On 2015-06-21 02:57, Nasser M. Abbasi wrote:
I just started to learn some python today for first time,
so be easy on me.
I am having some trouble figuring how do the problem shown in this link
http://12000.org/my_notes/mma_matlab_control/KERNEL/KEse44.htm
Given 4 column vectors, v1,v2,v3,v4, each is 3 rows.
I want to use these to construct matrix mat, which is
[[v1,v2],
[v3,v4]]
So the resulting matrix is as shown in the link. i.e.
it will be 6 rows and 2 columns.
This is what I tried:
import numpy as np
v1=np.array([1,2,3]);
v2=np.array([4,5,6]);
v3=np.array([7,8,9]);
v4=np.array([10,11,12]);
And now I get stuck, I tried
m=np.array([[v1,v2],[v3,v4]]) #no good
Also
m=np.array([v1,v2,v3,v4])
m.shape
Out[153]: (4L, 3L)
m.T
array([[ 1, 4, 7, 10],
[ 2, 5, 8, 11],
[ 3, 6, 9, 12]])
Not what I want.
I need to get the shape as in the above link, 6 rows by 2 columns,
where each column vector is stacked as shown. I also tried
v1=np.array([1,2,3]); v1.shape=3,1
v2=np.array([4,5,6]); v2.shape=3,1
v3=np.array([7,8,9]); v3.shape=3,1
v4=np.array([10,11,12]); v4.shape=3,1
mat=np.array([[v1,v2],[v3,v4]])
What is the correct way to do this in Python?
Here's one way, one step at a time:
r1 = np.concatenate([v1, v2])
r1
array([1, 2, 3, 4, 5, 6])
r2 = np.concatenate([v3, v4])
r2
array([ 7, 8, 9, 10, 11, 12])
m = np.array([r1, r2])
m
array([[ 1, 2, 3, 4, 5, 6],
[ 7, 8, 9, 10, 11, 12]])
m.transpose()
array([[ 1, 7],
[ 2, 8],
[ 3, 9],
[ 4, 10],
[ 5, 11],
[ 6, 12]])
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