I have a fairly large 2x2 matrix containing date and temperatures. There is a cluster of NaNs and incorrect data. I used find to get the index that contains the incorrect data. These indexes are stored in another variable. How do i remove the rows (date and value) corresponding to the indices?
Thanks.
fairly large 2x2 matrix makes little or no sense.
This is part from MATLAB documentation
You can delete rows and columns from a matrix by assigning the empty array [] to those rows or columns. Start with
A = magic(4)
A =
16 2 3 13
5 11 10 8
9 7 6 12
4 14 15 1
Then, delete the second column of A using
A(:, 2) = []
This changes matrix A to
A =
16 3 13
5 10 8
9 6 12
4 15 1
Also you can delete multiple rows/columns at once:
A([1 3],:)=[]
A =
5 10 8
4 15 1
Related
Let's say we have the following matrix
A=magic(4)
16 2 3 13
5 11 10 8
9 7 6 12
4 14 15 1
and we want to extract 3 submatrices, identified by the indexes for top left and bottom right corners. The indexes for a submatrix are contained in a row of the matrix i; columns 1 and 2 of i are the row indexes of the corners, columns 3 and 4 of i are the column indexes of the corners.
i.e.
i =
1 1 1 3
2 4 1 2
3 4 3 4
>> A(i(1,1):i(1,2),i(1,3):i(1,4))
ans =
16 2 3
>> A(i(2,1):i(2,2),i(2,3):i(2,4))
ans =
5 11
9 7
4 14
>> A(i(3,1):i(3,2),i(3,3):i(3,4))
ans =
6 12
15 1
The command A(i(,):i(,),i(,):i(,)) which I used to extract the submatrices is not very convenient, so I wonder is there a better way to do the job ?
If you don't want to type it all out then why not write a wrapper function?
A = magic(4);
S = #(r) A(i(r,1):i(r,2),i(r,3):i(r,4));
S(1)
S(2)
S(3)
If A may change after the definition of S then you would need to make it a parameter to the function.
S = #(A,r) A(i(r,1):i(r,2),i(r,3):i(r,4));
A = magic(4)
S(A,1)
S(A,2)
S(A,3)
Similarly if i may change then you would need to make it a parameter as well.
Edit
Unfortunately, contrary to my comment, if you want to perform assignment then A(I(r)) won't work exactly the same as what you've posted since this always returns an array instead of a matrix. One possible workaround is to use cell arrays in place of comma-separated-lists, but this isn't as elegant as the read only option. For example
S = #(r) {i(r,1):i(r,2) , i(r,3):i(r,4)};
s = S(1); A(s{:})
s = S(2); A(s{:})
s = S(3); A(s{:})
Following the same principle you could pre-define a cell array from i to make access one line.
s = arrayfun(#(r) {i(r,1):i(r,2),i(r,3):i(r,4)}, 1:size(i,1), 'UniformOutput', false);
A(s{1}{:})
A(s{2}{:})
A(s{3}{:})
Basically the sum function calculate the sum of the columns, that is to say if we have a 4x4 matrix we would get a 1X4 vector
A = magic(4)
A =
16 2 3 13
5 11 10 8
9 7 6 12
4 14 15 1
sum(A)
ans =
34 34 34 34
But if I want to get the Summation of the rows then i have 2 methods, the first is to get the transpose of the matrix then get the summation of the transposed matrix,and finally get the transpose of the result...., The Second method is to use dimension argument for the Sum function "sum(A, 2)"
A = magic(4)
A =
16 2 3 13
5 11 10 8
9 7 6 12
4 14 15 1
sum(A,2)
ans =
34
34
34
34
The problem is here I cannot understand how this is done, If anyone could please tell me the idea/concept behind this method,
It's hard to tell exactly how sum internally works, but we can guess it does something similar to this.
Matlab stores matrices (or N-dimensional arrays) in memory using column-major order. This means the order for the elements in memory for a 3 x 4 matrix is
1 4 7 10
2 5 8 11
3 6 9 12
So it first stores element (1,1), then (1,2), then (13), then (2,1), ...
In fact, this is the order you use when you apply linear indexing (that is, index a matrix with a single number). For example, let
A = [7 8 6 2
9 0 3 5
6 3 2 1];
Then A(4) gives 8.
With this in mind, it's easy to guess that what sum(A,1) does is traverse elements consecutively: A(1)+A(2)+A(3) to obtain the sum of the first column, then A(4)+A(5)+A(6) to sum the second column, etc. In contrast, sum(A,2) proceeds in steps of size(A,1) (3 in this example): A(1)+A(4)+A(7)+A(10) to compute the sum of the first row, etc.
As a side note, this is probably related with the observed fact that sum(A,1) is faster than sum(A,2).
