jax.numpy.corrcoef(x, y=None, rowvar=True)[source]#

Return Pearson product-moment correlation coefficients.

LAX-backend implementation of numpy.corrcoef().

Original docstring below.

Please refer to the documentation for cov for more detail. The relationship between the correlation coefficient matrix, R, and the covariance matrix, C, is

\[R_{ij} = \frac{ C_{ij} } { \sqrt{ C_{ii} C_{jj} } }\]

The values of R are between -1 and 1, inclusive.

  • x (array_like) – A 1-D or 2-D array containing multiple variables and observations. Each row of x represents a variable, and each column a single observation of all those variables. Also see rowvar below.

  • y (array_like, optional) – An additional set of variables and observations. y has the same shape as x.

  • rowvar (bool, optional) – If rowvar is True (default), then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed: each column represents a variable, while the rows contain observations.


R – The correlation coefficient matrix of the variables.

Return type: