examples/opf/clients/nyctaxi/nyctaxi_anomaly.py
def _setRandomEncoderResolution(minResolution=0.001):
  """
  Given model params, figure out the correct resolution for the
  RandomDistributed encoder. Modifies params in place.
  """
  encoder = (
    model_params.MODEL_PARAMS["modelParams"]["sensorParams"]["encoders"]["value"]
  )

  if encoder["type"] == "RandomDistributedScalarEncoder":
    rangePadding = abs(_INPUT_MAX - _INPUT_MIN) * 0.2
    minValue = _INPUT_MIN - rangePadding
    maxValue = _INPUT_MAX + rangePadding
    resolution = max(minResolution,
                     (maxValue - minValue) / encoder.pop("numBuckets")
                    )
    encoder["resolution"] = resolution
def _setRandomEncoderResolution(minResolution=0.001):
  """
  Given model params, figure out the correct resolution for the
  RandomDistributed encoder. Modifies params in place.
  """
  encoder = (
    model_params.MODEL_PARAMS["modelParams"]["sensorParams"]["encoders"]["value"]
  )

  if encoder["type"] == "RandomDistributedScalarEncoder":
    rangePadding = abs(_INPUT_MAX - _INPUT_MIN) * 0.2
    minValue = _INPUT_MIN - rangePadding
    maxValue = _INPUT_MAX + rangePadding
    resolution = max(minResolution,
                     (maxValue - minValue) / encoder.pop("numBuckets")
                    )
    encoder["resolution"] = resolution
def _setRandomEncoderResolution ( minResolution = 0.001 ) : encoder = ( model_params . MODEL_PARAMS [ "modelParams" ] [ "sensorParams" ] [ "encoders" ] [ "value" ] ) if encoder [ "type" ] == "RandomDistributedScalarEncoder" : rangePadding = abs ( _INPUT_MAX - _INPUT_MIN ) * 0.2 minValue = _INPUT_MIN - rangePadding maxValue = _INPUT_MAX + rangePadding resolution = max ( minResolution , ( maxValue - minValue ) / encoder . pop ( "numBuckets" ) ) encoder [ "resolution" ] = resolution
Given model params, figure out the correct resolution for the
  RandomDistributed encoder. Modifies params in place.
Given model params figure out the correct resolution for the RandomDistributed encoder . Modifies params in place .