Consider the following table:
myTable:
a b
-------
1
2
3 10
4 50
5 30
How do I replace the empty cells of b with a zero? So the result would be:
a b
-------
1 0
2 0
3 10
4 50
5 30
Right now I'm doing:
myTable: update b:{$[x~0Ni;0;x]}'b from myTable
But I am wondering whether there is a better/easier solution for doing this.
Using the fill operator (^)
Example Table:
q) tbl:flip`a`b!(2;0N)#10?0N 0N 0N,til 3
a b
---
0 2
1 1
1 1
1
1
Fill nulls in all columns with 0:
q)0^tbl
a b
---
0 2
1 1
1 1
0 1
1 0
Fill nulls only in selective columns with 0:
q)update 0^b from tbl
a b
---
0 2
1 1
1 1
1
1 0
In some cases when you want to fill the null values from the previous non-null value, you can use fills functions.
q)tbl:flip`a`b!(2;0N)#10?0N 0N 0N,til 3
a b
---
2 1
1
1 2
0
update fills a, fills b from tbl
q)a b
---
2 1
2 1
2 1
1 2
0 2
using fills with aggregation
q)tbl:update s:5?`g`a from flip`a`b!(2;0N)#10?0N 0N 0N,til 3
a b s
-----
1 a
2 a
0 0 g
2 2 g
0 a
q)update fills a, fills b by s from tbl
a b s
-----
1 a
1 2 a
0 0 g
2 2 g
0 2 a
Related
I have table like this
ID
MW1
1
0
2
0
3
0
4
1
5
1
6
1
7
0
9
1
10
0
11
0
My output should look like this
ID
MW1
CountSequenz
1
0
1
2
0
1
3
0
1
4
1
2
5
1
2
6
1
2
7
0
3
9
1
4
10
0
5
11
0
5
The window functions would be a nice fit here.
Select ID
,MW1
,Seq = sum(Flg) over (order by ID)
From (
Select *
,Flg =case when lag(MW1,1) over (order by ID)=MW1 then 0 else 1 end
From YourTable
) A
Order by ID
Results
I need to transform a neural network output matrix with size 2 X N in zeros and ones, where 0 will represent the minimum value of the column and 1 contrariwise. This will be necessary in order to calculate the confusion matrix.
For example, consider this matrix 2 X 8:
2 33 4 5 6 7 8 9
1 44 5 4 7 5 2 1
I need to get this result:
1 0 0 1 0 1 1 1
0 1 1 0 1 0 0 0
How can I do this in MATLAB without for loops? Thanks in advance.
>> d = [ 2 33 4 5 6 7 8 9;
1 44 5 4 7 5 2 1];
>> bsxfun(#rdivide, bsxfun(#minus, d, min(d)), max(d) - min(d))
ans =
1 0 0 1 0 1 1 1
0 1 1 0 1 0 0 0
The bsxfun function is necessary to broadcast the minus and division operations to matrices of different dimensions (min and max have only 1 row each).
Other solution is the following (works only for 2 rows):
>> [d(1,:) > d(2,:); d(1,:) < d(2,:)]
ans =
1 0 0 1 0 1 1 1
0 1 1 0 1 0 0 0
If it's just 2xN, then this will work:
floor(A./[max(A); max(A)])
In general:
floor(A./repmat(max(A),size(A,1),1))
I have written some code that compresses a matrix to remove zero columns and rows, but I can't work out how to reconstruct the original matrix.
Say I have a matrix:
A = [ 0 3 0 2 1 0 6
3 0 0 4 8 0 5
0 0 0 0 0 0 0
2 4 0 0 2 0 1
1 8 0 2 0 0 7
0 0 0 0 0 0 0
6 5 0 1 7 0 0 ]
Here rows/columns 3 and 6 are empty, so my compression function will give the output:
A_dash = [ 0 3 2 1 6
3 0 4 8 5
2 4 0 2 1
1 8 2 0 7
6 5 1 7 0 ]
A_map = [ 1 2 4 5 7]
Where A_map is a vector mapping the indicies of the rows/columns of A_dash to A. This means that if A_map(3) = 4, then row/column 4 of A is the same as row/column 3 of A_dash - ie. a row/column of zeroes must be inserted between columns/rows 2 and 3 in A_dash
What is the easiest way people can suggest for me to recreate matrix A from A_dash, using the information in A_map?
