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() }))
