#Numpy permute how to#

Print('Time for direct shuffle: '.format((time. There is a good answer on SO which discusses the meaning of contiguous in Numpy. view() vs reshape() and transpose() view() vs transpose() Both view() and reshape() can be used to change the size or shape of tensors. Rand_num = np.random.randint(5, size=(6000, 2000)) Here, I would like to talk about view() vs reshape(), transpose() vs permute(). 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. 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. 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() For 2-D arrays, transpose by omitting the axes order argument. In : %timeit np.random.permutation(X.shape) Numpy transpose can also permute by specifying a tuple of the desired order. 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. Parameters: input ( Tensor) the input tensor. 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. Returns a view of the original tensor input with its dimensions permuted. 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.

#Numpy permute how to#

In : import numpy as npįor other functionalities you can also check out the following functions: currently im facing a problem regarding the permutation of 2 numpy arrays of different row sizes, i know how to to utilize the np.random.shuffle function but i cannot seem to find a solution to my specific problem, the examples from the numpy documentation only refers to nd arrays with the same row sizes, e.g x.shape10784 y.shape10784.

The order of sub-arrays is changed but theirĬontents remains the same. This function only shuffles the array along the first axis of a