I'm trying to create a band matriz in Matlab, which should looks like this for matrix_size = 6 and band_width = 1:
[1,1,0,0,0,0]
[1,2,2,0,0,0]
[0,2,3,3,0,0]
[0,0,3,4,4,0]
[0,0,0,4,5,5]
[0,0,0,0,5,6]
The values should be like this.
I did function, which gives me result:
[1,1,0,0,0,0]
[1,1,1,0,0,0]
[0,1,1,1,0,0]
[0,0,1,1,1,0]
[0,0,0,1,1,1]
[0,0,0,0,1,1]
Code of my function:
function M=bandmatrix(n,r)
% n -- matriz size
% r -- band width
% n >= r + 2
M = sign(conv2(eye(n),ones(r+1),'same'));
end
How I can do this function? I also would be grateful for the function, in which walues are the same as I want, but function doesn't depends on band width. Thank you!
you can use diag as follows:
diag(1:6)+diag(1:5,1)+diag(1:5,-1)
Generally, for any order n:
diag(1:n)+diag(1:n-1,1)+diag(1:n-1,-1)
You could also do something like:
clear;
m = 6;
n = 6;
diags = repmat((1:6)', 1, 3);
diagidx = [-1:1];
for k = 1:length(diagidx)
if(diagidx(k) > 0)
diags(:, k) = circshift(diags(:, k), diagidx(k), 1);
end
end
sparseMat = spdiags(diags, [-1:1], m, n);
myMat = full(sparseMat)
Using sparse matrix allows us to specify the data and allocate only once.
But it also means the values used in the diagonal are needed to be shifted according to the matrix being fat or skinny.
Related
Is there a function in MATLAB that generates the following matrix for a given scalar r:
1 r r^2 r^3 ... r^n
0 1 r r^2 ... r^(n-1)
0 0 1 r ... r^(n-2)
...
0 0 0 0 ... 1
where each row behaves somewhat like a power analog of the CUMSUM function?
You can compute each term directly using implicit expansion and element-wise power, and then apply triu:
n = 5; % size
r = 2; % base
result = triu(r.^max((1:n)-(1:n).',0));
Or, maybe a little faster because it doesn't compute unwanted powers:
n = 5; % size
r = 2; % base
t = (1:n)-(1:n).';
u = find(t>=0);
t = t(u);
result = zeros(n);
result(u) = r.^t;
Using cumprod and triu:
% parameters
n = 5;
r = 2;
% Create a square matrix filled with 1:
A = ones(n);
% Assign the upper triangular part shifted by one with r
A(triu(A,1)==1)=r;
% cumprod along the second dimension and get only the upper triangular part
A = triu(cumprod(A,2))
Well, cumsum accumulates the sum of a vector but you are asking for a specially design matrix, so the comparison is a bit problematic....
Anyway, it might be that there is a function for this if this is a common special case triangular matrix (my mathematical knowledge is limited here, sorry), but we can also build it quite easily (and efficiently=) ):
N = 10;
r = 2;
% allocate arry
ary = ones(1,N);
% initialize array
ary(2) = r;
for i = 3:N
ary(i) = ary(i-1)*r;
end
% build matrix i.e. copy the array
M = eye(N);
for i = 1:N
M(i,i:end) = ary(1:end-i+1);
end
This assumes that you want to have a matrix of size NxN and r is the value that you want calculate the power of.
FIX: a previous version stated in line 13 M(i,i:end) = ary(i:end);, but the assignment needs to start always at the first position of the ary
I am looking for an effective way in order to index into a matrix using a moving window.
% Create logical matrix
startRows = repmat([10,10,30,10,40], 1, 3);
logicalMat = true(100, 5, 3);
logicalMat((1:100)' < startRows) = 0;
% Create source matrix
window = 10;
sourceMat = randi(10, [100, 5, 3]);
sourceMat((1:100)' < startRows-(window-1)) = NaN;
% Compute target matrix: For every cell in targetMat, where logicalMat is true,
% compute the product of multVec and the previous 10 rows.
targetMat = nan(100,5);
multVec = randi(10, [1, 10]);
% For instance, targetMat(10,1) should be the product of multVec and sourceMat(1:10,1).
% targetMat(11,1) = multVec * sourceMat(2:11, 1) and so on...
% targetMat(30,3) = multVec * sourceMat(21:30, 3) and so on...
% I am interested in the technique how to address the "moving window".
One possible solution might be using a for-loop. However, I would like to know whether there exists a more elegant solution. Also, if I expand the example into the third dimension, I would need a third for-loop. Is it possible to vectorize this problem?
