I have a 5-by-200 matrix where the i:50:200, i=1:50 are related to each other, so for example the matrix columns 1,51,101,151 are related to each other, and columns 49,99,149,199 are also related to each other.
I want to use a for-loop to create another matrix that re-sorts the previous matrix based on this relationship.
My code is
values=zeros(5,200);
for j=1:50
for m=1:4:200
a=factor_mat(:,j:50:200)
values(:,m)=a
end
end
However, the code does not work.
Here's what's happening. Let's say we're on the first iteration of the outer loop, so j == 1. This effectively gives you:
j = 1;
for m=1:4:200
a=factor_mat(:,j:50:200)
values(:,m)=a;
end
So you're creating the same submatrix for a (j doesn't change) 50 times and storing it at different places in the values matrix. This isn't really what you want to do.
To create each 4-column submatrix once and store them in 50 different places, you need to use j to tell you which of the 50 you're currently processing:
for j=1:50
a=factor_mat(:,j:50:200);
m=j*4; %// This gives us the **end** of the current range
values(:,m-3:m)=a;
end
I've used a little trick here, because the indices of Matlab arrays start at 1 rather than 0. I've calculated the index of the last column we want to insert. For the first group, this is column 4. Since j == 1, j * 4 == 4. Then I subtract 3 to find the first column index.
That will fix the problem you have with your loops. But loops aren't very Matlab-ish. They used to be very slow; now they're adequate. But they're still not the cool way to do things.
To do this without loops, you can use reshape and permute:
a=reshape(factor_mat,[],50,4);
b=permute(a,[1,3,2]);
values=reshape(b,[],200);
Related
I have a matrix with a large number of rows. I have another matrix that I will loop through one row at a time. For each row in the second matrix, I need to look for similar rows in the first matrix. Once all the similar rows are found, I need to know the row numbers of the similar rows. These rows will almost never be exact, so ismember does not work.
Also, the solution would preferably (not necessarily, however) give some way to set a level of similarity that would trigger the code to say it is similar and give me the row number.
Is there any way to do this? I've looked around, and I can't find anything.
You could use cosine distance, which finds the angle between two vectors. Similar vectors (in your case, a row and your comparison vector) have a value close to 1 and dissimilar vectors have a value close to 0.
function d = cosSimilarity(u, v)
d = dot(u,v)/(norm(u)*norm(v));
end
To apply this function to each to all pairs of rows in the matrices M and V you could use nested for loops. Hardly the most elegant, but it will work:
numRowsM = size(M, 1)
numRowsV = size(V, 1)
similarThresh = .9
for m = 1:numRowsM
for v = 1:numRowsV
similarity = cosSimilarity(V(v,:), M(m, :))
% Notify about similar rows
if similarity > similarThresh
disp([num2str(m) ' is similar to a row in V'])
end
end
end
Instead of nested for loops, there are definitely other ways. You could start by looking at the solution from this question, which will help you avoid the loop by converting the rows of the matrix into cells of a cell array and then applying the function with cellfun.
I'm struggling with one of my matlab assignments. I want to create 10 different models. Each of them is based on the same original array of dimensions 1x100 m_est. Then with for loop I am choosing 5 random values from the original model and want to add the same random value to each of them. The cycle repeats 10 times chosing different values each time and adding different random number. Here is a part of my code:
steps=10;
for s=1:steps
for i=1:1:5
rl(s,i)=m_est(randi(numel(m_est)));
rl_nr(s,i)=find(rl(s,i)==m_est);
a=-1;
b=1;
r(s)=(b-a)*rand(1,1)+a;
end
pert_layers(s,:)=rl(s,:)+r(s);
M=repmat(m_est',s,1);
end
for k=steps
for m=1:1:5
M_pert=M;
M_pert(1:k,rl_nr(k,1:m))=pert_layers(1:k,1:m);
end
end
In matrix M I am storing 10 initial models and want to replace the random numbers with indices from rl_nr matrix into those stored in pert_layers matrix. However, the last loop responsible for assigning values from pert_layers to rl_nr indices does not work properly.
Does anyone know how to solve this?
Best regards
Your code uses a lot of loops and in this particular circumstance, it's quite inefficient. It's better if you actually vectorize your code. As such, let me go through your problem description one point at a time and let's code up each part (if applicable):
I want to create 10 different models. Each of them is based on the same original array of dimensions 1x100 m_est.
I'm interpreting this as you having an array m_est of 100 elements, and with this array, you wish to create 10 different "models", where each model is 5 elements sampled from m_est. rl will store these values from m_est while rl_nr will store the indices / locations of where these values originated from. Also, for each model, you wish to add a random value to every element that is part of this model.
