Missions for the User¶

An overview on the different algorithms categories.

General¶

`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