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mindspore.numpy.cov

mindspore.numpy.cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None, dtype=None)[source]

Estimates 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 $C_{i j}$ is the covariance of $x_{i}$ and $x_{j}$ . The element $C_{i i}$ is the variance of $x_{i}$ .

Note

fweights and aweights must be all positive, in Numpy if negative values are detected, a value error will be raised, in MindSpore we converts all values to positive instead.

Parameters

m (Union[Tensor, list, tuple]) – A 1-D or 2-D tensor containing multiple variables and observations. Each row of m represents a variable, and each column represents a single observation of all those variables. Also see rowvar below.
y (Union[Tensor, list, tuple], optional) – An additional set of variables and observations. y has the same form as that of m, default is None.
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.
ddof (int, optional) – If not None, the default value implied by bias is overridden. Note that $d d o f = 1$ will return the unbiased estimate, even if both fweights and aweights are specified, and $d d o f = 0$ will return the simple average. See the notes for the details. The default value is None.
fweights (Union[Tensor, list, tuple], optional) – 1-D tensor of integer frequency weights; the number of times each observation vector should be repeated. The default value is None.
aweights (Union[Tensor, list, tuple], optional) – 1-D tensor of observation vector weights. These relative weights are typically larger for observations considered more important and smaller for observations considered less important. If $d d o f = 0$ the tensor of weights can be used to assign probabilities to observation vectors. The default value is None.
dtype (Union[mindspore.dtype, str], optional) – Data-type of the result. By default, the return data-type will have mstype.float32 precision.

Returns

Tensor, the covariance matrix of the variables.

Raises

TypeError – if the inputs have types not specified above.
ValueError – if m and y have wrong dimensions.
RuntimeError – if aweights and fweights have dimensions > 2.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore.numpy as np
>>> output = np.cov([[2., 3., 4., 5.], [0., 2., 3., 4.], [7., 8., 9., 10.]])
>>> print(output)
[[1.6666666 2.1666667 1.6666666]
[2.1666667 2.9166667 2.1666667]
[1.6666666 2.1666667 1.6666666]]