I wanted to plot the following
y=linspace(0,D,100)
temp=y^2;
plot(y,temp);
i am getting an error with y^2, it says matrix should be square.
is there another way to plot.
You are not getting that error because of plot. You are getting it because of
temp=y^2
Instead, you should be using
temp=y.^2
^ means matrix power. .^ is elementwise power. You can find more about MATLAB operators here.
Let's say you have a 3x3 matrix, magic(3).
A=magic(3)
A =
8 1 6
3 5 7
4 9 2
Here is square of matrix A (which is A*A, as Dan suggested):
A^2
ans =
91 67 67
67 91 67
67 67 91
Here is the matrix which contains squares of A's elements:
A.^2
ans =
64 1 36
9 25 49
16 81 4
Just as an alternative to the above answer, you can consider the following case:
A = magic(3);
temp = bsxfun(#times,A,A);
which retrieves the same results as
temp = A.^2;
the . operator will apply your square element-wise. bsxfun makes exactly the same.
I hope this helps.
Related
I tried to resample my data from a block of matrix that defined its indices. Hopefully this example can make it clear:
A=rand(18400,100);
A_IDX=randi([1 100],[18400 100]);
A_IDX consist 18400 rows and 100 columns. I wanted to extract the matrix A at the A_IDX indices. Result would be something like:
A=[1 2 3; 4 5 6];
A_IDX=[1 3; 2 3];
A_Result=[1 3; 5 6];
I tried A(:,A_IDX) but that gave me 1840x184000 matrix size, which is not what I wanted to do in the first place. Anyone can help? Thanks in advance!
We could get the linear index equivalent for those indices and then simply indexing into the input array would give us the desired output. Now, to get those linear indices, we would make use of bsxfun for the math computations related to the index computations, which would basically involve scaling and offsetting.
Indexing with 2D array of column indices
For a 2D array of column indices, we would have -
function out = take_cols(a, col_idx)
n = size(a,1);
lidx = bsxfun(#plus,(col_idx-1)*n,(1:n).');
out = a(lidx);
Sample run -
>> a
a =
39 83 39 48 36
58 74 20 19 50
69 97 65 34 57
47 58 80 24 51
>> col_idx
col_idx =
2 4
3 5
1 4
2 5
>> take_cols(a, col_idx)
ans =
83 48
20 50
69 34
58 51
Indexing with 2D array of row indices
For a 2D array of row indices, it would be -
function out = take_rows(a, row_idx)
[m,n] = size(a);
lidx = bsxfun(#plus,row_idx, (0:n-1)*m);
out = a(lidx);
Sample run -
>> a
a =
39 83 39 48 36
58 74 20 19 50
69 97 65 34 57
47 58 80 24 51
>> row_idx
row_idx =
3 2 3 1 2
4 3 4 2 4
>> take_rows(a, row_idx)
ans =
69 74 65 48 50
47 97 80 19 51
This weird monster of code will give you what you want. It generates proper subscripts for each index and converts them to linear, then just indexes A linearly.
A_IDX_aux=A_IDX';
reshape(A(sub2ind(size(A),repelem(1:size(A,1),1,size(A_IDX,1)).',A_IDX_aux(:))),[size(A,1), size(A_IDX,2)]).';
I find my solution for this task too, but not so fast, as Divakar and Ander :)
Behold:
res = cell2mat(arrayfun( #(x) A(x,A_IDX(x,:)), (1:size(A,1))', 'UniformOutput',false));
It use cell2mat and I suppose it is not so fast as bsxfun, but hope is still alive and I was curios to test all the 3 solutions. And I got unobvious results!
Elapsed time is 0.000058 seconds. % Divakar
Elapsed time is 0.000077 seconds. % Andres
Elapsed time is 0.000339 seconds. % Me
This mean bsxf is fastest! But using right indexing give fast result too! And my solution was really slow. I suppose it's because of 'UniformOutput', false - I forced to convert to cells and then back, so it slow my method a lot.
Conclusion:
If you can use bsxf - use it!
Despite the fact that my method looks more visually pleasing than that of Andres, it is still slower.
So there is no any sense to post this answer :D I spend some time for current work, maybe it will help someone in future
I have a few multidimensional matrices of dimensions mxnxt, where each element in mxn is an individual sensor input, and t is time. What I want to do is analyse only the peak values for each element in mxn over t, so I would end up with a single 2D matrix of mxn containing only max values.
I know there are are ways to get a single overall max value, but is there a way to combine this with element-by-element operations like bsxfun so that it examines each individual element over t?
I'd be grateful for any help you can give because I'm really stuck at the moment. Thanks in advance!
