def kmeans(request):
k = 2
global norm, champs, name_champ
norm = Normalizer()
workpath = os.path.dirname(os.path.abspath(__file__)) #Returns the Path your .py file is in
datafile = os.path.join(workpath, 'dataset/spambase.data.txt')
norm.load_csv(os.path.join(workpath, 'dataset/spambase.data.txt'))
champs = []
name_champ = []
if request.method == 'GET':
if(request.GET['nb'] == '3'):
champs.append(int(request.GET['champs1']))
champs.append(int(request.GET['champs2']))
champs.append(int(request.GET['champs3']))
name_champ.append(nomChamp[champs[0]])
name_champ.append(nomChamp[champs[1]])
name_champ.append(nomChamp[champs[2]])
elif (request.GET['nb'] == '2'):
champs.append(int(request.GET['champs1']))
champs.append(int(request.GET['champs2']))
name_champ.append(nomChamp[champs[0]])
name_champ.append(nomChamp[champs[1]])
global kMeanClusterer
kMeanClusterer = KMeanClusterer(k, datafile, champs)
kMeanClusterer.assignement()
centroids = []
clusters = []
for i in range(k):
centroids.append(kMeanClusterer.getCluster(i).getCentroid())
#centroids.append(kMeanClusterer.getCluster(i).normalizeCentroid(0.0, 1.0, len(champs)))
for i in range(k):
clusters.append(kMeanClusterer.getCluster(i).getPoints())
#clusters.append(norm.normalization(kMeanClusterer.getCluster(i).getPoints(), 0.0, 1.0, len(champs)))
splitedData = norm.get_splitedData(champs)
spams = splitedData[0]
nospams = splitedData[1]
html = render_to_string('kmeans.html', {'k': len(champs), 'centroids': centroids, 'clusters': clusters,
'spams': spams, 'no_spams': nospams, 'nomChamps': name_champ})
return HttpResponse(html)
else:
form = DocumentForm() # A empty, unbound form
return redirect('index.html', {'form': form})
