Rectified an issue which remained from a mistaken commit (ac61810). F…

…ixes weighting error (closing #5) and accuracy (closes #11) [had to fix votes calculations due to accidental  in defining , and tentatively but incorrectly implemented zerosarray---kept the function just in case].  Woohoo!  That's the algorithm.

example.jl

@@ -71,7 +71,7 @@ function main(alt::Bool=false)
       #       println(typeof(c))
       # end
 
-      println("Loading test faces...")
+      println("\nLoading test faces...")
       facesTesting = loadImages(posTestingPath)
       facesIITesting = map(toIntegralImage, facesTesting) # list(map(...))
       println("...done. ", length(facesTesting), " faces loaded.\n\nLoading test non-faces..")

src/AdaBoost.jl

@@ -96,40 +96,48 @@ function learn(positiveIIs::AbstractArray, negativeIIs::AbstractArray, numClassi
 
     # println(typeof(numImgs));println(typeof(numFeatures))
     # create an empty array (of zeroes) with dimensions (numImgs, numFeautures)
-    global votes = zeros((numImgs, numFeatures)) # necessarily different from `zero.((numImgs, numFeatures))`
+    global votes = zeros((numImgs, numFeatures)) # necessarily different from `zero.((numImgs, numFeatures))`; previously zerosarray
 
     # bar = progressbar.ProgressBar()
     # @everywhere numImgs begin
+    # println(size(votes))
     # println(votes)
-
+    # displaymatrix(votes)
+    # println(length(features))
+    # displaymatrix(features)
     # show progress bar
+    # displaymatrix(images)
+    # println(size(images))
     @everywhere begin
         n = numImgs
-        processes = length(numImgs) # i.e., hypotheses
-        p = Progress(n, 1)   # minimum update interval: 1 second
-        for t in 1:processes # bar(range(num_imgs)):
+        processes = numImgs # i.e., hypotheses
+        # println(processes)
+        # p = Progress(n, 1)   # minimum update interval: 1 second
+        @showprogress for t in 1:processes # bar(range(num_imgs)):
+            # println(t)
             # votes[i, :] = np.array(list(Pool(processes=None).map(partial(_get_feature_vote, image=images[i]), features)))
-            # votes[i, :] = Array(map(partial(getVote, images[i]), features))
-            votes[t, :] = Array(map(feature -> getVote(feature, images[t]), features))
+            # votes[t, :] = Array(map(partial(_get_feature_vote, images[t]), features))
+            votes[t, :] = Array(map(f -> _get_feature_vote(f, images[t]), features))
             # votes[i, :] = [map(feature -> getVote(feature, images[i]), features)]
-            next!(p)
+            # next!(p)
         end
     end # end everywhere (end parallel processing)
+    # displaymatrix(votes)
 
     # select classifiers
     # classifiers = Array()
     classifiers = []
 
-    println("Selecting classifiers...")
+    println("\nSelecting classifiers...")
 
     n = numClassifiers
-    p = Progress(n, 1)   # minimum update interval: 1 second
-    for t in 1:numClassifiers
+    # p = Progress(n, 1)   # minimum update interval: 1 second
+    @showprogress for t in 1:numClassifiers
     # for t in processes
         # println(typeof(length(featureIndices)))
         # print_matrix(stdout, weights)
         # println(weights)
-        classificationErrors = zeros(length(featureIndices))
+        classificationErrors = zeros(length(featureIndices)) # previously, zerosarray
 
         # normalize the weights $w_{t,i}\gets \frac{w_{t,i}}{\sum_{j=1}^n w_{t,j}}$
         # weights *= 1. / np.sum(weights)
@@ -164,6 +172,7 @@ function learn(positiveIIs::AbstractArray, negativeIIs::AbstractArray, numClassi
 
             classificationErrors[j] = ε
         end
+        # print_matrix(stdout, weights)
 
         # choose the classifier $h_t$ with the lowest error $\varepsilon_t$
         minErrorIDX = argmin(classificationErrors) # returns the index of the minimum in the array
@@ -179,7 +188,9 @@ function learn(positiveIIs::AbstractArray, negativeIIs::AbstractArray, numClassi
         # featureWeight = (1 - bestError) / bestError # β
         # println(typeof(featureWeight))
         # [println(f.weight) for f in features]
+        # print_matrix(stdout, weights)
         bestFeature.weight = featureWeight
+        # print_matrix(stdout, weights)
 
