# Path: glimix_core/_util/format.py
# def format_function(func, params, attrs=None):
#     if attrs is None:
#         attrs = []
#     tname = type(func).__name__
#     name = func.name
#     kwargs_input = [f"{arg}={val}" for arg, val in params.items()]
#     input = ", ".join(kwargs_input)
#     msg = f"{tname}({input})"
#     if name is not None:
#         msg += f": {name}"
# 
#     msg += "\n"
#     for a in attrs:
#         msg += _format_named_arr(a[0], a[1])
#     return msg


from numpy import exp, log
from optimix import Function, Scalar
from .._util import format_function
        from numpy_sugar.linalg import check_symmetry
        from numpy_sugar import epsilon

    @scale.setter
    def scale(self, scale):

        scale = max(scale, epsilon.tiny)
        self._logscale.value = log(scale)

    def value(self):
        """
        Covariance matrix, s⋅K₀.

        Returns
        -------
        K : ndarray
            s⋅K₀.
        """
        return self.scale * self._K0

    def gradient(self):
        """
        Derivative of the covariance matrix over log(s).

        Returns
        -------
        logscale : float
            s⋅K₀.
        """
        return dict(logscale=self.scale * self._K0)

    def __str__(self):

# Path: glimix_core/_util/format.py
# def format_function(func, params, attrs=None):
#     if attrs is None:
#         attrs = []
#     tname = type(func).__name__
#     name = func.name
#     kwargs_input = [f"{arg}={val}" for arg, val in params.items()]
#     input = ", ".join(kwargs_input)
#     msg = f"{tname}({input})"
#     if name is not None:
#         msg += f": {name}"
# 
#     msg += "\n"
#     for a in attrs:
#         msg += _format_named_arr(a[0], a[1])
#     return msg


# Path: glimix_core/cov/_given.py
from numpy import exp, log
from optimix import Function, Scalar
from .._util import format_function
        from numpy_sugar.linalg import check_symmetry
        from numpy_sugar import epsilon


    @scale.setter
    def scale(self, scale):

        scale = max(scale, epsilon.tiny)
        self._logscale.value = log(scale)

    def value(self):
        """
        Covariance matrix, s⋅K₀.

        Returns
        -------
        K : ndarray
            s⋅K₀.
        """
        return self.scale * self._K0

    def gradient(self):
        """
        Derivative of the covariance matrix over log(s).

        Returns
        -------
        logscale : float
            s⋅K₀.
        """
        return dict(logscale=self.scale * self._K0)

    def __str__(self):

        return format_function(self, {"K0": "..."}, [("scale", self.scale)])