stream_windowing

Module: missions.nodes.meta.stream_windowing

Perform windowing on stream of windows

Inheritance diagram for pySPACE.missions.nodes.meta.stream_windowing:

Inheritance diagram of pySPACE.missions.nodes.meta.stream_windowing

StreamWindowingNode

class pySPACE.missions.nodes.meta.stream_windowing.StreamWindowingNode(windower_spec_file, windower_spec_file_train=None, local_window_conf=False, nullmarker_stride_ms=1000, *args, **kwargs)[source]

Bases: pySPACE.missions.nodes.base_node.BaseNode

Get a stream of time series objects and window them inside a flow.

Node that interprets a stream of incoming time series objects as a raw data stream. The markers stored in marker_name attribute are used as the markers for a MarkerWindower. This should done before any splitter, since all incoming windows are regarded as parts of a consecutive data stream.

Parameters

windower_spec_file:
 The window specification file for the MarkerWindower. Used for testing and training, if windower_spec_file_train is not specified.
windower_spec_file_train:
 A separate window file for training only. If not specified, windower_spec_file is used for training and testing.

Parameters

Exemplary Call

-
    node : Stream_Windowing
    parameters :
        windower_spec_file : "example_lrp_window_spec.yaml"
Authors:

Hendrik Woehrle (hendrik.woehrle@dfki.de)
Created:

2012/07/09
POSSIBLE NODE NAMES:
 
  • Stream_Windowing
  • StreamWindowingNode
  • StreamWindowing
POSSIBLE INPUT TYPES:
 
  • PredictionVector
  • FeatureVector
  • TimeSeries

Class Components Summary

__getstate__() Return a pickable state for this object
get_output_type(input_type[, as_string])
input_types
process() Processes all data that is provided by the input node
request_data_for_testing() Returns the data for testing of subsequent nodes
request_data_for_training(use_test_data) Returns the data that can be used for training of subsequent nodes
window_stream(data)
__init__(windower_spec_file, windower_spec_file_train=None, local_window_conf=False, nullmarker_stride_ms=1000, *args, **kwargs)[source]
request_data_for_training(use_test_data)[source]

Returns the data that can be used for training of subsequent nodes

request_data_for_testing()[source]

Returns the data for testing of subsequent nodes

process()[source]

Processes all data that is provided by the input node

Returns a generator that yields the data after being processed by this node.

window_stream(data)[source]
__getstate__()[source]

Return a pickable state for this object

get_output_type(input_type, as_string=True)[source]
input_types = ['PredictionVector', 'FeatureVector', 'TimeSeries']