numpy.vander()
function is used to generate a Vandermonde matrix.
Syntax : numpy.vander(arr, N = None, increasing = False)
Parameters :
arr : [ array_like] 1-D input array.
N : [int, optional] Number of columns in the output. If N is not specified, a square array is returned (N = len(x)).
increasing : [bool, optional] Order of the powers of the columns. If True, the powers increase from left to right, if False (the default) they are reversed.
Return : [ndarray] dVandermonde matrix. If increasing is False, the first column is x^(N-1), the second x^(N-2) and so forth. If increasing is True, the columns are x^0, x^1, …, x^(N-1).
Code #1 :
# Python program explaining # numpy.vander() function # importing numpy as geek import numpy as geek arr = geek.array([ 1 , 2 , 3 , 4 , 5 ]) gfg = geek.vander(arr) print (gfg) |
Output :
[[ 1 1 1 1 1] [ 16 8 4 2 1] [ 81 27 9 3 1] [256 64 16 4 1] [625 125 25 5 1]]
Code #2 :
# Python program explaining # numpy.vander() function # importing numpy as geek import numpy as geek arr = geek.array([ 1 , 2 , 3 , 4 , 5 ]) N = 3 gfg = geek.vander(arr, N) print (gfg) |
Output :
[[ 1 1 1] [ 4 2 1] [ 9 3 1] [16 4 1] [25 5 1]]
Code #3 :
# Python program explaining # numpy.vander() function # importing numpy as geek import numpy as geek arr = geek.array([ 1 , 2 , 3 , 4 , 5 ]) gfg = geek.vander(arr, increasing = True ) print (gfg) |
Output :
[[ 1 1 1 1 1] [ 1 2 4 8 16] [ 1 3 9 27 81] [ 1 4 16 64 256] [ 1 5 25 125 625]]