Estimate a covariance matrix, given data.
Covariance indicates the level to which two variables vary together. If we examine Ndimensional samples, , then the covariance matrix element is the covariance of and . The element is the variance of .
Parameters:  m : array_like
y : array_like, optional
rowvar : int, optional
bias : int, optional


Returns:  out : ndarray

See also
Examples
Consider two variables, and , which correlate perfectly, but in opposite directions:
>>> x = np.array([[0, 2], [1, 1], [2, 0]]).T
>>> x
array([[0, 1, 2],
[2, 1, 0]])
Note how increases while decreases. The covariance matrix shows this clearly:
>>> np.cov(x)
array([[ 1., 1.],
[1., 1.]])
Note that element , which shows the correlation between and , is negative.
Further, note how x and y are combined:
>>> x = [2.1, 1, 4.3]
>>> y = [3, 1.1, 0.12]
>>> X = np.vstack((x,y))
>>> print np.cov(X)
[[ 11.71 4.286 ]
[ 4.286 2.14413333]]
>>> print np.cov(x, y)
[[ 11.71 4.286 ]
[ 4.286 2.14413333]]
>>> print np.cov(x)
11.71