"Cloning" Row or Column Vectors

Cloning row or column vectors

Here's an elegant, Pythonic way to do it:

>>> array([[1,2,3],]*3)
array([[1, 2, 3],
       [1, 2, 3],
       [1, 2, 3]])

>>> array([[1,2,3],]*3).transpose()
array([[1, 1, 1],
       [2, 2, 2],
       [3, 3, 3]])

the problem with [16] seems to be that the transpose has no effect for an array. you're probably wanting a matrix instead:

>>> x = array([1,2,3])
>>> x
array([1, 2, 3])
>>> x.transpose()
array([1, 2, 3])
>>> matrix([1,2,3])
matrix([[1, 2, 3]])
>>> matrix([1,2,3]).transpose()
matrix([[1],
        [2],
        [3]])

Cloning row in numpy.tile

3 is the number of times he have to clone it horizontally

and 1 is the number of times he have to clone it vertically:

for example if you have:

import numpy as np

tile = np.tile(
np.array([
    [1,2,3], #<- row 1 here
    [1,2,3]  #<- row 2 here
]),(2,2)
)
print(tile)

you would get the array, cloned 2 times vertically and 2 times horizontally:

[[1 2 3 1 2 3]
 [1 2 3 1 2 3]
 [1 2 3 1 2 3]
 [1 2 3 1 2 3]]

column vector times row vector to form a matrix

You can try

numpy.matmul

Notes from the documentation

The behavior depends on the arguments in the following way.

• If both arguments are 2-D they are multiplied like conventional
  matrices.
• If either argument is N-D, N > 2, it is treated as a stack of
  matrices residing in the last two indexes and broadcast accordingly.
• If the first argument is 1-D, it is promoted to a matrix by
  prepending a 1 to its dimensions. After matrix multiplication the
  prepended 1 is removed.
• If the second argument is 1-D, it is promoted to a matrix by
  appending a 1 to its dimensions. After matrix multiplication the
  appended 1 is removed.

Repeat a unidimensional array over columns

Try this code:

np.array([x] * 3).T

Here 3 is the number of times you want to repeat those values

How to broadcast a row to a column in Python NumPy?

R + C[:, numpy.newaxis]

Does the trick for me.

For example

import numpy as np
r = np.ones(5)
c = np.ones(4) * 2
r + c[:, np.newaxis]

gives

array([[ 3.,  3.,  3.,  3.,  3.],
       [ 3.,  3.,  3.,  3.,  3.],
       [ 3.,  3.,  3.,  3.,  3.],
       [ 3.,  3.,  3.,  3.,  3.]])

How do you multiply a column vector with a row vector using @ in Python?

What you can see here, is a property of how numpy arrays and matrices work.

You've created numpy arrays, each 2 long, which if you transpose, you still get an array of length 2. You can see this if you do a.shape. You have to create a 1x2 matrix, where it will work as you expected:

>>> a=np.array([[1,2]])
>>> b=np.array([[3,4]])
>>> a@b
Error
>>> a.T@b
array([[3, 4],
       [6, 8]])
>>> a@b.T
array([[11]])
>>> a.T@b.T
Error

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