# 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 typing import Any, Dict
from numpy import diag_indices_from, dot, exp, eye, inf, log, tril_indices_from, zeros
from optimix import Function, Vector
from .._util import format_function
        from numpy_sugar import epsilon
        from numpy.linalg import svd
        from numpy.linalg import slogdet
        return K + self._epsilon * eye(K.shape[0])

    def gradient(self):
        """
        Derivative of the covariance matrix over the parameters of L.

        Returns
        -------
        Lu : ndarray
            Derivative of K over the lower triangular part of L.
        """
        L = self.L
        self._grad_Lu[:] = 0

        for i in range(len(self._tril1[0])):
            row = self._tril1[0][i]
            col = self._tril1[1][i]
            self._grad_Lu[row, :, i] = L[:, col]
            self._grad_Lu[:, row, i] += L[:, col]

        m = len(self._tril1[0])
        for i in range(len(self._diag[0])):
            row = self._diag[0][i]
            col = self._diag[1][i]
            self._grad_Lu[row, :, m + i] = L[row, col] * L[:, col]
            self._grad_Lu[:, row, m + i] += L[row, col] * L[:, col]

        return {"Lu": self._grad_Lu}

    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/_free.py
from typing import Any, Dict
from numpy import diag_indices_from, dot, exp, eye, inf, log, tril_indices_from, zeros
from optimix import Function, Vector
from .._util import format_function
        from numpy_sugar import epsilon
        from numpy.linalg import svd
        from numpy.linalg import slogdet

        return K + self._epsilon * eye(K.shape[0])

    def gradient(self):
        """
        Derivative of the covariance matrix over the parameters of L.

        Returns
        -------
        Lu : ndarray
            Derivative of K over the lower triangular part of L.
        """
        L = self.L
        self._grad_Lu[:] = 0

        for i in range(len(self._tril1[0])):
            row = self._tril1[0][i]
            col = self._tril1[1][i]
            self._grad_Lu[row, :, i] = L[:, col]
            self._grad_Lu[:, row, i] += L[:, col]

        m = len(self._tril1[0])
        for i in range(len(self._diag[0])):
            row = self._diag[0][i]
            col = self._diag[1][i]
            self._grad_Lu[row, :, m + i] = L[row, col] * L[:, col]
            self._grad_Lu[:, row, m + i] += L[row, col] * L[:, col]

        return {"Lu": self._grad_Lu}

    def __str__(self):

        return format_function(self, {"dim": self._L.shape[0]}, [("L", self.L)])