BayesNet/docs/algorithm.md

1. // initialization

2. W_0 \leftarrow (w_1, \dots, w_m) \leftarrow 1/m

3. W \leftarrow W_0

4. Vars \leftarrow {\cal{X}}

5. \delta \leftarrow 10^{-4}

6. convergence \leftarrow True

7. maxTolerancia \leftarrow 3

8. bisection \leftarrow False

9. error \leftarrow \inf

10. finished \leftarrow False

11. AODE \leftarrow \emptyset // the ensemble

12. tolerance \leftarrow 0

13. numModelsInPack \leftarrow 0

14. // main loop

15. While (!finished)

    1. \pi \leftarrow SortFeatures(Vars, criterio, D[W])

    2. k \leftarrow 2^{tolerance}

    3. if (tolerance == 0) numItemsPack \leftarrow0

    4. P \leftarrow Head(\pi,k) // first k features in order

    5. i \leftarrow 0

    6. While (i < size(P))

       1. X \leftarrow P[i]

       2. i \leftarrow i + 1

       3. numItemsPack \leftarrow numItemsPack + 1

       4. Vars.remove(X)

       5. spode \leftarrow BuildSpode(X, {\cal{X}}, D[W])

       6. \hat{y}[] \leftarrow spode.Predict(D[W])

       7. \epsilon \leftarrow error(\hat{y}[], y[])

       8. \alpha \leftarrow \frac{1}{2} ln \left ( \frac{1-\epsilon}{\epsilon} \right )

       9. if (\epsilon > 0.5)

          1. finished \leftarrow True

          2. break

       10. AODE.add( (spode,\alpha_t) )

       11. W \leftarrow UpdateWeights(D[W],\alpha,y[],\hat{y}[])

    7. if (convergence \And ! finished)

       1. \hat{y}[] \leftarrow AODE.Predict(D[W])

       2. e \leftarrow error(\hat{y}[], y[])

       3. if (e > (error+\delta)) // result doesn't improve

          1. if (tolerance == maxTolerance)\; finished\leftarrow True

          2. else tolerance \leftarrow tolerance+1

       4. else

          1. tolerance \leftarrow 0

          2. error \leftarrow min(error,e)

    8. if (Vars == \emptyset) \; finished \leftarrow True

16. if (tolerance == maxTolerance) // algorithm finished because of lack of convergence

    1. removeModels(AODE, numItemsPack)

    2. W \leftarrow W_B

17. Return AODE