def classify(request): C = json.loads(request.POST["C"]) try: features, labels = get_multi_features(request) except ValueError as e: return HttpResponse(json.dumps({"status": e.message})) try: kernel = get_kernel(request, features) except ValueError as e: return HttpResponse(json.dumps({"status": e.message})) learn = "No" values=[] try: domain = json.loads(request.POST['axis_domain']) x, y, z = svm.classify_svm(sg.GMNPSVM, features, labels, kernel, domain, learn, values, C, False) except Exception as e: return HttpResponse(json.dumps({"status": repr(e)})) # z = z + np.random.rand(*z.shape) * 0.01 z_max = np.nanmax(z) z_min = np.nanmin(z) z_delta = 0.1*(np.nanmax(z)-np.nanmin(z)) data = {"status": "ok", "domain": [z_min-z_delta, z_max+z_delta], "max": z_max+z_delta, "min": z_min-z_delta, "z": z.tolist()} return HttpResponse(json.dumps(data))
def classify(request): C = json.loads(request.POST["C"]) try: features, labels = get_multi_features(request) except ValueError as e: return HttpResponse(json.dumps({"status": e.message})) try: kernel = get_kernel(request, features) except ValueError as e: return HttpResponse(json.dumps({"status": e.message})) learn = "No" values = [] try: domain = json.loads(request.POST['axis_domain']) x, y, z = svm.classify_svm(sg.GMNPSVM, features, labels, kernel, domain, learn, values, C, False) except Exception as e: return HttpResponse(json.dumps({"status": repr(e)})) # z = z + np.random.rand(*z.shape) * 0.01 z_max = np.nanmax(z) z_min = np.nanmin(z) z_delta = 0.1 * (np.nanmax(z) - np.nanmin(z)) data = { "status": "ok", "domain": [z_min - z_delta, z_max + z_delta], "max": z_max + z_delta, "min": z_min - z_delta, "z": z.tolist() } return HttpResponse(json.dumps(data))
def classify(request): value=[] try: features, labels = get_binary_features(request) except ValueError as e: return HttpResponse(json.dumps({"status": e.message})) try: kernel = get_kernel(request, features) except ValueError as e: return HttpResponse(json.dumps({"status": e.message})) try: learn = request.POST["learn"] except ValueError as e: return HttpResponse(json.dumps({"status": e.message})) if kernel.get_name() == 'PolyKernel' and learn == "GridSearch": value.append(int(request.POST["polygrid1"])) value.append(int(request.POST["polygrid2"])) if value[1] <= value[0]: return HttpResponse(json.dumps({"status": "Bad values"})) try: C = float(request.POST["C"]) domain = json.loads(request.POST['axis_domain']) x, y, z = svm.classify_svm(sg.LibSVM, features, labels, kernel, domain, learn, value, C=C) except Exception as e: import traceback return HttpResponse(json.dumps({"status": repr(traceback.format_exc(0))})) return HttpResponse(json.dumps({ 'status': 'ok', 'domain': [np.min(z), np.max(z)], 'z': z.tolist() }))
def classify(request): value = [] try: features, labels = get_binary_features(request) except ValueError as e: return HttpResponse(json.dumps({"status": e.message})) try: kernel = get_kernel(request, features) except ValueError as e: return HttpResponse(json.dumps({"status": e.message})) try: learn = request.POST["learn"] except ValueError as e: return HttpResponse(json.dumps({"status": e.message})) if int(features.get_num_vectors()) > 100 and learn == "GridSearch": return HttpResponse( json.dumps({ "status": ("Model Selection " "allowed only for less than 100 samples due to computational costs" ) })) if kernel.get_name() == 'PolyKernel' and learn == "GridSearch": value.append(int(request.POST["polygrid1"])) value.append(int(request.POST["polygrid2"])) if value[1] <= value[0]: return HttpResponse(json.dumps({"status": "Bad values for degree"})) try: C = float(request.POST["C"]) domain = json.loads(request.POST['axis_domain']) x, y, z = svm.classify_svm(sg.LibSVM, features, labels, kernel, domain, learn, value, C=C) except Exception as e: import traceback return HttpResponse( json.dumps({"status": repr(traceback.format_exc(0))})) return HttpResponse( json.dumps({ 'status': 'ok', 'domain': [np.min(z), np.max(z)], 'z': z.tolist() }))
def classify(request): try: features, labels = get_binary_features(request) except ValueError as e: return HttpResponse(json.dumps({"status": e.message})) try: kernel = get_kernel(request, features) except ValueError as e: return HttpResponse(json.dumps({"status": e.message})) try: C = float(request.POST["C"]) x, y, z = svm.classify_svm(sg.LibSVM, features, labels, kernel, domain, C=C) except Exception as e: import traceback return HttpResponse(json.dumps({"status": repr(traceback.format_exc())})) return HttpResponse(json.dumps({ 'status': 'ok', 'domain': [np.min(z), np.max(z)], 'z': z.tolist() }))