text: """
  
  =============================================================
  Online Latent Dirichlet Allocation with variational inference
  =============================================================
  
  This implementation is modified from Matthew D. Hoffman's onlineldavb code
  Link: http://matthewdhoffman.com/code/onlineldavb.tar
  """
  
  # Author: Chyi-Kwei Yau
  # Author: Matthew D. Hoffman (original onlineldavb implementation)
  
  import numpy as np
  import scipy.sparse as sp
  from scipy.special import gammaln
  import warnings
  
  from ..base import BaseEstimator, TransformerMixin
  from ..utils import (check_random_state, check_array,
                       gen_batches, gen_even_slices, _get_n_jobs)
  from ..utils.validation import check_non_negative
  from ..utils.extmath import logsumexp
  from ..externals.joblib import Parallel, delayed
  from ..externals.six.moves import xrange
  from ..exceptions import NotFittedError
  
  from ._online_lda import (mean_change, _dirichlet_expectation_1d,
                            _dirichlet_expectation_2d)
  
  EPS = np.finfo(np.float).eps
  
  
  def _update_doc_distribution(X, exp_topic_word_distr, doc_topic_prior,
                               max_iters,
                               mean_change_tol, cal_sstats, random_state):
      """E-step: update document-topic distribution.
  
      Parameters
      ----------
      X : array-like or sparse matrix, shape=(n_samples, n_features)
          Document word matrix.
  
      exp_topic_word_distr : dense matrix, shape=(n_topics, n_features)
          Exponential value of expection of log topic word distribution.
          In the literature, this is `exp(E[log(beta)])`.
  
      doc_topic_prior : float
          Prior of document topic distribution `theta`.
  
      max_iters : int
          Max number of iterations for updating document topic distribution in
          the E-step.
  
      mean_change_tol : float
          Stopping tolerance for updating document topic distribution in E-setp.
  
      cal_sstats : boolean
          Parameter that indicate to calculate sufficient statistics or not.
          Set `cal_sstats` to `True` when we need to run M-step.
  
      random_state : RandomState instance or None
          Parameter that indicate how to initialize document topic distribution.
          Set `random_state` to None will initialize document topic distribution
          to a constant number.
  
      Returns
      -------
      (doc_topic_distr, suff_stats) :
          `doc_topic_distr` is unnormalized topic distribution for each document.
          In the literature, this is `gamma`. we can calculate `E[log(theta)]`
          from it.
          `suff_stats` is expected sufficient statistics for the M-step.
              When `cal_sstats == False`, this will be None.
  
      """
      is_sparse_x = sp.issparse(X)
      n_samples, n_features = X.shape
      n_topics = exp_topic_word_distr.shape[0]
  
      if random_state:
          doc_topic_distr = random_state.gamma(100., 0.01, (n_samples, n_topics))
      else:
          doc_topic_distr = np.ones((n_samples, n_topics))
  
      # In the literature, this is `exp(E[log(theta)])`
      exp_doc_topic = np.exp(_dirichlet_expectation_2d(doc_topic_distr))
  
      # diff on `component_` (only calculate it when `cal_diff` is True)
      suff_stats = np.zeros(exp_topic_word_distr.shape) if cal_sstats else None
  
      if is_sparse_x:
          X_data = X.data
          X_indices = X.indices
          X_indptr = X.indptr
  
      for idx_d in xrange(n_samples):
          if is_sparse_x:
              ids = X_indices[X_indptr[idx_d]:X_indptr[idx_d + 1]]
              cnts = X_data[X_indptr[idx_d]:X_indptr[idx_d + 1]]
          else:
              ids = np.nonzero(X[idx_d, :])[0]
              cnts = X[idx_d, ids]
  
          doc_topic_d = doc_topic_distr[idx_d, :]
          # The next one is a copy, since the inner loop overwrites it.
          exp_doc_topic_d = exp_doc_topic[idx_d, :].copy()
          exp_topic_word_d = exp_topic_word_distr[:, ids]
  
