nlcpy.cov

nlcpy.cov(a, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None)

Estimates a covariance matrix, given data and weights.

Covariance indicates the level to which two variables vary together. If we examine N-dimensional samples, $X = [x_1, x_2, ... x_N]^T$ , then the covariance matrix element $C_{ij}$ is the covariance of $x_i$ and $x_j$ . The element $C_{ii}$ is the variance of $x_i$ . See the notes for an outline of the algorithm.

Parameters

marray_like: A 1-D or 2-D array containing multiple variables and observations. Each row of m represents a variable, and each column a single observation of all those variables. Also see rowvar below.
yarray_like, optional: An additional set of variables and observations. y has the same form as that of m.
rowvarbool, optional: 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.
biasbool, optional: Default normalization (False) is by (N - 1), where N is the number of observations given (unbiased estimate). These arguments had no effect on the return values of the function and can be safely ignored in this and previous versions of nlcpy. If bias is True, then normalization is by N.
ddofint, optional: If not None the default value implied by bias is overridden. Note that ddof=1 will return the unbiased estimate, even if both fweights and aweights are specified, and ddof=0 will return the simple average. See the notes for the details. The default value is None.
fweightsarray_like, int, optional: 1-D array of integer frequency weights; the number of times each observation vector should be repeated.
aweightsarray_like, optional: 1-D array of observation vector weights. These relative weights are typically large for observations considered “important” and smaller for observations considered less “important”. If ddof=0 the array of weights can be used to assign probabilities to observation vectors.

Returns

outndarray: The covariance matrix of the variables.

See also

corrcoef: Normalized covariance matrix

Note

Assume that the observations are in the columns of the observation array m and let f = fweights and a = aweights for brevity. The steps to compute the weighted covariance are as follows:

>>> import nlcpy as vp
>>> m = vp.arange(10, dtype=vp.float64)
>>> f = vp.arange(10) * 2
>>> a = vp.arange(10) ** 2.
>>> ddof = 9 # N - 1
>>> w = f * a
>>> v1 = vp.sum(w)
>>> v2 = vp.sum(w * a)
>>> m -= vp.sum(m * w, axis=None, keepdims=True) / v1
>>> cov = vp.dot(m * w, m.T) * v1 / (v1**2 - ddof * v2)

Note that when a == 1, the normalization factor v1 / (v1**2 - ddof * v2) goes over to 1 / (vp.sum(f) - ddof) as it should.

Examples

Consider two variables, $x_0$ and $x_1$ , which correlate perfectly, but in opposite directions:

>>> import nlcpy as vp
>>> x = vp.array([[0, 2], [1, 1], [2, 0]]).T
>>> x
array([[0, 1, 2],
       [2, 1, 0]])

Note how $x_0$ increases while $x_1$ decreases. The covariance matrix shows this clearly:

>>> vp.cov(x) 
array([[ 1., -1.],
       [-1.,  1.]])

Note that element $C_{0,1}$ , which shows the correlation between $x_0$ and $x_1$ , is negative.

Further, note how x and y are combined:

>>> x = [-2.1, -1,  4.3]
>>> y = [3,  1.1,  0.12]
>>> vp.cov(x, y) 
array([[11.71      , -4.286     ],
       [-4.286     ,  2.14413333]])
>>> vp.cov(x)    
array(11.71)