class_docstr: Encapsulate the inner State Space Language Model for DTM.
  
  Some important attributes of this class:
  
      * `obs` is a matrix containing the document to topic ratios.
      * `e_log_prob` is a matrix containing the topic to word ratios.
      * `mean` contains the mean values to be used for inference for each word for a time slice.
      * `variance` contains the variance values to be used for inference of word in a time slice.
      * `fwd_mean` and`fwd_variance` are the forward posterior values for the mean and the variance.
      * `zeta` is an extra variational parameter with a value for each time slice.
class_docstr_tok: ["Encapsulate","the","inner","State","Space","Language","Model","for","DTM",".","Some","important","attributes","of","this","class",":","*","`","obs","`","is","a","matrix","containing","the","document","to","topic","ratios",".","*","`","e_log_prob","`","is","a","matrix","containing","the","topic","to","word","ratios",".","*","`","mean","`","contains","the","mean","values","to","be","used","for","inference","for","each","word","for","a","time","slice",".","*","`","variance","`","contains","the","variance","values","to","be","used","for","inference","of","word","in","a","time","slice",".","*","`","fwd_mean","`","and","`","fwd_variance","`","are","the","forward","posterior","values","for","the","mean","and","the","variance",".","*","`","zeta","`","is","an","extra","variational","parameter","with","a","value","for","each","time","slice","."]
code: def __init__(
      self,
      vocab_len=None,
      num_time_slices=None,
      num_topics=None,
      obs_variance=0.5,
      chain_variance=0.005,
  ):
      self.vocab_len = vocab_len
      self.num_time_slices = num_time_slices
      self.obs_variance = obs_variance
      self.chain_variance = chain_variance
      self.num_topics = num_topics
  
      # setting up matrices
      self.obs = np.zeros((vocab_len, num_time_slices))
      self.e_log_prob = np.zeros((vocab_len, num_time_slices))
      self.mean = np.zeros((vocab_len, num_time_slices + 1))
      self.fwd_mean = np.zeros((vocab_len, num_time_slices + 1))
      self.fwd_variance = np.zeros((vocab_len, num_time_slices + 1))
      self.variance = np.zeros((vocab_len, num_time_slices + 1))
      self.zeta = np.zeros(num_time_slices)
  
      # the following are class variables which are to be integrated during Document Influence Model
      self.m_update_coeff = None
      self.mean_t = None
      self.variance_t = None
      self.influence_sum_lgl = None
      self.w_phi_l = None
      self.w_phi_sum = None
      self.w_phi_l_sq = None
      self.m_update_coeff_g = None
  
imports: import logging
  import numpy
  from scipy.special import digamma, gammaln
  from scipy import optimize
  from gensim import utils, matutils
  from gensim.models import ldamodel
label_desc: Use image node_id 1 to create a new sslm object from inherited base classes: utils with example: obj = sslm(vocab_len, num_time_slices, num_topics, obs_variance, chain_variance)