          # Iterate between `doc_topic_d` and `norm_phi` until convergence
          for _ in xrange(0, max_iters):
              last_d = doc_topic_d
  
              # The optimal phi_{dwk} is proportional to
              # exp(E[log(theta_{dk})]) * exp(E[log(beta_{dw})]).
              norm_phi = np.dot(exp_doc_topic_d, exp_topic_word_d) + EPS
  
              doc_topic_d = (exp_doc_topic_d *
                             np.dot(cnts / norm_phi, exp_topic_word_d.T))
              # Note: adds doc_topic_prior to doc_topic_d, in-place.
              _dirichlet_expectation_1d(doc_topic_d, doc_topic_prior,
                                        exp_doc_topic_d)
  
              if mean_change(last_d, doc_topic_d) < mean_change_tol:
                  break
          doc_topic_distr[idx_d, :] = doc_topic_d
  
          # Contribution of document d to the expected sufficient
          # statistics for the M step.
          if cal_sstats:
              norm_phi = np.dot(exp_doc_topic_d, exp_topic_word_d) + EPS
              suff_stats[:, ids] += np.outer(exp_doc_topic_d, cnts / norm_phi)
  
      return (doc_topic_distr, suff_stats)
  
  
  class LatentDirichletAllocation(BaseEstimator, TransformerMixin):
      """Latent Dirichlet Allocation with online variational Bayes algorithm
  
      .. versionadded:: 0.17
  
      Read more in the :ref:`User Guide <LatentDirichletAllocation>`.
  
      Parameters
      ----------
      n_topics : int, optional (default=10)
          Number of topics.
  
      doc_topic_prior : float, optional (default=None)
          Prior of document topic distribution `theta`. If the value is None,
          defaults to `1 / n_topics`.
          In the literature, this is called `alpha`.
  
      topic_word_prior : float, optional (default=None)
          Prior of topic word distribution `beta`. If the value is None, defaults
          to `1 / n_topics`.
          In the literature, this is called `eta`.
  
      learning_method : 'batch' | 'online', default='online'
          Method used to update `_component`. Only used in `fit` method.
          In general, if the data size is large, the online update will be much
          faster than the batch update.
          The default learning method is going to be changed to 'batch' in the 0.20 release.
          Valid options::
  
              'batch': Batch variational Bayes method. Use all training data in
                  each EM update.
                  Old `components_` will be overwritten in each iteration.
              'online': Online variational Bayes method. In each EM update, use
                  mini-batch of training data to update the ``components_``
                  variable incrementally. The learning rate is controlled by the
                  ``learning_decay`` and the ``learning_offset`` parameters.
  
      learning_decay : float, optional (default=0.7)
          It is a parameter that control learning rate in the online learning
          method. The value should be set between (0.5, 1.0] to guarantee
          asymptotic convergence. When the value is 0.0 and batch_size is
          ``n_samples``, the update method is same as batch learning. In the
          literature, this is called kappa.
  
      learning_offset : float, optional (default=10.)
          A (positive) parameter that downweights early iterations in online
          learning.  It should be greater than 1.0. In the literature, this is
          called tau_0.
  
      max_iter : integer, optional (default=10)
          The maximum number of iterations.
  
      total_samples : int, optional (default=1e6)
          Total number of documents. Only used in the `partial_fit` method.
  
      batch_size : int, optional (default=128)
          Number of documents to use in each EM iteration. Only used in online
          learning.
  
      evaluate_every : int optional (default=0)
          How often to evaluate perplexity. Only used in `fit` method.
          set it to 0 or negative number to not evalute perplexity in
          training at all. Evaluating perplexity can help you check convergence
          in training process, but it will also increase total training time.
          Evaluating perplexity in every iteration might increase training time
          up to two-fold.
  
