def main(fname, N, n, params):
"""Run GMM EM on the data in @fname"""
gmm = GaussianMixtureModel.from_file(fname)
k, d, M, S, w = gmm.k, gmm.d, gmm.means, gmm.sigmas, gmm.weights
X = gmm.sample(N, n)
# Set seed for the algorithm
sc.random.seed(int(params.seed))
algo = GaussianMixtureEM(k, d)
O = M, S, w
def report(i, O_, lhood):
M_, _, _ = O_
lhood, Z, O_ = algo.run(X, None, report)
M_, S_, w_ = O_
M_ = closest_permuted_matrix(M.T, M_.T).T
# Table
print column_aerr(M, M_), column_rerr(M, M_)
def make_table(model, data, methods):
tbl = []
tbl.append(["Method","Parameter error", "Likelihood"])
tbl.append(["True", 0., model.llikelihood(data)])
print "true", model["M"]
for name, method in methods:
w, params = method(model, data)
w, params = fix_parameters(model["M"], params, w)
print name, params
lhood = model.using(M=params, w=w).llikelihood(data)
tbl.append([name, column_rerr(model["M"], params), lhood])
return tbl
def main(fname, N, n, params):
"""Run GMM EM on the data in @fname"""
gmm = GaussianMixtureModel.from_file( fname )
k, d, M, S, w = gmm.k, gmm.d, gmm.means, gmm.sigmas, gmm.weights
X = gmm.sample( N, n )
# Set seed for the algorithm
sc.random.seed( int( params.seed ) )
algo = GaussianMixtureEM( k, d )
O = M, S, w
def report( i, O_, lhood ):
M_, _, _ = O_
lhood, Z, O_ = algo.run( X, None, report )
M_, S_, w_ = O_
M_ = closest_permuted_matrix( M.T, M_.T ).T
# Table
print column_aerr( M, M_ ), column_rerr( M, M_ )