GPy/models/new_gp_heteroscedastic_regression.py
def _grads_without_kernels(self, x):
        """
        Gets the gradients from the likelihood and the priors without kernels

        Failures are handled robustly. The algorithm will try several times to
        return the gradients, and will raise the original exception if
        the objective cannot be computed.

        :param x: the parameters of the model.
        :type x: np.array
        """
        try:
            self.optimizer_array = x
            self.obj_grads_without_kernels = self._transform_gradients(self.objective_function_gradients_without_kernels())
            self._fail_count = 0
        except (LinAlgError, ZeroDivisionError, ValueError):
            if self._fail_count >= self._allowed_failures:
                raise
            self._fail_count += 1
            self.obj_grads_without_kernels = np.clip(self._transform_gradients(self.objective_function_gradients_without_kernels()), -1e100, 1e100)
        return self.obj_grads_without_kernels

        Gets the gradients from the likelihood and the priors without kernels

        Failures are handled robustly. The algorithm will try several times to
        return the gradients, and will raise the original exception if
        the objective cannot be computed.

        :param x: the parameters of the model.
        :type x: np.array
        
Gets the gradients from the likelihood and the priors without kernels
Failures are handled robustly. The algorithm will try several times to
return the gradients, and will raise the original exception if
the objective cannot be computed.
Gets the gradients from the likelihood and the priors without kernels Failures are handled robustly . The algorithm will try several times to return the gradients and will raise the original exception if the objective cannot be computed .
def _grads_without_kernels(self, x):
        try:
            self.optimizer_array = x
            self.obj_grads_without_kernels = self._transform_gradients(self.objective_function_gradients_without_kernels())
            self._fail_count = 0
        except (LinAlgError, ZeroDivisionError, ValueError):
            if self._fail_count >= self._allowed_failures:
                raise
            self._fail_count += 1
            self.obj_grads_without_kernels = np.clip(self._transform_gradients(self.objective_function_gradients_without_kernels()), -1e100, 1e100)
        return self.obj_grads_without_kernels
def _grads_without_kernels ( self , x ) : try : self . optimizer_array = x self . obj_grads_without_kernels = self . _transform_gradients ( self . objective_function_gradients_without_kernels ( ) ) self . _fail_count = 0 except ( LinAlgError , ZeroDivisionError , ValueError ) : if self . _fail_count >= self . _allowed_failures : raise self . _fail_count += 1 self . obj_grads_without_kernels = np . clip ( self . _transform_gradients ( self . objective_function_gradients_without_kernels ( ) ) , - 1e100 , 1e100 ) return self . obj_grads_without_kernels
Gets the gradients from the likelihood and the priors without kernels
Failures are handled robustly.
Gets the gradients from the likelihood and the priors without kernels Failures are handled robustly .
"""
        Gets the gradients from the likelihood and the priors without kernels

        Failures are handled robustly. The algorithm will try several times to
        return the gradients, and will raise the original exception if
        the objective cannot be computed.

        :param x: the parameters of the model.
        :type x: np.array
        """
self
x
the parameters of the model.
the parameters of the model .