = 1;
b = 2;
c = 3;
fcn = #(x) a+x;
a = 42;
And indeed, it captures a copy of the whole enclosing workspace.
>> f = so_many_variables;
>> f_info = functions(f);
>> f_info.workspace{1}
ans =
a: 1
>> f_info.workspace{2}
ans =
fcn: #(x)a+x
a: 1
b: 2
c: 3
This was a surprise to me at first. But it makes sense when you think about it: because of the presence of feval and eval, Matlab can't actually know at construction time what variables the anonymous function is actually going to end up referencing. So it has to capture everything in scope just in case they get referenced dynamically, like in this contrived example. This uses the value of foo but Matlab won't know that until you invoke the returned function handle.
function fcn = so_many_variables()
a = 1;
b = 2;
foo = 42;
fcn = #(x) x + eval(['f' 'oo']);
The workaround you're doing - isolating the function construction in a separate function with a minimal workspace - sounds like the right fix.
Here's a generalized way to get that restricted workspace to build your anonymous function in.
function eval_with_vars_out = eval_with_vars(eval_with_vars_expr, varargin)
% Assign variables to the local workspace so they can be captured
ewvo__reserved_names = {'varargin','eval_with_vars_out','eval_with_vars_expr','ewvo__reserved_names','ewvo_i'};
for ewvo_i = 2:nargin
if ismember(inputname(ewvo_i), ewvo__reserved_names)
error('variable name collision: %s', inputname(ewvo_i));
end
eval([ inputname(ewvo_i) ' = varargin{ewvo_i-1};']);
end
clear ewvo_i ewvo__reserved_names varargin;
% And eval the expression in that context
eval_with_vars_out = eval(eval_with_vars_expr);
The long variable names here hurt readability, but reduce the likelihood of collision with the caller's variables.
You just call eval_with_vars() instead of eval(), and pass in all the input variables as additional arguments. Then you don't have to type up a static function definition for each of your anonymous function builders. This'll work as long as you know up front what variables are actually going to be referenced, which is the same limitation as the approach with getJacobianHandle.
Jv = eval_with_vars_out(['#(v,f,rho) ' Jv_str],listConnected,areaPipes,endNodes,D,L,g,dz);
Anonymous functions capture everything within their scope and store them in the function workspace. See MATLAB documentation for anonymous functions
In particular:
"Variables specified in the body of the expression. MATLAB captures these variables and holds them constant throughout the lifetime of the function handle.
The latter variables must have a value assigned to them at the time you construct an anonymous function that uses them. Upon construction, MATLAB captures the current value for each variable specified in the body of that function. The function will continue to associate this value with the variable even if the value should change in the workspace or go out of scope."
An alternative workaround to your problem, is to use the fact that the matlab save function can be used to save only the specific variables you need. I have had issues with the save function saving way too much data (very different context from yours), but some judicial naming conventions, and use of wildcards in the variables list made all my problems go away.
when I am doing a function in Matlab. Sometimes I have equations and every one of these have constants. Then, I have to declare these constants inside my function. I wonder if there is a way to call the values of that constants from outside of the function, if I have their values on the workspace.
I don't want to write this values as inputs of my function in the function declaration.
In addition to the solutions provided by Iterator, which are all great, I think you have some other options.
First of all, I would like to warn you about global variables (as Iterator also did): these introduce hidden dependencies and make it much more cumbersome to reuse and debug your code. If your only concern is ease of use when calling the functions, I would suggest you pass along a struct containing those constants. That has the advantage that you can easily save those constants together. Unless you know what you're doing, do yourself a favor and stay away from global variables (and functions such as eval, evalin and assignin).
Next to global, evalin and passing structs, there is another mechanism for global state: preferences. These are to be used when it concerns a nearly immutable setting of your code. These are unfit for passing around actual raw data.
If all you want is a more or less clean syntax for calling a certain function, this can be achieved in a few different ways:
You could use a variable number of parameters. This is the best option when your constants have a default value. I will explain by means of an example, e.g. a regular sine wave y = A*sin(2*pi*t/T) (A is the amplitude, T the period). In MATLAB one would implement this as:
function y = sinewave(t,A,T)
y = A*sin(2*pi*t/T);
When calling this function, we need to provide all parameters. If we extend this function to something like the following, we can omit the A and T parameters:
function y = sinewave(t,A,T)
if nargin < 3
T = 1; % default period is 1
if nargin < 2
A = 1; % default amplitude 1
end
end
y = A*sin(2*pi*t/T);
This uses the construct nargin, if you want to know more, it is worthwhile to consult the MATLAB help for nargin, varargin, varargout and nargout. However, do note that you have to provide a value for A when you want to provide the value of T. There is a more convenient way to get even better behavior:
function y = sinewave(t,A,T)
if ~exists('T','var') || isempty(T)
T = 1; % default period is 1
end
if ~exists('A','var') || isempty(A)
A = 1; % default amplitude 1
end
y = A*sin(2*pi*t/T);
This has the benefits that it is more clear what is happening and you could omit A but still specify T (the same can be done for the previous example, but that gets complicated quite easily when you have a lot of parameters). You can do such things by calling sinewave(1:10,[],4) where A will retain it's default value. If an empty input should be valid, you should use another invalid input (e.g. NaN, inf or a negative value for a parameter that is known to be positive, ...).
Using the function above, all the following calls are equivalent:
t = rand(1,10);
y1 = sinewave(t,1,1);
y2 = sinewave(t,1);
y3 = sinewave(t);
If the parameters don't have default values, you could wrap the function into a function handle which fills in those parameters. This is something you might need to do when you are using some toolboxes that impose constraints onto the functions that are to be used. This is the case in the Optimization Toolbox.
I will consider the sinewave function again, but this time I use the first definition (i.e. without a variable number of parameters). Then you could work with a function handle:
f = #(x)(sinewave(x,1,1));
You can work with f as you would with an other function:
e.g. f(10) will evaluate sinewave(10,1,1).
That way you can write a general function (i.e. sinewave that is as general and simple as possible) but you create a function (handle) on the fly with the constants substituted. This allows you to work with that function, but also prevents global storage of data.
You can of course combine different solutions: e.g. create function handle to a function with a variable number of parameters that sets a certain global variable.
The easiest way to address this is via global variable:
http://www.mathworks.com/help/techdoc/ref/global.html
You can also get the values in other workspaces, including the base or parent workspace, but this is ill-advised, as you do not necessarily know what wraps a given function.
If you want to go that route, take a look at the evalin function:
http://www.mathworks.com/help/techdoc/ref/evalin.html
Still, the standard method is to pass all of the variables you need. You can put these into a struct, if you wish, and only pass the one struct.