GPy/models/new_gp_heteroscedastic_regression.py
def objective_function_gradients_without_kernels(self):
        """
        The gradients for the objective function without kernels
        This function is the true objective, which wants to be minimized in case of training without kernels.
        """
        allDerivates = np.append(self.derivate_obj_func,np.zeros([self.input_dim+1,1]))
        allDerivates = np.append(allDerivates, np.zeros([self.input_dim+1,1]))
        allDerivates = np.append(allDerivates, self.derivate_mean)
        return allDerivates

        The gradients for the objective function without kernels
        This function is the true objective, which wants to be minimized in case of training without kernels.
        
The gradients for the objective function without kernels
This function is the true objective, which wants to be minimized in case of training without kernels.
The gradients for the objective function without kernels This function is the true objective which wants to be minimized in case of training without kernels .
def objective_function_gradients_without_kernels(self):
        allDerivates = np.append(self.derivate_obj_func,np.zeros([self.input_dim+1,1]))
        allDerivates = np.append(allDerivates, np.zeros([self.input_dim+1,1]))
        allDerivates = np.append(allDerivates, self.derivate_mean)
        return allDerivates
def objective_function_gradients_without_kernels ( self ) : allDerivates = np . append ( self . derivate_obj_func , np . zeros ( [ self . input_dim + 1 , 1 ] ) ) allDerivates = np . append ( allDerivates , np . zeros ( [ self . input_dim + 1 , 1 ] ) ) allDerivates = np . append ( allDerivates , self . derivate_mean ) return allDerivates
The gradients for the objective function without kernels
This function is the true objective, which wants to be minimized in case of training without kernels.
The gradients for the objective function without kernels This function is the true objective which wants to be minimized in case of training without kernels .
"""
        The gradients for the objective function without kernels
        This function is the true objective, which wants to be minimized in case of training without kernels.
        """
self