Is this what you want?
out = max(A,[],3); %// checking maximum values in 3rd dimension
Example:
A = randi(50,3,3,3); %// Random 3x3x3 dim matrix
out = max(A,[],3);
Results:
A(:,:,1) =
35 5 8
38 12 42
23 46 27
A(:,:,2) =
50 6 39
4 49 41
23 1 44
A(:,:,3) =
5 41 10
20 22 14
13 46 8
>> out
out =
50 41 39
38 49 42
23 46 44
You can call max() with the matrix and select the dimension (look the documentation) on which the operation will be calculated, e.g
M = max(A,[],3)
I am trying to plot as below:-
x=0:0.1:1;
plot(x,2*x-x^2);
Why does this give the following error:-
Error using ^
Inputs must be a scalar and a square matrix.
To compute elementwise POWER, use POWER (.^) instead.
The objective is to plot a quadratic function only. SO i modified the above as follows:-
x=0:0.1:1;
plot(x,2*x-x*x);
The error persisted:-
Error using *
Inner matrix dimensions must agree.
Where am i going wrong?
You want either
x=0:0.1:1;
plot(x,2*x-x.^2);
or
x=0:0.1:1;
plot(x,2*x-x.*x);
MATLAB automatically uses the * operator for matrix multipilcation when both operands are arrays, and uses the ^ for matrix multiplication when the left operand is an array. This applies to both one- and two-dimensional arrays.
x*x and x^2 are trying to matrix-multiply a 1x11 array by a 1x11 array, which makes no sense, hence the Inner matrix dimensions must agree. error.
To perform element-wise operations on arrays, you must prefix the operator with a .. For example, x.*x performs element-wise multiplication and x.^2 performs element-wise exponentiation.
See below:
>> A = magic(3)
A =
8 1 6
3 5 7
4 9 2
>> A*A % or A^2 do matrix multiplication
ans =
91 67 67
67 91 67
67 67 91
>> A.*A % or A.^2 do element-wise multiplication, (the square of each element)
64 1 36
9 25 49
16 81 4
I want to calculate the sum of the elements surrounding a given element in a matrix. So far, I have written these lines of code:
for i=1:m,
rij(1:n)=0
for j=1:n,
alive = tijdelijk(i-1,j)+tijdelijk(i+1,j)+tijdelijk(i-1,j-1)+tijdelijk(i+1,j-1)+tijdelijk(i,j+1)+tijdelijk(i,j-1)+tijdelijk(i-1,j+1)+tijdelijk(i+1,j+1)
This results in an error because, for example, i-1 becomes zero for i=1. Anyone got an idea how to do this without getting this error?
You can sum the elements via filtering. conv2 can be used for this manner.
Let me give an example. I create a sample matrix
>> A = reshape(1:20, 4, 5)
A =
1 5 9 13 17
2 6 10 14 18
3 7 11 15 19
4 8 12 16 20
Then, I create a filter. The filter is like a mask where you put the center on the current cell and the locations corresponding to the 1's on the filter are summed. For eight-connected neighbor case, the filter should be as follows:
>> B = [1 1 1; 1 0 1; 1 1 1]
B =
1 1 1
1 0 1
1 1 1
Then, you simply convolve the matrix with this small matrix.
>> conv2(A, B, 'same')
ans =
13 28 48 68 45
22 48 80 112 78
27 56 88 120 83
18 37 57 77 50
If you want four-connected neighbors, you can make the corners of your filter 0. Similarly, you can design any filter for your purpose, such as for averaging all neighbors instead of summing them.
For details, please see the convolution article in Wikipedia.
Two possibilities : change the limits of the loops to i=k:(m-k) and j=k:(n-k) or use blkproc
ex :
compute the 2-D DCT of each 8-by-8 block
I = imread('cameraman.tif');
fun = #dct2;
J = blkproc(I,[8 8],fun);
imagesc(J), colormap(hot)
There are lots of things you can do at the edges. Which you do depends very specifically on your problem and is different from usage case to usage case. Typical things to do:
If (i-1) or (i+1) is out of range, then just ignore that element. This is equivalent to zero padding the matrix with zeros around the outside and adjusting the loop limits accordingly
Wrap around the edges. In other words, for an MxN matrix, if (i-1) takes you to 0 then instead of taking element (i-1, j) = (0, j) you take element (M, j).
Since your code mentions "your teacher" I'd guess that you can ask what should happen at the edges (or working it out in a sensible manner may well be part of the task!!).
I have a challenge to order my matrix. The provided functions like sortrows work in the opposite way...
Take this 2D matrix
M =
40 45 68
50 65 58
60 55 48
57 67 44
,
The objective is to find matrix O that indicates the sorting index (rank) per row, i.e.:
O =
1 2 3
1 3 2
3 2 1
2 3 1
.
So for the second row 50 is the smallest element (1), 65 the largest (3), and 58 is the second largest (2), therefore row vector [1 3 2].
[~,sorted_inds] = sort(M,2);
will do.
I think you're looking for the second output of the regular sort function:
[~,I] = sort(M,2)
This syntax supresses the actual sorted matrix Msorted, and returns the indices I such that
for j = 1:n, Msorted(j,:) = M(I(j,:),j); end
Type doc sort for more information.