torch.corrcoef#

torch.corrcoef(input)→Tensor#

Estimates the Pearson product-moment correlation coefficient matrix of the variables given by theinput 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$R_{ij}=\frac{C_{ij}}{\sqrt{C_{ii}\ast C_{jj}}}$Rij​=Cii​∗Cjj​​Cij​​

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 aScalar 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)>>>xtensor([[-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.)