It turns out that naively generating the matrix of permutations is the bottleneck in my code. I can calculate the property of interest for each permutation using numpy operations that vectorise/broadcast nicely over the resulting matrix of permutations. My use case is for a brute-force search to find permutations that minimise a specific property (constituting a "good enough" search solution). # x is big enough to not want to enumerate all permutations This is not ideal, mostly because the loop is in Python rather than inside a compiled numpy function. A naive way of generating many permutations is np.array(). Given a (1d) numpy array x, I want to sample n permutations of x (each of length len(x)), and have the result as a numpy array with shape (n, len(x)). Print('Time for direct shuffle: ".format((time.Unlike many other numpy/random functions, () doesn't provide an obvious way to return multiple results in a single function call. 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(): (n-1)->0 p np.random.permutation(n) P np.eye(n)p random. rand_num2 = np.random.randint(5, size=(6000, 2000)) TSP example using numpy functions (for efficiency) (C) Fair Isaac Corp. First, shuffle the index of an array then, use the shuffled index to get the data. import numpy as np x1 np. This is very helpful when you are generating random data, the example code is: Create two sequeces with the same shape. Permute two sequences by the same random order. 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.Īfter a bit of experiment (i) found the most memory and time-efficient way to shuffle data(row-wise)in an nD array. Example 1 : In this example we can see that by using () method, we are able to get the sequence of permutation and it will return the sequence by using this method. From the result we will find, () will randomly permute a sequence by its first aixs. 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) New code should use the permutation method of a Generator instance instead please see the Quick Start. 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. For example, it is now possible to permute the rows or 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
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