      perp_tol : float, optional (default=1e-1)
          Perplexity tolerance in batch learning. Only used when
          ``evaluate_every`` is greater than 0.
  
      mean_change_tol : float, optional (default=1e-3)
          Stopping tolerance for updating document topic distribution in E-step.
  
      max_doc_update_iter : int (default=100)
          Max number of iterations for updating document topic distribution in
          the E-step.
  
      n_jobs : int, optional (default=1)
          The number of jobs to use in the E-step. If -1, all CPUs are used. For
          ``n_jobs`` below -1, (n_cpus + 1 + n_jobs) are used.
  
      verbose : int, optional (default=0)
          Verbosity level.
  
      random_state : int, RandomState instance or None, optional (default=None)
          If int, random_state is the seed used by the random number generator;
          If RandomState instance, random_state is the random number generator;
          If None, the random number generator is the RandomState instance used
          by `np.random`.
  
      Attributes
      ----------
      components_ : array, [n_topics, n_features]
          Topic word distribution. ``components_[i, j]`` represents word j in
          topic `i`.
  
      n_batch_iter_ : int
          Number of iterations of the EM step.
  
      n_iter_ : int
          Number of passes over the dataset.
  
      References
      ----------
      [1] "Online Learning for Latent Dirichlet Allocation", Matthew D. Hoffman,
          David M. Blei, Francis Bach, 2010
  
      [2] "Stochastic Variational Inference", Matthew D. Hoffman, David M. Blei,
          Chong Wang, John Paisley, 2013
  
      [3] Matthew D. Hoffman's onlineldavb code. Link:
          http://matthewdhoffman.com//code/onlineldavb.tar
  
      """
  
      def __init__(self, n_topics=10, doc_topic_prior=None,
                   topic_word_prior=None, learning_method=None,
                   learning_decay=.7, learning_offset=10., max_iter=10,
                   batch_size=128, evaluate_every=-1, total_samples=1e6,
                   perp_tol=1e-1, mean_change_tol=1e-3, max_doc_update_iter=100,
                   n_jobs=1, verbose=0, random_state=None):
          self.n_topics = n_topics
          self.doc_topic_prior = doc_topic_prior
          self.topic_word_prior = topic_word_prior
          self.learning_method = learning_method
          self.learning_decay = learning_decay
          self.learning_offset = learning_offset
          self.max_iter = max_iter
          self.batch_size = batch_size
          self.evaluate_every = evaluate_every
          self.total_samples = total_samples
          self.perp_tol = perp_tol
          self.mean_change_tol = mean_change_tol
          self.max_doc_update_iter = max_doc_update_iter
          self.n_jobs = n_jobs
          self.verbose = verbose
          self.random_state = random_state
  
      def _check_params(self):
          """Check model parameters."""
  
          if self.n_topics <= 0:
              raise ValueError("Invalid 'n_topics' parameter: %r"
                               % self.n_topics)
  
          if self.total_samples <= 0:
              raise ValueError("Invalid 'total_samples' parameter: %r"
                               % self.total_samples)
  
          if self.learning_offset < 0:
              raise ValueError("Invalid 'learning_offset' parameter: %r"
                               % self.learning_offset)
  
          if self.learning_method not in ("batch", "online", None):
              raise ValueError("Invalid 'learning_method' parameter: %r"
                               % self.learning_method)
  
      def _init_latent_vars(self, n_features):
          """Initialize latent variables."""
  
          self.random_state_ = check_random_state(self.random_state)
          self.n_batch_iter_ = 1
          self.n_iter_ = 0
  
          if self.doc_topic_prior is None:
              self.doc_topic_prior_ = 1. / self.n_topics
          else:
              self.doc_topic_prior_ = self.doc_topic_prior
  
          if self.topic_word_prior is None:
              self.topic_word_prior_ = 1. / self.n_topics
          else:
              self.topic_word_prior_ = self.topic_word_prior
  
          init_gamma = 100.
          init_var = 1. / init_gamma
          # In the literature, this is called `lambda`
          self.components_ = self.random_state_.gamma(
              init_gamma, init_var, (self.n_topics, n_features))
  
          # In the literature, this is `exp(E[log(beta)])`
          self.exp_dirichlet_component_ = np.exp(
              _dirichlet_expectation_2d(self.components_))
  
      def _e_step(self, X, cal_sstats, random_init, parallel=None):
          """E-step in EM update.
  
