Shortcuts

# 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 $R_{ij} = \frac{ C_{ij} } { \sqrt{ C_{ii} * C_{jj} } }$

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.

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)
tensor(1.) ## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials