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Weberni69795
2021-10-14 13:47:35 +02:00
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Masterarbeit/openvibe/extras-master/contrib/applications/developer-tools/pybox-manager/doc/Doc_BoxAlgorithm_DataViz.dox-part
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/**
* \page BoxAlgorithmDataViz DataViz
__________________________________________________________________
Detailed description
__________________________________________________________________
* |OVP_DocBegin_BoxAlgorithmDataViz_Description|
This box aims to transform the data with a LDA or a PCA and plot the data in 2D or 3D.
* |OVP_DocEnd_BoxAlgorithmDataViz_Description|
__________________________________________________________________
Settings description
__________________________________________________________________
* |OVP_DocBegin_BoxAlgorithmDataViz_Settings|
* |OVP_DocEnd_BoxAlgorithmDataViz_Settings|
* |OVP_DocBegin_BoxAlgorithmDataViz_Setting1|
The box clock frequency. The Python process function is called at
each tick.
* |OVP_DocEnd_BoxAlgorithmDataViz_Setting1|
* |OVP_DocBegin_BoxAlgorithmDataViz_Setting2|
If specified, path to save the model which transform the data.
* |OVP_DocEnd_BoxAlgorithmDataViz_Setting2|
* |OVP_DocBegin_BoxAlgorithmDataViz_Setting3|
If specified, path to load the model which transform the data.
If empty, a new model will be created depending on the input's data.
* |OVP_DocEnd_BoxAlgorithmDataViz_Setting3|
* |OVP_DocBegin_BoxAlgorithmDataViz_Setting4|
Algorithm to use to reduce the data's dimensions. Two options are available :
PCA or LDA. By default, the box will use PCA.
* |OVP_DocEnd_BoxAlgorithmDataViz_Setting4|
* |OVP_DocBegin_BoxAlgorithmDataViz_Setting5|
Number of dimensions to plot the data. The user can only choose between 2 or 3.
* |OVP_DocEnd_BoxAlgorithmDataViz_Setting5|
* |OVP_DocBegin_BoxAlgorithmDataViz_Setting6|
Labels of the data, if not specified then labels will be "1, 2, 3, etc".
* |OVP_DocEnd_BoxAlgorithmDataViz_Setting6|
__________________________________________________________________
Examples description
__________________________________________________________________
* |OVP_DocBegin_BoxAlgorithmDataViz_Examples|
See <a href="http://openvibe.inria.fr/tutorial-using-python-with-openvibe">this page</a> for commented examples.
* |OVP_DocEnd_BoxAlgorithmDataViz_Examples|
__________________________________________________________________
Miscellaneous description
__________________________________________________________________
* |OVP_DocBegin_BoxAlgorithmDataViz_Miscellaneous|
* |OVP_DocEnd_BoxAlgorithmDataViz_Miscellaneous|
*/
Masterarbeit/openvibe/extras-master/contrib/applications/developer-tools/pybox-manager/doc/Doc_BoxAlgorithm_DatasetCreator.dox-part
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/**
* \page BoxAlgorithmDatasetCreator DatasetCreator
__________________________________________________________________
Detailed description
__________________________________________________________________
* |OVP_DocBegin_BoxAlgorithmDatasetCreator_Description|
This box aims to monitor the user to create a dataset.
The user first configure the box and chooses some labels, then
the box will randomly determine an order between them. It will
then record the user's brain activity while verbally indicating
the current action. In this way, we will be able to create a
labeled dataset that can be used for learning.
* |OVP_DocEnd_BoxAlgorithmDatasetCreator_Description|
__________________________________________________________________
Settings description
__________________________________________________________________
* |OVP_DocBegin_BoxAlgorithmDatasetCreator_Settings|
* |OVP_DocEnd_BoxAlgorithmDatasetCreator_Settings|
* |OVP_DocBegin_BoxAlgorithmDatasetCreator_Setting1|
The box clock frequency. The Python process function is called at
each tick.
* |OVP_DocEnd_BoxAlgorithmDatasetCreator_Setting1|
* |OVP_DocBegin_BoxAlgorithmDatasetCreator_Setting2|
The path to the dir where the user want to save the data.