          Parameters
          ----------
          X : array-like or sparse matrix, shape=(n_samples, n_features)
              Document word matrix.
  
          cal_sstats : boolean
              Parameter that indicate whether to calculate sufficient statistics
              or not. Set ``cal_sstats`` to True when we need to run M-step.
  
          random_init : boolean
              Parameter that indicate whether to initialize document topic
              distribution randomly in the E-step. Set it to True in training
              steps.
  
          parallel : joblib.Parallel (optional)
              Pre-initialized instance of joblib.Parallel.
  
          Returns
          -------
          (doc_topic_distr, suff_stats) :
              `doc_topic_distr` is unnormalized topic distribution for each
              document. In the literature, this is called `gamma`.
              `suff_stats` is expected sufficient statistics for the M-step.
              When `cal_sstats == False`, it will be None.
  
          """
  
          # Run e-step in parallel
          random_state = self.random_state_ if random_init else None
  
          # TODO: make Parallel._effective_n_jobs public instead?
          n_jobs = _get_n_jobs(self.n_jobs)
          if parallel is None:
              parallel = Parallel(n_jobs=n_jobs, verbose=max(0, self.verbose - 1))
          results = parallel(
              delayed(_update_doc_distribution)(X[idx_slice, :],
                                                self.exp_dirichlet_component_,
                                                self.doc_topic_prior_,
                                                self.max_doc_update_iter,
                                                self.mean_change_tol, cal_sstats,
                                                random_state)
              for idx_slice in gen_even_slices(X.shape[0], n_jobs))
  
          # merge result
          doc_topics, sstats_list = zip(*results)
          doc_topic_distr = np.vstack(doc_topics)
  
          if cal_sstats:
              # This step finishes computing the sufficient statistics for the
              # M-step.
              suff_stats = np.zeros(self.components_.shape)
              for sstats in sstats_list:
                  suff_stats += sstats
              suff_stats *= self.exp_dirichlet_component_
          else:
              suff_stats = None
  
          return (doc_topic_distr, suff_stats)
  
      def _em_step(self, X, total_samples, batch_update, parallel=None):
          """EM update for 1 iteration.
  
          update `_component` by batch VB or online VB.
  
          Parameters
          ----------
          X : array-like or sparse matrix, shape=(n_samples, n_features)
              Document word matrix.
  
          total_samples : integer
              Total umber of documents. It is only used when
              batch_update is `False`.
  
          batch_update : boolean
              Parameter that controls updating method.
              `True` for batch learning, `False` for online learning.
  
          parallel : joblib.Parallel
              Pre-initialized instance of joblib.Parallel
  
          Returns
          -------
          doc_topic_distr : array, shape=(n_samples, n_topics)
              Unnormalized document topic distribution.
          """
  
          # E-step
          _, suff_stats = self._e_step(X, cal_sstats=True, random_init=True,
                                       parallel=parallel)
  
          # M-step
          if batch_update:
              self.components_ = self.topic_word_prior_ + suff_stats
          else:
              # online update
              # In the literature, the weight is `rho`
              weight = np.power(self.learning_offset + self.n_batch_iter_,
                                -self.learning_decay)
              doc_ratio = float(total_samples) / X.shape[0]
              self.components_ *= (1 - weight)
              self.components_ += (weight * (self.topic_word_prior_
                                             + doc_ratio * suff_stats))
  
          # update `component_` related variables
          self.exp_dirichlet_component_ = np.exp(
              _dirichlet_expectation_2d(self.components_))
          self.n_batch_iter_ += 1
          return
  
      def _check_non_neg_array(self, X, whom):
          """check X format
  
          check X format and make sure no negative value in X.
  
