numpy covariance matrix

Question

Suppose I have two vectors of length 25, and I want to compute their covariance matrix. I try doing this with numpy.cov, but always end up with a 2x2 matrix.

>>> import numpy as np >>> x=np.random.normal(size=25) >>> y=np.random.normal(size=25) >>> np.cov(x,y) array([[ 0.77568388,  0.15568432],        [ 0.15568432,  0.73839014]]) 

Using the rowvar flag doesn't help either - I get exactly the same result.

>>> np.cov(x,y,rowvar=0) array([[ 0.77568388,  0.15568432],        [ 0.15568432,  0.73839014]]) 

How can I get the 25x25 covariance matrix?

Answer 1

You have two vectors, not 25. The computer I'm on doesn't have python so I can't test this, but try:

z = zip(x,y) np.cov(z) 

Of course.... really what you want is probably more like:

n=100 # number of points in each vector num_vects=25 vals=[] for _ in range(num_vects):     vals.append(np.random.normal(size=n)) np.cov(vals) 

This takes the covariance (I think/hope) of num_vects 1xn vectors

Answer 2

Try this:

import numpy as np x=np.random.normal(size=25) y=np.random.normal(size=25) z = np.vstack((x, y)) c = np.cov(z.T)