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_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_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_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_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_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_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_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_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 _create_history(options, input_seq):
if options.bytes:
seq = byting.to_binseq(input_seq)
else:
seq = input_seq
return formatting.to_bits(seq)
def _assert_context(extractor, history_seq, expected_seq):
eq_(extractor.extract_context(to_bits(history_seq)),
to_bits(expected_seq))
def _create_history(options, input_seq):
if options.bytes:
seq = byting.to_binseq(input_seq)
else:
seq = input_seq
return formatting.to_bits(seq)
