numpy.ones_like (array, dtype = None, order = & # 39; K & # 39 ;, subok = True): return an array of the given shape and type as a given array with ones,
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-rise 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 (byDeafult)] 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 will not work to an online ID. Please run them on your systems to see how they work
This article is courtesy of Mohit Gupta_OMG