def Test_weightened_knn():
k_fold = 10
trainingSet=[]
testSet = []
predictions = []
dataset = misc.loadData('pc1.arff')
trainingSet = np.asarray(trainingSet)
testSet = np.asarray(testSet)
predictions = []
trainingSet,testSet = partition(dataset,k_fold)
accuracy = []
for k in [1,2,3,5,7,9,11,13,15]:
print "Weightened KNN with K = " + repr(k)
print "K-fold = " + repr(k_fold)
for x in range(k_fold):
predictions = []
for i in range(len(testSet[x])):
result = calculateNeighborsWeightened(trainingSet[x], testSet[x][i], k)
predictions.append(result)
accuracy.append(misc.calculateAccuracy(testSet[x],predictions))
print 'iteration: '+ repr(x) + " accuracy:" + repr(accuracy[x]) + '%'
accuracy_avg = sum(accuracy)/k_fold
print ("Average accuracy : " + repr(accuracy_avg) + '%')
print
accuracy = []
accuracy_avg = 0
print
def Test_vdm_weightened_knn():
k_fold = 10
trainingSet=[]
testSet = []
predictions = []
dataset = misc.loadData('chess.csv')
trainingSet = np.asarray(trainingSet)
testSet = np.asarray(testSet)
predictions = []
trainingSet,testSet = partition(dataset,k_fold)
accuracy = []
for k in [1,2,3,5,7,9,11,13,15]:
print "Weightened KNN-VDM with K = " + repr(k)
print "K-fold = " + repr(k_fold)
for x in range(k_fold):
predictions = []
lookup_ = misc.buildLookUp(trainingSet[x])
classes = misc.getClasses(trainingSet[x])
for i in range(len(testSet[x])):
result = calculateNeighborsWeightenedVDM(trainingSet[x],testSet[x][i],k,lookup_,classes)
predictions.append(result)
accuracy.append(misc.calculateAccuracy(testSet[x],predictions))
print 'iteration: '+ repr(x) + " accuracy:" + repr(accuracy[x]) + '%'
accuracy_avg = sum(accuracy)/k_fold
print ("Average accuracy : " + repr(accuracy_avg) + '%')
print
accuracy = []
accuracy_avg = 0
print
def Test_hvdm_unweightened_knn():
k_fold = 10
trainingSet=[]
testSet = []
predictions = []
dataset = misc.loadData('crx.data')
for i in range(len(dataset)):
if dataset[i][13] != '?':
dataset[i][13] = float(dataset[i][13])
trainingSet = np.asarray(trainingSet)
testSet = np.asarray(testSet)
predictions = []
trainingSet,testSet = partition(dataset,k_fold)
accuracy = []
for k in [1,2,3,5,7,9,11,13,15]:
print "UnWeightened KNN-HVDM with K = " + repr(k)
print "K-fold = " + repr(k_fold)
for x in range(k_fold):
predictions = []
maxmin = misc.maxMin(trainingSet[x])
lookup_ = misc.buildLookUp(trainingSet[x])
classes = misc.getClasses(trainingSet[x])
for i in range(len(testSet[x])):
result = calculateNeighborsUnWeightenedHVDM(trainingSet[x],testSet[x][i],k,lookup_,classes,maxmin)
predictions.append(result)
accuracy.append(misc.calculateAccuracy(testSet[x],predictions))
print 'iteration: '+ repr(x) + " accuracy:" + repr(accuracy[x]) + '%'
accuracy_avg = sum(accuracy)/k_fold
print ("Average accuracy : " + repr(accuracy_avg) + '%')
print
accuracy = []
accuracy_avg = 0
print