         # classifiers = vcat(classifiers, bestFeature)
         # println(classifiers)
@@ -188,8 +199,22 @@ function learn(positiveIIs::AbstractArray, negativeIIs::AbstractArray, numClassi
         # update image weights $w_{t+1,i}=w_{t,i}\beta_{t}^{1-e_i}$
         # weights = list(map(lambda img_idx: weights[img_idx] * np.sqrt((1-best_error)/best_error) if labels[img_idx] != votes[img_idx, best_feature_idx] else weights[img_idx] * np.sqrt(best_error/(1-best_error)), range(num_imgs)))
         # weights = (imgIDX -> (labels[imgIDX] ≠ votes[imgIDX, bestFeatureIDX]) ? weights[imgIDX]*sqrt((1-bestError)/bestError) : weights[imgIDX]*sqrt(bestError/(1-bestError)), 1:numImgs)
+        # print_matrix(stdout, weights)
+        # weights = Array(map(imgIDX -> labels[imgIDX] ≠ votes[imgIDX, bestFeatureIDX] ? weights[imgIDX] * sqrt((1 - bestError) / bestError) : weights[imgIDX] * sqrt(bestError / (1 - bestError)), 1:numImgs))
+        weights = Array(map(i -> labels[i] ≠ votes[i, bestFeatureIDX] ? weights[i] * sqrt((1 - bestError) / bestError) : weights[i] * sqrt(bestError / (1 - bestError)), 1:numImgs))
+        # println(votes[:,bestFeatureIDX])
+
+        # print_matrix(stdout, weights)
+        # println(typeof(weights))
 
-        weights = Array(map(imgIDX -> (labels[imgIDX] ≠ votes[imgIDX, bestFeatureIDX]) ? weights[imgIDX] * featureWeight : weights[imgIDX] * featureWeight, 1:numImgs))
+        # imgIDX -> labels[imgIDX] ≠ votes[imgIDX, bestFeatureIDX] ? weights[imgIDX] * sqrt((1 - bestError) / bestError) : weights[imgIDX] * sqrt(bestError / (1 - bestError))
+
+        # weights = np.array(list(map(
+        #
+        # lambda img_idx:
+        #     weights[img_idx] * np.sqrt((1-best_error)/best_error) if labels[img_idx] != votes[img_idx, best_feature_idx] else weights[img_idx] * np.sqrt(best_error/(1-best_error)),
+        #
+        #     range(num_imgs))))
 
         # β = ε / (1 - ε)
         #
@@ -207,15 +232,22 @@ function learn(positiveIIs::AbstractArray, negativeIIs::AbstractArray, numClassi
         featureIndices = filter!(e -> e ∉ bestFeatureIDX, featureIndices) # note: without unicode operators, `e ∉ [a, b]` is `!(e in [a, b])`
         # println(bestFeature)
 
-        next!(p)
+        # next!(p)
     end
+    # println(weights)
 
     # println(typeof(classifiers[1]))
+    # println(votes)
     return classifiers
 
 end
 
 
+function _get_feature_vote(feature::HaarLikeFeature, image::AbstractArray)
+    return getVote(feature, image)
+end
+
+
 # function _get_feature_vote(feature::HaarLikeFeature, image::Int64)
 #     # return getVote(image)
 #     # return partial(getVote)(image)

src/Utils.jl

@@ -43,6 +43,32 @@ deepfloat(a::Number) = a * 1.0
 deepfloat(a) = deepfloat.(a)
 
 
+function displaymatrix(M::AbstractArray)
+    return show(IOContext(stdout, :limit => true, :compact => true, :short => true), "text/plain", M)
+    # return show(IOContext(stdout, :limit => true, :compact => true, :short => true, :displaysize), "text/plain", M)
+end
+
+
+function zerosarray(a::Int64, b::Int64=1)
+    #=
+    To replicate numpy.zeros(...)
+    
+    parameter `a`: A number of length for array to be [type: Integer]
+    parameter `b`: A number of the second dimension for array of arrays of zeros (defaults to one) [type: Integer]
+     
+    return: Either an array of zeros (i.e., `[0, 0, 0]`), or an array of arrays of zeros (if b is given; i.e., [0, 0, 0, 0; 0, 0, 0, 0; 0, 0, 0, 0]) [type: Abstract Array]
+    =#
+
+    if isone(b)
+        # return [zeros(a) for _ in 1:b]
+        return collect(eachrow(zeros(b, a)))
+    else
+        # return [zeros(b) for _ in 1:a]
+        return collect(eachrow(zeros(a, b)))
+    end
+end
+
+
 # for adaboost
 function partial(f,a...)
     #=
@@ -218,7 +244,7 @@ function reconstruct(classifiers::AbstractArray, imgSize::Tuple)
                     sign = mod((sign + 1), 2)
                 end
                 for y in 1:c.height
-                    image[c.topLeft[0] + x, c.topLeft[1] + y] += 1 * sign * c.weight
+                    image[c.topLeft[1] + x, c.topLeft[2] + y] += 1 * sign * c.weight
                 end
             end
         elseif c.featureType == FeatureTypes[3] # three horizontal