I want to sort in Matlab the element of each row of a matrix A in a matrix B and obtain a matrix C reporting the column index of each sorted element in the original matrix A. If two elements of a row of A are the same the reported column indices should be in increasing order, e.g.
A=[3 2 1 4; 5 6 7 8; 9 0 10 2; 2 1 1 0]
B=[1 2 3 4; 5 6 7 8; 0 2 9 10; 0 1 1 2]
C=[3 2 1 4; 1 2 3 4; 2 4 1 3; 4 2 3 1]
The builtin sort function will do this, when ran on rows (dimension 2 in Matlab).
First output will be the elements sorted within each row giving B
Second output will be the column indices of the elements of B from A within each row giving C
[B,C]=sort(A,2)
or if you just want C replace B with ~ in the above line..
Related
I have a Matrix A. I want to iterate over the inner part of the matrix (B), while also working with the rows and columns that are not part of B.
A = [1 4 5 6 7 1; B = [2 2 2 2;
8 2 2 2 2 1; 2 3 3 2;
9 2 3 3 2 1; 2 8 2 2];
0 2 8 2 2 1;
1 1 1 1 1 1];
I know it is possible to select the part of A like this:
[rows,columns] = size(A);
B = A([2:1:rows-1],[2:1:columns-1]);
for i = 1:(rows*columns)
%do loop stuff
endfor
This however wont work because I also need the outer rows and columns for my calculations. How can I achieve a loop without altering A?
So, why do not use two indexes for the inner matrix?
%....
for i=2:rows-1
for j=2:cols-1
% here, A(i,j) are the B elements, but you
% can still access to A(i-1, j+1) if you want.
end
end
%....
It is the first time I deal with column-compress storage (CCS) format to store matrices. After googling a bit, if I am right, in a matrix having n nonzero elements the CCS is as follows:
-we define a vector A_v of dimensions n x 1 storing the n non-zero elements
of the matrix
- we define a second vector A_ir of dimensions n x 1 storing the rows of the
non-zero elements of the matrix
-we finally define a third vector A_jc whose elements are the indices of the
elements of A_v which corresponds to the beginning of new column, plus a
final value which is by convention equal t0 n+1, and identifies the end of
the matrix (pointing theoretically to a virtual extra-column).
So for instance,
if
M = [1 0 4 0 0;
0 3 5 2 0;
2 0 0 4 6;
0 0 7 0 8]
we get
A_v = [1 2 3 4 5 7 2 4 6 8];
A_ir = [1 3 2 1 2 4 2 3 3 4];
A_jc = [1 3 4 7 9 11];
my questions are
I) is what I wrote correct, or I misunderstood anything?
II) what if I want to represent a matri with some columns which are zeroes, e.g.,
M2 = [0 1 0 0 4 0 0;
0 0 3 0 5 2 0;
0 2 0 0 0 4 6;
0 0 0 0 7 0 8]
wouldn't the representation of M2 in CCS be identical to the one of M?
Thanks for the help!
I) is what I wrote correct, or I misunderstood anything?
You are perfectly correct. However, you have to take care that if you use a C or C++ library offsets and indices should start at 0. Here, I guess you read some Fortran doc for which indices are starting at 1. To be clear, here is below the C version, which simply translates the indices of your Fortran-style correct answer:
A_v = unmodified
A_ir = [0 2 1 0 1 3 1 2 2 4] (in short [1 3 2 1 2 4 2 3 3 4] - 1)
A_jc = [0 2 3 6 8 10] (in short [1 3 4 7 9 11] - 1)
II) what if I want to represent a matri with some columns which are
zeroes, e.g., M2 = [0 1 0 0 4 0 0;
0 0 3 0 5 2 0;
0 2 0 0 0 4 6;
0 0 0 0 7 0 8]
wouldn't the representation of M2 in CCS be identical to the one of M?
I you have an empty column, simply add a new entry in the offset table A_jc. As this column contains no element this new entry value is simply the value of the previous entry. For instance for M2 (with index starting at 0) you have:
A_v = unmodified
A_ir = unmodified
A_jc = [0 0 2 3 6 8 10] (to be compared to [0 2 3 6 8 10])
Hence the two representations are differents.
If you just start learning about sparse matrices there is an excelllent free book here: http://www-users.cs.umn.edu/~saad/IterMethBook_2ndEd.pdf
A matrix of size nxn needs to be constructed with the desired properties.
n is even. (given as input to the algorithm)
Matrix should contain integers from 0 to n-1
Main diagonal should contain only zeroes and matrix should be symmetric.
All numbers in each row should be different.
For various n , any one of the possible output is required.
input
2
output
0 1
1 0
input
4
output
0 1 3 2
1 0 2 3
3 2 0 1
2 3 1 0
Now the only idea that comes to my mind is to brute-force build combinations recursively and prune.
How can this be done in a iterative way perhaps efficiently?
