def test_manual_example():
model = ctw.create_model()
model.see_generated([1])
eq_(_get_history_p(model), 0.5)
model.see_generated([1])
# pw = 0.5 * (3/8 + 1 * 0.5 * 0.5) = 5/16.0
eq_float_(_get_history_p(model), 5/16.0)
def test_manual_example():
model = ctw.create_model()
model.see_generated([1])
eq_(_get_history_p(model), 0.5)
model.see_generated([1])
# pw = 0.5 * (3/8 + 1 * 0.5 * 0.5) = 5/16.0
eq_float_(_get_history_p(model), 5 / 16.0)
def test_impossible_history():
model = ctw.create_model(deterministic=True, max_depth=2)
model.see_generated([0, 0, 0])
eq_(0.0, model.predict_one())
model.revert_generated(3)
eq_(0.5, model.predict_one())
model.see_generated([1, 1, 1])
eq_(1.0, model.predict_one())
def test_continue_example():
# A test of the documentation example:
# ./continue.py -n 10 01101
model = ctw.create_model()
model.see_generated(to_bits("01101"))
p_given = _get_history_p(model)
model.see_generated(to_bits("1011011011"))
p_seq = _get_history_p(model)
eq_float_(p_seq/float(p_given), 0.052825, precision=6)
def test_impossible_history():
model = ctw.create_model(deterministic=True, max_depth=2)
model.see_generated([0, 0, 0])
eq_(0.0, model.predict_one())
model.revert_generated(3)
eq_(0.5, model.predict_one())
model.see_generated([1, 1, 1])
eq_(1.0, model.predict_one())
def test_max_depth_sum():
for seq_len in xrange(10):
total = 0.0
for seq in iter_all_seqs(seq_len):
model = ctw.create_model(max_depth=8)
model.see_generated(to_bits(seq))
total += _get_history_p(model)
eq_float_(total, 1.0, precision=15)
def test_max_depth():
model = ctw.create_model(max_depth=0)
eq_(_get_history_p(model), 1.0)
model.see_generated([1])
eq_(model.root.log_p_estim, math.log(0.5))
eq_(model.root.log_pw, model.root.log_p_estim)
model.see_generated([1])
eq_(_get_history_p(model), naive_ctw._estim_kt_p(0, 2))
def test_continue_example():
# A test of the documentation example:
# ./continue.py -n 10 01101
model = ctw.create_model()
model.see_generated(to_bits("01101"))
p_given = _get_history_p(model)
model.see_generated(to_bits("1011011011"))
p_seq = _get_history_p(model)
eq_float_(p_seq / float(p_given), 0.052825, precision=6)
def test_max_depth():
model = ctw.create_model(max_depth=0)
eq_(_get_history_p(model), 1.0)
model.see_generated([1])
eq_(model.root.log_p_estim, math.log(0.5))
eq_(model.root.log_pw, model.root.log_p_estim)
model.see_generated([1])
eq_(_get_history_p(model), naive_ctw._estim_kt_p(0, 2))
def test_max_depth_sum():
for seq_len in xrange(10):
total = 0.0
for seq in iter_all_seqs(seq_len):
model = ctw.create_model(max_depth=8)
model.see_generated(to_bits(seq))
total += _get_history_p(model)
eq_float_(total, 1.0, precision=15)
def test_predict_one():
seq_len = 8
for determ in [False, True]:
for seq in iter_all_seqs(seq_len):
model = ctw.create_model(determ)
verifier = naive_ctw.create_model(determ)
for c in seq:
model.see_generated(to_bits(c))
verifier.see_generated(to_bits(c))
eq_float_(model.predict_one(), verifier.predict_one(),
precision=10)
def test_children_respecting():
model = ctw.create_model(deterministic=True)
model.see_generated([0, 1])
model.switch_history()
model.see_generated([0])
p_estim = 0
p_uncovered = 0.5 * 0.5
p_child = 0.5
eq_(model.get_history_log_p(),
math.log(0.5 * (p_estim + p_child * 1.0 * p_uncovered)))
def test_children_respecting():
model = ctw.create_model(deterministic=True)
model.see_generated([0, 1])
model.switch_history()
model.see_generated([0])
p_estim = 0
p_uncovered = 0.5 * 0.5
p_child = 0.5
eq_(model.get_history_log_p(),
math.log(0.5 * (p_estim + p_child * 1.0 * p_uncovered)))
def test_invalid_history():
model = ctw.create_model(deterministic=True)
model.see_generated([0, 1, 1])
model.switch_history()
model.see_generated([0, 1])
try:
model.see_generated([0])
assert False, "ImpossibleHistoryError is expected"
except ctw.ImpossibleHistoryError, e:
assert str(e).startswith(
"Impossible history. Try non-deterministic prior.")
