Rectify a remaining Pythonic reference

src/Adaboost.jl

@@ -155,9 +155,10 @@ function learn(positiveIIs::AbstractArray, negativeIIs::AbstractArray, numClassi
         # featureWeight = 0.5 * log((1 - bestError) / bestError)
         featureWeight = (1 - bestError) / bestError # β
         # println(typeof(featureWeight))
-        bestFeature.weight = featureWeight # need to element-wise alter the struct `weight`; else we get `setfield! immutable struct of type HaarLikeFeature cannot be changed`
+        bestFeature.weight = featureWeight
 
         # classifiers = vcat(classifiers, bestFeature)
+        # println(classifiers)
         classifiers = push!(classifiers, bestFeature)
 
         # update image weights $w_{t+1,i}=w_{t,i}\beta_{t}^{1-e_i}$
@@ -184,6 +185,7 @@ function learn(positiveIIs::AbstractArray, negativeIIs::AbstractArray, numClassi
         next!(p)
     end
 
+    # println(typeof(classifiers[1]))
     return classifiers
 
 end

src/FDA.jl

@@ -64,6 +64,12 @@ function main(alt::Bool=false)
       println("Determining classifiers; this may take a while...")
       classifiers = learn(facesIITraining, nonFacesIITraining, numClassifiers, minFeatureHeight, maxFeatureHeight, minFeatureWidth, maxFeatureWidth)
 
+      # [println(c) for c in classifiers]
+      # println(typeof(classifiers))
+      # for c in classifiers
+      #       println(typeof(c))
+      # end
+
       println("Loading test faces...")
       facesTesting = loadImages(posTestingPath)
       facesIITesting = map(toIntegralImage, facesTesting) # list(map(...))
@@ -79,9 +85,9 @@ function main(alt::Bool=false)
       correctNonFaces = length(nonFacesTesting) - sum(ensembleVoteAll(nonFacesIITesting, classifiers))
 
       println("...done.\n\nResult:\n      Faces: ", correctFaces, "/", length(faces_testing)
-            , "  (", ((float(correctFaces) / len(facesTesting)) * 100), "%)\n  non-Faces: "
-            , (correct_non_faces), "/", len(non_faces_testing), "  ("
-            , ((float(correct_non_faces) / len(non_faces_testing)) * 100), "%)")
+            , "  (", ((deepfloat(correctFaces) / length(facesTesting)) * 100), "%)\n  non-Faces: "
+            , (correct_non_faces), "/", length(non_faces_testing), "  ("
+            , ((deepfloat(correct_non_faces) / length(non_faces_testing)) * 100), "%)")
 
       # Just for fun: putting all haar-like features over each other generates a face-like image
       recon = reconstruct(classifiers, size(facesTesting[1]))

src/Utils.jl

@@ -132,7 +132,11 @@ function ensembleVote(intImg::AbstractArray, classifiers::AbstractArray)
     #     return 0
     # end
 
-    return (sum([c.get_vote(int_img) for c in classifiers]) >= 0 ? 1 : 0)
+    # println([typeof(c) for c in classifiers])
+    # println(typeof(classifiers))
+    return deepsum([getVote(c, intImg) for c in classifiers]) >= 0 ? 1 : 0
+
+     # >= 0 ? 1 : 0
 end
 
 function ensembleVoteAll(intImgs::AbstractArray, classifiers::AbstractArray)
@@ -148,9 +152,28 @@ function ensembleVoteAll(intImgs::AbstractArray, classifiers::AbstractArray)
     [type: Abstract Arrays (array of Integers)]
     =#
 
-    votePartial = partial(ensembleVote, classifiers)
+    # [println(typeof(c)) for c in classifiers]
+    # println(typeof(classifiers))
+
+    # votePartial = [partial(ensembleVote, c) for c in classifiers]
+    # votePartial = partial(ensembleVote, [c for c in classifiers])
+    # votePartial = partial(ensembleVote, classifiers)
+    #
+    # return map(votePartial, intImgs)
+
+    # map(i -> ensembleVote(classifiers, i), intImgs)
+    return map(i -> ensembleVote(i, classifiers), intImgs)
+
+    # votePartial = partial(ensembleVote, intImgs)
+    #
+    # return map(votePartial, classifiers)
+
+
+    # map(imgIDX -> (labels[imgIDX] ≠ votes[imgIDX, bestFeatureIDX]) ? weights[imgIDX] * featureWeight : weights[imgIDX] * featureWeight, 1:numImgs)
+    #
+    # map(i -> (), intImgs)
+    #
 
-    return map(votePartial, intImgs)
 end