cov

numeric.stats.cov(m, y=None, rowvar=True, bias=False)

Estimate a covariance matrix.

Parameters:

• m – (array_like) A 1-D or 2-D array containing multiple variables and observations.

• y – (array_like) Optional. An additional set of variables and observations. y has the same form as that of m.

• rowvar – (boolean) If rowvar is True (default), then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed: each column represents a variable, while the rows contain observations.

• bias – (boolean) Default normalization (False) is by (N - 1), where N is the number of observations given (unbiased estimate). If bias is True, then normalization is by N.

Returns:

Covariance.

Examples:

from mipylib.numeric import stats

x1 = [12, 2, 1, 12, 2]
x2 = [1, 4, 7, 1, 0]
c = stats.cov(x1, x2)
print c

x = array([[0, 2], [1, 1], [2, 0]]).T
print stats.cov(x)

x = array([[12, 2, 1, 12, 2], [1, 4, 7, 1, 0]])
print stats.cov(x)
print stats.cov(x, x)

Result:

>>> run script...
array([[32.2, -9.1]
      [-9.1, 8.3]])
array([[1.0, -1.0]
      [-1.0, 1.0]])
array([[32.2, -9.1]
      [-9.1, 8.3]])
array([[32.2, -9.1, 32.2, -9.1]
      [-9.1, 8.3, -9.1, 8.300000000000002]
      [32.2, -9.1, 32.2, -9.1]
      [-9.1, 8.300000000000002, -9.1, 8.3]])