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 \(C_{ij}\) is the covariance of \(x_i\) and \(x_j\). The element \(C_{ii}\) 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.

  • 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 \(ddof=1\) will return the unbiased estimate, even if both fweights and aweights are specified, and \(ddof=0\) 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.

  • 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 \(ddof=0\) the tensor of weights can be used to assign probabilities to observation vectors.

  • 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]]