Ky = kern.white(1,0.01)
m2 = MOHGP(X,Kf,Ky,Y, K=Nclust, prior_Z = 'DP', alpha=alpha)
m2.ensure_default_constraints()
m2.checkgrad(verbose=1)
m2.randomize()
m2.optimize()
m2.systematic_splits()
m2.systematic_splits()
m2.plot(1,1,1,0,0,1)
#construct a MOG model (can't recover the clusters)
Y_ = Y.copy()
Y_ -= Y_.mean(0)
Y_ /= Y_.std(0)
m3 = MOG(Y_, prior_Z='DP', alpha=alpha)
m3.randomize()
m3.optimize()
m3.systematic_splits()
m3.systematic_splits()
pb.figure()
pb.subplot(2,2,1)
pb.imshow(ground_truth_phi,aspect='auto',cmap=pb.cm.gray)
pb.title('ground truth')
pb.subplot(2,2,2)
pb.imshow(m.phi,aspect='auto',cmap=pb.cm.gray)
pb.title('structured GP-DP')
pb.subplot(2,2,3)
pb.imshow(m2.phi,aspect='auto',cmap=pb.cm.gray)
pb.title('unstructured GP-DP')
pb.subplot(2,2,4)
Nclust = 15
dim = 2
Nmin = 25
Nmax = 50
Ndata = np.random.randint(Nmin, Nmax, Nclust)
means = np.random.randn(Nclust, dim) * 5
aa = [np.random.randn(dim, dim + 1) for i in range(Nclust)]
Sigmas = [np.dot(a, a.T) for a in aa]
X = np.vstack([
np.random.multivariate_normal(mu, cov, (n, ))
for mu, cov, n in zip(means, Sigmas, Ndata)
]) / 100
Nrestarts = 3
Nclust = 15
m = MOG(X, Nclust, prior_Z='DP')
#starts = [np.random.randn(m.N*m.K) for i in range(Nrestarts)]
from scipy.cluster import vq
starts = []
for i in range(Nrestarts):
means = X[np.random.permutation(X.shape[0])[:Nclust]]
dists = np.square(X[:, :, None] - means.T[None, :, :]).sum(1)
starts.append(dists)
for method in ['steepest', 'PR', 'FR', 'HS']:
for st in starts:
m.set_vb_param(st)
m.optimize(method=method, maxiter=1e4)
m.plot_tracks()
Ky = kern.white(1, 0.01)
m2 = MOHGP(X, Kf, Ky, Y, K=Nclust, prior_Z='DP', alpha=alpha)
m2.ensure_default_constraints()
m2.checkgrad(verbose=1)
m2.randomize()
m2.optimize()
m2.systematic_splits()
m2.systematic_splits()
m2.plot(1, 1, 1, 0, 0, 1)
#construct a MOG model (can't recover the clusters)
Y_ = Y.copy()
Y_ -= Y_.mean(0)
Y_ /= Y_.std(0)
m3 = MOG(Y_, prior_Z='DP', alpha=alpha)
m3.randomize()
m3.optimize()
m3.systematic_splits()
m3.systematic_splits()
pb.figure()
pb.subplot(2, 2, 1)
pb.imshow(ground_truth_phi, aspect='auto', cmap=pb.cm.gray)
pb.title('ground truth')
pb.subplot(2, 2, 2)
pb.imshow(m.phi, aspect='auto', cmap=pb.cm.gray)
pb.title('structured GP-DP')
pb.subplot(2, 2, 3)
pb.imshow(m2.phi, aspect='auto', cmap=pb.cm.gray)
pb.title('unstructured GP-DP')
pb.subplot(2, 2, 4)
np.random.seed(0)
#make some Data which appears in clusters:
Nclust = 15
dim = 2
Nmin = 25
Nmax = 50
Ndata = np.random.randint(Nmin, Nmax, Nclust)
means = np.random.randn(Nclust, dim)*5
aa = [np.random.randn(dim, dim+1) for i in range(Nclust)]
Sigmas = [np.dot(a, a.T) for a in aa]
X = np.vstack([np.random.multivariate_normal(mu, cov, (n,)) for mu, cov, n in zip(means, Sigmas, Ndata)])/100
Nrestarts=3
Nclust = 15
m = MOG(X, Nclust, prior_Z='DP')
#starts = [np.random.randn(m.N*m.K) for i in range(Nrestarts)]
from scipy.cluster import vq
starts = []
for i in range(Nrestarts):
means = X[np.random.permutation(X.shape[0])[:Nclust]]
dists = np.square(X[:,:,None]-means.T[None,:,:]).sum(1)
starts.append(dists)
for method in ['steepest', 'PR', 'FR', 'HS']:
for st in starts:
m.set_vb_param(st)
m.optimize(method=method, maxiter=1e4)
m.plot_tracks()
