I have a huge vector. I have to count values falling within certain ranges.
the ranges are like 0-10, 10-20 etc. I have to count the number of values which fall in certain range.
I did something like this :
for i=1:numel(m1)
if (0<m1(i)<=10)==1
k=k+1;
end
end
Also:
if not(isnan(m1))==1
x=(0<m1<=10);
end
But both the times it gives array which contains all 1s. What wrong am I doing?
You can do something like this (also works for non integers)
k = sum(m1>0 & m1<=10)
You can use logical indexing. Observe:
>> x = randi(40, 1, 10) - 20
x =
-2 17 -12 -9 -14 -14 15 4 2 -14
>> x2 = x(0 < x & x < 10)
x2 =
4 2
>> length(x2)
ans =
2
and the same done in one step:
>> length(x(0 < x & x < 10))
ans =
2
to count the values in a specific range you can use ismember,
if m1 is vector use,
k = sum(ismember(m1,0:10));
If m1 is matrix use k = sum(sum(ismember(m1,0:10)));
for example,
m1=randi(20,[5 5])
9 10 6 10 16
8 9 14 20 6
16 13 14 7 11
16 15 4 12 14
4 16 3 5 18
sum(sum(ismember(m1,1:10)))
12
Why not simply do something like this?
% Random data
m1 = 100*rand(1000,1);
%Count elements between 10 and 20
m2 = m1(m1>10 & m1<=20);
length(m2) %number of elements of m1 between 10 and 20
You can then put things in a loop
% Random data
m1 = 100*rand(1000,1);
nb_elements = zeros(10,1);
for k=1:length(nb_elements)
temp = m1(m1>(10*k-10) & m1<=(10*k));
nb_elements(k) = length(temp);
end
Then nb_elements contains your data with nb_elements(1) for the 0-10 range, nb_elements(2) for the 10-20 range, etc...
Matlab does not know how to evaluate the combined logical expression
(0<m1(i)<=10)
Insted you should use:
for i=1:numel(m1)
if (0<m1(i)) && (m1(i)<=10)
k=k+1;
end
end
And to fasten it up probably something like this:
sum((0<m1) .* (m1<=10))
Or you can create logical arrays and then use element-wise multiplication. Don't know how fast this is though and it might use a lot of memory for large arrays.
Something like this
A(find((A>0.2 .* (A<0.8)) ==1))
Generate values
A= rand(5)
A =
0.414906 0.350930 0.057642 0.650775 0.525488
0.573207 0.763477 0.120935 0.041357 0.900946
0.333857 0.241653 0.421551 0.737704 0.162307
0.517501 0.491623 0.016663 0.016396 0.254099
0.158867 0.098630 0.198298 0.223716 0.136054
Find the intersection where the values > 0.8 and < 0.2. This will give you two logical arrays and the values where A>0.2 and A<0.8 will be =1 after element-wise multiplication.
find((A>0.2 .* (A<0.8)) ==1)
Then apply those indices to A
A(find((A>0.2 .* (A<0.8)) ==1))
ans =
0.41491
0.57321
0.33386
0.51750
0.35093
0.76348
0.24165
0.49162
0.42155
0.65077
0.73770
0.22372
0.52549
0.90095
0.25410
Related
I have 3d matrix A that has my data. At multiple locations defined by row and column indcies as shown by matrix row_col_idx I want to extract all data along the third dimension as shown below:
A = cat(3,[1:3;4:6], [7:9;10:12],[13:15;16:18],[19:21;22:24]) %matrix(2,3,4)
row_col_idx=[1 1;1 2; 2 3];
idx = sub2ind(size(A(:,:,1)), row_col_idx(:,1),row_col_idx(:,2));
out=nan(size(A,3),size(row_col_idx,1));
for k=1:size(A,3)
temp=A(:,:,k);
out(k,:)=temp(idx);
end
out
The output of this code is as follows:
A(:,:,1) =
1 2 3
4 5 6
A(:,:,2) =
7 8 9
10 11 12
A(:,:,3) =
13 14 15
16 17 18
A(:,:,4) =
19 20 21
22 23 24
out =
1 2 6
7 8 12
13 14 18
19 20 24
The output is as expected. However, the actual A and row_col_idx are huge, so this code is computationally expensive. Is there away to vertorize this code to avoid the loop and the temp matrix?
