def segment_words():
"""
Using the final list of affixes, segment the words using a longest
match approach, where you segment based on the longest occuring morpheme
in the word.
"""
global CUTS
global CORRECT_CUTS
global TRUE_BOUNDARIES
#build the chunks
for word in STANDARD:
affixes = trim_affixes([x for x in AFFIXES if x.strip(dash) in word])
segments = splits(word, affixes)
a,b,c = metrics(' '.join(segments), STANDARD[word])
CUTS += b
CORRECT_CUTS += a
TRUE_BOUNDARIES += c
def predecessor_segmentor():
"""
User our global list of test words and segment them basing on predecessor
counts that exist within the trie RTRIE that we have globally
instantiated.
"""
global CUTS
global CORRECT_CUTS
global TRUE_BOUNDARIES
#initialize
CUTS, CORRECT_CUTS, TRUE_BOUNDARIES = (0,0,0)
correct = 0 #number of correct annotations
for word in WORDS:
a,b,c = metrics(segment_word(word, reverse=True), WORDS[word])
CUTS += b
CORRECT_CUTS += a
TRUE_BOUNDARIES += c
def generate_states_from_rule_and_seed(
f_name=None,
rule=None,
seed=[],
scale_num=None,
scale_type="maj",
states=[],
steps=DEFAULT_SEQUENCE_STEPS,
sampler_name=None,
debug=False,
save_png=False,
save_json=False,
save_midi=False,
beat_duration=DEFAULT_BEAT_DURATION,
dont_ignore_odd=False,
):
sc_num = scale_num
sc_type = scale_type
if len(seed):
width = len(seed)
else:
width = 128
scale = get_scale(scale_num, scale_type, debug)
if not len(seed):
# Start from a default seed with 1 activated bit
seed = DEFAULT_SEED
k = rule["k"]
a = np.array(rule["rule"])
# THIS IS SUPER IMPORTANT TO GETTING GOOD RESULTS. It prevents [0,0,0] -> 1 transitions which clutter up CA
# Flip final bit
if not dont_ignore_odd:
a[-1] = 0
k_states = np.array(list(map(np.int64, rule["k_states"])))
# generate rule from k_states / mask
r_set = k_states[a.astype(bool)]
r = lambda x, k: eca(x, k, r_set)
states = run(steps, seed=seed, kernel=k, f=r)
# apply a sampling filter to the states.
if sampler_name:
sampler = getattr(sampling, sampler_name)
states = sampler(states)
# TODO: add a conditional flag for image generation
if save_png:
f_name_img = f_name + ".tendril.png"
image_from_states(states, f_name_img)
if debug:
print_states(states[0:10])
mets = metrics(states)
# g(states, steps, beat_duration)
if sc_num != None:
g = lambda x, y, z: generate_pianoroll(x, y, z, scale[0:width])
else:
g = lambda x, y, z: generate_pianoroll(x, y, z, CHROMATIC_SCALE[0:width
])
if save_json or save_midi:
write_files_from_states(
states,
mets,
seed,
[],
f_name,
g=g,
save_json=save_json,
save_midi=save_midi,
debug=debug,
beat_duration=beat_duration,
steps=steps,
)
return
def calculate_elos():
process.processYears(start_year, end_year)
stats.metrics()
stats.mean()
