mars.tensor.cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None)[source]#

Estimate 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.

  • m (array_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.

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

  • rowvar (bool, 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.

  • bias (bool, optional) – 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. These values can be overridden by using the keyword ddof in numpy versions >= 1.5.

  • ddof (int, 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.

  • fweights (array_like, int, optional) – 1-D tensor of integer freguency weights; the number of times each observation vector should be repeated.

  • aweights (array_like, optional) – 1-D tensor 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.


out – The covariance matrix of the variables.

Return type


See also


Normalized covariance matrix


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:

>>> w = f * a
>>> v1 = mt.sum(w)
>>> v2 = mt.sum(w * a)
>>> m -= mt.sum(m * w, axis=1, keepdims=True) / v1
>>> cov = * 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 / (np.sum(f) - ddof) as it should.


Consider two variables, \(x_0\) and \(x_1\), which correlate perfectly, but in opposite directions:

>>> import mars.tensor as mt
>>> x = mt.array([[0, 2], [1, 1], [2, 0]]).T
>>> x.execute()
array([[0, 1, 2],
       [2, 1, 0]])

Note how \(x_0\) increases while \(x_1\) decreases. The covariance matrix shows this clearly:

>>> mt.cov(x).execute()
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]
>>> X = mt.stack((x, y), axis=0)
>>> print(mt.cov(X).execute())
[[ 11.71        -4.286     ]
 [ -4.286        2.14413333]]
>>> print(mt.cov(x, y).execute())
[[ 11.71        -4.286     ]
 [ -4.286        2.14413333]]
>>> print(mt.cov(x).execute())