Can anyone direct me to the section of numpy manual where i can get functions to accomplish root mean square calculations ... (i know this can be accomplished using np.mean and np.abs .. isn't there a built in ..if no why?? .. just curious ..no offense)
can anyone explain the complications of matrix and arrays (just in the following case):
U is a matrix(T-by-N,or u say T cross N) , Ue is another matrix(T-by-N)
I define k as a numpy array
U[ind,:] is still matrix
in the following fashion
k = np.array(U[ind,:])
when I print k or type k in ipython
it displays following
K = array ([[2,.3 .....
......
9]])
You see the double square brackets (which makes it multi-dim i guess) which gives it the shape = (1,N)
but I can't assign it to array defined in this way
l = np.zeros(N)
shape = (,N) or perhaps (N,) something like that
l[:] = k[:]
error:
matrix dimensions incompatible
Is there a way to accomplish the vector assignment which I intend to do ... Please don't tell me do this l = k (that defeats the purpose ... I get different errors in program .. I know the reasons ..If you need I may attach the piece of code)
writing a loop is the dumb way .. which I'm using for the time being ...
I hope I was able to explain .. the problems I'm facing ..
regards ...
Try this:
U = np.zeros((N,N))
ind = 1
k = np.zeros(N)
k[:] = U[ind,:]