          Parameters
          ----------
          X :  array-like or sparse matrix
  
          """
          X = check_array(X, accept_sparse='csr')
          check_non_negative(X, whom)
          return X
  
      def partial_fit(self, X, y=None):
          """Online VB with Mini-Batch update.
  
          Parameters
          ----------
          X : array-like or sparse matrix, shape=(n_samples, n_features)
              Document word matrix.
  
          Returns
          -------
          self
          """
          self._check_params()
          X = self._check_non_neg_array(X,
                                        "LatentDirichletAllocation.partial_fit")
          n_samples, n_features = X.shape
          batch_size = self.batch_size
  
          # initialize parameters or check
          if not hasattr(self, 'components_'):
              self._init_latent_vars(n_features)
  
          if n_features != self.components_.shape[1]:
              raise ValueError(
                  "The provided data has %d dimensions while "
                  "the model was trained with feature size %d." %
                  (n_features, self.components_.shape[1]))
  
          n_jobs = _get_n_jobs(self.n_jobs)
          with Parallel(n_jobs=n_jobs, verbose=max(0, self.verbose - 1)) as parallel:
              for idx_slice in gen_batches(n_samples, batch_size):
                  self._em_step(X[idx_slice, :],
                                total_samples=self.total_samples,
                                batch_update=False,
                                parallel=parallel)
  
          return self
  
      def fit(self, X, y=None):
          """Learn model for the data X with variational Bayes method.
  
          When `learning_method` is 'online', use mini-batch update.
          Otherwise, use batch update.
  
          Parameters
          ----------
          X : array-like or sparse matrix, shape=(n_samples, n_features)
              Document word matrix.
  
          Returns
          -------
          self
          """
          self._check_params()
          X = self._check_non_neg_array(X, "LatentDirichletAllocation.fit")
          n_samples, n_features = X.shape
          max_iter = self.max_iter
          evaluate_every = self.evaluate_every
          learning_method = self.learning_method
          if learning_method == None:
              warnings.warn("The default value for 'learning_method' will be "
                            "changed from 'online' to 'batch' in the release 0.20. "
                            "This warning was introduced in 0.18.",
                            DeprecationWarning)
              learning_method = 'online'
  
          batch_size = self.batch_size
  
          # initialize parameters
          self._init_latent_vars(n_features)
          # change to perplexity later
          last_bound = None
          n_jobs = _get_n_jobs(self.n_jobs)
          with Parallel(n_jobs=n_jobs, verbose=max(0, self.verbose - 1)) as parallel:
              for i in xrange(max_iter):
                  if learning_method == 'online':
                      for idx_slice in gen_batches(n_samples, batch_size):
                          self._em_step(X[idx_slice, :], total_samples=n_samples,
                                        batch_update=False, parallel=parallel)
                  else:
                      # batch update
                      self._em_step(X, total_samples=n_samples,
                                    batch_update=True, parallel=parallel)
  
                  # check perplexity
                  if evaluate_every > 0 and (i + 1) % evaluate_every == 0:
                      doc_topics_distr, _ = self._e_step(X, cal_sstats=False,
                                                         random_init=False,
                                                         parallel=parallel)
                      bound = self._perplexity_precomp_distr(X, doc_topics_distr,
                                                             sub_sampling=False)
                      if self.verbose:
                          print('iteration: %d, perplexity: %.4f'
                                % (i + 1, bound))
  
                      if last_bound and abs(last_bound - bound) < self.perp_tol:
                          break
                      last_bound = bound
                  self.n_iter_ += 1
  
          # calculate final perplexity value on train set
          doc_topics_distr, _ = self._e_step(X, cal_sstats=False,
                                             random_init=False,
                                             parallel=parallel)
          self.bound_ = self._perplexity_precomp_distr(X, doc_topics_distr,
                                                       sub_sampling=False)
  
          return self
  
      def _unnormalized_transform(self, X):
          """Transform data X according to fitted model.
  
