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: lik = get_likelihood(request) 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})) try: scale = float(request.POST["scale"]) except: raise ValueError("Scale is not correct") try: domain = json.loads(request.POST['axis_domain']) x, y, z, width, param, best_scale = gaussian_process.classify_gp(features, labels, kernel, domain, lik, learn, scale) except Exception as e: return HttpResponse(json.dumps({"status": repr(e)})) return HttpResponse(json.dumps({ 'status': 'ok', 'best_width': float(width), 'best_param': float(param), 'best_scale': float(best_scale), '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 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: rate = float(request.POST["rate"]) bias = float(request.POST['bias']) z_value, z_label = classify_perceptron(sg.Perceptron, features, labels, rate, bias) except Exception as e: return HttpResponse(json.dumps({"status": e})) return HttpResponse(json.dumps({ 'status': 'ok', 'domain': [np.min(z_value), np.max(z_value)], 'z': z_value.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: lik = get_likelihood(request) 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 == "ML2": return HttpResponse( json.dumps({ "status": ("Model Selection " "allowed only for less than 100 samples due to computational costs" ) })) try: scale = float(request.POST["scale"]) except: raise ValueError("Scale is not correct") try: domain = json.loads(request.POST['axis_domain']) x, y, z, width, param, best_scale = gaussian_process.classify_gp( features, labels, kernel, domain, lik, learn, scale) except Exception as e: return HttpResponse(json.dumps({"status": repr(e)})) return HttpResponse( json.dumps({ 'status': 'ok', 'best_width': float(width), 'best_param': float(param), 'best_scale': float(best_scale), '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() }))
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: lik = get_likelihood(request) 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 == "ML2": return HttpResponse(json.dumps({"status": ("Model Selection " "allowed only for less than 100 samples due to computational costs")})) try: scale = float(request.POST["scale"]) except: raise ValueError("Scale is not correct") try: domain = json.loads(request.POST['axis_domain']) x, y, z, width, param, best_scale = gaussian_process.classify_gp(features, labels, kernel, domain, lik, learn, scale) except Exception as e: return HttpResponse(json.dumps({"status": repr(e)})) return HttpResponse(json.dumps({ 'status': 'ok', 'best_width': float(width), 'best_param': float(param), 'best_scale': float(best_scale), 'domain': [np.min(z), np.max(z)], 'z': z.tolist() }))