本文整理汇总了Python中statistics.mode函数的典型用法代码示例。如果您正苦于以下问题:Python mode函数的具体用法?Python mode怎么用?Python mode使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了mode函数的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: resetPass
def resetPass(customCommand,test=False):
from random import sample as randomize
from random import random
from os.path import exists
# Opens the Adj, Adv, and Noun files as arrays
av = open(sys.path[0]+"/Adv").read().splitlines()
aj = open(sys.path[0]+"/Adj").read().splitlines()
nn = open(sys.path[0]+"/Noun").read().splitlines()
# Just for fun, some statistics!
totalCombos = len(av)*len(aj)*len(nn)
combosFormatted = "{:,}".format(totalCombos)
avLengths=[]
for item in av:
avLengths.append(len(item))
ajLengths=[]
for item in aj:
ajLengths.append(len(item))
nnLengths=[]
for item in nn:
nnLengths.append(len(item))
from statistics import mean,median,mode
print("-"*25+"\n"+
"Total adverbs: "+str(len(av))+"\n"+
"Total adjectives: "+str(len(aj))+"\n"+
"Total nouns: "+str(len(nn))+"\n"+
"Total possible combinations: "+combosFormatted+" (not factoring in numbers)\n"+
"Shortest possible passphrase length: "+str(min(avLengths)+min(ajLengths)+min(nnLengths))+"\n"+
"Longest possible passphrase length: "+str(max(avLengths)+max(ajLengths)+max(nnLengths)+5)+"\n"+
"Mean passphrase length: "+str(int(mean(avLengths)+mean(ajLengths)+mean(nnLengths)+4))+"\n"+
"Median passphrase length: "+str(int(median(avLengths)+median(ajLengths)+median(nnLengths))+4)+"\n"+
"Mode passphrase length: "+str(int(mode(avLengths)+mode(ajLengths)+mode(nnLengths))+4)+"\n"+
"-"*25)
# Randomize the order of the arrays
av = randomize(av,len(av))
aj = randomize(aj,len(aj))
nn = randomize(nn,len(nn))
# Pick a random word from each randomized array
newAdverb = av[int(random()*len(av))].capitalize()
newAdjective = aj[int(random()*len(aj))].capitalize()
newNoun = nn[int(random()*len(nn))].capitalize()
# Possibly add a random number from 1 to 10,000
if maybeNumber():
from math import ceil
number = str(ceil(random()*10000))
else:
number = ''
# Assemble the passphrase
newPassphrase = number+newAdverb+newAdjective+newNoun
#################################################################### Needs attention
print("The new passphrase will be: "+newPassphrase)
print("Total entropy: ~"+str(int(entropy(newPassphrase))))
if customCommand == ' {PASSPHRASE}':
print("Password display command not found. Aborting.")
exit()
if not test:
import RouterPasswording
RouterPasswording.newPassphrase(newPassphrase)
from os import system as execute
execute(customCommand.replace("{password}",newPassphrase).replace("{passphrase}",newPassphrase))
示例2: find_hit_regions
def find_hit_regions(primer, alignment): #this one is for all the sequences in the alignment
'''this is currently super inefficient... It basically does the work of primer_coverage() for every single possible
frame in a sliding window for every sequence... If I'm ok with this I should just have this function return the
number of mismatches for the positions which best match... If I do that then I could have the amplicon length be
something that was returned as well.....hmmm very tempting... I think I should do this. what else besides amplicon
length would this allow me to do? I could also have it output potential mispriming sites, and then the amplicon
length for the misprimed sites.... I could include a condition where it would print a warning if mispriming
is likely, output a spreadsheet that tells you what sequences are likely to misprime, how big the amplicon
for the mispriming would be... But this mispriming would only be for these particular sequences that you are
tyring to amplify, A much more liekly source of mispriming would just be other random genomic DNA. A metagenome
might be a good thing to run this, but that would really take a long time.....'''
