code: # -*- coding: utf-8 -*-
  # pylint: disable=E1101
  """
      Deprecated. Use named_interface.BVBQMixMVN.
      Won't be documented due to this
  """
  import torch
  
  from . import utils
  from . import bvbq
  from . import distributions
  from . import gp
  from . import acquisition
  from . import metrics
  
  
  class BVBQMixMVN(object):
      def __init__(self, eval_function, ndim):
          self.set_eval_function(eval_function)
          self.ndim = ndim
          self.logprobgp = None
          self.mixmeans = None
          self.mixvars = None
          self.mixweights = None
          self.nmixtures = 0
  
      def initialize_data(self, xdata, ydata, kind='smatern52',
                          noise=0.0, mean=-30.0, empirical_params=False,
                          **kwargs):
          # TODO : Assertions, customizations and new policies
          logprobgp = gp.SimpleGP(self.ndim, kind=kind,
                                  noise=noise, zeromax=True)
          logprobgp.mean = mean
          logprobgp.fix_mean()
          logprobgp.fix_noise()
          logprobgp.set_data(xdata, ydata, empirical_params=empirical_params)
          self.logprobgp = logprobgp
  
      def initialize_components(self, init_policy='manual', **kwargs):
          # TODO : Assertions, customization and new policies
          assert init_policy in ['manual', 'manual_mix']
          if init_policy == 'manual':
              mean = kwargs.get('mean')
              var = kwargs.get('var')
              mixmeans = torch.atleast_2d(utils.tensor_convert(mean))
              mixvars = torch.atleast_2d(utils.tensor_convert(var))
              mixweights = torch.ones(1)
              nmixtures = 1
          elif init_policy == 'manual_mix':
              nmixtures = mixmeans.shape[0]
          self.mixmeans = mixmeans
          self.mixvars = mixvars
          self.mixweights = mixweights
          self.nmixtures = nmixtures
  
      def update_distribution(self):
          #TODO : Customization
          mean, var = bvbq.propose_component_mvn_mixmvn_relbo(
              self.logprobgp,
              self.mixmeans,
              self.mixvars,
              self.mixweights)
          mixmeans, mixvars, mixweights = bvbq.update_distribution_mvn_mixmvn(
              self.logprobgp,
              mean, var,
              self.mixmeans,
              self.mixvars,
              self.mixweights)
          self.mixmeans = mixmeans
          self.mixvars = mixvars
          self.mixweights = mixweights
  
      def update_evaluations(self, name='PP'):
          x0 = self.distribution.sample(1)[0, :]
          x = acquisition.acquire_next_point_mixmvn(x0,
                                                    self.logprobgp,
                                                    self.distribution,
                                                    name=name)
          y = self.evaluate_single(x)
  
          # FIXME: Fix this function
  #        self.logprobgp.update(x,y)
          # FIXME : Substitute below lines for actual (fixed) efficient update above
          X = torch.vstack([self.eval_points, x])
          y = torch.vstack([self.eval_values, y])
          self.logprobgp.set_data(X, y)
  
      def evaluate_single(self, x):
          return torch.squeeze(self.eval_function(x))
  
      def fit_all_parameters(self):
          #TODO : Customization
          mixmeans, mixvars, mixweights = bvbq.fit_mixmvn_elbo(
              self.logprobgp, self.mixmeans, self.mixvars, self.mixweights)
          self.mixmeans = mixmeans
          self.mixvars = mixvars
          self.mixweights = mixweights
          
      def fit_all_weights(self):
          #TODO : Customization
          mixmeans, mixvars, mixweights = bvbq.reweight_mixmvn_elbo(
              self.logprobgp, self.mixmeans, self.mixvars, self.mixweights)
          self.mixmeans = mixmeans
          self.mixvars = mixvars
          self.mixweights = mixweights
  
      def set_eval_function(self, eval_function):
          self._eval_function = eval_function
          self.eval_function = utils.numpy_to_torch_wrapper(eval_function)
  
      def elbo_metric(self, nsamples=1000):
          return metrics.bq_mixmvn_elbo_with_var(self.logprobgp,
                                                 self.mixmeans,
                                                 self.mixvars,
                                                 self.mixweights,
                                                 nsamples=nsamples)
  
      def optimize_gp_params(self, *args, **kwargs):
          baseopt = kwargs.get('baseopt', 'QN')
          kwargs.pop('baseopt', None)
          assert baseopt in ['QN', 'SGD']
          if baseopt == 'QN':
              return self.optimize_gp_params_qn(*args, **kwargs)
          elif baseopt == 'SGD':
              return self.optimize_gp_params_sgd(*args, **kwargs)
  
      def suggest_initialization_points(self, n):
          raise NotImplementedError
          #return xdata
  
      @property
      def distribution(self):
          return distributions.MixtureDiagonalNormalDistribution(
              self.mixmeans, self.mixvars, self.mixweights)
  
      # XXX: This actually performs computation
      @property
      def optimize_gp_params_qn(self):
          return self.logprobgp.optimize_params_qn
  
      @property
      def optimize_gp_params_sgd(self):
          return self.logprobgp.optimize_params_sgd
  
      @property
      def eval_points(self):
          return self.logprobgp.X
  
      @property
      def eval_values(self):
          return self.logprobgp.y
  
apis: torch.ones torch.vstack