def applyKNN(request):
#get the file on which we will be applying
docList = Document.objects.all()
l = len(docList)
revDocList = []
i = 0
while i<l:
revDocList.append(docList[l-i-1])
i += 1
document = revDocList[0]
if request.GET['K']!='' :
k = int(request.GET['K'])
else:
k = 7
classList = wKNN(str(document.docfile),k)
l = len(classList)
lis = []
for i in range(l):
lis.append((classList[i][1],classList[i][0]))
filename = str(document.docfile)
context = {
'title':'KNN Results',
'msg':'<br>For the file '+filename+' .<a href="/home/">Click here to go back!!</a>',
'colA':'Class Name',
'colB':'Percentage Chance',
'dataSet' : lis,
}
return render(request,'results.html',context)
def testFile(path,knnResult,bayesianResult,f):
i = 1
realClass = path.split('/')[1]
while i<8:
classList = wKNN(path,i)
knnResult.append((str(path)+' - '+str(i)+' - '+str(classList[0][1]),realClass,classList[0][1]))
print(str(path)+' - '+str(i)+' - '+str(classList[0][1])+' | '+realClass+' | '+classList[0][1])
f.write((str(path)+' - '+str(i)+' - '+str(classList[0][1])+' | '+realClass+' | '+classList[0][1])+'\n')
i += 2
classList = NaiveBayes(path)
bayesianResult.append((str(path)+' - '+str(classList[0][1]),realClass,classList[0][1]))
print(str(path)+' - '+str(classList[0][1])+' | '+realClass+' | '+classList[0][1])
f.write((str(path)+' - '+str(classList[0][1])+' | '+realClass+' | '+classList[0][1])+'\n')
def applyKNN(request):
#get the file on which we will be applying
docList = Document.objects.all()
l = len(docList)
revDocList = []
i = 0
while i<l:
revDocList.append(docList[l-i-1])
i += 1
document = revDocList[0]
k = int(request.GET['K'])
classList = wKNN(str(document.docfile),k)
filename = str(document.docfile)
return HttpResponse('<br>for the file '+filename+' .The class list is: <br>'+str(classList))