Then with for loop I am choosing 5 random values from the original model and want to add the same random value to each of them.
Instead of doing this with a for loop, generate all of your random indices in one go. Since you have 10 steps, and we wish to sample 5 points per step, you have 10*5 = 50 points in total. As such, why don't you use randperm instead? randperm is exactly what you're looking for, and we can use this to generate unique random indices so that we can ultimately use this to sample from m_est. randperm generates a vector from 1 to N but returns a random permutation of these elements. This way, you only get numbers enumerated from 1 to N exactly once and we will ensure no repeats. As such, simply use randperm to generate 50 elements, then reshape this array into a matrix of size 10 x 5, where the number of rows tells you the number of steps you want, while the number of columns is the total number of points per model. Therefore, do something like this:
num_steps = 10;
num_points_model = 5;
ind = randperm(numel(m_est));
ind = ind(1:num_steps*num_points_model);
rl_nr = reshape(ind, num_steps, num_points_model);
rl = m_est(rl_nr);
The first two lines are pretty straight forward. We are just declaring the total number of steps you want to take, as well as the total number of points per model. Next, what we will do is generate a random permutation of length 100, where elements are enumerated from 1 to 100, but they are in random order. You'll notice that this random vector uses only a value within the range of 1 to 100 exactly once. Because you only want to get 50 points in total, simply subset this vector so that we only get the first 50 random indices generated from randperm. These random indices get stored in ind.
Next, we simply reshape ind into a 10 x 5 matrix to get rl_nr. rl_nr will contain those indices that will be used to select those entries from m_est which is of size 10 x 5. Finally, rl will be a matrix of the same size as rl_nr, but it will contain the actual random values sampled from m_est. These random values correspond to those indices generated from rl_nr.
Now, the final step would be to add the same random number to each model. You can certainly use repmat to replicate a random column vector of 10 elements long, and duplicate them 5 times so that we have 5 columns then add this matrix together with rl.... so something like:
a = -1;
b = 1;
r = (b-a)*rand(num_steps, 1) + a;
r = repmat(r, 1, num_points_model);
M_pert = rl + r;
Now M_pert is the final result you want, where we take each model that is stored in rl and add the same random value to each corresponding model in the matrix. However, if I can suggest something more efficient, I would suggest you use bsxfun instead, which does this replication under the hood. Essentially, the above code would be replaced with:
a = -1;
b = 1;
r = (b-a)*rand(num_steps, 1) + a;
M_pert = bsxfun(#plus, rl, r);
Much easier to read, and less code. M_pert will contain your models in each row, with the same random value added to each particular model.
The cycle repeats 10 times chosing different values each time and adding different random number.
Already done in the above steps.
I hope you didn't find it an imposition to completely rewrite your code so that it's more vectorized, but I think this was a great opportunity to show you some of the more advanced functions that MATLAB has to offer, as well as more efficient ways to generate your random values, rather than looping and generating the values one at a time.
Hopefully this will get you started. Good luck!
I've just started using for loops in matlab in programming class and the basic stuff is doing me fine, However I've been asked to "Use loops to create a 3 x 5 matrix in which the value of each element is its row number to the power of its column number divided by the sum of its row number and column number for example the value of element (2,3) is (2^3 / 2+3) = 1.6
So what sort of looping do I need to use to enable me to start new lines to form a matrix?
Since you need to know the row and column numbers (and only because you have to use loops), for-loops are a natural choice. This is because a for-loop will automatically keep track of your row and column number for you if you set it up right. More specifically, you want a nested for loop, i.e. one for loop within another. The outer loop might loop through the rows and the inner loop through the columns for example.
As for starting new lines in a matrix, this is extremely bad practice to do in a loop. You should rather pre-allocate your matrix. This will have a major performance impact on your code. Pre-allocation is most commonly done using the zeros function.
e.g.
num_rows = 3;
num_cols = 5;
M = zeros(num_rows,num_cols); %// Preallocation of memory so you don't grow your matrix in your loop
for row = 1:num_rows
for col = 1:num_cols
M(row,col) = (row^col)/(row+col);
end
end
But the most efficient way to do it is probably not to use loops at all but do it in one shot using ndgrid:
[R, C] = ndgrid(1:num_rows, 1:num_cols);
M = (R.^C)./(R+C);
The command bsxfun is very helpful for such problems. It will do all the looping and preallocation for you.
eg:
bsxfun(#(x,y) x.^y./(x+y), (1:3)', 1:5)
Starting wish a 7x4 binary matrix I need to change a random bit in each column to simulate error. Have been trying to no avail.
A very straightforward way to do this is to use a for loop. It might not be the most efficient approach in MATLAB, but it's probably good enough considering your data set is so small.
