cleanup

				params_ubnd=params_ubnd,

				disp=True)

			# result = fmin_bfgs_2(loss_fun, params_init, fprime=loss_fun.gradient, gtol=1e-6, maxiter=1000, disp=2, init_hess=None)#(loss_fun.hessian(params_init)+loss_fun.hessian(params_init).T)/2)

			# result = fmin_bfgs_2(loss_fun, result.x, fprime=loss_fun.gradient, gtol=1e-6, maxiter=1000, disp=2, init_hess=loss_fun.hessian(result.x))

			# result = minimize(loss_fun,

			# 	jac  = loss_fun.gradient,

			# 	hess = loss_fun.hessian,

			# 	x0=result.x, bounds=tuple(zip(params_lbnd,params_ubnd)),

			# 	method='Newton-CG', options={'maxiter':1000, 'maxfun':1000, 'maxls':20, 'maxcor':100, 'ftol':1.e-8, 'gtol':1.e-6, 'xtol':1.e-6, 'disp':True} #_debug}

			# 	)

			# result = minimize(loss_fun,

			# 	jac  = loss_fun.gradient,

			# 	hess = loss_fun.hessian,

			# 	# hess = myBFGS(), #loss_fun.hessian,

			# 	x0=params_init, bounds=tuple(zip(params_lbnd,params_ubnd)),

			# 	method=solver, options={'maxiter':1000, 'maxfun':1000, 'maxls':20, 'maxcor':100, 'ftol':1.e-8, 'gtol':1.e-6, 'xtol':1.e-6, 'disp':True} #_debug}

			# 	# method='L-BFGS-B', options={'maxiter':1000, 'maxfun':1000, 'maxls':20, 'maxcor':100, 'ftol':1.e-6, 'gtol':1.e-6, 'disp':True}

			# 	# method='BFGS', options={'maxiter':1000, 'gtol':1.e-6, 'norm':np.inf, 'disp':True}

			# 	# method='CG', options={'maxiter':1000, 'gtol':1.e-5, 'norm':np.inf, 'disp':True}

			# 	# method='Newton-CG', options={'maxiter':1000, 'xtol':1.e-6, 'disp':True}

			# 	# method='Nelder-Mead', options={'maxiter':1000, 'disp':False}

			# 	# method='TNC', options={'scale':None, 'maxfun':1000, 'ftol':1.e-3, 'gtol':1.e-5, 'disp':True}

			# 	# method='dogleg', options={'maxiter':1000, 'tol':1.e-6, 'gtol':1.e-8, 'disp':True}

			# 	# method='trust-krylov', options={'maxiter':1000, 'tol':1.e-6, 'inexact':False, 'disp':True}

			# 	# method='trust-exact', options={'maxiter':1000, 'gtol':1.e-4, 'disp':True}

			# 	# method='trust-constr', options={'maxiter':1000, 'gtol':1.e-4, 'disp':True}

			# 	)

			self.init_loss.append(loss_fun(params_init))

			self.init_params.append(np.array(params_init))