I have a vector that contains a long list of data (time series). I would like to find the maximum of every 60 elements without going through manually C=[max(B(1:60)), etc... ] because it is a rather large data set. Is there a clean way of doing this? Thanks for any ideas! I appreciate it.
Oli's suggestion deserves to be made into a formal answer. Try this:
C = max(reshape(B,60,[]));
As another option you can look at blkproc.
A= randn(600,1);
blkproc( A, [60,1], 'max');
blkproc is being phased out, so you will also have to look at blockproc.
Though, reshaping and taking the max will probably be more efficient as was mentioned in the comments.
max( reshape(A, [60, 10] ) )
[update]
As a note... don't use blkproc :-). Using a very large array (A), blkproc is 100x slower than the max,reshape.
You can also use 'buffer' function.
A= randn(600,1);
max(buffer(A,60));
This solution works even when the length of the vector is not exact multiple of 60 and is faster in comparison to 'reshape' function.
Related
Obviously, float comparison is always tricky. I have a lot of assert-check in my (scientific) code, so very often I have to check for equality of sums to one, and similar issues.
Is there a quick-and easy / best-practice way of performing those checks?
The easiest way I can think of is to build a custom function for fixed tolerance float comparison, but that seems quite ugly to me. I'd prefer a built-in solution, or at least something that is extremely clear and straightforward to understand.
I think it's most likely going to have to be a function you write yourself. I use three things pretty constantly for running computational vector tests so to speak:
Maximum absolute error
return max(abs(result(:) - expected(:))) < tolerance
This calculates maximum absolute error point-wise and tells you whether that's less than some tolerance.
Maximum excessive error count
return sum( (abs(result(:) - expected(:))) < tolerance )
This returns the number of points that fall outside your tolerance range. It's also easy to modify to return percentage.
Root mean squared error
return norm(result(:) - expected(:)) < rmsTolerance
Since these and many other criteria exist for comparing arrays of floats, I would suggest writing a function which would accept the calculation result, the expected result, the tolerance and the comparison method. This way you can make your checks very compact, and it's going to be much less ugly than trying to explain what it is that you're doing in comments.
Any fixed tolerance will fail if you put in very large or very small numbers, simplest solution is to use eps to get the double precision:
abs(A-B)<eps(A)*4
The 4 is a totally arbitrary number, which is sufficient in most cases.
Don't know any special build in solution. Maybe something with using eps function?
For example as you probably know this will give False (i.e. 0) as a result:
>> 0.1 + 0.1 + 0.1 == 0.3
ans =
0
But with eps you could do the following and the result is as expected:
>> (0.1+0.1+0.1) - 0.3 < eps
ans =
1
I have had good experience with xUnit, a unit test framework for Matlab. After installing it, you can use:
assertVectorsAlmostEqual(a,b) (checks for normwise closeness between vectors; configurable absolute/relative tolerance and sane defaults)
assertElementsAlmostEqual(a,b) (same check, but elementwise on every single entry -- so [1 1e-12] and [1 -1e-9] will compare equal with the former but not with the latter).
They are well-tested, fast to use and clear enough to read. The function names are quite long, but with any decent editor (or the Matlab one) you can write them as assertV<tab>.
For those who understand both MATLAB and Python (NumPy), it would maybe be useful to check the code of the following Python functions, which do the job:
numpy.allclose(a, b, rtol=1e-05, atol=1e-08)
numpy.isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)
I am working on a code in Least Square Non Negative solution recovery context on Matlab, and I need (with no more details because it's not that important for this question) to know the number of non zero elements in my matrices and arrays.
The function NNZ on matlab does exactly what I want, but it happens that I need more information about what Matlab thinks of a "zero element", it could be 0 itself, or the numerical zero like 1e-16 or less.
Does anybody has this information about the NNZ function, cause I couldn't get the original script
Thanks.
PS : I am not an expert on Matlab, so accept my apologies if it's a really simple task.
I tried "open nnz", on Matlab but I only get a small script of commented code lines...
