- cupy.cov(a, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None, *, dtype=None)#
Returns the covariance matrix of an array.
This function currently does not support
a (cupy.ndarray) – Array to compute covariance matrix.
y (cupy.ndarray) – An additional set of variables and observations.
rowvar (bool) – If
True, then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed.
bias (bool) – If
False, normalization is by
(N - 1), where N is the number of observations given (unbiased estimate). If
True, then normalization is by
ddof (int) – If not
Nonethe default value implied by bias is overridden. Note that
ddof=1will return the unbiased estimate and
ddof=0will return the simple average.
fweights (cupy.ndarray, int) – 1-D array of integer frequency weights. the number of times each observation vector should be repeated. It is required that fweights >= 0. However, the function will not error when fweights < 0 for performance reasons.
aweights (cupy.ndarray) – 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=0the array of weights can be used to assign probabilities to observation vectors. It is required that aweights >= 0. However, the function will not error when aweights < 0 for performance reasons.
dtype – Data type specifier. By default, the return data-type will have at least numpy.float64 precision.
The covariance matrix of the input array.
- Return type: