I'm trying to do something like
f = [x+1 y+2]
values = [1 2]
f(values) = [2 4]
(not proper syntax)
f(values) only works for taking one variable?
Try this:
f = {#(x) (x+1); #(y) (y+2)}; %//create a cell array of your function handlers
values = [1 2];
%//convert your input values to a cell array
length = numel(values);
v = mat2cell( values, 1, ones(length,1) ).' ;
%// f(v)
results = cellfun(#(x,y) x(y), f, v);
Related
I want to get all unique values in A, where A is a cell array of matrices of different shapes and sizes:
A = {[], 1, [2 3], [4 5; 6 7]};
U = [];
for ii = 1: numel(A)
a = A{ii};
U = [U; a(:)];
end
U = unique(U);
That returns:
U =
1 2 3 4 5 6 7
If all elements in A where row vectors, I could use [A{:}] like:
U = unique([A{1:3}]);
That returns:
U =
1 2 3
But in my case it throws an exception:
Error using horzcat
Dimensions of matrices being concatenated are not
consistent.
So how can I avoid that for-loop?
You can use cellfun to reshape all elements in the cell.
U = unique(cell2mat(cellfun(#(x)reshape(x,1,numel(x)),A, 'UniformOutput', false)));
or avoiding the reshapewith
U = unique(cell2mat(cellfun(#(x)x(:).',A, 'UniformOutput', false)));
We can go this way:
A = {[], 1, [2 3], [2 0; 4 5; 6 7]};
AA = cellfun( #(x) unique(x(:)), A, 'UniformOutput' , false)
res = unique(cat(1, AA{:}))
First of all create unique array for each cell - it let us avoid converting all the cells to numeric just only unique values.
Lets convert cell array to one numeric array - cat(1, AA{:}) .
Find unique values through this resulting array.
Here is what I want, a 3-D matrix:
K = 2:2.5:10;
den = zeros(1,4,4);
for i = 1:1:4
den(:,:,i) = [1, 5, K(i)-6, K(i)];
end
Or, a cell array is also acceptable:
K = 2:2.5:10;
for i = 1:1:4
den{i} = [1, 5, K(i)-6, K(i)];
end
But I want to know if there is a more efficient way of doing this using vectorized code like:
K = 2:2.5:10;
den = [1, 5, K-6, K];
I know the last code will not get what I wanted. But, like I can use:
v = [1 2 3];
v2 = v.^2;
instead of:
v = [1 2 3];
for i = 1:length(v)
v(i) = v(i)^2;
end
to get the matrix I want. Is there a similar way of doing this so that I can get the 3-D matrix or cell array I mentioned at the beginning more efficiently?
You need to "broadcast" the scalar values in columns so they are of the same length as your K vector. MATLAB does not do this broadcasting automatically, so you need to repeat the scalars and create vectors of the appropriate size. You can use repmat() for this.
K = 2:2.5:10;
%% // transpose K to a column vector:
K = transpose(K);
%% // helper function that calls repmat:
f = #(v) repmat(v, length(K), 1);
%% // your matrix:
den = [f(1) f(5) K-6 K];
This should be more optimized for speed but requires a bit more intermediary memory than the loop does.
Just use reshape with a 1*3 size:
den = reshape([ones(1,length(K));ones(1,length(K))*5; K-6; K],[1 4 length(K)]);
I think the used extra memory by reshape should be low and constant (dependent only on the length of the vector of new sizes).
You can use the classic line equation y=a*x+b, extended to the matrix form:
k = 2:2.5:10 ;
fa = [0 0 1 1].' ; %' // "a" coefficients
fb = [1 5 -6 0].' ; %' // "b" coefficients
d(1,:,:) = fa*k + fb*ones(1,4) ;
The above is better for clarity, but if you're not bothered you can also pack everything in one line:
d(1,:,:) = [0 0 1 1].' * (2:2.5:10) + [1 5 -6 0].' * ones(1,4) ;
If you need to re-use the principle for many different values of k, then you can use an anonymous function to help:
fden = #(k) [0 0 1 1].' * k + [1 5 -6 0].' * ones(1,4) ; %// define anonymous function
k = 2:2.5:10 ;
d(1,:,:) = fden(k) ; %// use it for any value of "k"
In a previous question, a user asked about iterating over a cell array of anonymous functions. I am wondering whether there is a way to evaluate a set of functions without the explicit use of a for loop.
