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
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.
>>> 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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