def test_predict_one():
seq_len = 8
for determ in [False, True]:
for seq in iter_all_seqs(seq_len):
model = ctw.create_model(determ)
verifier = naive_ctw.create_model(determ)
for c in seq:
model.see_generated(to_bits(c))
verifier.see_generated(to_bits(c))
eq_float_(model.predict_one(),
verifier.predict_one(),
precision=10)
def test_invalid_history():
model = ctw.create_model(deterministic=True)
model.see_generated([0, 1, 1])
model.switch_history()
model.see_generated([0, 1])
try:
model.see_generated([0])
assert False, "ImpossibleHistoryError is expected"
except ctw.ImpossibleHistoryError, e:
assert str(e).startswith(
"Impossible history. Try non-deterministic prior.")
def test_max_depth_example():
# The calculated probablities are from the
# 'Reflections on "The Context-Tree Weighting Method: Basic Properties"'
# paper (figure 6 and 7).
model = ctw.create_model(max_depth=3)
model.see_added([1,1,0])
model.see_generated(to_bits("0100110"))
p_seq = _get_history_p(model)
eq_float_(p_seq, 7/2048.0)
model.see_generated([0])
p_seq2 = _get_history_p(model)
eq_float_(p_seq2, 153/65536.0)
def test_max_depth_example():
# The calculated probablities are from the
# 'Reflections on "The Context-Tree Weighting Method: Basic Properties"'
# paper (figure 6 and 7).
model = ctw.create_model(max_depth=3)
model.see_added([1, 1, 0])
model.see_generated(to_bits("0100110"))
p_seq = _get_history_p(model)
eq_float_(p_seq, 7 / 2048.0)
model.see_generated([0])
p_seq2 = _get_history_p(model)
eq_float_(p_seq2, 153 / 65536.0)
def test_predict_from_zero_history():
model = ctw.create_model(deterministic=True)
model.see_generated([1, 1])
model.switch_history()
p_estim = 0.5
p_uncovered = 0.5
p_child = 0.5
pw = 0.5 * (p_estim + p_uncovered * p_child * 1.0)
eq_float_(model.get_history_log_p(), math.log(pw))
p_estim = 0.5
p_uncovered = 0.5 * 0.5
p_child = 0.5
pw = 0.5 * (p_estim + p_uncovered * p_child * 1.0)
eq_float_(model.predict_one(), pw / math.exp(model.get_history_log_p()))
def test_predict_from_zero_history():
model = ctw.create_model(deterministic=True)
model.see_generated([1, 1])
model.switch_history()
p_estim = 0.5
p_uncovered = 0.5
p_child = 0.5
pw = 0.5 * (p_estim + p_uncovered * p_child * 1.0)
eq_float_(model.get_history_log_p(), math.log(pw))
p_estim = 0.5
p_uncovered = 0.5 * 0.5
p_child = 0.5
pw = 0.5 * (p_estim + p_uncovered * p_child * 1.0)
eq_float_(model.predict_one(), pw / math.exp(model.get_history_log_p()))
def create_model(deterministic=False, max_depth=None, num_factors=8):
cts = []
for i in xrange(num_factors):
cts.append(ctw.create_model(deterministic, max_depth))
return _Factored(cts)
def test_sure():
model = ctw.create_model(deterministic=True, max_depth=2)
model.see_generated([1, 1, 1])
eq_(1.0, model.predict_one())
def create_model(deterministic=False, max_depth=None, num_factors=8):
cts = []
for i in range(num_factors):
cts.append(ctw.create_model(deterministic, max_depth))
return _Factored(cts)
def test_see():
contexted = naive_ctw._Contexted(naive_ctw._estim_kt_p)
for seq in iter_all_seqs(seq_len=10):
model = ctw.create_model()
model.see_generated(to_bits(seq))
eq_float_(_get_history_p(model), contexted.calc_p("", seq))
def test_empty_context():
model = ctw.create_model()
eq_(model.predict_one(), 0.5)
def test_predict_first():
for determ in [False, True]:
model = ctw.create_model(determ)
verifier = naive_ctw.create_model(determ)
eq_float_(model.predict_one(), verifier.predict_one())
def test_sure():
model = ctw.create_model(deterministic=True, max_depth=2)
model.see_generated([1, 1, 1])
eq_(1.0, model.predict_one())
def test_empty_context():
model = ctw.create_model()
eq_(model.predict_one(), 0.5)
def test_predict_first():
for determ in [False, True]:
model = ctw.create_model(determ)
verifier = naive_ctw.create_model(determ)
eq_float_(model.predict_one(), verifier.predict_one())
def test_see():
contexted =naive_ctw._Contexted(naive_ctw._estim_kt_p)
for seq in iter_all_seqs(seq_len=10):
model = ctw.create_model()
model.see_generated(to_bits(seq))
eq_float_(_get_history_p(model), contexted.calc_p("", seq))