I have a huge waveform matrix:
[w,fs] = wavread('file.wav');
length(w)
ans =
258048
I want to go through this matrix in segments (say 50) and get the maximum of these segments to compare it to another value. I tried this:
thold = max(w) * .04;
nwindows = 50;
left = 1;
right = length(w)/nwindows;
counter = 0;
for i = 1:nwindows
temp = w(left:right);
if (max(temp) > thold)
counter = counter + 1;
end
left = right;
right = right+right;
end
But MATLAB threw tons of warnings and gave me this error:
Index exceeds matrix dimensions.
Error in wlengthdur (line 17)
temp = w(left:right);
Am I close or way off course?
An alternative approach would be to use reshaped to arrange you vector in to a 2D matrix with number of row n and columns equal to ceil(length(w) / n) i.e. round up so that it is divisible as matlab matrices must be rectangular. This way you can find the max or whatever you need in one step without looping.
w = randn(47, 1);
%this needs to be a column vector, if yours isn't call w = w(:) to ensure that it is
n = 5;
%Pad w so that it's length is divisible by n
padded = [w; nan(n - mod(length(w), n), 1)];
segmented_w = reshape(padded, n, []);
max(segmented_w)
Related
I want to compute the above summation, for a given 'x'. The summation is to be carried out over a block of lengths specified by an array , for example block_length = [5 4 3]. The summation is carried as follows: from -5 to 5 across one dimension, -4 to 4 in the second dimension and -3 to 3 in the last dimension.
The pseudo code will be something like this:
sum = 0;
for i = -5:5
for j = -4:4
for k = -3:3
vec = [i j k];
tv = vec * vec';
sum = sum + 1/(1+tv)*cos(2*pi*x*vec'));
end
end
end
The problem is that I want to find the sum when the number of dimensions are not known ahead of time, using some kind of variable nested loops hopefully. Matlab uses combvec, but it returns all possible combinations of vectors, which is not required as we only compute the sum. When there are many dimensions, combvec returning all combinations is not feasible memory wise.
Appreciate any ideas towards solutions.
PS: I want to do this at high number of dimensions, for example 650, as in machine learning.
Based on https://www.mathworks.com/matlabcentral/answers/345551-function-with-varying-number-of-for-loops I came up with the following code (I haven't tested it for very large number of indices!):
function sum = fun(x, block_length)
sum = 0;
n = numel(block_length); % Number of loops
vec = -ones(1, n) .* block_length; % Index vector
ready = false;
while ~ready
tv = vec * vec';
sum = sum + 1/(1+tv)*cos(2*pi*x*vec');
% Update the index vector:
ready = true; % Assume that the WHILE loop is ready
for k = 1:n
vec(k) = vec(k) + 1;
if vec(k) <= block_length(k)
ready = false;
break; % v(k) increased successfully, leave "for k" loop
end
vec(k) = -1 * block_length(k); % v(k) reached the limit, reset it
end
end
end
where x and block_length should be both 1-x-n vectors.
The idea is that, instead of using explicitly nested loops, we use a vector of indices.
How good/efficient is this when tackling the suggested use case where block_length can have 650 elements? Not much! Here's a "quick" test using merely 16 dimensions and a [-1, 1] range for the indices:
N = 16; tic; in = 0.1 * ones(1, N); sum = fun(in, ones(size(in))), toc;
which yields an elapsed time of 12.7 seconds on my laptop.
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
To compute the mean of every bins along a dimension of a nd array in matlab, for example, average every 10 elements along dim 4 of a 4d array
x = reshape(1:30*30*20*300,30,30,20,300);
n = 10;
m = size(x,4)/10;
y = nan(30,30,20,m);
for ii = 1 : m
y(:,:,:,ii) = mean(x(:,:,:,(1:n)+(ii-1)*n),4);
end
It looks a bit silly. I think there must be better ways to average the bins?
Besides, is it possible to make the script applicable to general cases, namely, arbitray ndims of array and along an arbitray dim to average?
For the second part of your question you can use this:
x = reshape(1:30*30*20*300,30,30,20,300);
dim = 4;
n = 10;
m = size(x,dim)/10;
y = nan(30,30,20,m);
idx1 = repmat({':'},1,ndims(x));
idx2 = repmat({':'},1,ndims(x));
for ii = 1 : m
idx1{dim} = ii;
idx2{dim} = (1:n)+(ii-1)*n;
y(idx1{:}) = mean(x(idx2{:}),dim);
end
For the first part of the question here is an alternative using cumsum and diff, but it may not be better then the loop solution:
function y = slicedmean(x,slice_size,dim)
s = cumsum(x,dim);
idx1 = repmat({':'},1,ndims(x));
idx2 = repmat({':'},1,ndims(x));
idx1{dim} = slice_size;
idx2{dim} = slice_size:slice_size:size(x,dim);
y = cat(dim,s(idx1{:}),diff(s(idx2{:}),[],dim))/slice_size;
end
Here is a generic solution, using the accumarray function. I haven't tested how fast it is. There might be some room for improvement though.
