def get_rrxor_tree():
rr = RRXOR()
rstr = rr.simulate(1000000*3)
bpt = BinaryParseTree(3, 2, 1)
bpt.parse(rstr)
return bpt
def test_golden_mean():
gm = GoldenMean()
gstr = gm.simulate(500000)
for L in range(2, 5):
expected_words = get_all_binary_words(L)
ewdist = empirical_word_distributions(gstr, L, expected_words)
awdist = analytical_word_distributions(gm, L)
dprobs = dict()
for ws,wp in ewdist.iteritems():
dprobs[ws] = list()
dprobs[ws].append(wp)
for ws,wp in awdist.iteritems():
dprobs[ws].append(wp)
print 'Distribution of length %d (empirical, analytical)' % L
for ws,(ewp,awp) in dprobs.iteritems():
print '%s: %0.4f, %0.4f' % (ws, ewp, awp)
Lvals = range(1, 8)
plot_process_info(gm, Lvals)
bpt = BinaryParseTree(5, 2, 2)
bpt.parse(gstr)
bpt.show()
def get_evenprocess_tree(dthresh=1e-1, D=5, M=2, L=3):
ep = EvenProcess()
estr = ep.simulate(1000000)
bpt = BinaryParseTree(D, M, L, dthresh=dthresh)
bpt.parse(estr)
return bpt
def get_rrxor_tree():
rr = RRXOR()
rstr = rr.simulate(1000000 * 3)
bpt = BinaryParseTree(3, 2, 1)
bpt.parse(rstr)
return bpt
def get_gm_tree():
gm = GoldenMean()
gstr = gm.simulate(500000)
bpt = BinaryParseTree(5, 2, 2)
bpt.parse(gstr)
return bpt
def get_evenprocess_tree(dthresh=1e-1, D=5, M=2, L=3):
ep = EvenProcess()
estr = ep.simulate(1000000)
bpt = BinaryParseTree(D, M, L, dthresh=dthresh)
bpt.parse(estr)
return bpt
def test_golden_mean():
gm = GoldenMean()
gstr = gm.simulate(500000)
for L in range(2, 5):
expected_words = get_all_binary_words(L)
ewdist = empirical_word_distributions(gstr, L, expected_words)
awdist = analytical_word_distributions(gm, L)
dprobs = dict()
for ws, wp in ewdist.iteritems():
dprobs[ws] = list()
dprobs[ws].append(wp)
for ws, wp in awdist.iteritems():
dprobs[ws].append(wp)
print 'Distribution of length %d (empirical, analytical)' % L
for ws, (ewp, awp) in dprobs.iteritems():
print '%s: %0.4f, %0.4f' % (ws, ewp, awp)
Lvals = range(1, 8)
plot_process_info(gm, Lvals)
bpt = BinaryParseTree(5, 2, 2)
bpt.parse(gstr)
bpt.show()
def get_gm_tree():
gm = GoldenMean()
gstr = gm.simulate(500000)
bpt = BinaryParseTree(5, 2, 2)
bpt.parse(gstr)
return bpt
def test_biasedcoin(p=0.5):
bc = BiasedCoin(p=p)
bstr = bc.simulate(100000)
bpt = BinaryParseTree(4, 2, 4)
bpt.parse(bstr)
bpt.show()
def test_biasedcoin(p=0.5):
bc = BiasedCoin(p=p)
bstr = bc.simulate(100000)
bpt = BinaryParseTree(4, 2, 4)
bpt.parse(bstr)
bpt.show()