alignment_len = len(alignment[0])
primer_length = len(primer)
number_of_frames = (alignment_len - primer_length) + 1
range_of_frames = range(0, number_of_frames)
list_of_indexes = []
first_indexes = []
last_indexes = []
frame_indexes = {}
for frame in range_of_frames:
frame_indexes[frame] = {}
frame_indexes[frame]["first"] = frame
frame_indexes[frame]["last"] = frame + primer_length
hit_regions = {}
for seq in alignment:
sequences = {}
for frame in frame_indexes:
sequence = seq[frame_indexes[frame]["first"]:frame_indexes[frame]["last"]]
#print(sequence)
sequences[frame] = sequence
number_mismatches = {}
for key in sequences:
number_mismatches[key] = 0
for count, position in enumerate(sequences[key].upper()):
#print(count, position)
if position not in ambiguous_dna_values[primer[count]]:
number_mismatches[key] += 1
indexes = frame_indexes[min(number_mismatches, key=number_mismatches.get)]
hit_regions[seq.id] = indexes
#print("number of sequences checked: {}".format(len(hit_regions)))
#print("Percent complete: {}".format(len(hit_regions)/len(alignment)))
#hit_regions = set(hit_regions)
#print(hit_regions)
starting = []
ending = []
for key in hit_regions:
#print(key)
starting.append(hit_regions[key]["first"])
ending.append(hit_regions[key]["last"])
#print(starting)
#print(ending)
starting = mode(starting)
ending = mode(ending)
return starting, ending
示例3: classify
def classify(self, text):
features = self.find_features(text)
votes = []
for c in self._classifiers:
v = c.classify(features)
votes.append(v)
choice_votes = votes.count(mode(votes))
conf = choice_votes / float(len(votes))
return (mode(votes), conf)
示例4: main
def main():
print(stats.mean(range(6)))
print(stats.median(range(6)))
print(stats.median_low(range(6)))
print(stats.median_high(range(6)))
print(stats.median_grouped(range(6)))
try:
print(stats.mode(range(6)))
except Exception as e:
print(e)
print(stats.mode(list(range(6)) + [3]))
print(stats.pstdev(list(range(6)) + [3]))
print(stats.stdev(list(range(6)) + [3]))
print(stats.pvariance(list(range(6)) + [3]))
print(stats.variance(list(range(6)) + [3]))
示例5: classify
def classify(self,features):
votes=[]
for c in self._classifier:
v=c.classify(features)
votes.append(v)
votes.append("pos")
return mode(votes)
示例6: classify
def classify(self, features):
votes = []
for c in self._classifiers:
v = c.classify(features)
votes.append(v)
print(v)
return mode(votes)
示例7: basic_stats
def basic_stats(total_data):
mean = statistics.mean(total_data)
median = statistics.median(total_data)
mode = statistics.mode(total_data)
standard_dev = statistics.stdev(total_data)
return [mean, median, mode, standard_dev]
示例8: diff1
def diff1(listy):
pie=listy
awe=[]
d=reduce(gcd,listy)
for elem in listy:
awe.append(elem/d)
listy=awe
new=[listy]
old=[pie]
for elem in listy:
new.append(diff(new[-1]))
for elem in listy:
old.append(diff(old[-1]))
new=new[0:-1]
old=old[0:-1]
loop=-1
oth=0
for elem in new:
loop=loop+1
if elem.count(elem[0])==len(elem):
me=loop
oth=1
if oth==1:
old=old[0:me]
old=list(reversed(old))
start=new[0][0]
loop=0
for elem in old:
loop=loop+elem[-1]
return(loop)
else:
return(mode(pie))
示例9: classify
def classify(self, features):
votes = []
for c in self._classifiers:
v = c.classify(features)
votes.append(v)
result = mode(votes)
return result.lower()
示例10: vote
def vote(self, training_set):
votes = []
for c in self.classifiers:
v = c.classify(training_set)
votes.append(v)
return mode(votes)
示例11: mode
def mode(RGB_list, count):
''' Gets mode element of a list given'''
temp = []
for index in RGB_list:
temp.append(index[count])
return statistics.mode(temp)
示例12: classify
def classify(self, features):
votes = []
for c in self._classifiers: #c for classifiers
v = c.classify(features) #v for votes
votes.append(v)
#print(votes)
return mode(votes)
示例13: process_file
def process_file(filename):
# data = np.recfromcsv(filename, delimiter=',', filling_values=numpy.nan, case_sensitive=True, deletechars='', replace_space=' ')
with io.open(filename, "r", encoding="UTF-8") as source_file:
data_iter = csv.DictReader(source_file)
# data = [data for data in data_iter]
pricelist = []
unitlist = []
for line in data_iter:
pricelist.append(float(line["product_price"]))
unitlist.append(line["OKEI_name"])
price_med = statistics.median(pricelist)
unit_mode = statistics.mode(unitlist)
# df = pd.DataFrame(data)
med_outliers = []
mod_outliers = []
with io.open(filename, "r", encoding="UTF-8") as source_file:
data_iter = csv.DictReader(source_file)
for line in data_iter:
if line["OKEI_name"] != unit_mode:
mod_outliers.append(line)
if (float(line["product_price"]) / price_med) > 3:
med_outliers.append(line)
return price_med, unit_mode, med_outliers, mod_outliers
示例14: print_posts
def print_posts(posts, post_type, print_num):
price_list = []
for post in posts:
try:
price_list.append(float(post.price))
except ValueError:
pass
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%{}'
'%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'.format(post_type))
if price_list:
print('NUM of POSTS: ', len(posts))
print('MEAN: ', statistics.mean(price_list))
print('MEDIAN: ', statistics.median(price_list))
try:
print('MODE: ', statistics.mode(price_list))
print('STDEV: ', statistics.stdev(price_list))
except statistics.StatisticsError:
pass
for post in posts[:print_num]:
pprint(post.price)
pprint(post.title)
pprint(post.carrier)
pprint(post.description)
pprint('www.kijiji.ca' + post.link)
示例15: validate_array
def validate_array(self, arr):
'''
given arr
if mean and stdev of *arr* is close to target_mean and target_stdev,
return true
'''
#print('there are {} elements'.format(len(arr)))
mean = statistics.mean(arr)
#median = statistics.median(arr)
stdev = statistics.stdev(arr)
mode = 0
# most time we could not get *mode* from this array, pass it
try:
mode = statistics.mode(arr)
except statistics.StatisticsError:
pass
#print('median: {:.3f}\n'.format(media))
#print('mean: {:.3f}\nstdev: {:.3f}\n'.format(mean, stdev))
if abs(self.target_mean[0] - mean) < self.target_mean[1] \
and abs(self.target_stdev[0] - stdev) < self.target_stdev[1]:
self.result_mean = mean
self.result_stdev = stdev
self.result_mode = mode
return True
return False