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})