* |OVP_DocEnd_BoxAlgorithmDatasetCreator_Setting2|
* |OVP_DocBegin_BoxAlgorithmDatasetCreator_Setting3|
Name of the first label. That label has to be configured with the
manager to associate a sound.
* |OVP_DocEnd_BoxAlgorithmDatasetCreator_Setting3|
* |OVP_DocBegin_BoxAlgorithmDatasetCreator_Setting4|
Name of the second label. That label has to be configured with the
manager to associate a sound.
* |OVP_DocEnd_BoxAlgorithmDatasetCreator_Setting4|
* |OVP_DocBegin_BoxAlgorithmDatasetCreator_Setting5|
Name of the third label. That label has to be configured with the
manager to associate a sound.
* |OVP_DocEnd_BoxAlgorithmDatasetCreator_Setting5|
* |OVP_DocBegin_BoxAlgorithmDatasetCreator_Setting6|
Name of the fourth label. That label has to be configured with the
manager to associate a sound.
* |OVP_DocEnd_BoxAlgorithmDatasetCreator_Setting6|
* |OVP_DocBegin_BoxAlgorithmDatasetCreator_Setting7|
True if you want one csv per label, false if you want one csv
containing all the labels.
* |OVP_DocEnd_BoxAlgorithmDatasetCreator_Setting7|
* |OVP_DocBegin_BoxAlgorithmDatasetCreator_Setting3|
Number of folds you want to create.
* |OVP_DocEnd_BoxAlgorithmDatasetCreator_Setting3|
* |OVP_DocBegin_BoxAlgorithmDatasetCreator_Setting3|
Number of action to record. For exemple, if you want two record for
4 labels, then number of actions if 8.
* |OVP_DocEnd_BoxAlgorithmDatasetCreator_Setting3|
__________________________________________________________________
Examples description
__________________________________________________________________
* |OVP_DocBegin_BoxAlgorithmDatasetCreator_Examples|
See <a href="http://openvibe.inria.fr/tutorial-using-python-with-openvibe">this page</a> for commented examples.
* |OVP_DocEnd_BoxAlgorithmDatasetCreator_Examples|
__________________________________________________________________
Miscellaneous description
__________________________________________________________________
* |OVP_DocBegin_BoxAlgorithmDatasetCreator_Miscellaneous|
* |OVP_DocEnd_BoxAlgorithmDatasetCreator_Miscellaneous|
*/
Masterarbeit/openvibe/extras-master/contrib/applications/developer-tools/pybox-manager/doc/Doc_BoxAlgorithm_ProcessML.dox-part
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/**
* \page BoxAlgorithmProcessML Process Sklearn
__________________________________________________________________
Detailed description
__________________________________________________________________
* |OVP_DocBegin_BoxAlgorithmProcessML_Description|
This box aims to load a machine learning model from scikit-learn
or pyriemann already trained and to use it to predict its input's data.
* |OVP_DocEnd_BoxAlgorithmProcessML_Description|
__________________________________________________________________
Settings description
__________________________________________________________________
* |OVP_DocBegin_BoxAlgorithmProcessML_Settings|
* |OVP_DocEnd_BoxAlgorithmProcessML_Settings|
* |OVP_DocBegin_BoxAlgorithmProcessML_Setting1|
The box clock frequency. The Python process function is called at
each tick.
* |OVP_DocEnd_BoxAlgorithmProcessML_Setting1|
* |OVP_DocBegin_BoxAlgorithmProcessML_Setting2|
The path to the model the user want to load.
* |OVP_DocEnd_BoxAlgorithmProcessML_Setting2|
* |OVP_DocBegin_BoxAlgorithmProcessML_Setting3|
If not empty, the path where the user want to save the predictions
results.
* |OVP_DocEnd_BoxAlgorithmProcessML_Setting3|
__________________________________________________________________
Examples description
__________________________________________________________________
* |OVP_DocBegin_BoxAlgorithmProcessML_Examples|
See <a href="http://openvibe.inria.fr/tutorial-using-python-with-openvibe">this page</a> for commented examples.
