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):
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 _read_toy_data(request):
y_set = []
x_set = []
toy_data = json.loads(request.POST['point_set'])
for pt in toy_data:
y_set.append(float(pt["y"]))
x_set.append(float(pt["x"]))
noise_level = float(request.POST['noise_level'])
scale = float(request.POST['scale'])
domain = json.loads(request.POST['axis_domain'])
labels = np.array(y_set, dtype = np.float64)
num = len(x_set)
if num == 0:
raise Http404
examples = np.zeros((1, num))
for i in xrange(num):
examples[0,i] = x_set[i]
feat_train = sg.RealFeatures(examples)
labels = sg.RegressionLabels(labels)
kernel = get_kernel(request, feat_train)
try:
learn = request.POST["learn"]
except ValueError as e:
return HttpResponse(json.dumps({"status": e.message}))
return (feat_train, labels, noise_level, scale, kernel, domain, learn)
def _read_toy_data(request):
y_set = []
x_set = []
x_set_induc = []
points = []
points_induc = []
model_sel_error = False
toy_data = json.loads(request.POST['point_set'])
for pt in toy_data:
if int(pt['label']) == 1:
points.append(pt)
elif pt['label'] == -1:
points_induc.append(pt)
for pt in points:
y_set.append(float(pt["y"]))
x_set.append(float(pt["x"]))
for pt in points_induc:
x_set_induc.append(float(pt["x"]))
noise_level = float(request.POST['noise_level'])
scale = float(request.POST['scale'])
inf = request.POST['inf']
domain = json.loads(request.POST['axis_domain'])
labels = np.array(y_set, dtype=np.float64)
num = len(x_set)
if num == 0:
raise Http404
examples = np.zeros((1, num))
for i in xrange(num):
examples[0, i] = x_set[i]
feat_train = sg.RealFeatures(examples)
labels = sg.RegressionLabels(labels)
#Get inducing points
num_induc = len(x_set_induc)
if num_induc != 0:
examples_induc = np.zeros((1, num_induc))
for i in xrange(num_induc):
examples_induc[0, i] = x_set_induc[i]
feat_train_induc = sg.RealFeatures(examples_induc)
elif num_induc == 0:
feat_train_induc = None
kernel = get_kernel(request, feat_train)
try:
learn = request.POST["learn"]
except:
raise ValueError("Argument Error")
if int(feat_train.get_num_vectors()) > 100 and learn == "ML2":
model_sel_error = True
return (feat_train, labels, noise_level, scale, kernel, domain, learn,
feat_train_induc, inf), model_sel_error
def _read_toy_data(request):
y_set = []
x_set = []
x_set_induc=[]
points=[]
points_induc=[]
model_sel_error=False
toy_data = json.loads(request.POST['point_set'])
for pt in toy_data:
if int(pt['label'])==1:
points.append(pt)
elif pt['label']==-1:
points_induc.append(pt)
for pt in points:
y_set.append(float(pt["y"]))
x_set.append(float(pt["x"]))
for pt in points_induc:
x_set_induc.append(float(pt["x"]))
noise_level = float(request.POST['noise_level'])
scale = float(request.POST['scale'])
inf = request.POST['inf']
domain = json.loads(request.POST['axis_domain'])
labels = np.array(y_set, dtype = np.float64)
num = len(x_set)
if num == 0:
raise Http404
examples = np.zeros((1, num))
for i in xrange(num):
examples[0,i] = x_set[i]
feat_train = sg.RealFeatures(examples)
labels = sg.RegressionLabels(labels)
#Get inducing points
num_induc = len(x_set_induc)
if num_induc != 0:
examples_induc = np.zeros((1, num_induc))
for i in xrange(num_induc):
examples_induc[0,i] = x_set_induc[i]
feat_train_induc = sg.RealFeatures(examples_induc)
elif num_induc == 0:
feat_train_induc = None
kernel = get_kernel(request, feat_train)
try:
learn = request.POST["learn"]
except:
raise ValueError("Argument Error")
if int(feat_train.get_num_vectors()) > 100 and learn == "ML2":
model_sel_error=True
return (feat_train, labels, noise_level, scale, kernel, domain, learn, feat_train_induc, inf), model_sel_error
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:
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 _read_toy_data(request):
y_set = []
x_set = []
toy_data = json.loads(request.POST['point_set'])
for pt in toy_data:
y_set.append(float(pt["y"]))
x_set.append(float(pt["x"]))
noise_level = float(request.POST['noise_level'])
domain = json.loads(request.POST['axis_domain'])
labels = np.array(y_set, dtype = np.float64)
num = len(x_set)
if num == 0:
raise Http404
examples = np.zeros((1, num))
for i in xrange(num):
examples[0,i] = x_set[i]
feat_train = sg.RealFeatures(examples)
labels = sg.RegressionLabels(labels)
kernel = get_kernel(request, feat_train)
return (feat_train, labels, noise_level, kernel, domain)
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 _read_data(request):
labels = []
features = []
data = json.loads(request.POST['point_set'])
cost = float(request.POST['C'])
tubeeps = float(request.POST['tube'])
kernel_name = request.POST['kernel']
for pt in data:
labels.append(float(pt["y"]))
features.append(float(pt["x"]))
labels = np.array(labels, dtype=np.float64)
num = len(features)
if num == 0:
raise TypeError
examples = np.zeros((1,num))
for i in xrange(num):
examples[0,i] = features[i]
lab = sg.RegressionLabels(labels)
train = sg.RealFeatures(examples)
kernel = get_kernel(request, train)
return (cost, tubeeps, lab, kernel)
