cupy.cov#
- cupy.cov(a, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None, *, dtype=None)[source]#
Returns the covariance matrix of an array.
This function currently does not support
fweights
andaweights
options.- Parameters:
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). IfTrue
, then normalization is byN
.ddof (int) – If not
None
the default value implied by bias is overridden. Note thatddof=1
will return the unbiased estimate andddof=0
will 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=0
the 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.
- Returns:
The covariance matrix of the input array.
- Return type:
See also