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torch.corrcoef
torch.corrcoef(input) → Tensor
-
Estimates the Pearson product-moment correlation coefficient matrix of the variables given by the
input
matrix, where rows are the variables and columns are the observations.Note
The correlation coefficient matrix R is computed using the covariance matrix C as given by
Note
Due to floating point rounding, the resulting array may not be Hermitian and its diagonal elements may not be 1. The real and imaginary values are clipped to the interval [-1, 1] in an attempt to improve this situation.
- Parameters
-
input (Tensor) – A 2D matrix containing multiple variables and observations, or a Scalar or 1D vector representing a single variable.
- Returns
-
(Tensor) The correlation coefficient matrix of the variables.
See also
torch.cov()
covariance matrix.Example:
>>> x = torch.tensor([[0, 1, 2], [2, 1, 0]]) >>> torch.corrcoef(x) tensor([[ 1., -1.], [-1., 1.]]) >>> x = torch.randn(2, 4) >>> x tensor([[-0.2678, -0.0908, -0.3766, 0.2780], [-0.5812, 0.1535, 0.2387, 0.2350]]) >>> torch.corrcoef(x) tensor([[1.0000, 0.3582], [0.3582, 1.0000]]) >>> torch.corrcoef(x[0]) tensor(1.)
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https://pytorch.org/docs/2.1/generated/torch.corrcoef.html