The numpy.one_like() function returns an array of given shape and type as a given array, with ones.
Syntax: numpy.ones_like(array, dtype = None, order = 'K', subok = True)
array : array_like input subok : [optional, boolean]If true, then newly created array will be sub-class of array; otherwise, a base-class array order : C_contiguous or F_contiguous C-contiguous order in memory(last index varies the fastest) C order means that operating row-wise on the array will be slightly quicker FORTRAN-contiguous order in memory (first index varies the fastest). F order means that column-wise operations will be faster. dtype : [optional, float(byDefault)] Data type of returned array.
ndarray of ones having given shape, order and datatype.
Original array : [[0 1] [2 3] [4 5] [6 7] [8 9]] Matrix b : [[ 1. 1.] [ 1. 1.] [ 1. 1.] [ 1. 1.] [ 1. 1.]] Matrix c : [1 1 1 1 1 1 1 1]
Also, these codes won’t run on online-ID. Please run them on your systems to explore the working
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