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()
m.plot()
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()
m.plot()
