本文整理汇总了Python中statistics.mean函数的典型用法代码示例。如果您正苦于以下问题:Python mean函数的具体用法?Python mean怎么用?Python mean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mean函数的20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,您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: show_result2
def show_result2():
fd_list = db.session.query(Price_History).all()
# Some simple statistics for sample questions
GPDALC = []
GPWALC = []
MSWALC = []
MSDALC = []
for el in fd_list:
if(el.SCHOOL=='GP'):
GPWALC.append(el.WALC)
GPDALC.append(el.DALC)
elif(el.SCHOOL=='MS'):
MSWALC.append(el.WALC)
MSDALC.append(el.DALC)
else:
print("School error")
mean_GPWALC = statistics.mean(GPWALC)
mean_GPDALC = statistics.mean(GPDALC)
mean_MSWALC = statistics.mean(MSWALC)
mean_MSDALC = statistics.mean(MSDALC)
# Prepare data for google charts
data = [['GP School Workday Alcohol Consumption', mean_GPDALC], ['GP School Weekend Alcohol Consumption', mean_GPWALC],
['MS School Workday Alcohol Consumption', mean_MSDALC], ['MS School Weekend Alcohol Consumption', mean_MSWALC]]
return render_template('result2.html', data=data)
开发者ID:Imielin,项目名称:git,代码行数:28,代码来源:dataeng.py
示例2: sample
def sample(self, borrowers, threshold, n_iterations=1000, eps=0.0001, target=None):
"""
:param borrowers: list of borrower (and information about them)
:type borrowers: list[Borrower]
:param threshold: big losses threshold
:type threshold: float
:param n_iterations: number of simulations
:type n_iterations: int
:return:
"""
weights_matrix, independent_weight, losses, vitality = self.get_parameters(borrowers)
res = []
iteration = 0
for iteration in range(n_iterations):
res.append(self.one_loss(weights_matrix, independent_weight, losses, vitality, threshold))
if iteration > 100 and target is not None and abs(target - mean(res)) < eps:
break
elif iteration > 100 and (max(res) - min(res)) / (iteration ** 0.5) < eps:
break
print("TwoStepSampler break after {} iterations".format(iteration))
return mean(res)
开发者ID:DaryaPopova,项目名称:diplom,代码行数:26,代码来源:two_step_sampler.py
示例3: process_result
def process_result(self, t_frame, r_frame):
print(t_frame, r_frame)
try:
stat_const = float(2.776)
res2 = [] # frame transmission
res3 = [] # throughput
for i in range(int(self.T[0])):
# frame transmission
res2.append(t_frame[i]/r_frame[i])
res3.append(self.F * r_frame[i] / self.R)
# print(res2, res3)
avg_res2 = statistics.mean(res2)
sd2 = statistics.stdev(res2)
dif2 = sd2/math.sqrt(int(self.T[0]))*stat_const
upper_bound2 = avg_res2 + dif2
lower_bound2 = avg_res2 - dif2
avg_res3 = statistics.mean(res3)
sd3 = statistics.stdev(res3)
dif3 = sd3/math.sqrt(int(self.T[0]))*stat_const
upper_bound3 = avg_res3 + dif3
lower_bound3 = avg_res3 - dif3
except ZeroDivisionError:
return float("inf"), float("inf"), float("inf"), 0, 0, 0
return avg_res2, lower_bound2, upper_bound2, avg_res3, lower_bound3, upper_bound3
开发者ID:Hank-TNguyen,项目名称:W16,代码行数:31,代码来源:Simulation.py
示例4: mean_dev
def mean_dev(training_set):
'''
Calculates and returns the mean and standard deviation to the classes yes and no of a given training set
'''
class_yes = []
class_no = []
mean_yes = {}
mean_no = {}
dev_yes = {}
dev_no = {}
for key in training_set[0]:
for i in range(len(training_set)):
if training_set[i]['DiabetesClass'] == 'yes':
class_yes.append(training_set[i][key])
else:
class_no.append(training_set[i][key])
if not key == 'DiabetesClass':
mean_yes[key] = statistics.mean(class_yes)
mean_no[key] = statistics.mean(class_no)
dev_yes[key] = statistics.stdev(class_yes)
dev_no[key] = statistics.stdev(class_no)
else:
prob_yes = float(len(class_yes) / len(training_set))
prob_no = float(len(class_no) / len(training_set))
class_yes = []
class_no = []
return mean_yes, mean_no, dev_yes, dev_no, prob_yes, prob_no
开发者ID:pedrotst,项目名称:trab1-ai,代码行数:27,代码来源:old_main.py
示例5: get_parts_closeness
def get_parts_closeness(part1, part2) -> float:
