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svr.py
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svr.py
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from django.http import HttpResponse, HttpResponseNotFound
from django.template import RequestContext
from django.shortcuts import render_to_response
import modshogun as sg
import numpy as np
import json
def entrance(request):
properties = { 'title': 'Supported Vector Regression Demo' }
return render_to_response("svr/index.html", properties, context_instance=RequestContext(request))
def point(request):
try:
arguments=_read_data(request)
svm=_train_svr(*arguments)
x=np.linspace(0, 1, 100)
y=np.array(svm.apply(sg.RealFeatures(np.array([x]))).get_labels(), dtype=np.float64)
line_dot = []
for i in xrange(len(x)):
line_dot.append({'x_value' : x[i], 'y_value' : y[i]})
return HttpResponse(json.dumps(line_dot))
except:
return HttpResponseNotFound()
def _read_data(request):
labels = []
features = []
data = json.loads(request.POST['data'])
cost = float(request.POST['C'])
tubeeps = float(request.POST['tube'])
degree = int(request.POST['d'])
width = float(request.POST['sigma'])
kernel_name = request.POST['kernel']
for pt in data["points"]:
labels.append(float(pt["y"]))
features.append(float(pt["x"]))
return (cost, tubeeps, degree, width, kernel_name, labels, features)
def _train_svr(cost, tubeeps, degree, width, kernel_name, labels, features):
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)
if kernel_name == "line":
gk = sg.LinearKernel(train, train)
gk.set_normalizer(sg.IdentityKernelNormalizer())
elif kernel_name == "poly":
gk = sg.PolyKernel(train, train, degree, True)
gk.set_normalizer(sg.IdentityKernelNormalizer())
elif kernel_name == "gaus":
gk = sg.GaussianKernel(train, train, width)
else:
raise TypeError
svm = sg.LibSVR(cost, tubeeps, gk, lab)
svm.train()
svm.set_epsilon(1e-2)
return svm