![]() ![]() But v is created once, and I might need to look for many different values of x over it. 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. 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. 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. Import numpy as np from itertools import chain, izip def find( a, predicate, chunk_size = 1024): Here is the implemented function: function = myind2ind(ii, N) The column number can be found by subtracting current linear index from the linear index of the first element of the current row and adding R to it. R = N + 1 -floor(r) įor the column number we find the index of the first element idx_first of the current row R: idx_first=(floor(r+1). R can be rounded and subtracted from N to get the first element (row number of triangular matrix) of the desired output. Given jj = size(I,1) + 1 - ii as a row index I that begins from the end of I and using N * (N -1) / 2 we can formulate a quadratic equation: N * (N -1) / 2 = jj We can get the number of rows of I with the Gauss formula for triangular number (N-1) * (N-1+1) /2 = The I matrix can be generated by nchoosek. ![]()
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