% Possible solution using loop
[nRows, nCols, nPages] = size(sourceMat);
for p = 1 : nPages
for c = 1 : nCols
for r = startRows(c) : nRows
targetMat(r, c, p) = multVec * sourceMat((r - window + 1) : r, c, p);
end
end
end
As stated in the comments, it's really hard to advise without more specifics, but you could try try the filter2 or imfilter functions for this type of thing. With more description of what you are trying to achieve and why, someone may be able to give a more specific answer
I am trying to evaluate all values the expression f = 2y-exp(z) can take for different values of z and y. Were y and z are two vectors of length M. I am wondering why the two approaches for generating the expression f yields different results.
Using meshgrid:
[Y,Z] = meshgrid(y,z);
argument = 2*Y-exp(Z);
and with double for-loops
argument_new = zeros(M,M);
for i = 1:length(y)
for j = 1:length(z)
argument_new(i,j) = 2*y(i)-exp(z(j));
end
end
Any hints will be highly appreciated!
That's because of the way meshgrid creates 'inverted' directions. I don't find the right words, but here is an example illustrating with your code.You see that if you uncomment option 2 and use argument_new(j,i) instead of argument_new(i,j) both matrices are equal (as obtained with isequal).
clear
clc
M = 20;
y = 1:M;
z = 1:M;
[Y,Z] = meshgrid(y,z);
argument = 2*Y-exp(Z);
argument_new = zeros(M,M);
for i = 1:length(y)
for j = 1:length(z)
%// 1)
argument_new(i,j) = 2*y(i)-exp(z(j));
%// 2)
%// argument_new(j,i) = 2*y(i)-exp(z(j));
end
end
isequal(argument,argument_new) %// Gives 0 for option 1 and 1 for option 2.
Blame that on meshgrid:
MESHGRID is like NDGRID except that the order of the first two input
and output arguments are switched (i.e., [X,Y,Z] = MESHGRID(x,y,z)
produces the same result as [Y,X,Z] = NDGRID(y,x,z)).
Solution: use ndgrid, which doesn't do that switching, and is thus more "natural":
[Y,Z] = ndgrid(y,z);
argument = 2*Y-exp(Z);
Or in your code, after meshgrid, add a transpose operation: argument = argument.';)
They are the same, you should just transpose either one (' in Matlab), or you can replace i by j and vice versa in the for loops
I have a function that transforms R^N to R^M. For simplicity, lets just let it be the identity function #(z) z where z may be a vector. I want to apply a function to a list of parameters of size K x N and have it map to K x M output.
Here is my attempt:
function out_matrix = array_fun_matrix(f, vals)
for i=1:size(vals,1)
f_val = f(vals(i,:));
if(size(f_val,1) > 1) %Need to stack up rows, so convert as required.
f_val = f_val';
end
out_matrix(i,:) = f_val;
end
end
You can try it with
array_fun_matrix(#(z) z(1)^2 + z(2)^2 + z(3), [0 1 0; 1 1 1; 1 2 1; 2 2 2])
The question: Is there a better and more efficient way to do this with vectorization, etc.? Did I miss a built-in function?
Examples of non-vectorizable functions: There are many, usually involving elaborate sub-steps and numerical solutions. A trivial example is something like looking for the numerical solution to an equation, which in term is using numerical quadrature. i.e. let params = [b c] and solve for the a such that int_0^a ((z + b)^2) dz = c
(I know here you could do some calculus, but the integral here is stripped down). Implementing this example,
find_equilibrium = #(param) fzero(#(a) integral(#(x) (x + param(1)).^2 - param(2), 0, a), 1)
array_fun_matrix(find_equilibrium, [0 1; 0 .8])
You can use the cellfun function, but you'll need to manipulate your data a bit:
function out_matrix = array_fun_matrix(f, vals)
% Convert your data to a cell array:
cellVals = mat2cell(vals, ones(1,size(vals,1)));
% apply the function:
out_cellArray = cellfun(f, cellVals, 'UniformOutput', false);
% Convert back to matrix:
out_matrix = cell2mat(out_cellArray);
end
If you don't like this implementation, you can improve the performance of yours by preallocating the out_matrix:
function out_matrix = array_fun_matrix(f, vals)
firstOutput = f(vals(1,:));
out_matrix = zeros(size(vals,1), length(firstOutput)); % preallocate for speed.
for i=1:size(vals,1)
f_val = f(vals(i,:));
if(size(f_val,1) > 1) %Need to stack up rows, so convert as required.
f_val = f_val';
end
out_matrix(i,:) = f_val;
end
end
I want to vectorize the following MATLAB code. I think it must be simple but I'm finding it confusing nevertheless.
r = some constant less than m or n
[m,n] = size(C);
S = zeros(m-r,n-r);
for i=1:m-r+1
for j=1:n-r+1
S(i,j) = sum(diag(C(i:i+r-1,j:j+r-1)));
end
end
The code calculates a table of scores, S, for a dynamic programming algorithm, from another score table, C.