Here is what I have got so far:
% orig_size is original number of columns/rows
c_count = size(A_dash,1);
A = zeros(c_count, orig_size); % c_count rows to avoid dimension mismatch
for ii = 1:c_count
A(:,A_map(ii)) == A_dash(:,ii);
end
This gives me the right result column-wise:
A = [ 0 3 0 2 1 0 6
3 0 0 4 8 0 5
2 4 0 0 2 0 1
1 8 0 2 0 0 7
6 5 0 1 7 0 0 ]
However, I'm not sure how i should go about inserting the rows, i suppose i could copy the first 1:i rows into one matrix, i:end rows to a second matrix and concatenate those with a zero row in between, but that feels like a bit of a
clunky solution, and probably not very efficient for large sized matrices..
Otherwise, is there a better way that people can suggest I store the map information? I was thinking instead of storing the mapping between column/row indices, that I just store the indices of the zero columns/rows and then insert columns/rows of zeros where appropriate. Would this be a better way?
You've got the indices of the valid rows/columns. Now all you've got to do is put them in a new matrix of zeros the same size as A:
B=zeros(size(A));
B(A_map,A_map)=A_dash
B =
0 3 0 2 1 0 6
3 0 0 4 8 0 5
0 0 0 0 0 0 0
2 4 0 0 2 0 1
1 8 0 2 0 0 7
0 0 0 0 0 0 0
6 5 0 1 7 0 0
Just to check...
>> A==B
ans =
1 1 1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1 1 1
A and B are equal everywhere, so we've reconstructed A.
I am trying to create an 8x8 matrix containing 0s, 1s and 2s. Each row and each column should contain two 0s, three 1s and three 2s.
Previously I have used the below to generate an example containing only 1s and 0s.
output = zeros(8, 8);
for i=1:8
tmp = (1:8) + (i);
tmp = rem(tmp, 4);
output(i,:) = tmp;
output(i,:) = tmp > 0;
end
output =
1 1 0 1 1 1 0 1
1 0 1 1 1 0 1 1
0 1 1 1 0 1 1 1
1 1 1 0 1 1 1 0
1 1 0 1 1 1 0 1
1 0 1 1 1 0 1 1
0 1 1 1 0 1 1 1
1 1 1 0 1 1 1 0
However I would now like something similar to the following:
output =
1 1 0 1 2 2 0 2
1 0 1 2 2 0 2 1
0 1 2 2 0 2 1 1
1 2 2 0 2 1 1 0
2 2 0 2 1 1 0 1
2 0 2 1 1 0 1 2
0 2 1 1 0 1 2 2
2 1 1 0 1 2 2 0
Thanks for your help.
What you have in your example is a Hankel matrix so you could use the hankel function
c = [1 1 0 1 2 2 0 2];
k = [2 1 1 0 1 2 2 0];
A = hankel(c,k)
where c is the first column of the output matrix and k is the last row.
Making your output matrix a Hankel matrix is a good idea (based on your requirements) as it will enforce the row and column frequency counts for each value. You would not necessarily get this just by creating rows that are random permutations of a base row (using randperm for example) as duplicate rows would be possible which would break your column requirements.
As an example, if you want random c with fixed numbers of specific elements, you can randomly permute a base vector containing the required values and frequencies - as per your requirement this would be
c = [0 0 1 1 1 2 2 2];
index = randperm(numel(c));
c = c(index);
c =
0 2 0 2 2 1 1 1
To get the square Hankel structure then choose k to be the next cyclic permutation of c
k = circshift(c',1)'
k =
1 0 2 0 2 2 1 1
and just call hankel with these as mentioned above
A = hankel(c,k)
A =
0 2 0 2 2 1 1 1
2 0 2 2 1 1 1 0
0 2 2 1 1 1 0 2
2 2 1 1 1 0 2 0
2 1 1 1 0 2 0 2
1 1 1 0 2 0 2 2
1 1 0 2 0 2 2 1
1 0 2 0 2 2 1 1
The above output is based on what I got on my machine based on the output from randperm.
Any output matrix generated using the above will meet your requirements specified in the question.
It was hard to phrase the question, but here's an example of what I'm looking for:
1 2 3 4
2 1 1 1
2 2 3 1
0 0 0 0
and in column one, I add all the value of all of the first three rows and save it to the third and so on, so that it becomes:
1 2 3 4
2 1 1 1
2 2 3 1
5 5 7 6
I think you can use sum:
octave:23> m = [1 2 3 4; 2 1 1 1; 2 2 3 1; 0 0 0 0]
m =
1 2 3 4
2 1 1 1
2 2 3 1
0 0 0 0
octave:24> m(length(m), :) = sum(m)
m =
1 2 3 4
2 1 1 1
2 2 3 1
5 5 7 6