          Parameters
          ----------
          X : array-like or sparse matrix, shape=(n_samples, n_features)
              Document word matrix.
  
          Returns
          -------
          doc_topic_distr : shape=(n_samples, n_topics)
              Document topic distribution for X.
          """
          if not hasattr(self, 'components_'):
              raise NotFittedError("no 'components_' attribute in model."
                                   " Please fit model first.")
  
          # make sure feature size is the same in fitted model and in X
          X = self._check_non_neg_array(X, "LatentDirichletAllocation.transform")
          n_samples, n_features = X.shape
          if n_features != self.components_.shape[1]:
              raise ValueError(
                  "The provided data has %d dimensions while "
                  "the model was trained with feature size %d." %
                  (n_features, self.components_.shape[1]))
  
          doc_topic_distr, _ = self._e_step(X, cal_sstats=False,
                                            random_init=False)
  
          return doc_topic_distr
  
      def transform(self, X):
          """Transform data X according to the fitted model.
  
             .. versionchanged:: 0.18
                *doc_topic_distr* is now normalized
  
          Parameters
          ----------
          X : array-like or sparse matrix, shape=(n_samples, n_features)
              Document word matrix.
  
          Returns
          -------
          doc_topic_distr : shape=(n_samples, n_topics)
              Document topic distribution for X.
          """
          doc_topic_distr = self._unnormalized_transform(X)
          doc_topic_distr /= doc_topic_distr.sum(axis=1)[:, np.newaxis]
          return doc_topic_distr
  
      def _approx_bound(self, X, doc_topic_distr, sub_sampling):
          """Estimate the variational bound.
  
          Estimate the variational bound over "all documents" using only the
          documents passed in as X. Since log-likelihood of each word cannot
          be computed directly, we use this bound to estimate it.
  
          Parameters
          ----------
          X : array-like or sparse matrix, shape=(n_samples, n_features)
              Document word matrix.
  
          doc_topic_distr : array, shape=(n_samples, n_topics)
              Document topic distribution. In the literature, this is called
              gamma.
  
          sub_sampling : boolean, optional, (default=False)
              Compensate for subsampling of documents.
              It is used in calculate bound in online learning.
  
          Returns
          -------
          score : float
  
          """
  
          def _loglikelihood(prior, distr, dirichlet_distr, size):
              # calculate log-likelihood
              score = np.sum((prior - distr) * dirichlet_distr)
              score += np.sum(gammaln(distr) - gammaln(prior))
              score += np.sum(gammaln(prior * size) - gammaln(np.sum(distr, 1)))
              return score
  
          is_sparse_x = sp.issparse(X)
          n_samples, n_topics = doc_topic_distr.shape
          n_features = self.components_.shape[1]
          score = 0
  
          dirichlet_doc_topic = _dirichlet_expectation_2d(doc_topic_distr)
          dirichlet_component_ = _dirichlet_expectation_2d(self.components_)
          doc_topic_prior = self.doc_topic_prior_
          topic_word_prior = self.topic_word_prior_
  
          if is_sparse_x:
              X_data = X.data
              X_indices = X.indices
              X_indptr = X.indptr
  
          # E[log p(docs | theta, beta)]
          for idx_d in xrange(0, n_samples):
              if is_sparse_x:
                  ids = X_indices[X_indptr[idx_d]:X_indptr[idx_d + 1]]
                  cnts = X_data[X_indptr[idx_d]:X_indptr[idx_d + 1]]
              else:
                  ids = np.nonzero(X[idx_d, :])[0]
                  cnts = X[idx_d, ids]
              temp = (dirichlet_doc_topic[idx_d, :, np.newaxis]
                      + dirichlet_component_[:, ids])
              norm_phi = logsumexp(temp)
              score += np.dot(cnts, norm_phi)
  
