GPy/models/new_gp_heteroscedastic_regression.py
def objective_function_gradients_with_kernels(self):
        """
        The gradients for the objective function with kernels
        This function is the true objective, which wants to be minimized in case of training with kernels.
        """
        allDerivates = np.append(self.derivate_obj_func,self.derivate_mean_kernel_obj_func)
        allDerivates = np.append(allDerivates, self.derivate_var_kernel_obj_func)
        allDerivates = np.append(allDerivates, self.derivate_mean)
        return allDerivates

        The gradients for the objective function with kernels
        This function is the true objective, which wants to be minimized in case of training with kernels.
        
The gradients for the objective function with kernels
This function is the true objective, which wants to be minimized in case of training with kernels.
The gradients for the objective function with kernels This function is the true objective which wants to be minimized in case of training with kernels .
def objective_function_gradients_with_kernels(self):
        allDerivates = np.append(self.derivate_obj_func,self.derivate_mean_kernel_obj_func)
        allDerivates = np.append(allDerivates, self.derivate_var_kernel_obj_func)
        allDerivates = np.append(allDerivates, self.derivate_mean)
        return allDerivates
def objective_function_gradients_with_kernels ( self ) : allDerivates = np . append ( self . derivate_obj_func , self . derivate_mean_kernel_obj_func ) allDerivates = np . append ( allDerivates , self . derivate_var_kernel_obj_func ) allDerivates = np . append ( allDerivates , self . derivate_mean ) return allDerivates
The gradients for the objective function with kernels
This function is the true objective, which wants to be minimized in case of training with kernels.
The gradients for the objective function with kernels This function is the true objective which wants to be minimized in case of training with kernels .
"""
        The gradients for the objective function with kernels
        This function is the true objective, which wants to be minimized in case of training with kernels.
        """
self