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numpy.ma.cov(x, y=None, rowvar=True, bias=False, allow_masked=True, ddof=None)
[source] -
Estimate the covariance matrix.
Except for the handling of missing data this function does the same as
numpy.cov
. For more details and examples, seenumpy.cov
.By default, masked values are recognized as such. If
x
andy
have the same shape, a common mask is allocated: ifx[i,j]
is masked, theny[i,j]
will also be masked. Settingallow_masked
to False will raise an exception if values are missing in either of the input arrays.Parameters: x : array_like
A 1-D or 2-D array containing multiple variables and observations. Each row of
x
represents a variable, and each column a single observation of all those variables. Also seerowvar
below.y : array_like, optional
An additional set of variables and observations.
y
has the same form asx
.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)
, whereN
is the number of observations given (unbiased estimate). Ifbias
is True, then normalization is byN
. This keyword can be overridden by the keywordddof
in numpy versions >= 1.5.allow_masked : bool, optional
If True, masked values are propagated pair-wise: if a value is masked in
x
, the corresponding value is masked iny
. If False, raises aValueError
exception when some values are missing.ddof : {None, int}, optional
If not
None
normalization is by(N - ddof)
, whereN
is the number of observations; this overrides the value implied bybias
. The default value isNone
.New in version 1.5.
Raises: ValueError
Raised if some values are missing and
allow_masked
is False.See also
numpy.ma.cov()
2017-01-10 18:15:11
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