I don't understand this piece of code of a for loop in matlab, I know that loops in matlab usually look like: for ii=1:2:100 so that it starts in 1 until 100 and in each iteration you add 2.
But here I've got this condition in the loop and I don't get what it does:
for ii=[1:w:rd(1)-w-border, rd(1)-w-border+1],
...
end;
w and border are integers passed as arguments and rd is size of the image/matrix (rd = size(image);)
Can someone explain me how for loops work in matlab with this kind of condition?
Thanks in advance.
For loop in matlab can execute statements for a defined set of index values:
For example, the following code will display all the element in the set [1,5,8,17]:
for s = [1,5,8,17]
disp(s)
end
Your code for ii=[1:w:rd(1)-w-border, rd(1)-w-border+1] is similar.
Its just like a set 1:w:rd(1)-w-border with an additional element rd(1)-w-border+1.
Its like writing this set [1,2,3,4,5,8] as [1:1:5, 8]
I hope its clear now.
the for argument is a vector. the loop iterator ii takes one value for the vector for each iteration of the loop. As you mentioned, the vector can be equally spaced one like 1:2:100. But it can also be arbitrary, for example for ii = [4,6,1,8] ....
In you case the for argument vector is partly "equally spaced" vector: 1:w:rd(1)-w-border plus another element rd(1)-border+1.
Related
I want to paralyze my forloop in Matlab.I use parfor function for that, but I get error because the way I used variable inside of the loop. would someone help me to fix that. I'm new in matlab.
Here is part of my try.
Here is part of problematic part:
CV_err=zeros(length(gamma), (Num_Tasks + 1));
parfor k=1:length(gamma)
#block of code
#
CV_err(k,1:Num_Tasks)= sum(In_Fold_Error)./size(In_Fold_Error,1);
CV_err(k,Lambda_location)= Lambda;
CV_err(k,(Num_Tasks +2))= sum(CV_err(k,1:Num_Tasks))/Num_Tasks;
end
Error: parfor loop can not run due to way CV_err is used.
CV_err is indexed in different ways, potentially causing dependencies
Seems that valid indices are restricted in parfor .
While your variable is sliced, you only access the k-th row in the k-th iteration, the code analyser does not understand it. Give a little help to matlab, first put all data into a vector and then write all at once to the sliced variable.
CV_err=zeros(length(gamma), (Num_Tasks + 2));
parfor k=1:length(gamma)
%block of code
%
temp=zeros(1,(Num_Tasks + 2));
temp(1,1:Num_Tasks)= sum(In_Fold_Error)./size(In_Fold_Error,1);
temp(1,Lambda_location)= Lambda;
temp(1,(Num_Tasks +2))= sum(temp(1,1:Num_Tasks))/Num_Tasks;
CV_err(k,:)=temp;
end
The limitation is explained in the documentation:
Form of Indexing. Within the list of indices for a sliced variable, one of these indices is of the form i, i+k, i-k, k+i, or k-i, where i is the loop variable and k is a constant or a simple (nonindexed) broadcast variable; and every other index is a scalar constant, a simple broadcast variable, a nested for-loop index, colon, or end.
Source
To fix pre-allocation, don't pre-allocate. You're just telling to MATLAB how it should split the work among workers; parfor doesn't like that.
The answer is: don't make loops change common variables, write your results separately, grow cell arrays instead of matrices, i.e.
clear CV_err;
parfor k=1:length(gamma)
%// here your other code
this_CV_err = zeros(Num_Tasks+2,1);
this_CV_err(1:Num_Tasks) = sum(In_Fold_Error)./size(In_Fold_Error,1);
this_CV_err(Lambda_location) = Lambda;
this_CV_err(Num_Tasks+2) = mean(this_CV_err(1:Num_Tasks));
CV_err{k} = this_CV_err;
end;
I have written a function that is the beginning of a Poisson Process
function n_t = PoisProc2(t,tao,SIZE)
n_t=0;
for n=1:SIZE
if t>tao(1,n)
n_t=n_t+1;
end
end
end
tao is simply an array of random doubles of length SIZE. For simplicity we'll say [1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,20]
So this functions purpose is to count how many elements of tao that t is greater than for any given t.
This code works fine when I simply write
PoisProc2(3,tao,20);
the answer I get is 19 as expected, but if I write
x=1:.01:20;
y=PoisProc2(x,tao,20);
plot(x,y,'-')
y shows up as 0 in the workspace (I would expect an array of length 1901) and my plot also reads 0. I'm pretty new to Matlab, but this seems like a pretty simply thing I'm trying to do and I must be missing something obvious. Please help!
Your code does not work as you are giving a vector. So your if condition is not working as you expect.
First initialize n_t with a vector :
n_t=zeros(1,length(t))
instead of
if t>tao(1,n)
n_t=n_t+1;
end
Vectorize your expression :
n_t = n_t + (t>tao(1,n))
Cheers
Because x is a vector in your last example, the "if t>tao(1,n)" statement in your function behave totally different from what you think.
This function below should give you the right result.
function ret = PoisProc2(thresholds, vec)
ret = zeros(size(thresholds));
for k = 1:numel(thresholds)
ret(k) = numel(nonzeros(vec > thresholds(k)));
end
Side comments:
Your original function is quite C/Java style. You can see in my function, it's replaced by a one-liner "numel(nonzeros(vec > thresholds(k)))", which is more MATLAB style.
