![]() Let's encapsulate the function written by import math Furthermore, assessing the complexity requires to know how many time the endless-loop will actually loop before a new random tuple is found and break it. If we want to be sure that the loop could end, we must enforce N <= k!, but it is not guaranteed. There is something interesting about the algorithm design and complexity. This naïve version does not directly suffer to factorial complexity O(k!) of itertools.permutations function which does generate all k! permutations before sampling from it. Then to permute the original list using the index permutation. ![]() to check if permutation already exists and store it (as tuple of int because it must hash) to prevent duplicates.to generate a new random permutation (index randomly permuted).It relies on which randomly permute a sequence. If key not in perms: # (4) Check if permutation already has been drawn (hash table) Perm = np.random.permutation(k) # (3) Generate a random permutation form U Print('Time for direct shuffle: ".format((time.Bellow the naïve implementation I did (well implemented by pure PSL using generator): import numpy as npįor i in range(n): # (1) Draw N samples from permutations Universe U (#U = k!) ![]() Here, I am using memory_profiler to find memory usage and python's builtin "time" module to record time and comparing all previous answers def main(): First, shuffle the index of an array then, use the shuffled index to get the data. For example, it is now possible to permute the rows orĪfter a bit of experiment (i) found the most memory and time-efficient way to shuffle data(row-wise)in an nD array. Treated as a separate 1-D array for every combination of the other Subarrays indexed by an axis are permuted rather than the axis being The new function differs from shuffle and permutation in that the The function is introduced in Numpy's 1.20.0 Release. In : import numpy as npįor other functionalities you can also check out the following functions: The order of sub-arrays is changed but theirĬontents remains the same. This function only shuffles the array along the first axis of a So, it seems using these np.take based could be used only if memory is a concern or else np.random.shuffle based solution looks like the way to go. In : %timeit np.take(X,np.random.rand(X.shape).argsort(),axis=0,out=X) In : %timeit np.take(X,np.random.permutation(X.shape),axis=0,out=X) These tests include the two approaches listed in this post and np.shuffle based one in solution. Thus, the shuffling solution could be modified to - np.take(X,np.random.rand(X.shape).argsort(),axis=0,out=X) In : %timeit np.random.rand(X.shape).argsort() In : %timeit np.random.permutation(X.shape) Speedup results - In : X = np.random.random((6000, 2000)) Here's a trick to speed up np.random.permutation(X.shape) with np.argsort() - np.random.rand(X.shape).argsort() In : np.take(X,np.random.permutation(X.shape),axis=0,out=X) ![]() Thus, the implementation would look like this - np.take(X,np.random.permutation(X.shape),axis=0,out=X) Also, np.take facilitates overwriting to the input array X itself with out= option, which would save us memory. You can also use np.random.permutation to generate random permutation of row indices and then index into the rows of X using np.take with axis=0. If you have 3d array, loop through the 1st axis (axis=0) and apply this function, like: np.array() ![]() I tried many solutions, and at the end I used this simple one: from sklearn.utils import shuffle ![]()
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