IMO, You can handle your answer by an algorithm to handle this:
If 8x8 result is:
0 1 2 3 4 5 6 7
1 0 3 2 5 4 7 6
2 3 0 1 6 7 4 5
3 2 1 0 7 6 5 4
4 5 6 7 0 1 2 3
5 4 7 6 1 0 3 2
6 7 4 5 2 3 0 1
7 6 5 4 3 2 1 0
You have actually a matrix of two 4x4 matrices in below pattern:
m0 => 0 1 2 3 m1 => 4 5 6 7 pattern => m0 m1
1 0 3 2 5 4 7 6 m1 m0
2 3 0 1 6 7 4 5
3 2 1 0 7 6 5 4
And also each 4x4 is a matrix of two 2x2 matrices with a relation to a power of 2:
m0 => 0 1 m1 => 2 3 pattern => m0 m1
1 0 3 2 m1 m0
In other explanation I should say you have a 2x2 matrix of 0 and 1 then you expand it to a 4x4 matrix by replacing each cell with a new 2x2 matrix:
0 => 0+2*0 1+2*0 1=> 0+2*1 1+2*1
1+2*0 0+2*0 1+2*1 0+2*1
result => 0 1 2 3
1 0 3 2
2 3 0 1
3 2 1 0
Now expand it again:
0,1=> as above 2=> 0+2*2 1+2*2 3=> 0+2*3 1+2*3
1+2*2 0+2*2 1+2*3 0+2*3
I can calculate value of each cell by this C# sample code:
// i: row, j: column, n: matrix dimension
var v = 0;
var m = 2;
do
{
var p = m/2;
v = v*2 + (i%(n/p) < n/m == j%(n/p) < n/m ? 0 : 1);
m *= 2;
} while (m <= n);
We know each row must contain each number. Likewise, each row contains each number.
Let us take CS convention of indices starting from 0.
First, consider how to place the 1's in the matrix. Choose a random number k0, from 1 to n-1. Place the 1 in row 0 at position (0,k0). In row 1, if k0 = 1 in which case there is already a one placed. Otherwise, there are n-2 free positions and place the 1 at position (1,k1). Continue in this way until all the 1 are placed. In the final row there is exactly one free position.
Next, repeat with the 2 which have to fit in the remaining places.
Now the problem is that we might not be able to actually complete the square. We may find there are some constraints which make it impossible to fill in the last digits. The problem is that checking a partially filled latin square is NP-complete.(wikipedia) This basically means pretty compute intensive and there no know short-cut algorithm. So I think the best you can do is generate squares and test if they work or not.
If you only want one particular square for each n then there might be simpler ways of generating them.
The link Ted Hopp gave in his comment Latin Squares. Simple Construction does provide a method for generating a square starting with the addition of integers mod n.
I might be wrong, but if you just look for printing a symmetric table - a special case of latin squares isomorphic to the symmetric difference operation table over a powerset({0,1,..,n}) mapped to a ring {0,1,2,..,2^n-1}.
One can also produce such a table, using XOR(i,j) where i and j are n*n table indexes.
For example:
def latin_powerset(n):
for i in range(n):
for j in range(n):
yield (i, j, i^j)
Printing tuples coming from previously defined special-case generator of symmetric latin squares declared above:
def print_latin_square(sq, n=None):
cells = [c for c in sq]
if n is None:
# find the length of the square side
n = 1; n2 = len(cells)
while n2 != n*n:
n += 1
rows = list()
for i in range(n):
rows.append(" ".join("{0}".format(cells[i*n + j][2]) for j in range(n)))
print("\n".join(rows))
square = latin_powerset(8)
print(print_latin_square(square))
outputs:
0 1 2 3 4 5 6 7
1 0 3 2 5 4 7 6
2 3 0 1 6 7 4 5
3 2 1 0 7 6 5 4
4 5 6 7 0 1 2 3
5 4 7 6 1 0 3 2
6 7 4 5 2 3 0 1
7 6 5 4 3 2 1 0
See also
This covers more generic cases of latin squares, rather than that super symmetrical case with the trivial code above:
https://www.cut-the-knot.org/arithmetic/latin2.shtml (also pointed in the comments above for symmetric latin square construction)
https://doc.sagemath.org/html/en/reference/combinat/sage/combinat/matrices/latin.html
I have a problem with removing the rows when the columns are identical.
I have used a for and if loop but the run time is too long.
I was thinking if there are any more efficient and faster run time method.
say
A=[ 2 4 6 8;
3 9 7 9;
4 8 7 6;
8 5 4 6;
2 10 11 2]
I would want the result to be
A=[ 2 4 6 8;
4 8 7 6;
8 5 4 6]
eliminating the 2nd row because of the repeated '9' and remove the 5th row because of repeated '2'.
You can use sort and diff to identify the rows with repeated values
A = A(all(diff(sort(A'))),:)
returns
A =
2 4 6 8
4 8 7 6
8 5 4 6
The trick here is how to find the rows with repeated values in an efficient manner.
How about this:
% compare all-vs-all for each row using `bsxfun`
>> c = bsxfun( #eq, A, permute( A, [1 3 2] ) );
>> c = sum( c, 3 ); % count the number of matches each element has in the row
>> c = any( c > 1, 2 ); % indicates rows with repeated values - an element with more than one match
>> A = A( ~c, : )
A =
2 4 6 8
4 8 7 6
8 5 4 6
If I have a 2d array the way to represent each element in 1 dimension is to use row_num * row_width + column if i want the element at row_num, column. But what I'm struggling with is how big should the 1 dimensional array be if I have a 3x3 2d array (just as an example). Shouldn't 3^3 = 9 be enough for the 1d array? But then for element 3,2 the index would be 3 * 3 + 2 = 11. Or should the size be that of the biggest index I want to address - e.g. 3 * 3 + 3 = 12 if I want to address all elements from a 3x3 2d array?
You need to start counting from zero (zero-indexing), where the rows and columns are 0,1,2.
Then element "(3,2)" is really "(2, 1)", or 2*3+1=7, and the final element "(3,3)" is really "(2,2)", which is 2*3+2=8. This is the last element in the 1-D array, because they're counted from 0 too, so the 9 elements are 0,1,2,3,4,5,6,7,8.
For example:
>>> for r in 0,1,2:
... for c in 0,1,2:
... print r, c, r*3+c
...
0 0 0
0 1 1
0 2 2
1 0 3
1 1 4
1 2 5
2 0 6
2 1 7
2 2 8