jax.numpy.corrcoef#
- 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.
- Parameters:
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.
- Returns:
R – The correlation coefficient matrix of the variables.
- Return type:
ndarray