def corrcoef(x, y=None, rowvar=<NUM_LIT:1>, bias=<NUM_LIT:0>, ddof=None):
c = cov(x, y, rowvar, bias, ddof)<EOL>try:<EOL><INDENT>d = diag(c)<EOL><DEDENT>except ValueError:  <EOL><INDENT>return c / c<EOL><DEDENT>return c / sqrt(multiply.outer(d, d))<EOL>
Return correlation coefficients.

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

.. math:: P_{ij} = \\frac{ C_{ij} } { \\sqrt{ C_{ii} * C_{jj} } }

The values of `P` 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 `m` 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 `m`.
rowvar : int, optional
    If `rowvar` is non-zero (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.
bias : int, optional
    Default normalization is by ``(N - 1)``, where ``N`` is the number of
    observations (unbiased estimate). If `bias` is 1, then
    normalization is by ``N``. These values can be overridden by using
    the keyword ``ddof`` in numpy versions >= 1.5.
ddof : {None, int}, optional
    .. versionadded:: 1.5
    If not ``None`` normalization is by ``(N - ddof)``, where ``N`` is
    the number of observations; this overrides the value implied by
    ``bias``. The default value is ``None``.

Returns
-------
out : ndarray
    The correlation coefficient matrix of the variables.

See Also
--------
cov : Covariance matrix