Since nnz counts everything that isn't an exact zero (i.e. 1e-100 is non-zero), you just have to apply a relational operator to your data first to find how many values exceed some tolerance around zero. For a matrix A:
n = nnz(abs(A) > 1e-16);
Also, this discussion of floating-point comparison might be of interest to you.
You can add in a tolerance by doing something like:
nnz(abs(myarray)>tol);
This will create a binary array that is 1 when abs(myarray)>tol and 0 otherwise and then count the number of non-zero entries.
A quick question because i fear there may already be an answer (although i cant find it)
i am getting the error: Matrix dimensions must agree.
because i am useing '<'
now with all the other operators there is a way around this either by putting '.' infront or by using a different formula. So what do people do about the less than operator????
i don't see why the greater than or equal to (>=) works but yet less than does not!?
am i being stupid and missed something really obvious??
code snippet
matrix 1 represents an array of 16 numbers
matrix 2 can represents anywhere between 10 and 20 numbers
idx = (matrix2 >= matrix1 * 0.1 & matrix2 < matrix1 * 1.5);
any help guidance or advice on the topic would be much appreciated! thank you!
EDIT
i know the matrices are different sizes but is there a way to use less then with different size arrays? as im not bothered about the size of the array but the numbers within
If you want to compare parts of matrices, like M(1:3,10:12)>A(5:7,1:3), you, probably, have to use the function squeeze():
squeeze(M(1:3,10:12))>squeeze(A(5:7,1:3))
This function remotes singleton dimensions and everything works fine after.
This question already has answers here:
How to find the index of the n smallest elements in a vector
(2 answers)
Closed 9 years ago.
Is there an efficient way to find the m-th smallest number in a vector of length n in Matlab? Do I have to use sort() function? Thanks and regards!
You don't need to sort the list of numbers to find the mth smallest number. The mth smallest number can be found out in linear time. i.e. if there are n elements in your array you can get a solution in O(n) time by using the selection algorithm and median of median algorithm.
The link is to the Wikipedia article,
http://en.wikipedia.org/wiki/Selection_algorithm#Linear_general_selection_algorithm_-_Median_of_Medians_algorithm
Edit 2: As Eitan pointed the first part of the answer doesn't address the question of finding the smallest m-th value but regarding the m-th element after the min value. The rest of the answer remains... +1 for Eitan's sharpness.
While sort is probably very efficient to begin with, you can try to see whether a find will be better. For example:
id=find(X>min(X),m,'first');
id(end) % is the index of the smallest m-th element in X
the function find has added functionality that lets you find the 'first' or 'last' elements that meet some criterion. For example, if you want to find the first n elements in array X less than a value y, use find(X<y,n,'first')
This operation stops as soon as the first element meeting the condition is encountered, which can result in significant time savings if the array is large and the value you find happens to be far from the end.
I'd also like to recap what #woodchips said already in this SO discussion that is somewhat relevant to your question:
The best way to speed up basic built-in algorithms such as sort is to get a faster hardware. It will speed everything else up too. MATLAB is already doing that in an efficient manner, using an optimized code internally. Saying this, maybe a GPU add-on can improve this too...
Edit:
For what it's worth, adding to Muster's comment, there is a FEX file called nth_element that is a MEX wrap of C++ that will get a solution in O(n) time for what you need. (similar to what #DDD pointed to)
As alternative solution, you may follow this way:
A = randi(100,4000,1);
A = sort(A,'ascend');
m = 5; % the 5 smallest numbers in array A
B = A(1:5);
I hope this helps.
I have a data of 9 columns 14470 rows,
The first column is filled with 0 or 1. Zero means that there is no measurment and the whole row is not in my interest.... can some body help me in writing a loop which go through all lines and filter the data when in first column 1 exist?
You do not need a loop for this, remember Matlab is a matrix oriented programming language, loops should be avoided. I won't give you the answer, I think you can figure it out yourself, it's easy. This tutorial will help.
Have fun.