class_docstr: Estimate Dynamic Topic Model parameters based on a training corpus.
class_docstr_tok: ["Estimate","Dynamic","Topic","Model","parameters","based","on","a","training","corpus","."]
docstr: Initialize State Space Language Model, topic-wise.
  
  Parameters
  ----------
  topic_chain_variance : float
      Gaussian parameter defined in the beta distribution to dictate how the beta values evolve.
  topic_obs_variance : float
      Observed variance used to approximate the true and forward variance as shown in
      `David M. Blei, John D. Lafferty: "Dynamic Topic Models"
      <https://mimno.infosci.cornell.edu/info6150/readings/dynamic_topic_models.pdf>`_.
  alpha : float
      The prior probability for the model.
  init_suffstats : numpy.ndarray
      Sufficient statistics used for initializing the model, expected shape (`self.vocab_len`, `num_topics`).
docstr_tok: ["Initialize","State","Space","Language","Model",",","topic-wise",".","Parameters","--","--","--","--","--","topic_chain_variance",":","float","Gaussian","parameter","defined","in","the","beta","distribution","to","dictate","how","the","beta","values","evolve",".","topic_obs_variance",":","float","Observed","variance","used","to","approximate","the","true","and","forward","variance","as","shown","in","`","David","M.","Blei",",","John","D.","Lafferty",":","``","Dynamic","Topic","Models","''","<","https",":","\/\/mimno.infosci.cornell.edu\/info6150\/readings\/dynamic_topic_models.pdf",">","`","_",".","alpha",":","float","The","prior","probability","for","the","model",".","init_suffstats",":","numpy.ndarray","Sufficient","statistics","used","for","initializing","the","model",",","expected","shape","(","`","self.vocab_len","`",",","`","num_topics","`",")","."]
code: def init_ldaseq_ss(
      self,
      topic_chain_variance,
      topic_obs_variance,
      alpha,
      init_suffstats,
  ):
      """Initialize State Space Language Model, topic-wise.
  
      Parameters
      ----------
      topic_chain_variance : float
          Gaussian parameter defined in the beta distribution to dictate how the beta values evolve.
      topic_obs_variance : float
          Observed variance used to approximate the true and forward variance as shown in
          `David M. Blei, John D. Lafferty: "Dynamic Topic Models"
          <https://mimno.infosci.cornell.edu/info6150/readings/dynamic_topic_models.pdf>`_.
      alpha : float
          The prior probability for the model.
      init_suffstats : numpy.ndarray
          Sufficient statistics used for initializing the model, expected shape (`self.vocab_len`, `num_topics`).
  
      """
      self.alphas = alpha
      for k, chain in enumerate(self.topic_chains):
          sstats = init_suffstats[:, k]
          sslm.sslm_counts_init(
              chain, topic_obs_variance, topic_chain_variance, sstats
          )
  
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 2 for calling the LdaSeqModel obj's underlying member method code with example usage: obj.init_ldaseq_ss(topic_chain_variance, topic_obs_variance, alpha, init_suffstats) without return types