parameters: (prob, mean, var, rng)
return_statement: return ret
docstring: Sample from independent mixture of gaussian (MoG) distributions
  
      Each batch is an independent MoG distribution.
  
      Parameters
      ----------
      prob : numpy.ndarray
        mixture probability of each gaussian. Shape --> (batch_num, center_num)
      mean : numpy.ndarray
        mean of each gaussian. Shape --> (batch_num, center_num, sample_dim)
      var : numpy.ndarray
        variance of each gaussian. Shape --> (batch_num, center_num, sample_dim)
      rng : numpy.random.RandomState
  
      Returns
      -------
      ret : numpy.ndarray
        sampling result. Shape --> (batch_num, sample_dim)
docstring_summary: Sample from independent mixture of gaussian (MoG) distributions
docstring_tokens: Sample from independent mixture of gaussian ( MoG ) distributions
function: def sample_mog(prob, mean, var, rng):
      """Sample from independent mixture of gaussian (MoG) distributions
  
      Each batch is an independent MoG distribution.
  
      Parameters
      ----------
      prob : numpy.ndarray
        mixture probability of each gaussian. Shape --> (batch_num, center_num)
      mean : numpy.ndarray
        mean of each gaussian. Shape --> (batch_num, center_num, sample_dim)
      var : numpy.ndarray
        variance of each gaussian. Shape --> (batch_num, center_num, sample_dim)
      rng : numpy.random.RandomState
  
      Returns
      -------
      ret : numpy.ndarray
        sampling result. Shape --> (batch_num, sample_dim)
      """
      gaussian_inds = sample_categorical(prob, rng).astype(numpy.int32)
      mean = mean[numpy.arange(mean.shape[0]), gaussian_inds, :]
      var = var[numpy.arange(mean.shape[0]), gaussian_inds, :]
      ret = sample_normal(mean=mean, var=var, rng=rng)
      return ret
function_tokens: def sample_mog ( prob , mean , var , rng ) : gaussian_inds = sample_categorical ( prob , rng ) . astype ( numpy . int32 ) mean = mean [ numpy . arange ( mean . shape [ 0 ] ) , gaussian_inds , : ] var = var [ numpy . arange ( mean . shape [ 0 ] ) , gaussian_inds , : ] ret = sample_normal ( mean = mean , var = var , rng = rng ) return ret