def test_mom():
"""Test the whether spectral can recover from simple mixture of 3 identical"""
model = MixtureModel.generate(2, 3, dirichlet_scale = 0.9)
_, _, M, _ = model["k"], model["d"], model["M"], model["w"]
N = 1e5
xs = model.sample(N)
moments = model.exact_moments(model.observed_monomials(3))
moments_ = model.empirical_moments(xs, model.observed_monomials(3))
print "moment diff", dict_diff(moments, moments_)
w_true, params = solve_mixture_model(model, moments)
w_data, params_ = solve_mixture_model(model, moments_)
w_true, params = fix_parameters(M, params, w_true)
w_data, params_ = fix_parameters(M, params_, w_data)
print "true", M
print "with exact moments", params
print "error with exact moments", norm(M - params)/norm(M)
print "with empirical moments", params_
print "error with empirical moments", norm(M - params_)/norm(M)
assert norm(M - params)/norm(M) < 1e-10
assert norm(M - params_)/norm(M) < 1e-1
def test_mom():
"""Test the whether spectral can recover from simple mixture of 3 identical"""
model = MixtureModel.generate(2, 3, dirichlet_scale=0.9)
_, _, M, _ = model["k"], model["d"], model["M"], model["w"]
N = 1e5
xs = model.sample(N)
moments = model.exact_moments(model.observed_monomials(3))
moments_ = model.empirical_moments(xs, model.observed_monomials(3))
print "moment diff", dict_diff(moments, moments_)
w_true, params = solve_mixture_model(model, moments)
w_data, params_ = solve_mixture_model(model, moments_)
w_true, params = fix_parameters(M, params, w_true)
w_data, params_ = fix_parameters(M, params_, w_data)
print "true", M
print "with exact moments", params
print "error with exact moments", norm(M - params) / norm(M)
print "with empirical moments", params_
print "error with empirical moments", norm(M - params_) / norm(M)
assert norm(M - params) / norm(M) < 1e-10
assert norm(M - params_) / norm(M) < 1e-1
def test_mixture_em():
model = MixtureModel(k = 2, d = 3, M = array([[0.7, 0.3],[0.2, 0.3],[0.1, 0.4]]), w = array([0.6,0.4]))
k, d, M, w = model["k"], model["d"], model["M"], model["w"]
N = 1e5
xs = model.sample(N)
lhood, _, (params, w_true) = MixtureModelEM(k, d).run(xs, params=(M, w))
w_true, params = fix_parameters(M, params, w_true)
lhood_, _, (params_, w_guess) = MixtureModelEM(k, d).run(xs)
w_data, params_ = fix_parameters(M, params_, w_guess)
print "true", M
print "true lhood", model.llikelihood(xs)
print "with em*", params
print "em lhood*", lhood
print "em lhood*", model.using(M = params, w = w_true).llikelihood(xs)
print "error with em*", norm(M - params)/norm(M)
print "with em", params_
print "em lhood", lhood_
print "em lhood", model.using(M = params_, w = w_guess).llikelihood(xs)
print "error with em", norm(M - params_)/norm(M)
assert norm(M - params)/norm(M) < 1e-1
assert norm(M - params_)/norm(M) < 1e-1
def do_command(args):
np.random.seed(args.seed)
#methods = [("EM", do_em), ("TPM", do_tpm), ("Lasserre", do_lasserre), ("Dreesen", do_dreesen)]
#methods = [("EM", do_em), ("TPM", do_tpm),("Lasserre", do_lasserre)] #, ("Dreesen", do_dreesen)]
methods = [("EM", do_em), ("TPM", do_tpm),("Lasserre2", do_lasserre), ("Lasserre3", do_lasserreconstr)] #, ("Dreesen", do_dreesen)]
avg_paramserror = Counter(); avg_nll = Counter();
for i in xrange(args.trials):
model = MixtureModel.generate(k = args.k, d = args.d)
data = model.sample(int(args.N))
tbl = make_table(model, data, methods)
for mname,_ in methods:
for row in tbl:
if row[0] == mname:
avg_paramserror[mname] += row[1]/args.trials
avg_nll[mname] += row[2]/args.trials
print_table(tbl)
print avg_paramserror
print avg_nll
print args
def test_mom():
"""Test the whether spectral can recover from simple mixture of 3 identical"""
model = MixtureModel.generate(2, 2, dirichlet_scale = 0.9)
k, d, M, w = model["k"], model["d"], model["M"], model["w"]
N = 1e5
print "M", M
xs = model.sample(N)
print model.exact_moments(model.observed_monomials(3))
print model.empirical_moments(xs, model.observed_monomials(3))
M_ = find_means(xs, k)
print "M_", M_
M_ = closest_permuted_matrix( M.T, M_.T ).T
print "M", M
print "M_", M_
print norm( M - M_ )/norm(M)
assert( norm( M - M_ )/norm(M) < 1e-1 )