def dataset_config(request):
has_preview = False
name = request.session.get('typeDataset')
if(request.method == 'POST'):
form = _get_dataset_form(request.session.get('typeDataset'), request.POST, doc=request.FILES)
# a = request.session.get('typeDataset')
if(form.is_valid()):
if(name == 'random'):
request.session['dataset'] = random_generation(form.cleaned_data['nbPoints'],
form.cleaned_data['nbClass'],)
elif(name == 'uniformGroup'):
request.session['dataset'] = group_generation(form.cleaned_data['nbClass'],
form.cleaned_data['nbPointPerClass'],)
elif(name == 'percentGroup'):
request.session['dataset'], loss = percent_generation(form.cleaned_data['percents'],
form.cleaned_data['nbPoints'],)
elif(name == 'custom'):
request.session['dataset'] = read_file(request.FILES['docfile'])
# Generate the preview of the training set
preview(request.session['dataset'])
has_preview = True
# Redirect if the user wants to go to the algorithm selection
if('nextstep' in request.POST):
return redirect('algo_select')
else:
form = _get_dataset_form(request.session.get('typeDataset'))
return render(request, 'integration/dataset_configuration.html', {'form': form,
'has_preview': has_preview,
'typeDataset': request.session.get('typeDataset')})
def execute_all(request):
if(request.method == 'POST'):
form = ExecuteAllForm(request.POST)
if(form.is_valid()):
new = form.cleaned_data['newPoint']
dataset = request.session.get('dataset')
knnRes = kmeans(new, dataset)
pairBasedRes = PairBased(new, dataset)
fadanaRes = fadana(new, dataset)
preview(request.session['dataset'], new=new) # genère l'image a afficher (juste un plan du dataset)
results = {'kmeans': knnRes[-1], 'fadana': fadanaRes[0], 'pair based': pairBasedRes[0], 'lazy analogical': "Not implemented yet"}
return render(request, 'integration/algorithm_comparison.html', {'results': results})
else:
form = ExecuteAllForm()
return render(request, 'integration/algorithm_selection.html', {'form': SelectAlgorithmForm(), 'formAll': form})
