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pythonnumpycovariance
jtam
jtam

Reputation: 902

Numpy covariance command returning matrix with more dimensions than input

I have an arbitrary row vector "u" and an arbitrary matrix "e" as follows:

u = np.resize(np.array([8,3]),[1,2])
e = np.resize(np.array([[2,2,5,5],[1, 6, 7, 4]]),[4,2])
np.cov(u,e)

array([[ 12.5,   0. ,   0. , -12.5,   7.5],
   [  0. ,   0. ,   0. ,   0. ,   0. ],
   [  0. ,   0. ,   0. ,   0. ,   0. ],
   [-12.5,   0. ,   0. ,  12.5,  -7.5],
   [  7.5,   0. ,   0. ,  -7.5,   4.5]])

The matrix that this returns is 5x5. This is confusing to me because the largest dimension of the inputs is only 4.

Thus, this may be less of a numpy question and more of a math question...not sure...

Upvotes: 0

Views: 1436

Answers (1)

audio
audio

Reputation: 404

Please refer to the official numpy documentation (https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.cov.html) and check whether you usage of the numpy.cov function is consistent with what you are trying to achieve and you understand what you are trying to do.

When looking at the signature

numpy.cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None)

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.

Note how m and y are combined as shown in the last example on the page

>>> x = [-2.1, -1,  4.3]
>>> y = [3,  1.1,  0.12]
>>> X = np.stack((x, y), axis=0)
>>> 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

Upvotes: 1

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