From the documentation, we see the following example:
g = gallery('integerdata',3,[15,1],1);
x = gallery('uniformdata',[15,1],9);
y = gallery('uniformdata',[15,1],2);
A = table(g,x,y)
func = #(x, y) (x - y);
B = rowfun(func,A,...
'GroupingVariable','g',...
'OutputVariableName','MeanDiff')
When the function func is applied to A in rowfun how does it know that there are variables in A called x and y?
EDIT: I feel that my last statement must not be true, as you do not get the same result if you did A = table(g, y, x).
I am still very confused by how rowfun can use a function that does not actually use any variables defined within the calling environment.
Unless you specify the rows (and their order) with the Name/Value argument InputVariables, Matlab will simply take column 1 as first input, column 2 as second input etc, ignoring eventual grouping columns.
Consequently, for better readability and maintainability of your code, I consider it good practice to always specify InputVariables explicitly.
Related
Consider the arbitrary function:
function myFunc_ = myFunc(firstInput, secondInput)
myFunc_ = firstInput * secondInput;
end
Now imagine I want to map the above function to an array for the first input firstInput, while the second input secondInput is constant. For example, something like:
firstVariable = linspace(0., 1.);
plot(firstVariable, map(myFunc, [firstVariable , 0.1]))
where 0.1 is an arbitrary scalar value for the secondInput and firstVariable array is an arbitrary array for the firstInput.
I have looked into the arrayfun() function. However, I don't know how to include the constant variable. Plus it seems like the syntax between MATLAB and Octave are different, or maybe I'm mistaken. It is important for me to have a cross-compatible code that I can share with colleagues.
Assuming in the original function you were multiplying two scalars and you want to vectorise, then
function myFunc_ = myFunc(firstInput, secondInput)
myFunc_ = firstInput .* secondInput;
end
should work just fine.
Then plot it directly:
plot( firstVariable, myFunc(firstVariable , 0.1) )
I'm afraid the arbitrary examples given in the original question were too simplified and as a result, they do not represent the actual issue I'm facing with my code. But I did manage to find the right syntax that works inside Octave:
plot(firstVariable, arrayfun(#(tempVariable) myFunc(tempVariable, 0.1), firstVariable))
basically the
#(tempVariable) myFunc(tempVariable, 0.1)
creates what is so-called an anonymous function and the
arrayfun(<function>, <array>)
maps the function over the given array.
I have a segment of code where a composition of nested loops needs to be run at various times; however, each time the operations within the nested loops are different. Is there a way to make the outer portion (loop composition) somehow a functional piece, so that the internal operations are variable. For example, below, two code blocks are shown which both use the same loop introduction, but have different purposes. According to the principle of DRY, how can I improve this, so as not to need to repeat myself each time a similar loop needs to be used?
% BLOCK 1
for a = 0:max(aVec)
for p = find(aVec'==a)
iDval = iDauVec{p};
switch numel(iDval)
case 2
r = rEqVec(iDval);
qVec(iDval(1)) = qVec(p) * (r(2)^0.5 / (r(1)^0.5 + r(2)^0.5));
qVec(iDval(2)) = qVec(p) - qVec(iDval(1));
case 1
qVec(iDval) = qVec(p);
end
end
end
% BLOCK 2
for gen = 0:max(genVec)-1
for p = find(genVec'==gen)
iDval = iDauVec{p};
QinitVec(iDval) = QinitVec(p)/numel(iDval);
end
end
You can write your loop structure as a function, which takes a function handle as one of its inputs. Within the loop structure, you can call this function to carry out your operation.