I think this can be done with hist() function. But this probably is easier to understand.
I am trying to use MATLAB in order to generate a variable whose elements are either 0 or 1. I want to define this variable using some kind of concatenation (equivalent of Java string append) so that I can add as many 0's and 1's according to some upper limit.
I can only think of using a for loop to append values to an existing variable. Something like
variable=1;
for i=1:N
if ( i%2==0)
variable = variable.append('0')
else
variable = variable.append('1')
i=i+1;
end
Is there a better way to do this?
In MATLAB, you can almost always avoid a loop by treating arrays in a vectorized way.
The result of pseudo-code you provided can be obtained in a single line as:
variable = mod((1:N),2);
The above line generates a row vector [1,2,...,N] (with the code (1:N), use (1:N)' if you need a column vector) and the mod function (as most MATLAB functions) is applied to each element when it receives an array.
That's not valid Matlab code:
The % indicates the start of a comment, hence introducing a syntax error.
There is no append method (at least not for arrays).
Theres no need to increment the index in a for loop.
Aside of that it's a bad idea to have Matlab "grow" variables, as memory needs to be reallocated at each time, slowing it down considerably. The correct approach is:
variable=zeros(N,1);
for i=1:N
variable(i)=mod(i,2);
end
If you really do want to grow variables (some times it is inevitable) you can use this:
variable=[variable;1];
Use ; for appending rows, use , for appending columns (does the same as vertcat and horzcat). Use cat if you have more than 2 dimensions in your array.
function letter=read_char(imgn, num_letters)
global templates
comp=[ ];
for n=1:num_letters
sem=corr2(imgn, templates{1,n});
comp=[comp sem];
end
vd=find(comp==max(comp));
Can someon please explain what the 'FOR' loop does and how 'vd' is calculated?
Also, what is the variable 'comp' and what will it looks like as the array also contains itself and another variable 'sem' which is calculated from the corr2 function.
thanks
The for loop creates a loop variable n that starts at 1 for the first loop and is incremented by 1 for each successive loop until it reaches num_letters. This means the loop will execute num_letters times.
The variable comp is first initialized to the empty matrix []. Within the loop, a 2-D correlation coefficient is computed between the matrix imgn and another matrix templates{1,n} (indexed from a cell array) using the function CORR2. The correlation coefficient sem is appended to the array comp using horizontal concatenation. This will ultimately result in comp being a 1-by-num_letters array of correlation coefficients.
The variable vd stores the array indices where values in comp are equal to the maximum value found in comp. This is done using the functions MAX and FIND and the comparison operator ==.
In the future, I would urge you to first use the online documentation to try and help you better understand how MATLAB works. It is very good documentation. I've learned most of what I know from it. ;)
I want to apply a function to all columns in a matrix with MATLAB. For example, I'd like to be able to call smooth on every column of a matrix, instead of having smooth treat the matrix as a vector (which is the default behaviour if you call smooth(matrix)).
I'm sure there must be a more idiomatic way to do this, but I can't find it, so I've defined a map_column function:
function result = map_column(m, func)
result = m;
for col = 1:size(m,2)
result(:,col) = func(m(:,col));
end
end
which I can call with:
smoothed = map_column(input, #(c) (smooth(c, 9)));
Is there anything wrong with this code? How could I improve it?
The MATLAB "for" statement actually loops over the columns of whatever's supplied - normally, this just results in a sequence of scalars since the vector passed into for (as in your example above) is a row vector. This means that you can rewrite the above code like this:
function result = map_column(m, func)
result = [];
for m_col = m
result = horzcat(result, func(m_col));
end
If func does not return a column vector, then you can add something like
f = func(m_col);
result = horzcat(result, f(:));
to force it into a column.
Your solution is fine.
Note that horizcat exacts a substantial performance penalty for large matrices. It makes the code be O(N^2) instead of O(N). For a 100x10,000 matrix, your implementation takes 2.6s on my machine, the horizcat one takes 64.5s. For a 100x5000 matrix, the horizcat implementation takes 15.7s.
If you wanted, you could generalize your function a little and make it be able to iterate over the final dimension or even over arbitrary dimensions (not just columns).
Maybe you could always transform the matrix with the ' operator and then transform the result back.
smoothed = smooth(input', 9)';
That at least works with the fft function.
A way to cause an implicit loop across the columns of a matrix is to use cellfun. That is, you must first convert the matrix to a cell array, each cell will hold one column. Then call cellfun. For example:
A = randn(10,5);
See that here I've computed the standard deviation for each column.
cellfun(#std,mat2cell(A,size(A,1),ones(1,size(A,2))))
ans =
0.78681 1.1473 0.89789 0.66635 1.3482
Of course, many functions in MATLAB are already set up to work on rows or columns of an array as the user indicates. This is true of std of course, but this is a convenient way to test that cellfun worked successfully.
std(A,[],1)
ans =
0.78681 1.1473 0.89789 0.66635 1.3482
Don't forget to preallocate the result matrix if you are dealing with large matrices. Otherwise your CPU will spend lots of cycles repeatedly re-allocating the matrix every time it adds a new row/column.
If this is a common use-case for your function, it would perhaps be a good idea to make the function iterate through the columns automatically if the input is not a vector.
This doesn't exactly solve your problem but it would simplify the functions' usage. In that case, the output should be a matrix, too.
You can also transform the matrix to one long column by using m(:,:) = m(:). However, it depends on your function if this would make sense.