Iterate through each of the four columns. On each iteration, randomly chose a number from 1 to 7 to represent the row in that column that you have selected to change. Finally, flip the bit at that row/column. The following code does just this. Assume that "A" is a binary matrix with 7 rows and 4 columns
for col=1:4; %// Iterate through each column
row = ceil(7*rand()); %// Randomly chose a number from 1 to 7 to represent row
A(row,col) = ~A(row,col); %// Flip the bit at the specified row/col
end
Another possibility is to create 4 random numbers in one call, and assign in a vectorized fashion:
rowNumbers = randi(4,[1 4])
A(rowNumbers,:) = ~A(rowNumbers,:);
Could somebody explain the following code snippet? I have no background in computer science or programming and just recently became aware of Matlab. I understand the preallocation part from data=ceil(rand(7,5)*10)... to ...N*(N-1)/2).
I need to understand every aspect of how matlab processes the code from kk=0 to the end. Also, the reasons why the code is codified in that manner. There's no need to explain the function of: bsxfun(#minus), just how it operates in the scheme of the code.
data=ceil(rand(7,5)*10);
N = size(data,2);
b=cell(N-1,1);
c=NaN(size(data,1),N*(N-1)/2);
kk=0;
for ii=1:N-1
b{ii} = bsxfun(#minus,data(:,ii),data(:,ii+1:end));
c(:,kk+(1:N-ii)) = bsxfun(#minus,data(:,ii),data(:,ii+1:end));
kk=kk+N-ii;
end
Start at zero
kk=0;
Loop with ii going from 1 up to N-1 incrementing by 1 every iteration. Type 1:10 in the command line of matlab and you'll see that it outputs 1 2 3 4 5 6 7 8 9 10. Thuis colon operator is a very important operator to understand in matlab.
for ii=1:N-1
b{ii} = ... this just stores a matrix in the next element of the cell vector b. Cell arrays can hold anything in each of their elements, this is necessary as in this case each iteration is creating a matrix with one fewer column than the previous iteration.
data(:,ii) --> just get the iith column of the matrix data (: means get all the rows)
data(:, ii + 1:end) means get a subset of the matrix data consisting of all the rows but only of columns that appear after column ii
bsxfun(#minus, data(:,ii), data(:,ii+1:end)) --> for each column in the matrix data(:, ii+1:end), subtract the single column data(:,ii)
b{ii} = bsxfun(#minus,data(:,ii),data(:,ii+1:end));
%This does the same thing as the line above but instead of storing the resulting matrix of the loop in a separate cell of a cell array, this is appending the original array with the new matrix. Note that the new matrix will have the same number of rows each time but one fewer column, so this appends as new columns.
%c(:,kk + (1:N-ii)) = .... --> So 1:(N-ii) produces the numbers 1 up to the number of columns in the result of this iteration. In matlab, you can index an array using another array. So for example try this in the command line of matlab: a = [0 0 0 0 0]; a([1 3 5]) = 1. The result you should see is a = 1 0 1 0 1. but you can also extend a matrix like this so for example now type a(6) = 2. The result: a = 1 0 1 0 1 2. So by using c(:, 1:N-ii) we are indexing all the rows of c and also the right number of columns (in order). Adding the kk is just offsetting it so that we do not overwrite our previous results.
c(:,kk+(1:N-ii)) = bsxfun(#minus,data(:,ii),data(:,ii+1:end));
Now we just increment kk by the number of new columns we added so that in the next iteration, c is appended at the end.
kk=kk+N-ii;
end;
I suggest that you put a breakpoint in this code and step through it line by line and look at how the variables change in matlab. To do this click on the little dashed line next to k=0; in the mfile, you will see a red dot appear there, and then run the code. The code will only execute as far as the dot, you are now in debug mode. If you hover over a variable in debug mode matlab will show its contents in a tool tip. For a really big variable check it out in the workspace. Now step through the code line by line and use my explanations above to make sure you understand how each line is changing each variable. For more complex lines like b{ii} = bsxfun(#minus,data(:,ii),data(:,ii+1:end)); you should highlight code snippets and ruin these in the command line to see what each part is doing so for example run data(:,ii) to see what that does and then try data(:,ii+1:end)) or even just ii+1:end (well in that case it wont work, replace end with size(data, 2)). Debugging is the best way to understand code that confuses you.
bsxfun(#minus,A,B)
is almost the same as
A-B
The difference is that the bsxfun version will handle inputs of different size: In each dimension (“direction,” if you find it easier to think about that way), if one of the inputs is scalar and the other one a vector, the scalar one will simply be repeated sufficiently often.
http://www.mathworks.com/help/techdoc/ref/bsxfun.html