* |OVP_DocEnd_BoxAlgorithmProcessML_Examples|
__________________________________________________________________
Miscellaneous description
__________________________________________________________________
* |OVP_DocBegin_BoxAlgorithmProcessML_Miscellaneous|
* |OVP_DocEnd_BoxAlgorithmProcessML_Miscellaneous|
*/
Masterarbeit/openvibe/extras-master/contrib/applications/developer-tools/pybox-manager/doc/Doc_BoxAlgorithm_TrainerML.dox-part
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/**
* \page BoxAlgorithmTrainerML Trainer Sklearn
__________________________________________________________________
Detailed description
__________________________________________________________________
* |OVP_DocBegin_BoxAlgorithmTrainerML_Description|
This box aims to create a machine learning model based on algorithm implemented in scikit-learn.
The user can modify nearly all the settings from scikit-learn.
It is possible for the user to test his model with a test set.
He can also save the model in a file which he can use with the ProcessML box.
An AdaBoost classifier is a meta-estimator that begins by fitting
a classifier on the original dataset and then fits additional
copies of the classifier on the same dataset but where the weights
of incorrectly classified instances are adjusted such that
subsequent classifiers focus more on difficult cases.
* |OVP_DocEnd_BoxAlgorithmTrainerML_Description|
__________________________________________________________________
Settings description
__________________________________________________________________
* |OVP_DocBegin_BoxAlgorithmTrainerML_Settings|
* |OVP_DocEnd_BoxAlgorithmTrainerML_Settings|
* |OVP_DocBegin_BoxAlgorithmTrainerML_Setting1|
The box clock frequency. The Python process function is called at
each tick.
* |OVP_DocEnd_BoxAlgorithmTrainerML_Setting1|
* |OVP_DocBegin_BoxAlgorithmTrainerML_Setting2|
The path to the file where the user want to save the model.
If empty, the model is not saved.
* |OVP_DocEnd_BoxAlgorithmTrainerML_Setting2|
* |OVP_DocBegin_BoxAlgorithmTrainerML_Setting3|
The path to the file where the user want to load a model.
If empty, a new model will be created.
* |OVP_DocEnd_BoxAlgorithmTrainerML_Setting3|
* |OVP_DocBegin_BoxAlgorithmTrainerML_Setting4|
The name of the scikit-learn algorithm. The user should not try
to modify this setting.
* |OVP_DocEnd_BoxAlgorithmTrainerML_Setting4|
* |OVP_DocBegin_BoxAlgorithmTrainerML_Setting5|
Float between 0 and 1 (1 not included) which represents the proportion
of the input dataset transformed into a test set, allowing us to
evaluate our model once the training on the set train is done. If 0
is filled in, then all the data will constitute the train set and no
metrics will be displayed.
* |OVP_DocEnd_BoxAlgorithmTrainerML_Setting5|
* |OVP_DocBegin_BoxAlgorithmTrainerML_Setting6|
Names of the labels of input's data. This parameter is only usefull
when the PolyMode is used (several streamed matrix in input, one for
each label). Then the labels as to be described as follows :
"my_label_1, mylabel_2, my_label_3, etc"
Otherwise the labels will be "1, 2, 3, etc".
If the input mode is ov-mode (one input streamed matrix and one
stimulation), the labels will be read by the stimulations.
* |OVP_DocEnd_BoxAlgorithmTrainerML_Setting6|
The rest of the settings are from scikit-learn. You can find
their purpose here :
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html
__________________________________________________________________
Examples description
__________________________________________________________________
* |OVP_DocBegin_BoxAlgorithmTrainerML_Examples|
See <a href="http://openvibe.inria.fr/tutorial-using-python-with-openvibe">this page</a> for commented examples.
* |OVP_DocEnd_BoxAlgorithmTrainerML_Examples|
__________________________________________________________________
Miscellaneous description
__________________________________________________________________
* |OVP_DocBegin_BoxAlgorithmTrainerML_Miscellaneous|
* |OVP_DocEnd_BoxAlgorithmTrainerML_Miscellaneous|
*/