This can be vectorized using linear indexing and implicit expansion:
out = A( row_col_idx(:,1) + ...
(row_col_idx(:,2)-1)*size(A,1) + ...
(0:size(A,1)*size(A,2):numel(A)-1) ).';
The above builds an indexing matrix as large as the output. If this is unacceptable due to memory limiations, it can be avoided by reshaping A:
sz = size(A); % store size A
A = reshape(A, [], sz(3)); % collapse first two dimensions
out = A(row_col_idx(:,1) + (row_col_idx(:,2)-1)*sz(1),:).'; % linear indexing along
% first two dims of A
A = reshape(A, sz); % reshape back A, if needed
A more efficient method is using the entries of the row_col_idx vector for selecting the elements from A. I have compared the two methods for a large matrix, and as you can see the calculation is much faster.
For the A given in the question, it gives the same output
A = rand([2,3,10000000]);
row_col_idx=[1 1;1 2; 2 3];
idx = sub2ind(size(A(:,:,1)), row_col_idx(:,1),row_col_idx(:,2));
out=nan(size(A,3),size(row_col_idx,1));
tic;
for k=1:size(A,3)
temp=A(:,:,k);
out(k,:)=temp(idx);
end
time1 = toc;
%% More efficient method:
out2 = nan(size(A,3),size(row_col_idx,1));
tic;
for jj = 1:size(row_col_idx,1)
out2(:,jj) = [A(row_col_idx(jj,1),row_col_idx(jj,2),:)];
end
time2 = toc;
fprintf('Time calculation 1: %d\n',time1);
fprintf('Time calculation 2: %d\n',time2);
Gives as output:
Time calculation 1: 1.954714e+01
Time calculation 2: 2.998120e-01
I have a n x m array (could be any size array but it will not be a 1 x m) and I want to rotate / shift each square loop individually no matter the array size.
How can I alternate the rotation / shift each square loop no matter the size of the array.
Please note: I'm not trying to calculate the values in the array but shift the values.
My thought process was to get the values of each "square loop" and place them into one row and do a circshift then place them back into another array.
I ran into problems trying to get the values back into the original n x m array size and I wasn't sure how I could loop through the process for different n x m arrays.
The pink highlighted section, left of the arrows is the starting position of the array and it's "loops" and the green highlighted section, right of the arrows is the type of rotation / shift of the values that I'm trying to create. The array could have more than 3 "loops" this is just an example.
Code below:
I=[1:5;6:10;11:15;16:20;21:25;26:30]
[rw,col] = size(I);
outer_1=[I(1,:),I(2:end-1,end).',I(end,end:-1:1),I(end-1:-1:2,1).'] %get values in one row (so I can shift values)
outer_1_shift=circshift(outer_1,[0 1]) %shift values
new_array=zeros(rw,col);
Ps: I'm using Octave 4.2.2 Ubuntu 18.04
Edit: The circshift function was changed for Octave 5.0, the last edit made it compatible with previous versions
1;
function r = rndtrip (n, m, v)
rv = #(x) x - 2 * (v - 1);
r = [v * ones(1,rv(m)-1) v:n-v+1 (n-v+1)*ones(1,rv(m)-2)];
if (rv(m) > 1)
r = [r n-v+1:-1:v+1];
endif
endfunction
function idx = ring (n, m , v)
if (2*(v-1) > min (n, m))
r = [];
else
r = rndtrip (n, m, v);
c = circshift (rndtrip (m, n, v)(:), - n + 2 * v - 1).';
idx = sub2ind ([n m], r, c);
endif
endfunction
# your I
I = reshape (1:30, 5, 6).';
# positive is clockwise, negative ccw
r = [1 -1 1];
for k = 1:numel(r)
idx = ring (rows(I), columns(I), k);
I(idx) = I(circshift(idx(:), r(k)));
endfor
I
gives
I =
6 1 2 3 4
11 8 9 14 5
16 7 18 19 10
21 12 13 24 15
26 17 22 23 20
27 28 29 30 25
run it on tio
So, I had the same idea as in Andy's comment. Nevertheless, since I was already preparing some code, here is my suggestion:
% Input.