part1_distances = part1.distances
part2_distances = part2.distances
mean1 = statistics.mean(part1_distances)
mean2 = statistics.mean(part2_distances)
difference = abs(mean1 - mean2)
return difference
开发者ID:notnami,项目名称:signify,代码行数:7,代码来源:substring_parser.py
示例6: main
def main(graph, nbk, delta_max, mu, max_eval, iter, move_operator, tabuSize, logsPath):
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
fh = logging.FileHandler(logsPath + "/tabusearch.log")
fh.setLevel(logging.INFO)
frmt = logging.Formatter('%(message)s')
fh.setFormatter(frmt)
log.addHandler(fh)
all_num_evaluations = []
all_best_score = []
all_time = []
log.info("-------RUNNING TABU SEARCH-------")
for i in range(iter):
start = timeit.default_timer()
num_evaluations, best_score, best = test_file_tabusearch(graph, nbk, delta_max, mu, max_eval, move_operator, tabuSize)
stop = timeit.default_timer()
log.debug('time : %f' % (stop - start))
all_num_evaluations.append(num_evaluations)
all_best_score.append(best_score)
all_time.append(stop - start)
log.info("nbS = %d; nbK = %d; delta_max = %d; mu = %r; move_operator= %s; tabu_maxsize = %d" % (graph.get_nbVertices(), nbk, delta_max, mu, move_operator.__name__, tabuSize))
log.info("for %d iteration with %d max_evaluations each, "
"\n best score found is %d,"
"\n total time in sec : %r"
"\n mean time in sec : %r,"
"\n mean best_score : %r, EcT : %r"
"\n mean num_eval : %r"
% (iter,
max_eval,
min(score for score in all_best_score),
sum(all_time),
statistics.mean(all_time),
statistics.mean(all_best_score), statistics.stdev(all_best_score),
statistics.mean(all_num_evaluations)))
开发者ID:BaaptM,项目名称:project_metaheuristics,代码行数:34,代码来源:test_tabusearch.py
示例7: calculate_latencies
def calculate_latencies(version_dates):
linux_latencies = latency(version_dates['linux'], OrderedDict(avo.os_to_kernel))
set_latex_value('linuxMeanUpdateLatency', ufloat(statistics.mean(linux_latencies.values()),statistics.stdev(linux_latencies.values())))
openssl_latencies = latency(version_dates['openssl'], OrderedDict(avo.os_to_project['openssl']))
set_latex_value('opensslMeanUpdateLatency', ufloat(statistics.mean(openssl_latencies.values()),statistics.stdev(openssl_latencies.values())))
bouncycastle_latencies = latency(version_dates['bouncycastle'], OrderedDict(avo.os_to_project['bouncycastle']))
set_latex_value('bouncycastleMeanUpdateLatency',ufloat(statistics.mean(bouncycastle_latencies.values()),statistics.stdev(bouncycastle_latencies.values())))
开发者ID:ucam-cl-dtg,项目名称:paper-da-securityupdates,代码行数:7,代码来源:versions.py
示例8: nutritionfacts
def nutritionfacts(self):
# print keys
svgdata = ""
frame_x = self.width * self.bins + 100 - 90
frame_y = (self.graphheight + 700) // 2 + 25 - self.graphheight
for i, s in enumerate([l for l in self.points if l[2]]):
mu = "μ = —"
sigma = "σ = —"
if len(s[0]) != 0:
xmean = stat.mean([t[0] for t in s[0]])
xsigma = stat.pstdev([t[0] for t in s[0]], xmean)
ymean = stat.mean([t[1] for t in s[0]])
ysigma = stat.pstdev([t[1] for t in s[0]], ymean)
mu = "μ = (" + str(round(xmean, 4)) + ", " + str(round(ymean, 4)) + ")"
sigma = "σ = (" + str(round(xsigma, 4)) + ", " + str(round(ysigma, 4)) + ")"
line_y = frame_y + i * 65
svgdata += circle(frame_x - 4, line_y + 3, 2, s[1])
svgdata += circle(frame_x + 4, line_y + 4, 2, s[1])
svgdata += circle(frame_x - 1, line_y + 10, 2, s[1])
svgdata += text(frame_x + 20, line_y + 10, s[2], align=-1, color=s[1], font="Neue Frutiger 65")
svgdata += text(frame_x + 28, line_y + 25, "n = " + str(len(s[0])), align=-1, color=s[1])
svgdata += text(frame_x + 28, line_y + 40, mu, align=-1, color=s[1])
svgdata += text(frame_x + 28, line_y + 55, sigma, align=-1, color=s[1])
self._frostbyte(svgdata)
开发者ID:kelvin13,项目名称:svgplot,代码行数:30,代码来源:graph.py
示例9: get_stats_window
def get_stats_window(depth_iterator, length, window_size):
"""Calculate min/max/mean and min/max windowed mean.