          # compute E[log p(theta | alpha) - log q(theta | gamma)]
          score += _loglikelihood(doc_topic_prior, doc_topic_distr,
                                  dirichlet_doc_topic, self.n_topics)
  
          # Compensate for the subsampling of the population of documents
          if sub_sampling:
              doc_ratio = float(self.total_samples) / n_samples
              score *= doc_ratio
  
          # E[log p(beta | eta) - log q (beta | lambda)]
          score += _loglikelihood(topic_word_prior, self.components_,
                                  dirichlet_component_, n_features)
  
          return score
  
      def score(self, X, y=None):
          """Calculate approximate log-likelihood as score.
  
          Parameters
          ----------
          X : array-like or sparse matrix, shape=(n_samples, n_features)
              Document word matrix.
  
          Returns
          -------
          score : float
              Use approximate bound as score.
          """
          X = self._check_non_neg_array(X, "LatentDirichletAllocation.score")
  
          doc_topic_distr = self._unnormalized_transform(X)
          score = self._approx_bound(X, doc_topic_distr, sub_sampling=False)
          return score
  
      def _perplexity_precomp_distr(self, X, doc_topic_distr=None,
                                    sub_sampling=False):
          """Calculate approximate perplexity for data X with ability to accept
          precomputed doc_topic_distr
  
          Perplexity is defined as exp(-1. * log-likelihood per word)
  
          Parameters
          ----------
          X : array-like or sparse matrix, [n_samples, n_features]
              Document word matrix.
  
          doc_topic_distr : None or array, shape=(n_samples, n_topics)
              Document topic distribution.
              If it is None, it will be generated by applying transform on X.
  
          Returns
          -------
          score : float
              Perplexity score.
          """
          if not hasattr(self, 'components_'):
              raise NotFittedError("no 'components_' attribute in model."
                                   " Please fit model first.")
  
          X = self._check_non_neg_array(X,
                                        "LatentDirichletAllocation.perplexity")
  
          if doc_topic_distr is None:
              doc_topic_distr = self._unnormalized_transform(X)
          else:
              n_samples, n_topics = doc_topic_distr.shape
              if n_samples != X.shape[0]:
                  raise ValueError("Number of samples in X and doc_topic_distr"
                                   " do not match.")
  
              if n_topics != self.n_topics:
                  raise ValueError("Number of topics does not match.")
  
          current_samples = X.shape[0]
          bound = self._approx_bound(X, doc_topic_distr, sub_sampling)
  
          if sub_sampling:
              word_cnt = X.sum() * (float(self.total_samples) / current_samples)
          else:
              word_cnt = X.sum()
          perword_bound = bound / word_cnt
  
          return np.exp(-1.0 * perword_bound)
  
      def perplexity(self, X, doc_topic_distr='deprecated', sub_sampling=False):
          """Calculate approximate perplexity for data X.
  
          Perplexity is defined as exp(-1. * log-likelihood per word)
  
          .. versionchanged:: 0.19
             *doc_topic_distr* argument has been deprecated and is ignored
             because user no longer has access to unnormalized distribution
  
          Parameters
          ----------
          X : array-like or sparse matrix, [n_samples, n_features]
              Document word matrix.
  
          doc_topic_distr : None or array, shape=(n_samples, n_topics)
              Document topic distribution.
              This argument is deprecated and is currently being ignored.
  
              .. deprecated:: 0.19
  
          Returns
          -------
          score : float
              Perplexity score.
          """
          if doc_topic_distr != 'deprecated':
              warnings.warn("Argument 'doc_topic_distr' is deprecated and is "
                            "being ignored as of 0.19. Support for this "
                            "argument will be removed in 0.21.",
                            DeprecationWarning)
  
          return self._perplexity_precomp_distr(X, sub_sampling=sub_sampling)
  