- #Matlab return index of nonzero elements generator#
- #Matlab return index of nonzero elements full#
- #Matlab return index of nonzero elements software#
But v is created once, and I might need to look for many different values of x over it.
#Matlab return index of nonzero elements full#
And yes, I do see it means making a full pass of the data if you do 3. I understand your concern, but I would never ask for np.first_square_equal precisely like I would never ask (and nobody, I hope, asked) for np.square_where. `np.first_square_equal(x*x, v)` - save a pass vs `first_equal(np.square(x), v)` `np.first_equal(x, v)` - save a pass vs `first(np.equal(x, v))`ģ. Difficulty or workload in library development constitutes a very good excuse of not implementing these features and is certainly understandable, but don't say they are not useful.ġ. What I'm trying to elaborate is that having the ability to do lazy processing is always desirable and can be crucial to the responsiveness of the system as well as the productivity of the people using the language. What's worse, there is nothing elegant he can do even if the bottleneck is figured out through profiling.
Making a function 10x slower without changing its complexity at all is sufficient to drive one data scientist crazy. At least the workstations are not scaling in the same way clusters are.
#Matlab return index of nonzero elements software#
However, every software is using 2x or 4x more memory than they used to. Three years ago when you purchase a standard laptop, they are equipped with 8GB memory and now you'll still find 8GB in the market. Let's ask a simple questions: are personal computers scaling today? The answer is definitely NO. However, I don't feel comfortable when performance tweaks are considered only in scalability. nonzero()):Īnyway it's good to know that it's impossible to do the lazy things in numpy, which clarifies current status of the issue. size, chunk_size),įor inds in izip( * predicate( chunk). I0 = 0 chunk_inds = chain( xrange( chunk_size, a. Raise ValueError( 'The array must be 1D, not. See Also - where, nonzero Notes - This function is best used for finding the first, or first few, data values which match the predicate.
#Matlab return index of nonzero elements generator#
Returns - index_generator : generator A generator of (indices, data value) tuples which make the predicate True. For high probability predicates, a smaller number will make this function quicker, similarly choose a larger number for low probabilities. chunk_size : integer The length of the chunks to use when searching for matching indices. predicate : function A function which operates on sections of the given array, returning element-wise True or False for each data value. Parameters - a : array_like Input data, must be 1D. """ Find the indices of array elements that match the predicate. Thus, the find method has a running time dictated by the overhead of copying the array and calling find, whereas the loop method time is dictated by how many array elements one needs to visit to find the first non-zero element.Import numpy as np from itertools import chain, izip def find( a, predicate, chunk_size = 1024): q is the location of the first non-zero element. Test results depend, of course, on the expected number of elements to visit before a non-zero element is found, as well as on the array length n. (I didn't add the bwconncomp answer because it's significantly slower). Method2 is the fixed version of this answer, using find and copying the array.
0 kommentar(er)