The diagonal summing is to generate scores for individual pieces of the data used to generate C, for all possible pieces (of size r).
Thanks in advance for any answers! Sorry if this one should be obvious...
Note
The built-in conv2 turned out to be faster than convnfft, because my eye(r) is quite small ( 5 <= r <= 20 ). convnfft.m states that r should be > 20 for any benefit to manifest.
If I understand correctly, you're trying to calculate the diagonal sum of every subarray of C, where you have removed the last row and column of C (if you should not remove the row/col, you need to loop to m-r+1, and you need to pass the entire array C to the function in my solution below).
You can do this operation via a convolution, like so:
S = conv2(C(1:end-1,1:end-1),eye(r),'valid');
If C and r are large, you may want to have a look at CONVNFFT from the Matlab File Exchange to speed up calculations.
Based on the idea of JS, and as Jonas pointed out in the comments, this can be done in two lines using IM2COL with some array manipulation:
B = im2col(C, [r r], 'sliding');
S = reshape( sum(B(1:r+1:end,:)), size(C)-r+1 );
Basically B contains the elements of all sliding blocks of size r-by-r over the matrix C. Then we take the elements on the diagonal of each of these blocks B(1:r+1:end,:), compute their sum, and reshape the result to the expected size.
Comparing this to the convolution-based solution by Jonas, this does not perform any matrix multiplication, only indexing...
I would think you might need to rearrange C into a 3D matrix before summing it along one of the dimensions. I'll post with an answer shortly.
EDIT
I didn't manage to find a way to vectorise it cleanly, but I did find the function accumarray, which might be of some help. I'll look at it in more detail when I am home.
EDIT#2
Found a simpler solution by using linear indexing, but this could be memory-intensive.
At C(1,1), the indexes we want to sum are 1+[0, m+1, 2*m+2, 3*m+3, 4*m+4, ... ], or (0:r-1)+(0:m:(r-1)*m)
sum_ind = (0:r-1)+(0:m:(r-1)*m);
create S_offset, an (m-r) by (n-r) by r matrix, such that S_offset(:,:,1) = 0, S_offset(:,:,2) = m+1, S_offset(:,:,3) = 2*m+2, and so on.
S_offset = permute(repmat( sum_ind, [m-r, 1, n-r] ), [1, 3, 2]);
create S_base, a matrix of base array addresses from which the offset will be calculated.
S_base = reshape(1:m*n,[m n]);
S_base = repmat(S_base(1:m-r,1:n-r), [1, 1, r]);
Finally, use S_base+S_offset to address the values of C.
S = sum(C(S_base+S_offset), 3);
You can, of course, use bsxfun and other methods to make it more efficient; here I chose to lay it out for clarity. I have yet to benchmark this to see how it compares with the double-loop method though; I need to head home for dinner first!
Is this what you're looking for? This function adds the diagonals and puts them into a vector similar to how the function 'sum' adds up all of the columns in a matrix and puts them into a vector.
function [diagSum] = diagSumCalc(squareMatrix, LLUR0_ULLR1)
%
% Input: squareMatrix: A square matrix.
% LLUR0_ULLR1: LowerLeft to UpperRight addition = 0
% UpperLeft to LowerRight addition = 1
%
% Output: diagSum: A vector of the sum of the diagnols of the matrix.
%
% Example:
%
% >> squareMatrix = [1 2 3;
% 4 5 6;
% 7 8 9];
%
% >> diagSum = diagSumCalc(squareMatrix, 0);
%
% diagSum =
%
% 1 6 15 14 9
%
% >> diagSum = diagSumCalc(squareMatrix, 1);
%
% diagSum =
%
% 7 12 15 8 3
%
% Written by M. Phillips
% Oct. 16th, 2013
% MIT Open Source Copywrite
% Contact mphillips#hmc.edu fmi.
%
if (nargin < 2)
disp('Error on input. Needs two inputs.');
return;
end
if (LLUR0_ULLR1 ~= 0 && LLUR0_ULLR1~= 1)
disp('Error on input. Only accepts 0 or 1 as input for second condition.');
return;
end
[M, N] = size(squareMatrix);
if (M ~= N)
disp('Error on input. Only accepts a square matrix as input.');
return;
end
diagSum = zeros(1, M+N-1);
if LLUR0_ULLR1 == 1
squareMatrix = rot90(squareMatrix, -1);
end
for i = 1:length(diagSum)
if i <= M
countUp = 1;
countDown = i;
while countDown ~= 0
diagSum(i) = squareMatrix(countUp, countDown) + diagSum(i);
countUp = countUp+1;
countDown = countDown-1;
end
end
if i > M
countUp = i-M+1;
countDown = M;
while countUp ~= M+1
diagSum(i) = squareMatrix(countUp, countDown) + diagSum(i);
countUp = countUp+1;
countDown = countDown-1;
end
end
end
Cheers