69 lines
2.6 KiB
TeX
69 lines
2.6 KiB
TeX
\begin{enumerate}
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\item[] // initialization
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\item $W_0 \leftarrow (w_1, \dots, w_m) \leftarrow 1/m$
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\item $W \leftarrow W_0$
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\item $Vars \leftarrow {\cal{X}}$
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\item $\delta \leftarrow 10^{-4}$
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\item $convergence \leftarrow True$
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\item $maxTolerancia \leftarrow 3$
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\item $bisection \leftarrow False$
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\item $error \leftarrow \inf$
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\item $finished \leftarrow False$
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\item $AODE \leftarrow \emptyset$ \hspace*{2cm} // the ensemble
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\item $tolerance \leftarrow 0$
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\item $numModelsInPack \leftarrow 0$
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\item[]
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\newpage
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\item[] // main loop
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\item While (!finished)
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\begin{enumerate}
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\item $\pi \leftarrow SortFeatures(Vars, criterio, D[W])$
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\item if $(bisection) \; k \leftarrow 2^{tolerance} \;$ else $k \leftarrow 1$
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\item if ($k tolerance == 0$) $W_B \leftarrow W$; $numItemsPack \leftarrow0$
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\item $P \leftarrow Head(\pi,k)$ \hspace*{2cm} // first k features in order
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\item $spodes \leftarrow \emptyset$
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\item $i \leftarrow 0$
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\item While ($ i < size(P)$)
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\begin{enumerate}
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\item $X \leftarrow P[i]$
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\item $i \leftarrow i + 1$
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\item $numItemsPack \leftarrow numItemsPack + 1$
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\item $Vars.remove(X)$
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\item $spode \leftarrow BuildSpode(X, {\cal{X}}, D[W])$
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\item $\hat{y}[] \leftarrow spode.Predict(D[W])$
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\item $e \leftarrow error(\hat{y}[], y[])$
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\item $\alpha \leftarrow \frac{1}{2} ln \left ( \frac{1-e}{e} \right )$
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\item if ($\alpha > 0.5$)
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\begin{enumerate}
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\item $finished \leftarrow True$
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\item break
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\end{enumerate}
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\item $spodes.add( (spode,\alpha_t) )$
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\item $W \leftarrow UpdateWeights(D[W],\alpha,y[],\hat{y}[])$
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\end{enumerate}
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\item $AODE.add( spodes )$
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\item if ($convergence \And ! finished$)
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\begin{enumerate}
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\item $\hat{y}[] \leftarrow Predict(D,spodes)$
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\item $e \leftarrow error(\hat{y}[], y[])$
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\item if $(e > (error+\delta))$ \hspace*{2cm} // result doesn't improve
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\begin{enumerate}
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\item if $(tolerance == maxTolerance) \;\; finished\leftarrow True$
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\item else $tolerance \leftarrow tolerance+1$
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\end{enumerate}
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\item else
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\begin{enumerate}
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\item $tolerance \leftarrow 0$
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\item $error \leftarrow min(error,e)$
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\end{enumerate}
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\end{enumerate}
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\item If $(Vars == \emptyset) \; finished \leftarrow True$
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\end{enumerate}
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\item if ($tolerance == maxTolerance$) // algorithm finished because of lack of convergence
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\begin{enumerate}
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\item $removeModels(AODE, numItemsPack)$
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\item $W \leftarrow W_B$
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\end{enumerate}
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\item Return $AODE$
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\end{enumerate} |