def generate_gmm_classification_data(request):
from modshogun import GMM, Math
num_classes = int(request.POST['num_classes'])
gmm = GMM(num_classes)
total = 40
rng = 4.0
num = total/num_classes
for i in xrange(num_classes):
gmm.set_nth_mean(np.array([Math.random(-rng, rng) for j in xrange(2)]), i)
cov_tmp = Math.normal_random(0.2, 0.1)
cov = np.array([[1.0, cov_tmp], [cov_tmp, 1.0]], dtype=float)
gmm.set_nth_cov(cov, i)
data=[]
labels=[]
for i in xrange(num_classes):
coef = np.zeros(num_classes)
coef[i] = 1.0
gmm.set_coef(coef)
data.append(np.array([gmm.sample() for j in xrange(num)]).T)
labels.append(np.array([i for j in xrange(num)]))
data = np.hstack(data)
data = data / (2.0 * rng)
xmin = np.min(data[0,:])
ymin = np.min(data[1,:])
labels = np.hstack(labels)
toy_data = []
for i in xrange(num_classes*num):
toy_data.append( { 'x': data[0, i] - xmin,
'y': data[1, i] - ymin,
'label': float(labels[i])})
return HttpResponse(json.dumps(toy_data))
def generate_gmm_classification_data(request):
from modshogun import GMM, Math
num_classes = int(request.POST['num_classes'])
gmm = GMM(num_classes)
total = 40
rng = 4.0
num = total / num_classes
for i in xrange(num_classes):
gmm.set_nth_mean(np.array([Math.random(-rng, rng) for j in xrange(2)]),
i)
cov_tmp = Math.normal_random(0.2, 0.1)
cov = np.array([[1.0, cov_tmp], [cov_tmp, 1.0]], dtype=float)
gmm.set_nth_cov(cov, i)
data = []
labels = []
for i in xrange(num_classes):
coef = np.zeros(num_classes)
coef[i] = 1.0
gmm.set_coef(coef)
data.append(np.array([gmm.sample() for j in xrange(num)]).T)
labels.append(np.array([i for j in xrange(num)]))
data = np.hstack(data)
data = data / (2.0 * rng)
xmin = np.min(data[0, :])
ymin = np.min(data[1, :])
labels = np.hstack(labels)
toy_data = []
for i in xrange(num_classes * num):
toy_data.append({
'x': data[0, i] - xmin,
'y': data[1, i] - ymin,
'label': float(labels[i])
})
return HttpResponse(json.dumps(toy_data))
def gen_data(ftype, num_samples, show_data = False): from modshogun import Math from modshogun import FactorType, Factor, TableFactorType, FactorGraph from modshogun import FactorGraphObservation, FactorGraphLabels, FactorGraphFeatures from modshogun import MAPInference, TREE_MAX_PROD Math.init_random(17) samples = FactorGraphFeatures(num_samples) labels = FactorGraphLabels(num_samples) for i in xrange(num_samples): vc = np.array([2,2,2], np.int32) fg = FactorGraph(vc) data1 = np.array([2.0*Math.random(0.0,1.0)-1.0 for i in xrange(2)]) vind1 = np.array([0,1], np.int32) fac1 = Factor(ftype[0], vind1, data1) fg.add_factor(fac1) data2 = np.array([2.0*Math.random(0.0,1.0)-1.0 for i in xrange(2)]) vind2 = np.array([1,2], np.int32) fac2 = Factor(ftype[0], vind2, data2) fg.add_factor(fac2) data3 = np.array([2.0*Math.random(0.0,1.0)-1.0 for i in xrange(2)]) vind3 = np.array([0], np.int32) fac3 = Factor(ftype[1], vind3, data3) fg.add_factor(fac3) data4 = np.array([2.0*Math.random(0.0,1.0)-1.0 for i in xrange(2)]) vind4 = np.array([1], np.int32) fac4 = Factor(ftype[1], vind4, data4) fg.add_factor(fac4) data5 = np.array([2.0*Math.random(0.0,1.0)-1.0 for i in xrange(2)]) vind5 = np.array([2], np.int32) fac5 = Factor(ftype[1], vind5, data5) fg.add_factor(fac5) data6 = np.array([1.0]) vind6 = np.array([0], np.int32) fac6 = Factor(ftype[2], vind6, data6) fg.add_factor(fac6) data7 = np.array([1.0]) vind7 = np.array([2], np.int32) fac7 = Factor(ftype[2], vind7, data7) fg.add_factor(fac7) samples.add_sample(fg) fg.connect_components() fg.compute_energies() infer_met = MAPInference(fg, TREE_MAX_PROD) infer_met.inference() fg_obs = infer_met.get_structured_outputs() labels.add_label(fg_obs) if show_data: state = fg_obs.get_data() print state return samples, labels
def gen_data(ftype, num_samples, show_data=False):
from modshogun import Math
from modshogun import FactorType, Factor, TableFactorType, FactorGraph
from modshogun import FactorGraphObservation, FactorGraphLabels, FactorGraphFeatures
from modshogun import MAPInference, TREE_MAX_PROD
Math.init_random(17)
samples = FactorGraphFeatures(num_samples)
labels = FactorGraphLabels(num_samples)
for i in range(num_samples):
vc = np.array([2, 2, 2], np.int32)
fg = FactorGraph(vc)
data1 = np.array([2.0 * Math.random(0.0, 1.0) - 1.0 for i in range(2)])
vind1 = np.array([0, 1], np.int32)
fac1 = Factor(ftype[0], vind1, data1)
fg.add_factor(fac1)
data2 = np.array([2.0 * Math.random(0.0, 1.0) - 1.0 for i in range(2)])
vind2 = np.array([1, 2], np.int32)
fac2 = Factor(ftype[0], vind2, data2)
fg.add_factor(fac2)
data3 = np.array([2.0 * Math.random(0.0, 1.0) - 1.0 for i in range(2)])
vind3 = np.array([0], np.int32)
fac3 = Factor(ftype[1], vind3, data3)
fg.add_factor(fac3)
data4 = np.array([2.0 * Math.random(0.0, 1.0) - 1.0 for i in range(2)])
vind4 = np.array([1], np.int32)
fac4 = Factor(ftype[1], vind4, data4)
fg.add_factor(fac4)
data5 = np.array([2.0 * Math.random(0.0, 1.0) - 1.0 for i in range(2)])
vind5 = np.array([2], np.int32)
fac5 = Factor(ftype[1], vind5, data5)
fg.add_factor(fac5)
data6 = np.array([1.0])
vind6 = np.array([0], np.int32)
fac6 = Factor(ftype[2], vind6, data6)
fg.add_factor(fac6)
data7 = np.array([1.0])
vind7 = np.array([2], np.int32)
fac7 = Factor(ftype[2], vind7, data7)
fg.add_factor(fac7)
samples.add_sample(fg)
fg.connect_components()
fg.compute_energies()
infer_met = MAPInference(fg, TREE_MAX_PROD)
infer_met.inference()
fg_obs = infer_met.get_structured_outputs()
labels.add_label(fg_obs)
if show_data:
state = fg_obs.get_data()
print(state)
return samples, labels