Assumes the depth_iterator will fill in all the implicit zero
entries which ``samtools depth`` may omit!
Assumes window_size < number of values in iterator!
"""
window = deque()
total_cov = 0
min_cov = None
max_cov = 0.0
assert 1 <= window_size <= length
prev_pos = 0
while len(window) < window_size:
try:
ref, pos, depth = next(depth_iterator)
except NoCoverage:
return 0, 0, 0.0, 0.0, 0.0
except StopIteration:
outstr = "Not enough depth values to fill %i window" % window_size
logger.info(outstr)
raise ValueError("%s" % outstr)
prev_pos += 1
assert pos == prev_pos, "Discontinuity in cov vals for %s position %i" % (ref,
pos)
total_cov += depth
if min_cov is None:
min_cov = depth
else:
min_cov = min(min_cov, depth)
max_cov = max(max_cov, depth)
window.append(depth)
assert len(window) == window_size
min_win = max_win = mean(window)
for ref, pos, depth in depth_iterator:
prev_pos += 1
assert pos == prev_pos, "Discontinuity in cov val for %s position %i" % (ref,
pos)
total_cov += depth
min_cov = min(min_cov, depth)
max_cov = max(max_cov, depth)
window.popleft()
window.append(depth)
assert len(window) == window_size
win_depth = mean(window)
min_win = min(min_win, win_depth)
max_win = max(max_win, win_depth)
mean_cov = total_cov / float(length)
assert prev_pos == length, "Missing final coverage?"
assert len(window) == window_size
assert min_cov <= mean_cov <= max_cov
assert min_cov <= min_win <= max_win <= max_cov
return min_cov, max_cov, mean_cov, min_win, max_win
开发者ID:Grindell,项目名称:public_scripts,代码行数:60,代码来源:Fix_five_prime_CDS.py
示例10: features_present1
def features_present1(self, othertmpft):
a=FeatureFinder()
a.train(othertmpft)
j=a.scan_data(othertmpft)
features=list()
dre=self.dict_process(othertmpft)
sendback=list()
final_list=list()
del j[0]
del j[len(j)-1]
for i in j:
#print(i.location)
if i.location<2:
final_list.append(Feature(i.location, statistics.mean(dre[i.location-1:i.location+3])))
else:
final_list.append(Feature(i.location, statistics.mean(dre[i.location-2:i.location+2])))
for i in self.outline:
if type(i)==Feature:features.append(i)
for i in features:
if len(final_list)>0:l=min(final_list, key=lambda x: abs(i.loc-x.loc))
else:return [0]*len(self.outline)
dis=len(othertmpft)-abs(i.loc-l.loc)
penalize_by=dis/len(othertmpft)
#print(penalize_by)
sendback.append(statistics.mean([penalize_by, self.t(abs(i.lo-l.lo))]))
# print(sendback)
#print("I am features1")
return self.find_outliar(sendback)
开发者ID:GGGG1020,项目名称:KineticEEG,代码行数:28,代码来源:SLICERZ.py
示例11: scan
def scan(self):
dre=list()
final=list()
dre=self.dict_process(self.data)
pol=[]
oo=list()
for d in self.listy:
r=self.__process(d)
if len(r[1])<2 and not len(r[1])==0:pol.append(statistics.mean(r[1]))
elif len(r[1])==0:pass
else:pol.append(statistics.mean(r[1]))
# print(pol)
for i in range(len(pol)):
final.append(Slope(self.download[i].location, pol[i]))
## print(final)
del self.download[0]
del self.download[-1]
last=1
for i in range(len(self.download)):
try:
final.insert(i+last, Feature(self.download[i].location, statistics.mean(dre[self.download[i].location-2:self.download[i].location+2])))
except statistics.StatisticsError:
#del final[i-1]
pass
last+=1
# print(final)
self.outline=final
开发者ID:GGGG1020,项目名称:KineticEEG,代码行数:27,代码来源:SLICERZ.py
