feature_vector_sink¶

Module: `missions.nodes.sink.feature_vector_sink`¶

Collect feature vectors

Inheritance diagram for pySPACE.missions.nodes.sink.feature_vector_sink:

class pySPACE.missions.nodes.sink.feature_vector_sink.FeatureVectorSinkNode(classes_names=[], num_features=None, **kwargs)[source]¶

Collect all FeatureVector elements that are passed through it in a collection of type feature_vector.

Parameters

Exemplary Call

- 
    node: FeatureVectorSink

POSSIBLE NODE NAMES:
Input:	FeatureVector
Output:	FeatureVectorDataset
Author:	Jan Hendrik Metzen (jhm@informatik.uni-bremen.de)
Created:	2008/09/02
	Feature_Vector_Sink FeatureVectorSink Labeled_Feature_Vector_Sink FeatureVectorSinkNode
POSSIBLE INPUT TYPES:
	FeatureVector

Class Components Summary

`_create_result_sets`(num_features[, ...])	Sets some object members that could not set during __init__ since the depend on the dimensionality of the data (i.e.
`_train`(data, label)
`get_result_dataset`()	Return the result
`input_types`
`is_supervised`()	Returns whether this node requires supervised training
`is_trainable`()	Returns whether this node is trainable.
`process_current_split`()	Compute the results of this sink node for the current split of the data
`reset`()	Reset the state of the object to the clean state it had after its

reset()[source]¶: Reset the state of the object to the clean state it had after its initialization

is_supervised()[source]¶: Returns whether this node requires supervised training

_create_result_sets(num_features, feature_names=None)[source]¶: Sets some object members that could not set during __init__ since the depend on the dimensionality of the data (i.e. the number of features)

process_current_split()[source]¶: Compute the results of this sink node for the current split of the data into train and test data