I = reshape(1:30, 5, 6).'
[m, n] = size(I);
% Determine number of loops.
nLoops = min(ceil([m, n] / 2));
% Iterate loops.
for iLoop = 1:nLoops
% Determine number of repetitions per row / column.
row = n - 2 * (iLoop - 1);
col = m - 2 * (iLoop - 1);
% Initialize indices.
idx = [];
% Add top row indices.
idx = [idx, [repelem(iLoop, row).']; iLoop:(n-(iLoop-1))];
% Add right column indices.
idx = [idx, [[iLoop+1:(m-(iLoop-1))]; repelem(n-(iLoop-1), col-1).']];
if (iLoop != m-(iLoop-1))
% Add bottom row indices.
idx = [idx, [repelem(m-(iLoop-1), row-1).'; (n-(iLoop-1)-1:-1:iLoop)]]
end
if (iLoop != n-(iLoop-1))
% Add left column indices.
idx = [idx, [[(m-(iLoop-1))-1:-1:iLoop+1]; repelem(iLoop, col-2).']]
end
% Convert subscript indices to linear indices.
idx = sub2ind(size(I), idx(1, :), idx(2, :));
% Determine direction for circular shift operation.
if (mod(iLoop, 2) == 1)
direction = [0 1];
else
direction = [0 -1];
end
% Replace values in I.
I(idx) = circshift(I(idx), direction);
end
% Output.
I
Unfortunately, I couldn't think of a smarter way to generate the indices, since you need to maintain the right order and avoid double indices. As you can see, I obtain subscript indices with respect to I, since this can be done quite easy using the matrix dimensions and number of loops. Nevertheless, for the circshift operation and later replacing of the values in I, linear indices are more handy, so that's why the sub2ind operation.
Input and output look like this:
I =
1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
16 17 18 19 20
21 22 23 24 25
26 27 28 29 30
I =
6 1 2 3 4
11 8 9 14 5
16 7 18 19 10
21 12 13 24 15
26 17 22 23 20
27 28 29 30 25
I was right, that the "shift direction" changes with every loop?
Hope that helps!
Caution: I haven't tested for generality, yet. So, please report any errors you might come across.
If there is a vector like this,
T = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16]
(the size of vector T can be flexible)
How can I get a array of 'sum of divisions'?
For example,
fn(T, 5) = [ (1+2+3+4+5) , (6+7+8+9+10), (11+12+13+14+15) , 16]
One option, which doesn't require the padding of zeros on the original array, is the use of accumarray and ceil:
div = 5;
out = accumarray(ceil((1:numel(T))/div).',T(:))
Another option using cumsum and diff instead:
div = 5;
T(ceil(numel(T)/div)*div) = 0;
cs = cumsum(T)
out = diff( [0 cs(div:div:end) ] )
Edit: once the padding is done, cumsum and diff are a little overkill and one should proceed as in Bentoy's answer.
Another way, close to the 2nd option of thewaywewalk:
div = 5;
T(ceil(numel(T)/div)*div) = 0;
out = sum(reshape(T,div,[])).'; % transpose if you really want a column vector
Also, one one-liner solution (I prefer this one):
out = blockproc(T,[1 5], #(blk) sum(blk.data), 'PadPartialBlocks',true);
Don't forget to set the parameter 'PadPartialBlocks', this is the key of avoiding explicit padding.
There is an in-built function vec2mat in Communications System Toolbox to convert a vector into a 2D matrix that cuts off after every N elements and puts into separate rows, padding the leftover places at the end with zeros to maintain 2D size . So, after using vec2mat, summing all the rows would be enough to give you the desired output. Here's the implementation -
sum(vec2mat(T,5),2)
Sample run -
>> T = 1:16;
>> vec2mat(T,5)
ans =
1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
16 0 0 0 0
>> sum(vec2mat(T,5),2)
ans =
15
40
65
16
I've an array
X =
10
5 (e)
20
5
30
6
40
4
50
3
60
8
70
12
and so on...