示例12: insertNormalizedModelInDB
def insertNormalizedModelInDB(idUser, idString, keystroke, isTest = False):
insertNormalizedRecord = replaceIfIsTest("INSERT INTO `mdl_user#isTest_keystroke_normalized`(`id_user`, `id_string`) VALUES (%s, %s)", isTest);
updateNormalizedRecord = replaceIfIsTest("UPDATE `mdl_user#isTest_keystroke_normalized` ", isTest);
executeSqlInDB(insertNormalizedRecord, (idUser, idString));
keyDimensionsExtractor = KeystrokeDimensionsExtractor(keystroke);
#extracting dimensions
timePressed = keyDimensionsExtractor.getTimePressed();
#geting avarage and standardDeviation
timePressedAverage = statistics.mean(timePressed);
timePressedstandardDeviation = statistics.pstdev(timePressed);
latencies = keyDimensionsExtractor.getLatencies();
latenciesAverage = statistics.mean(latencies);
latenciesStandardDeviation = statistics.pstdev(latencies);
dbModel = {
'id_user': idUser,
'id_string': idString,
'press_average': timePressedAverage,
'latency_avarage': latenciesAverage,
'press_standard_deviation': timePressedstandardDeviation,
'latency_standard_deviation': latenciesStandardDeviation,
}
#update in table created before
updateNormalizedRecord = updateNormalizedRecord + (" SET `press_average`= %(press_average)s,`latency_avarage`= %(latency_avarage)s, `press_standard_deviation`= %(press_standard_deviation)s,`latency_standard_deviation`= %(latency_standard_deviation)s "
" WHERE `id_user`= %(id_user)s AND `id_string`= %(id_string)s");
executeSqlInDB(updateNormalizedRecord, dbModel);
开发者ID:MarcoASCruz,项目名称:LearningPython,代码行数:32,代码来源:validate.py
示例13: csv_dict_reader
def csv_dict_reader(file_obj):
"""
Read a CSV file using csv.DictReader
"""
reader = csv.DictReader(file_obj, delimiter=',')
num_likes = []
num_comments = []
num_shares = []
for line in reader:
p = int(line["num_likes"])
q = int(line["first_page_comment"])
r = int(line["comments_beyond_pageone"])
num_likes.append(p)
num_comments.append(q)
num_shares.append(r)
mean_num_likes = statistics.mean(num_likes)
stdev_num_likes = statistics.stdev(num_likes)
mean_num_comments = statistics.mean(num_comments)
stdev_num_comments = statistics.stdev(num_comments)
mean_num_shares = statistics.mean(num_shares)
stdev_num_shares = statistics.stdev(num_shares)
covariance_likes = stdev_num_likes / mean_num_likes
covariance_comments = stdev_num_comments / mean_num_comments
covariance_shares = stdev_num_shares / mean_num_shares
w = csv.writer(open("svm_dataset.csv","a"),delimiter=',',quoting=csv.QUOTE_ALL)
w.writerow([mean_num_likes,stdev_num_likes,covariance_likes,mean_num_comments,stdev_num_comments,covariance_comments,mean_num_shares,stdev_num_shares,covariance_shares])
开发者ID:envious777,项目名称:Brand-Valuation-using-Social-Media-Data,代码行数:27,代码来源:save.py
示例14: show_result3
def show_result3():
fd_list = db.session.query(Price_History).all()
# Some simple statistics for sample questions
MDALC = []
MWALC = []
FWALC = []
FDALC = []
for el in fd_list:
if(el.SEX=='M'):
MWALC.append(el.WALC)
MDALC.append(el.DALC)
elif(el.SEX=='F'):
FWALC.append(el.WALC)
FDALC.append(el.DALC)
else:
print("Sex error")
mean_MWALC = statistics.mean(MWALC)
mean_MDALC = statistics.mean(MDALC)
mean_FWALC = statistics.mean(FWALC)
mean_FDALC = statistics.mean(FDALC)