I'm really not sure what you are asking. sum takes two inputs, the first of which is a multidimensional array A, say.
Now let's take sA = size(A), and d between 1 and ndims(A).
To understand what B = sum(A,d) does, first we find out what the size of B is.
That's easy, sB = sA; sB(d) = 1;. So in a way, it will "reduce" the size of A along dimension d.
The rest is trivial: every element in B is the sum of elements in A along dimension d.
Basically, sum(A) = sum(A,1) which outputs the sum of the columns in the matrix. 1 indicates the columns. So, sum(A,2) outputs the sum of the rows in the matrix. 2 indicating the rows. More than that, the sum command will output the entire matrix because there is only 2 dimensions (rows and columns)
I have got a nx3 adjacency matrix that contains nodes in the first two dimension and the correspondant weight in the third dimension. I want to filter the matrix for specific thresholds (for nodes indexing). For example, I want to keep the adjacency matrix for nodes smaller than 10.000, 20.000, etc. Which is the most efficient way to do so in matlab? I tried to do the following, find the index which correspond to nodes:
counter = 1;
for i=1: size(graph4, 1)
if (graph4(i,1) >30000) | (graph4(i,2) >30000)
bucket(counter) = i;
counter=counter+1;
end
end
Suppose the adjacency matrix is A as given below:
A =
8 1 6
3 5 7
4 9 2
11 4 9
6 8 10
7 12 5
17 10 15
12 14 16
13 18 11
If you want both column 1 and column 2 to be less than a value, you can do:
value = 10;
T = A(A(:,1) < value & A(:,2) < value, :)
T =
8 1 6
3 5 7
4 9 2
6 8 10
The following line seems to give the same results as your sample code (but it doesn't seem like it fits your description.
value = 10000;
bucket = find((A(:,1)>value) | A(:,2)>value)
I guess you made a mistake and want to increment the counter above the bucket-line and initialize it as counter = 0 before the loop? As it is now, it will be one more than the number of elements in the bucket-list.
I have a vector of values which represent an index of a row to be removed in some matrix M (an image). There's only one row value per column in this vector (i.e. if the image is 128 x 500, my vector contains 500 values).
I'm pretty new to MATLAB so I'm unsure if there's a more efficient way of removing a single pixel (row,col value) from a matrix so I've come here to ask that.
I was thinking of making a new matrix with one less row, looping through each column up until I find the row whose value I wish to remove, and "shift" the column up by one and then move onto the next column to do the same.
Is there a better way?
Thanks
Yes, there is a solution which avoids loops and is thus faster to write and to execute. It makes use of linear indexing, and exploits the fact that you can remove a matrix entry by assigning it an empty value ([]):
% Example data matrix:
M = [1 5 9 13 17
2 6 10 14 18
3 7 11 15 19
4 8 12 16 20];
% Example vector of rows to be removed for each column:
vector = [2 3 4 1 3];
[r c] = size(M);
ind = sub2ind([r c],vector,1:c);
M(ind) = [];
M = reshape(M,r-1,c);
This gives the result:
>> M =
1 5 9 14 17
3 6 10 15 18
4 8 11 16 20
This question already has answers here:
Closed 11 years ago.
Possible Duplicates:
How to subtract a vector from each row of a matrix?
How can I divide each row of a matrix by a fixed row?
I have matrix (M1) of M rows and 4 columns. I have another array (M2) of 1 row and 4 columns. I'd like to subtract every element in M1 by its respective column element in M2. In other words, each column of M1 needs to be subtraced by the scalar in the same column position in M2. I could call repmat(M2,M,1), which would create a NEW matrix of size MxN, where each element in a column was the same, and then do a element by element subtraction:
M2new = repmat(M2,M,1)
final = M1 - M2new
, however, this is two lines of code and creates a new element in memory. What is the fastest and least memory intensive way of performing this operation?
Use bsxfun like in the following example.
x=magic(4);
y=x(1,:);
z=bsxfun(#minus,x,y)
z =
0 0 0 0
-11 9 7 -5
-7 5 3 -1
-12 12 12 -12
Here z is obtained by subtracting the first row from every row. Just replace x with your matrix and y with your row vector, and you'r all set.
bsxfun(.) can potentially be more efficient, but personally as an old timer, I'll would recommend not to totally ignore linear algebra based solutions, like:
> M1= magic(4)
M1 =
16 2 3 13
5 11 10 8
9 7 6 12
4 14 15 1
> M2= M1(1, :)
M2 =
16 2 3 13
> M1- ones(4, 1)* M2
ans =
0 0 0 0
-11 9 7 -5
-7 5 3 -1
-12 12 12 -12
Let the actual use case and profiler to decide the functionality actually utilized.