Remove predict_single max_models

2024-03-19 11:35:43 +01:00
parent eb97a5a14b
commit 422129802a
5 changed files with 182 additions and 50 deletions
--- a/docs/algorithm.md
+++ b/docs/algorithm.md
@@ -0,0 +1,105 @@
+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.
+
+15. // main loop
+
+16. While (!finished)
+
+    1. $\pi \leftarrow SortFeatures(Vars, criterio, D[W])$
+
+    2. if $(bisection) \; k \leftarrow 2^{tolerance} \;$ else
+        $k \leftarrow 1$
+
+    3. if ($k tolerance == 0$) $W_B \leftarrow W$;
+        $numItemsPack \leftarrow0$
+
+    4. $P \leftarrow Head(\pi,k)$ // first k features in order
+
+    5. $spodes \leftarrow \emptyset$
+
+    6. $i \leftarrow 0$
+
+    7. 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. $e \leftarrow error(\hat{y}[], y[])$
+
+        8. $\alpha \leftarrow \frac{1}{2} ln \left ( \frac{1-e}{e} \right )$
+
+        9. if ($\alpha > 0.5$)
+
+            1. $finished \leftarrow True$
+
+            2. break
+
+        10. $spodes.add( (spode,\alpha_t) )$
+
+        11. $W \leftarrow UpdateWeights(D[W],\alpha,y[],\hat{y}[])$
+
+    8. $AODE.add( spodes )$
+
+    9. if ($convergence \And ! finished$)
+
+        1. $\hat{y}[] \leftarrow Predict(D,spodes)$
+
+        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. $error \leftarrow min(error,e)$
+
+    10. If $(Vars == \emptyset) \; finished \leftarrow True$
+
+17. if ($tolerance == maxTolerance$) // algorithm finished because of
+    lack of convergence
+
+    1. $removeModels(AODE, numItemsPack)$
+
+    2. $W \leftarrow W_B$
+
+18. Return $AODE$