def main(argv):
# stop execution if file not specified
if len(argv) == 0:
raise Exception("No file specified")
h = frequency.histogram(argv[1])
# call sample
print(sample(h))
def smpl_dstr(filename):
words_histogram = frequency.histogram(filename)
words_source = []
for each_word in words_histogram:
for i in range(0, frequency.frequency(each_word, words_histogram)):
words_source.append(each_word)
index = random.randint(0, len(words_source) - 1)
return(words_source[index])
def gen_word():
my_file = open("./words.txt", "r")
lines = my_file.readlines()
my_histogram = histogram(lines)
sentence = ""
num_words = 10
# for i in range(num_words):
# word = sample(my_histogram)
# sentence += " " + word
markovchain = MarkovChain(lines)
sentence = markovchain.walk(num_words)
return sentence
def generate_random_word(source_text):
histogram_sample = histogram(source_text)
count = 0
random_num = random.randint(0, int(total_weight(histogram_sample)))
for element in histogram_sample:
random_num = random_num - element[1]
if random_num <= 0:
generated_word = element[0]
return generated_word
break
else:
continue
def median(inlist: List(float)) -> float:
"""
Returns the computed median value of a list of numbers, given the
number of bins to use for the histogram (more bins brings the computed value
closer to the median score, default number of bins = 1000). See G.W.
Heiman's Basic Stats (1st Edition), or CRC Probability & Statistics.
Usage: lmedian (inlist, numbins=1000)
"""
numbins = 1000
(hist, smallest, binsize,
extras) = frequency.histogram(inlist, numbins,
[min(inlist), max(inlist)]) # make histog
cumhist = support.cumsum(hist) # make cumulative histogram
for i in range(len(cumhist)): # get 1st(!) index holding 50%ile score
if cumhist[i] >= len(inlist) / 2.0:
cfbin = i
break
LRL = smallest + binsize * cfbin # get lower read limit of that bin
cfbelow = cumhist[cfbin - 1]
freq = float(hist[cfbin]) # frequency IN the 50%ile bin
_median = LRL + ((len(inlist) / 2.0 - cfbelow) /
float(freq)) * binsize # median formula
return _median
length = len(hist)
num = random.randint(0, length)
for word in hist:
num -= hist[word]
if num <= 0:
return word
def test_probability(hist):
"""Checks to see if the weighted_select has a
non-uniform selection probability; returns a dictionary
mapping keys to probabilities
dictionary -> dictionary
"""
keys = {}
for key in hist.keys():
keys[key] = 0
for i in range(10000):
key = weighted_select(hist)
keys[key] += 1
for key in keys:
keys[key] = keys[key]/10000
return keys
if __name__ == "__main__":
args = sys.argv[1:]
text = ' '.join(args)
hist = histogram(text)
print(test_probability(hist))
def main(argv):
corpus = frequency.histogram(argv[1])
for i in range(50):
print(
stochastic.sample(corpus) + " " + stochastic.sample(corpus) +
" " + stochastic.sample(corpus))
print(moment.describe(lf))
print('\nFREQUENCY')
print('freqtable:')
print('itemfreq:')
print(frequency.itemfreq(l))
print(frequency.itemfreq(l))
print('scoreatpercentile:', frequency.scoreatpercentile(l, 40),
frequency.scoreatpercentile(lf, 40),
frequency.scoreatpercentile(l, 40),
frequency.scoreatpercentile(lf, 40))
print('percentileofscore:', frequency.percentileofscore(l, 12),
frequency.percentileofscore(lf, 12),
frequency.percentileofscore(l, 12),
frequency.percentileofscore(lf, 12))
print('histogram:', frequency.histogram(l, 10, [0, max(l)]),
frequency.histogram(l, 10, [0, max(l)]))
print('cumfreq:')
print(frequency.cumfreq(l))
print(frequency.cumfreq(lf))
print(frequency.cumfreq(l))
print(frequency.cumfreq(lf))
print('relfreq:')
print(frequency.relfreq(l))
print(frequency.relfreq(lf))
print(frequency.relfreq(l))
print(frequency.relfreq(lf))
print('\nVARIATION')
print('obrientransform:')
