(
    seq_model,
    low=0.0,
    high=1.0,
    precision=2,
    vocab_size=64,
    significance_decay=0.7,
    mode='train',
)
return SerializedModel(seq_model, obs_srl, act_srl, significance_decay, mode)
Simplified constructor for SerializedModel, for time series prediction.
Simplified constructor for SerializedModel, for time series prediction.
Simplified constructor for SerializedModel for time series prediction .
def TimeSeriesModel(
    seq_model,
    low=0.0,
    high=1.0,
    precision=2,
    vocab_size=64,
    significance_decay=0.7,
    mode='train',
):
  """Simplified constructor for SerializedModel, for time series prediction."""
  # Model scalar time series.
  obs_srl = space_serializer.BoxSpaceSerializer(
      space=gym.spaces.Box(shape=(), low=low, high=high),
      vocab_size=vocab_size,
      precision=precision,
  )
  # Artifact of the fact that we must provide some actions.
  # TODO(pkozakowski): Remove this requirement.
  act_srl = space_serializer.DiscreteSpaceSerializer(
      space=gym.spaces.Discrete(n=1), vocab_size=1
  )
  seq_model = functools.partial(seq_model, vocab_size=vocab_size)
  return SerializedModel(seq_model, obs_srl, act_srl, significance_decay, mode)
def TimeSeriesModel ( seq_model , low = 0.0 , high = 1.0 , precision = 2 , vocab_size = 64 , significance_decay = 0.7 , mode = 'train' , ) : # Model scalar time series. obs_srl = space_serializer . BoxSpaceSerializer ( space = gym . spaces . Box ( shape = ( ) , low = low , high = high ) , vocab_size = vocab_size , precision = precision , ) # Artifact of the fact that we must provide some actions. # TODO(pkozakowski): Remove this requirement. act_srl = space_serializer . DiscreteSpaceSerializer ( space = gym . spaces . Discrete ( n = 1 ) , vocab_size = 1 ) seq_model = functools . partial ( seq_model , vocab_size = vocab_size ) return SerializedModel ( seq_model , obs_srl , act_srl , significance_decay , mode )