def get_queryset(self):
data_kernel = testing.proses_testing()
context = {'data_kernel': data_kernel}
return context
def edit(request, pk, template_name='edit.html'):
data = get_object_or_404(DataTesting, pk=pk)
form = DataTestingForm(request.POST or None, instance=data)
if form.is_valid():
no = form.cleaned_data['no']
ppm_ch4 = float(form.cleaned_data['ppm_ch4'])
ppm_c2h4 = float(form.cleaned_data['ppm_c2h4'])
ppm_c2h2 = float(form.cleaned_data['ppm_c2h2'])
fault = form.cleaned_data['fault']
sum = ppm_ch4 + ppm_c2h4 + ppm_c2h2
ch4 = (ppm_ch4 / sum) * 100
c2h4 = (ppm_c2h4 / sum) * 100
c2h2 = (ppm_c2h2 / sum) * 100
try:
param = DataTesting.objects.get(no=no)
except DataTesting.DoesNotExist:
param = DataTesting()
param.no = no
param.ppm_ch4 = ppm_ch4
param.ppm_c2h4 = ppm_c2h4
param.ppm_c2h2 = ppm_c2h2
param.persen_ch4 = ch4
param.persen_c2h4 = c2h4
param.persen_c2h2 = c2h2
param.fault = fault
param.save()
# Testing
testing.proses_testing()
return redirect('home:data-testing')
return render(request, template_name, {'form': form})
def create(request):
form = DataTestingForm()
if request.method == 'POST':
if request.POST.get("input_excel"):
try:
excel_file = request.FILES['file-excel']
except MultiValueDictKeyError:
return render(request, 'home_data_testing_create.html', {'form': form})
if str(excel_file).split('.')[-1] == 'xls':
data = xls_get(excel_file, column_limit=5)
elif str(excel_file).split('.')[-1] == 'xlsx':
data = xlsx_get(excel_file, column_limit=5)
else:
return render(request, 'home_data_testing_create.html', {'form': form})
if data is not None:
save_excel_to_db(data)
# Testing
testing.proses_testing()
return redirect('home:data-testing')
else:
form = DataTestingForm(request.POST)
if form.is_valid():
no = form.cleaned_data['no']
ppm_ch4 = float(form.cleaned_data['ppm_ch4'])
ppm_c2h4 = float(form.cleaned_data['ppm_c2h4'])
ppm_c2h2 = float(form.cleaned_data['ppm_c2h2'])
fault = form.cleaned_data['fault']
sum = ppm_ch4 + ppm_c2h4 + ppm_c2h2
ch4 = (ppm_ch4 / sum) * 100
c2h4 = (ppm_c2h4 / sum) * 100
c2h2 = (ppm_c2h2 / sum) * 100
try:
param = DataTesting.objects.get(no=no)
except DataTesting.DoesNotExist:
param = DataTesting()
param.no = no
param.ppm_ch4 = ppm_ch4
param.ppm_c2h4 = ppm_c2h4
param.ppm_c2h2 = ppm_c2h2
param.persen_ch4 = ch4
param.persen_c2h4 = c2h4
param.persen_c2h2 = c2h2
param.fault = fault
param.save()
# Testing
testing.proses_testing()
return redirect('home:data-testing')
print(form)
return render(request, 'home_data_testing_create.html', {'form': form})
def post(self, request, *args, **kwargs):
form = ParameterFormKfold(request.POST)
if form.is_valid():
sigma = float(form.cleaned_data['sigma'])
lamda = float(form.cleaned_data['lamda'])
complexity = float(form.cleaned_data['complexity'])
gamma = float(form.cleaned_data['gamma'])
iterasi = int(form.cleaned_data['iterasi'])
split = int(form.cleaned_data['split'])
try:
param = Parameter.objects.get(id='1')
s = param.sigma
if s is None or not s.strip():
s = '2'
except Parameter.DoesNotExist:
param = Parameter()
param.id = '1'
s = '2'
param.sigma = sigma
param.lamda = lamda
param.complexity = complexity
param.gamma = gamma
param.iterasi = iterasi
param.save()
list_data_bias = []
cv = StratifiedKFold(n_splits=split, shuffle=True, random_state=42)
for i in range(7):
lv = i + 1
dt_normalisasi = normalisasi.get_normalisasi(lv)
x = dt_normalisasi['data_normalisasi_x']
y = dt_normalisasi['data_normalisasi_y']
xK = np.array(x)
yK = np.array(y)
data_bias = []
for train_index, test_index in cv.split(xK, yK):
matriks = training.get_matriks(xK[train_index].tolist(),
lamda, float(s), True)
n_data_normalisasi = matriks['n_data_normalisasi']
n_list_data_kernel = matriks['n_list_data_kernel']
n_list_data_matriks = matriks['n_list_data_matriks']
data_iterasi = training.get_iterasi(
n_list_data_matriks, complexity, gamma, iterasi)
data_alpha = data_iterasi[len(data_iterasi) -
1]['data_alfa_baru']
for j, x in enumerate(n_data_normalisasi):
x[5] = data_alpha[j]
bias = 0
if len(data_iterasi) > 0:
dt = training.get_bias(lv, n_data_normalisasi,
data_alpha, n_list_data_kernel,
False)
bias = dt['bias']
data_bias.append(bias)
list_data_bias.append(data_bias)
accuracy = []
for i in range(7):
lv = i + 1
dt_normalisasi = normalisasi.get_normalisasi(lv)
x = dt_normalisasi['data_normalisasi_x']
y = dt_normalisasi['data_normalisasi_y']
xK = np.array(x)
yK = np.array(y)
data_akurasi = []
y = 0
for train_index, test_index in cv.split(xK, yK):
y = y + 1
matriks = training.get_matriks(xK[train_index].tolist(),
lamda, float(s), True)
n_data_normalisasi = matriks['n_data_normalisasi']
n_list_data_matriks = matriks['n_list_data_matriks']
data_iterasi = training.get_iterasi(
n_list_data_matriks, complexity, gamma, iterasi)
data_alpha = data_iterasi[len(data_iterasi) -
1]['data_alfa_baru']
for j, x in enumerate(n_data_normalisasi):
x[5] = data_alpha[j]
data_testing = testing.proses_testing(
True, xK[test_index].tolist(), n_data_normalisasi,
list_data_bias[i][y - 1])
akurasi = []
for j, x in enumerate(data_testing):
akurasi.append(float(x['data_akurasi']))
data_akurasi.append(sum(akurasi) / len(akurasi))
ac = sum(data_akurasi) / y
accuracy.append(ac)
context = {
'scores': [],
'scores_mean': (sum(accuracy) / len(accuracy)),
'data_evaluasi': [],
'display': 'block',
'form': form
}
return render(request, self.template_name,
{self.context_object_name: context})
else:
context = {
'scores': [],
'scores_mean': 0,
'data_evaluasi': [],
'display': 'none',
'form': form
}
return render(request, self.template_name,
{self.context_object_name: context})