Basically, accumarray groups the value in x following a matrix of customized index for your question
x = reshape(1:30*30*20*300,30,30,20,300);
s = size(x);
% parameters for averaging
dimAv = 4;
n = 10;
% get linear index
ix = (1:numel(x))';
% transform them to a matrix of index per dimension
% this is a customized version of ind2sub
pcum = [1 cumprod(s(1:end-1))];
sub = zeros(numel(ix),numel(s));
for i = numel(s):-1:1,
ixtmp = rem(ix-1, pcum(i)) + 1;
sub(:,i) = (ix - ixtmp)/pcum(i) + 1;
ix = ixtmp;
end
% correct index for the given dimension
sub(:,dimAv) = floor((sub(:,dimAv)-1)/n)+1;
% run the accumarray to compute the average
sout = s;
sout(dimAv) = ceil(sout(dimAv)/n);
y = accumarray(sub,x(:), sout, #mean);
If you need a faster and memory efficient operation, you'll have to write your own mex function. It shouldn't be so difficult, I think !
I'm trying to make a kind of hsv histogram by converting the rgb values of a picture (80*120).
This is the code:
function Image_histogram = hsvHistogram(path, count_bins)
Image = double(imread(path));
Image_histogram = zeros(3 * count_bins);
[n m] = size(Image);
H_vect = zeros(n, m);
S_vect = zeros(n, m);
V_vect = zeros(n, m);
hue_vect = zeros(1, count_bins);
saturation_vect = zeros(1, count_bins);
value_vect = zeros(1, count_bins);
for line = 1 : n
for row = 1 : m
%[H_vect(line, row), S_vect(line, row), V_vect(line, row)] = rgb2hsv(Image(line, row, 1), Image(line, row, 2), Image(line, row, 3));
endfor
endfor
number = 100/count_bins;
for count = 0 : count_bins - 1
left = (number * count);
right = (number * count + number);
hue_vect(1, count + 1) = (sum(sum(H_vect(:,:) >= left & H_vect(:,:) < right)));
saturation_vect(1, count + 1) = (sum(sum(S_vect(:,:) >= left & S_vect(:,:) < right)));
value_vect(1, count + 1) = (sum(sum(V_vect(:,:) >= left & V_vect(:,:) < right)));
endfor
Image_histogram = horzcat(hue_vect, saturation_vect, value_vect);
endfunction
When i try to get the HSV matrix i always get the error : hsvHistogram: A(I,J,...): index to dimension 2 out of bounds; value 121 out of bound 120
rgb2hsv is a pixel by pixel converter. It converts R G B to H S V. It is not the built-in rgb2hsv function. The commented line seems to be the one with problems.
The problem is in the size() function.
If such image is RGB, the matrix Image will be a 3D matrix but in your size() function you just gather two outputs, which will lead to incorrect results.
You must gather all three outputs (for all three dimensions) and then eventually discard the third one (which we know it is 3). Try doing:
[n,m,~]=size(Image);
More into details, if your matrix has size n x m x q but you ask just for two outputs, like
[a,b]=size(Image);
you’ll have a=n and b=m*q.
These results are obviously incorrect because since q>1 then b>m where m is (again) the actual dimension size and you’ll experience the out-of-bounds error. In other words, the loop will run from 1 to b whereas the matrix has only dimension m (which is less than b).
As instead you must gather all three dimensions separately:
[a,b,c]=size(Image);
and (as above) eventually discard some unnecessary output arguments (thanks to the tilde ~ operator).
I have x and y data that has n number of points in each of the arrays.
I want to use polyfit on portions of the data.
I want to divide the data into a certain number of divisions(numDivisions).
My idea would be to do something along the lines of
n= size(x)%number of data points
numDivisions = 4;%number of times to divide the data
div = zeros(numDivisions,1)%number of points per division
p = zeros(numDivisions,4);% second number is degree of polynomial+1
S = zeros(numDivisions,1);
mu = zeros(numDivisions,1);
E = zeros(numDivisions,1);
for i = 1:numDivisions
div(i) = round(n(1,1)*i/numDivisions) %assign markers for divisions of points
end
for i = 1:size(div)
if i == 1
start = 1;
endpoint = div(i);
[p(i), S(i), mu(i)] = polyfit(x(start:endpoint), y(start:endpoint), 3);
else
[p(i), S(i), mu(i)] = polyfit(x(div(i-1):div(i)), y(div(i-1):div(i)), 3);
end
end
The goal would be to have an array of p values from the polyfits.
However, when I run it I get this error:
In an assignment A(I) = B, the number of elements in B
and I must be the same.
Error in (line 33)
[p(i), S(i), mu(i)] =
polyfit(x(start:endpoint),
y(start:endpoint), 3);