It looks as if the code inside the loop needs the values of p and iDval, and needs to assign to different elements of a vector variable in the workspace. In that case a suitable function definition might be something like this:
function vec = applyFunctionInLoop(aVec, vec, iDauVec, funcToApply)
for a = 0:max(aVec)
for p = find(aVec'==a)
iDval = iDauVec{p};
vec = funcToApply(vec, iDval, p);
end
end
end
You would need to put the code for each different operation you want to carry out in this way into a function with suitable input and output arguments:
function qvec = myFunc1(qVec, iDval, p)
switch numel(iDval)
case 2
r = rEqVec(iDval); % see note
qVec(iDval(1)) = qVec(p) * (r(2)^0.5 / (r(1)^0.5 + r(2)^0.5));
qVec(iDval(2)) = qVec(p) - qVec(iDval(1));
case 1
qVec(iDval) = qVec(p);
end
end
function v = myFunc2(v, ix, q)
v(ix) = v(q)/numel(ix);
end
Now you can use your loop structure to apply each function:
qvec = applyFunctionInLoop(aVec, qVec, iDauVec, myFunc1);
QinitVec = applyFunctionInLoop(aVec, QinitVec, iDauVec, myFunc2);
and so on.
In most of the answer I've kept to the same variable names you used in your question, but in the definition of myFunc2 I've changed the names to emphasise that these variables are local to the function definition - the function is not operating on the variables you passed in to it, but on the values of those variables, which is why we have to pass the final value of the vector out again.
Note that if you want to use the values of other variables in your functions, such as rEqVec in myFunc1, you need to think about whether those variables will be available in the function's workspace. I recommend reading these help pages on the Mathworks site:
Share Data Between Workspaces
Dynamic Function Creation with Anonymous and Nested Functions
Could you please help me with the following issue: I have the following function handle:
r1 = #(lambda) b + lambda*(r - b); % r and b are vectors of return data
I want to find the optimal lambdas that set me a mean function to zero, for a given set of powers within that function. What I tried to do and didn't work, as it returns me an error for undefined operators for input arguments of type 'function_handle' is:
lambda0 = 0.3;
for a = 2:10 %power coefficient
S1(a) = fzero(mean((r - b)*r1.^(1/(a - 1))),lambda0);
end
Any suggestion as to how to go about this problem is highly appreciated! Thank you in advance.
fzero accepts a function handle as the first input. As you currently have it, you're trying to pass a statement as the first input. This statement can't even be properly evaluated because you are trying to perform numerical operations on a function handle (more on this in a bit).
You need to instead do something like this where we create a new function handle that evaluates the original function handle and performs the other operations you need.
S1(a) = fzero(#(lambda)mean((r - b)*r1(lambda).^(1/(a - 1))),lambda0);
Further Explanation
Performing operations on a function handle is not the same as performing them on the result.
So for example, if we had a function handle:
func = #(x)2*x;
If we evaluation this, by calling it with an input value for x
func(2)
4
This works as we would expect. If now we really want the value (2*x)^2, we could try to write it the way that you wrote your statement in your question
func2 = func^2;
We will get an error!
Undefined operator '^' for input arguments of type 'function_handle'.
This does not work because MATLAB attempts to apply the ^ operation to the function handle itself and not the value of the evaluated function handle.
Instead, we would need to create a new function handle that essentially wraps the other one and performs any additional options:
func2 = #(x)func(x)^2;
func2(2)
16
Bringing it Full-Circle
So if we go back to your question, you defined your anonymous function r1 like this.
r1 = #(lambda) b + lambda*(r - b); % r and b are vectors of return data
This all looks great. You have one input argument and you reference r and b from the parent workspace.
Now when you call fzero you try to perform operations on this function handle in hopes of creating a new function handle.
mean((r - b)*r1.^(1/(a - 1)))
Like we just showed this will result in a very similar error
Undefined operator .^ for input arguments of type 'function_handle'
So we need to wrap this into a new function.
newfunc = #(lambda)mean((r - b)*r1(lambda).^(1 / (a - 1)));
Now we can safely pass this to fzero.
result = fzero(newfunc, lambda0);
I have been using MATLAB fminunc function to solve my optimization problem. I want to try the minFunc package :
http://www.di.ens.fr/~mschmidt/Software/minFunc.html
When using fminunc, I defined a function funObj.m which gives me the objective value and the gradient at any point 'x'. It also takes in several external inputs say, {a,b,c} which are matrices. So the function prototype looks like :
function [objVal,G] = funObj(x,a,b,c)
I want to use the same setup in the minFunc package. From the examples, I figured this should work :
options.Method='lbfgs';
f = #(x)funObj(x,a,b,c);
x = minFunc(f,x_init,options);
But when I call this way, I get an error as:
Error using funObj
Too many output arguments.
