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))
#setup the real GMM
real_gmm=GMM(3)
real_gmm.set_nth_mean(array([-2.0]), 0)
real_gmm.set_nth_mean(array([0.0]), 1)
real_gmm.set_nth_mean(array([2.0]), 2)
real_gmm.set_nth_cov(array([[0.3]]), 0)
real_gmm.set_nth_cov(array([[0.1]]), 1)
real_gmm.set_nth_cov(array([[0.2]]), 2)
real_gmm.set_coef(array([0.3, 0.5, 0.2]))
#generate training set from real GMM
generated=array([real_gmm.sample()])
for i in range(199):
generated=append(generated, array([real_gmm.sample()]), axis=1)
feat_train=RealFeatures(generated)
#train GMM using EM
est_gmm=GMM(3)
est_gmm.train(feat_train)
est_gmm.train_em(min_cov, max_iter, min_change)
#get and print estimated means and covariances
est_mean1=est_gmm.get_nth_mean(0)
est_mean2=est_gmm.get_nth_mean(1)
est_mean3=est_gmm.get_nth_mean(2)
est_cov1=est_gmm.get_nth_cov(0)
#setup the real GMM
real_gmm = GMM(3)
real_gmm.set_nth_mean(array([2.0, 2.0]), 0)
real_gmm.set_nth_mean(array([-2.0, -2.0]), 1)
real_gmm.set_nth_mean(array([2.0, -2.0]), 2)
real_gmm.set_nth_cov(array([[1.0, 0.2], [0.2, 0.5]]), 0)
real_gmm.set_nth_cov(array([[0.2, 0.1], [0.1, 0.5]]), 1)
real_gmm.set_nth_cov(array([[0.3, -0.2], [-0.2, 0.8]]), 2)
real_gmm.set_coef(array([0.3, 0.4, 0.3]))
#generate training set from real GMM
generated = array([real_gmm.sample()])
for i in range(199):
generated = append(generated, array([real_gmm.sample()]), axis=0)
generated = generated.transpose()
feat_train = RealFeatures(generated)
#train GMM using SMEM and print log-likelihood
est_smem_gmm = GMM(3, cov_type)
est_smem_gmm.train(feat_train)
est_smem_gmm.set_nth_mean(array([2.0, 0.0]), 0)
est_smem_gmm.set_nth_mean(array([-2.0, -2.0]), 1)
est_smem_gmm.set_nth_mean(array([-3.0, -3.0]), 2)
est_smem_gmm.set_nth_cov(array([[1.0, 0.0], [0.0, 1.0]]), 0)