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_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))