What is the correct way to call minFunc for my case?
**EDIT : Alright, here is a sample function that I want to use with minFunc. Lets say I want to find the minimum of a*(b-x)^2, where a,b are scalar parameters and x being a scalar too. The MATLAB objective function will then look like :
function obj = testFunc(x,a,b)
obj = a*(b-x)^2;
The function call to minimize this using fminunc (in MATLAB ) is simply:
f = #(x)testFunc(x,a,b);
x = fminunc(f,x_init);
This gives me the minimum of x = 10. Now, How do I do the same using minFunc ?
"Note that by default minFunc assumes that the gradient is supplied, unless the 'numDiff' option is set to 1 (for forward-differencing) or 2 (for central-differencing)."
The error is because only one argument is returned by the function. You can either return the gradient as a second argument or turn on numerical differencing.
Agree with Mark. I think the simplest way to solve it is
minFunc(#testFunc, x_init, a, b, c)
In MATLAB temporary function can only have one return value. So f = #(x)testFunc(x,a,b); let your method drop gradient part every time. Because minFunc can accept extra paramters, you can pass a, b and c after x_init. I think this would work.
A lot of MATLAB functions have an input structure such as:
output = function MyFun(a,b,c,'-setting1',s1,'-setting2',s2,'-setting3',s3)
I am wondering how I should implement this kind of functionality in my own functions. To be precise, I would like to find out how I can create a function such that:
The function has a variable number of inputs N + M
The first N inputs are ordered and unlabeled. In the example above, N = 3. The first input is always a, second input is always b, third input is always c. The function input is variable in that users do not necessarily need to send b, c; when they do not then these can take on default (hardcoded) values. As far as I know, this type of functionality is generally handled via varargin.
The remaining M inputs are unordered, but labeled. In the example above, M = 3, the variables are s1,s2,s3 and their labels are setting1,setting2 and setting3 respectively, I would like for users to be able to specify these variables in whatever order they want. If users choose not to specify one of these inputs (i.e. setting1), then I would like my function to assign default values for s1.
One example of such a function is the dlmwrite function.
Ideally, I am looking for an approach that is typically used by MATLAB developers so that my code is easy to understand.
The InputParser class addresses all of these issues. You can specify any number of:
Required parameters (ordered, unlabeled)
Optional parameters (ordered, unlabeled)
String parameter-value pairs in any order (unordered, labeled)
A very clear tutorial with examples is provided by MathWorks. For a function defined as function printPhoto(filename,varargin), the example boils down to the following.
Create the inputParser:
p = inputParser;
Specify defaults and define validation criteria:
defaultFinish = 'glossy';
validFinishes = {'glossy','matte'};
checkFinish = #(x) any(validatestring(x,validFinishes));
defaultColor = 'RGB';
validColors = {'RGB','CMYK'};
checkColor = #(x) any(validatestring(x,validColors));
defaultWidth = 6;
defaultHeight = 4;
Define required/optional/parameter input names, set their default values and validation functions:
addRequired(p,'filename',#ischar);
addOptional(p,'finish',defaultFinish,checkFinish);
addOptional(p,'color',defaultColor,checkColor);
addParameter(p,'width',defaultWidth,#isnumeric);
addParameter(p,'height',defaultHeight,#isnumeric);
Parse the inputs into a struct:
parse(p,filename,varargin{:});
Then you have the input arguments and their values in p.Results.
The InputParser class is used throughout newer MathWorks functions, so don't be afraid to use it yourself!