Missions for the User

An overview on the different algorithms categories.


missions Modules for data processing including large tasks (operations), signal processing algorithms (nodes) and interfaces to external packages
missions.nodes Nodes are elemental signal processing steps
missions.operations Large parameterized processing task, divided into smaller parts with different parameters
missions.support Algorithms to be included via wrappers in pySPACE


missions.nodes.source Load a special signal or data type as a stream of samples
missions.nodes.preprocessing Standard preprocessing of the incoming TimeSeries
missions.nodes.spatial_filtering Erase and/or recombine channels of multivariate TimeSeries
missions.nodes.spatial_filtering.sensor_selection Methods for sensor selection optimization algorithms
missions.nodes.feature_generation Generate features from a time series (amplitudes or frequency spectrum for example)
missions.nodes.splitter Control how data is split into training and testing data
missions.nodes.postprocessing Final modification or clean up of FeatureVector and PredictionVector
missions.nodes.classification Classification of the incoming signal
missions.nodes.meta Nodes, wrapping other groups of nodes or node chains
missions.nodes.meta.parameter_optimization Determine the optimal parameterization of a subflow
missions.nodes.sink Collect incoming signal types for further processing or to store in datasets
missions.nodes.visualization Visualize the single different data samples or averages

Together with the documentation generation, there automatically comes a list of all available nodes and corresponding name mappings.


missions.operations.node_chain Interface to node_chain using the BenchmarkNodeChain
missions.operations.weka_filter Use Weka’s Filter that transform one arff file into another.
missions.operations.weka_classification Classification using the WEKA experimenter
missions.operations.mmlf Execute MMLF experiments
missions.operations.merge Define train and test data for One versus Rest or Rest versus One in cross validation fashion
missions.operations.shuffle Take combinations of datasets in the summary for training and test each
missions.operations.concatenate Concatenate datasets of time series data
missions.operations.statistic Calculate a two-tailed paired t-test on a result collection for a certain parameter