emergent 8.5.1 PRerrVals Class Reference
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PRerrVals Class Reference

contains precision and recall error values

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 #include <PRerrVals>

defined at: /mnt/ssd/grey/local/include/Emergent/PRerrVals.h :29-70

Inherits From: taOBase, taBase

Inherited By: PRerrVals*, PRerrVals&, const PRerrVals

Index: SubTypes, Members, Methods, Static Members, Static Methods, Expert Members, Expert Methods

Sub Types

SubType Documentation

Regular (preferred) Member and Method Documentation


Member Category: _NoCategory


Method Category: UserData

Method Category: _NoCategory

Member Documentation

Member Category: _NoCategory

PRerrVals::fallout : float

fallout = fp / (fp + tn) -- fallout - false positive rate

PRerrVals::false_neg : float

false negative values -- e.g., how many active targets were incorrectly not activated by the network (actually a sum over graded activations -- if(targ > act) targ - act)

PRerrVals::false_pos : float

false positive values -- e.g., how many inactive targets were incorrectly activated by the network (actually a sum over graded activations -- if(act > targ) act - targ)

PRerrVals::fdr : float

false discovery rate = fp / (fp + tp)

PRerrVals::fmeasure : float

fmeasure = 2 * precision * recall / (precision + recall) -- harmonic mean of precision and recall -- is 1 when network is performing optimally

PRerrVals::mcc : float

mcc = ((tp*tn) - (fp*fn)) / sqrt((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn)) -- Matthews Correlation Coefficient. balanced statistic that reflects all of the kinds of errors the network could make. ranges from -1 to 1. this is the best overall value to look at for summary performance

PRerrVals::precision : float

precision = true_pos / (true_pos + false_pos) -- how many of the positive responses were true positives -- i.e., of the responses the network made, how many were correct -- does not include any of the false negatives, so the network could be under-responding (see recall)

PRerrVals::recall : float

recall = true_pos / (true_pos + false_neg) -- how many true positive responses were there relative to the total number of positive targets -- if it did not respond to a number of cases where it should have, then recall will be low

PRerrVals::specificity : float

specificity = true_neg / (false_pos + true_neg) -- proportion of true negatives correctly identified as such

PRerrVals::true_neg : float

true negative values -- e.g., to what extent was the unit correctly off

PRerrVals::true_pos : float

true positive values -- e.g., how many active targets were correctly activated by the network (actually a sum over graded activations -- if(targ > act) act else targ)

Method Documentation

Method Category: UserData

void taOBase::RemoveAllUserData ( )

get rid of our user data list entirely -- this is done automatically when saving something that has no user data items, but you can also force it with this method -- deletes the whole list

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Method Category: _NoCategory

void PRerrVals::ComputePR ( )

compute the precision, recall, and fmeasure values based on current raw stats values

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taBase* taOBase::GetOwner ( )

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TypeDef* PRerrVals::GetTypeDef ( )

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void PRerrVals::IncrVals ( PRerrVals& vls )

increment values from another set of values

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void PRerrVals::InitVals ( )

initialize all values to 0

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taSigLink** taOBase::addr_sig_link ( )

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Static Member and Method Documentation

Static Members

Static Methods

Static Method Documentation

Expert Member and Method Documentation

Expert Members

Expert Member Category: taBase

Expert Methods

Expert Method Category: UserData

Expert Member Documentation

Expert Member Category: taBase

taOBase::owner : taBase*

pointer to owner

taOBase::user_data_ : UserDataItem_List*

storage for user data (created if needed) DO NOT ACCESS this list directly -- use the GetUserData / SetUserData etc interface!

Expert Method Documentation

Expert Method Category: UserData

UserDataItem_List* taOBase::GetUserDataList ( bool force = false )

gets the userdatalist for this class

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emergent 8.5.1