def plot(
iterable, key=lambda item: item, sample_size=1, depth=1, colors=_plot_colors, basecase=_plot_basecase, _show=True
):
""" This function can be used for plotting an iterable graphically. If matplotlib isn't installed, this will only
throw a warning, and no GUI will be launched.. """
try:
pyplot
except NameError:
warnings.warn("matplotlib must be installed in order to see the plot graphic.", Warning)
else:
for i, iterable in enumerate(flattened(iterable, basecase)):
xs, ys = zip(
*(
(i, math.avg(key(item) for item in sample))
for i, sample in enumerate(iterate.chunked(iterable, sample_size, trail=True))
)
)
pyplot.plot(xs, ys, linewidth=1.0, color=colors[i % len(colors)])
if _show:
pyplot.show()
def __demo__ () :
from nlplib.exterior.util import plot
from nlplib.general.math import avg
def fitness (individual) :
return individual.traits['value']
def mutate (individual) :
individual.traits['value'] += round(random.triangular(-10, 10))
selection = Selection('foo', fitness, mutate)
for _ in range(100) :
individual = selection.individual()
individual.traits['value'] = 0
mutate(individual)
selection.population.add(individual)
fitnesses = [selection.generation() for _ in range(100)]
# Fitness increases with each successive generation.
plot([avg(fitness) for fitness in fitnesses])
def __demo__ (ut) :
from nlplib.data import builtin_db
from nlplib.core.model import Word, NeuralNetwork
from nlplib.exterior.train import usable
from nlplib.exterior.util import plot
top = 10
with builtin_db() as session :
top_words = list(session.access.most_common(Word, top=top)) + [None]
nn = NeuralNetwork(top_words, int(len(top_words)*2), top_words)
with builtin_db() as session :
patterns = usable(nn, list(session.access.all_documents())[:], gram_size=2)
errors = [nn.train(input_words, output_words, rate=0.1) for input_words, output_words in patterns]
from nlplib.general.math import avg
print(avg(errors[:]))
plot(errors,
sample_size=100)
def ask (string) :
with builtin_db() as session :
words = session.access.words(string)
scored = Scored(nn.predict(words)).sorted()
print('\n'.join(repr(score) for score in list(scored)[:12]))
return ask
