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):
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:
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() }))