mvpa2.clfs.transerror.SummaryStatistics¶
 
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class mvpa2.clfs.transerror.SummaryStatistics(targets=None, predictions=None, estimates=None, sets=None)¶
- Basic class to collect targets/predictions and report summary statistics - It takes care about collecting the sets, which are just tuples (targets, predictions, estimates). While ‘computing’ the matrix, all sets are considered together. Children of the class are responsible for computation and display. - Attributes - error- sets- stats- summaries- Return a list of separate summaries per each stored set - Methods - add(targets, predictions[, estimates])- Add new results to the set of known results - as_string([short, header, summary, description])- ‘Pretty print’ the matrix - compute()- Actually compute the confusion matrix based on all the sets - reset()- Cleans summary – all data/sets are wiped out - Initialize SummaryStatistics - targets or predictions cannot be provided alone (ie targets without predictions) - Parameters: - targets - Optional set of targets - predictions - Optional set of predictions - estimates - Optional set of estimates (which served for prediction) - sets - Optional list of sets - Attributes - error- sets- stats- summaries- Return a list of separate summaries per each stored set - Methods - add(targets, predictions[, estimates])- Add new results to the set of known results - as_string([short, header, summary, description])- ‘Pretty print’ the matrix - compute()- Actually compute the confusion matrix based on all the sets - reset()- Cleans summary – all data/sets are wiped out - 
add(targets, predictions, estimates=None)¶
- Add new results to the set of known results 
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as_string(short=False, header=True, summary=True, description=False)¶
- ‘Pretty print’ the matrix - Parameters: - short : bool - if True, ignores the rest of the parameters and provides consise 1 line summary - header : bool - print header of the table - summary : bool - print summary (accuracy) - description : bool - print verbose description of presented statistics 
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compute()¶
- Actually compute the confusion matrix based on all the sets 
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error¶
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reset()¶
- Cleans summary – all data/sets are wiped out 
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sets¶
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stats¶
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summaries¶
- Return a list of separate summaries per each stored set 
 
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