class_docstr: Inherits time series methods from TSM parent class.
  
  **** NEURAL NETWORK AUTOREGRESSIVE INTEGRATED (NNAR) MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.ndarray
      Field to specify the univariate time series data that will be used.
  
  ar : int
      Field to specify how many AR lags the model will have.
  
  integ : int (default : 0)
      Specifies how many times to difference the time series.
  
  units : int
      How many units for the neural network
  
  layers : int
      How many layers for the neural networ
  
  activation : function
      E.g. np.tanh, np.array (linear)
  
  target : str (if data is a pd.DataFrame) or int (if data is a np.ndarray)
      Specifies which column name or array index to use. By default, first
      column/array index will be selected as the dependent variable.
  
  family: family object
      E.g. pf.Normal()
  
  activation : function
      Non-linear transformation for the weights, e.g. np.tanh
  
  initialize_random : boolean
      Whether to initialzie the weights with N(0,1) random variables
class_docstr_tok: ["Inherits","time","series","methods","from","TSM","parent","class",".","*","*","*","*","NEURAL","NETWORK","AUTOREGRESSIVE","INTEGRATED","(","NNAR",")","MODELS","*","*","*","*","Parameters","--","--","--","--","--","data",":","pd.DataFrame","or","np.ndarray","Field","to","specify","the","univariate","time","series","data","that","will","be","used",".","ar",":","int","Field","to","specify","how","many","AR","lags","the","model","will","have",".","integ",":","int","(","default",":","0",")","Specifies","how","many","times","to","difference","the","time","series",".","units",":","int","How","many","units","for","the","neural","network","layers",":","int","How","many","layers","for","the","neural","networ","activation",":","function","E.g",".","np.tanh",",","np.array","(","linear",")","target",":","str","(","if","data","is","a","pd.DataFrame",")","or","int","(","if","data","is","a","np.ndarray",")","Specifies","which","column","name","or","array","index","to","use",".","By","default",",","first","column\/array","index","will","be","selected","as","the","dependent","variable",".","family",":","family","object","E.g",".","pf.Normal","(",")","activation",":","function","Non-linear","transformation","for","the","weights",",","e.g",".","np.tanh","initialize_random",":","boolean","Whether","to","initialzie","the","weights","with","N","(","0,1",")","random","variables"]
docstr: Calculates the negative log-likelihood of the model
  
  Parameters
  ----------
  beta : np.ndarray
      Contains untransformed starting values for latent variables
  
  Returns
  ----------
  The negative logliklihood of the model
docstr_tok: ["Calculates","the","negative","log-likelihood","of","the","model","Parameters","--","--","--","--","--","beta",":","np.ndarray","Contains","untransformed","starting","values","for","latent","variables","Returns","--","--","--","--","--","The","negative","logliklihood","of","the","model"]
code: def general_neg_loglik(self, beta):
      """Calculates the negative log-likelihood of the model
  
      Parameters
      ----------
      beta : np.ndarray
          Contains untransformed starting values for latent variables
  
      Returns
      ----------
      The negative logliklihood of the model
      """
  
      mu, Y = self._model(beta)
      parm = np.array(
          [
              self.latent_variables.z_list[k].prior.transform(beta[k])
              for k in range(beta.shape[0])
          ]
      )
      # TODO: Replace above with transformation that only acts on scale, shape, skewness in future (speed-up)
      (
          model_scale,
          model_shape,
          model_skewness,
      ) = self._get_scale_and_shape(parm)
      return self.family.neg_loglikelihood(
          Y, self.link(mu), model_scale, model_shape, model_skewness
      )
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  from ..None import families
  from ..None import output
  from ..None import tests
  from ..None import tsm
  from ..None import data_check
  from .nn_architecture import neural_network_tanh, neural_network_tanh_mb
label_desc: Use image node_id 12 for calling the NNAR obj's underlying member method code with example usage: obj.general_neg_loglik(beta) and returns: self