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
On 6/20/2015 9:20 PM, MRAB wrote:
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]])
On 6/20/2015 10:47 PM, Nasser M. Abbasi wrote:
I did manage to find a way:
-
r1 =np.hstack([(v1,v2)]).T
r2 =np.hstack([(v3,v4)]).T
mat = np.vstack((r1,r2))
-
Out[211]:
array([[ 1, 4],
[ 2, 5],
[ 3, 6
On 04/29/2012 05:17 PM, someone wrote:
I would also kindly ask about how to avoid this problem in
the future, I mean, this maybe means that I have to check the condition
number at all times before doing anything at all ? How to do that?
I hope you'll check the condition number all the time.
On 04/29/2012 07:59 PM, someone wrote:
Also, as was said, do not use INV(A) directly to solve equations.
In Matlab I used x=A\b.
good.
I used inv(A) in python. Should I use some kind of pseudo-inverse or
what do you suggest?
I do not use python much myself, but a quick google showed
On 04/29/2012 07:17 PM, someone wrote:
Ok. When do you define it to be singular, btw?
There are things you can see right away about a matrix A being singular
without doing any computation. By just looking at it.
For example, If you see a column (or row) being a linear combination of