As an example, the following code creates an array of (simple) functions, evaluates them for a fixed value and stores the results:
fcnList = {#(x) (x+1), #(x) (x+2)};
a = 2;
for i = 1:numel(fcnList)
y(i) = fcnList{i}(a);
end
Is there a way to do this without looping?
For your example, you could do the following using the cellfun function:
fcnList = {#(x) (x+1), #(x) (x+2)};
a = 2;
cellfun(#(func) func(a),fcnList)
ans =
3 4
Where I have created a handle called func which accepts as input a function from the fcnList variable. Each function is then evaluated for a.
If you need to pass a vector instead of a scalar, for instance b, you will need to set the 'UniformOutput' option to false:
b=[3 4]
fcnList = {#(x) (x+1), #(x) (x+2)};
cellfun(#(func) func(b),fcnList,'UniformOutput',false)
ans =
{
[1,1] =
4 5
[1,2] =
5 6
}
To avoid a for loop or cellfun (which is more or less the same as a loop), you can define a single function with vector output:
fcn = #(x) [x+1, x+2];
Then fcn(a) gives you a vector cointaining the results:
>> fcn = #(x) [x+1, x+2];
>> a = 2;
>> fcn(a)
ans =
3 4
If the results of each original function have different sizes you can define a single function with cell array output:
>> fcn = #(x) {x+1, [x+2; x+3]};
>> a = 2;
>> x = fcn(a)
x =
[3] [2x1 double]
>> celldisp(x)
x{1} =
3
x{2} =
4
5
I have a 1xm cell array A{}, with each element of the array being NxN matrix and a matrix W(N1,m).
I need to calculate
Sum(j) = W(j,1)*A{1,1} + W(j,2)*A{1,2}
and I am doing the following:
for j=1:N1
sum=false(N);
for k=1:m
sum = sum + W(j,k)*A{1,k};
end
Sum(j)=sum
end
Or more visually :
Matrix W(let's say N1=2)
|W11 W12||A{1,1}| = |W11*A{1,1} + W12*A{1,2}|
|W21 W22||A{1,2}| = |W21*A{1,1} + W22*A{1,2}|
Is there a way of doing it without using the loops?
To do that without for-loops, you can rape (pardon the expression) the arrayfun command:
w_func = #(j)arrayfun(#(k)(W(j, k) * A{k}), 1:m, 'Un', 0)
sum_func = #(x)sum(cat(3, x{:}), 3)
S = arrayfun(#(j)sum_func(w_func(j)), 1:N1, 'Un', 0);
This produces a cell array S that contains all the sums, from S{1} to S{N1}.
I'm confused over what you are trying to do, but if I understand it correctly, this code should work:
temp = cell2mat(A);
a_sum = temp*repmat(eye(n),m,1); % this reduces A by performing sum like operation so [1 1 1 3;0 1 0 2]
% becomes [2 4; 0 3]
Sum = W * a_sum
I am also not sure I understood the question, but here is some code to consider:
%# create some data resembling what you described
N = 2;
m = 4;
N1 = 5;
W = rand(N1,m);
A = cell(1,m); for i=1:m, A{i} = rand(N); end
%# do the multiplications
s = cell(N1,1);
for j=1:N1
AA = cellfun(#times, A, num2cell(W(j,:)), 'UniformOutput',false);
s{j} = sum(cat(3,AA{:}), 3);
end
The cell array s now contains the result such that:
s{j} = W(j,1)*A{1} + W(j,2)*A{2} + ... + W(j,m)*A{m}
thus s is a cell array of size N1-by-1, where each cell contains an N-by-N matrix
a = [1 1 1; 2 2 2; 3 3 3];
b = [1 2 3];
How can I call one function to get a vector v[i] = a[b[i],i]?
v = a(sub2ind(size(a), b, 1:length(b)))
sub2ind transforms subscripts into a single index.
Another thing to try, keeping very close to your description, you can use the arrayfun function.
First define a function that maps a value x to the desired output.
fn = #(x) a(b(x), x);
Then call that function on each input in the the i vector.
i = 1:3;
v = arrayfun(fn, i);
Or, this can all be done in a single line, in the obvious way:
v = arrayfun(#(x) a(b(x), x), 1:3);
This arrayfun is simply shorthand for the loop below:
for ixLoop = 1:3
v(ixLoop) = a(b(ixLoop),ixLoop);
end
The related functions arrayfun, cellfun, and structfun have similar uses, and are strangely empowering. This Joel article convinced me to be a believer.