# tensorplay.functional.corrcoef

Source: https://www.tensorplay.cn/docs/generated/tensorplay.functional.corrcoef.html

```python
tensorplay.functional.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](/docs/generated/tensorplay.Tensor.html#tensorplay.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**
>
> tensorplay.cov() covariance matrix.

Example:

```
>>> x = tensorplay.tensor([[0, 1, 2], [2, 1, 0]])
>>> tensorplay.corrcoef(x)
tensor([[ 1., -1.],
        [-1.,  1.]])
>>> x = tensorplay.randn(2, 4)
>>> x
tensor([[-0.2678, -0.0908, -0.3766,  0.2780],
        [-0.5812,  0.1535,  0.2387,  0.2350]])
>>> tensorplay.corrcoef(x)
tensor([[1.0000, 0.3582],
        [0.3582, 1.0000]])
>>> tensorplay.corrcoef(x[0])
tensor(1.)
```