I already know the value 5 which I've called e. I also know where it is located in the array. I want the following:
all the elements in X(2:2:end) within a certain range of +/-3 from e. (which are 5, 5, 6, 4, 3, 8).
the corresponding X(1:2:end) to the values we found within range. That means the final answer Y should be:
Y =
10
5
20
5
30
6
40
4
50
3
60
8
Thanks a lot!
In MATLAB/Octave, you can find the indexes of non-zero elements with the function find. Your problem is easily solved combining find with logic operators:
Y = X(find(X <= X(2)+3 & X >= X(2)-3));
Explained:
e = X(2)
X <= e+3 % Produces a Matrix with the element-wise result (1 or 0). The
X >= e-3 % values are determined by the logic operators >= and <=.
find(X) % Returns a matrix with the indeces of non-zero elements of X.
X(find(X)) % Returns the non-zero elements.
Tested in Octave (though should work in MATLAB too).
Solved!
X = [10; 5; 20; 5; 30; 6; 40; 4; 50; 3; 60; 8; 70; 12];
Xodd=X(1:2:end);
Xeven=X(2:2:end);
i=find(Xeven>5) %just an example could be done with other conditions
t=[Xodd(i) Xeven(i)];
%cascade them back!
Y=t';Y=Y(:);
I have a matlab problem to solve. In have two vectores that limit my space, x_low and x_high. The matrix pos needs to have values within this spaces and each column of the matrix has different bounds given by the two vectores. Now my problem is that randi gives valus between two integers but i need to change the bounds for each columns. There is another way to use randi or a different matlab function to do this?
I know there are better codes to do this but i'm starting to use matlab and i know to do it this way, any aid is welcome
x_low = [Io_low, Iirr_low, Rp_low, Rs_low, n_low]; % vector of constant values
x_high = [Io_high, Iirr_high, Rp_high, Rs_high, n_high]; % vector of constant values
pos = rand(particles, var);
var = length(x_high);
for i = 1: particles % rows
for k = 1: var %columns
if pos(i, k) < x_low(k) || pos(i, k) > x_high(k) % if the position is out of bounder
pos(i, k) = randi(x_low(k), x_high(k), 1); % fill it with a particle whithin the bounderies
end
end
end
If I understand correctly, you need to generate a matrix with integer values such that each column has different lower and upper limits; and those lower and upper limits are inclusive.
This can be done very simply with
rand (to generate random numbers between 0 and 1 ),
bsxfun (to take care of the lower and upper limits on a column basis), and
round (so that the results are integer values).
Let the input data be defined as
x_low = [1 6 11]; %// lower limits
x_high = [3 10 100]; %// upper limits
n_rows = 7; %// number of columns
Then:
r = rand(n_rows, numel(x_low)); %// random numbers between 0 and 1
r = floor(bsxfun(#times, r, x_high-x_low+1)); %// adjust span and round to integers
r = bsxfun(#plus, r, x_low); %// adjust lower limit
gives something like
r =
2 7 83
3 6 93
2 6 22
3 10 85
3 7 96
1 10 90
2 8 57
If you need to fill in values only at specific entries of matrix pos, you can use something like
ind = bsxfun(#lt, pos, x_low) | bsxfun(#gt, pos, x_high); %// index of values to replace
pos(ind) = r(ind);
This a little overkill, because the whole matrixd r is generated only to use some of its entries. To generate only the needed values the best way is probably to use loops.
You can use cellfun for this. Something like:
x_low = [Io_low, Iirr_low, Rp_low, Rs_low, n_low];
x_high = [Io_high, Iirr_high, Rp_high, Rs_high, n_high];
pos = cell2mat(cellfun(#randi, mat2cell([x_low' x_high'], ones(numel(x_low),1), 1), repmat({[particles 1]}, [numel(x_low) 1)])))';
Best,