# Prepare data for google charts
data = [['Female Workday Alcohol Consumption', mean_FDALC], ['Female Weekend Alcohol Consumption', mean_FWALC],
['Male Workday Alcohol Consumption', mean_MDALC], ['Male Weekend Alcohol Consumption', mean_MWALC]]
return render_template('result3.html', data=data)
开发者ID:Imielin,项目名称:git,代码行数:28,代码来源:dataeng.py
示例15: threshold
def threshold(imageArray):
balanceAr=[]
newAr = imageArray
#averages each pixle's RGB values
for evryRow in imageArray:
for evryPix in evryRow:
avgNum = mean(evryPix[:3])
balanceAr.append(avgNum)
#averages all pixle averages
balance = mean(balanceAr)
for evryRow in newAr:
for evryPix in evryRow:
#brighter pixles are made white
if mean(evryPix[:3]) > balance:
evryPix[0] = 255
evryPix[1] = 255
evryPix[2] = 255
#darker pixles made black
else:
evryPix[0] = 0
evryPix[1] = 0
evryPix[2] = 0
return newAr
开发者ID:adamwe1,项目名称:AdamWendlerProj3,代码行数:25,代码来源:SVCMethod.py
示例16: show_result
def show_result():
fd_list = db.session.query(Formdata).all()
# Some simple statistics for sample questions
satisfaction = []
q1 = []
q2 = []
for el in fd_list:
satisfaction.append(int(el.satisfaction))
q1.append(int(el.q1))
q2.append(int(el.q2))
if len(satisfaction) > 0:
mean_satisfaction = statistics.mean(satisfaction)
else:
mean_satisfaction = 0
if len(q1) > 0:
mean_q1 = statistics.mean(q1)
else:
mean_q1 = 0
if len(q2) > 0:
mean_q2 = statistics.mean(q2)
else:
mean_q2 = 0
# Prepare data for google charts
data = [['Satisfaction', mean_satisfaction], ['Python skill', mean_q1], ['Flask skill', mean_q2]]
return render_template('result.html', data=data)
开发者ID:mrevening,项目名称:FlaskAconda,代码行数:31,代码来源:unit5_webapp.py
示例17: survey
def survey(filtered=NO_FILTER):
from statistics import mean
# for lex_user in Lexicon.lex_users:
# for lex in Lexicon.lex[lex_user]:
# if lex.headword in Lexicon.common_vocabulary:
# lex["stats"][lex.headword].update(lex.timestamp, lex.start_time)
stats_by_lex = [
list(zip(*[(logic_entry["stats"][headword].count, logic_entry["stats"][headword].latency,
mean(logic_entry["stats"][headword].interval) if logic_entry["stats"][headword].interval else 0,
mean(logic_entry["stats"][headword].permanence) if logic_entry["stats"][
headword].permanence else 0)
for logic_entry in Logicon.logic.values() if filtered.filtered(logic_entry["logic"][0])]))
# for idiom_entry in Idiomaton.idiom.values() if idiom_entry["logic"][0].clazz == "E"]))
for headword in Logicon.common_logics]
print("Logicon.logic.", [(l, Logicon.logic[l]) for l in Logicon.logic])
print("stats_by_lex", stats_by_lex)
stat_props = [
(count, latency, interval,
permanence)
for count, latency, interval, permanence in stats_by_lex] if any(stats_by_lex) else \
[(0, 2.5e3, 0, 0), (1, 1.5e3, 1, 1)]
ticks = [Logicon.logicon[burst]["sequence"] for burst in Logicon.common_logics]
return list(zip(*stat_props)), \
["%s da Lógica" % stat for stat in "Contagem Latência Intervalo Permanência".split()], \
ticks, filtered.claz, "Índices das lógicas EICA"
开发者ID:labase,项目名称:eica,代码行数:26,代码来源:language.py
示例18: analyzeData
def analyzeData(purchases, times):
std = statistics.stdev(purchases, statistics.mean(purchases))
data = []
for i in range(len(purchases)):
if purchases[i] > (statistics.mean(purchases) + std * 1.5):
outliers.append(purchases[i])
else:
trimmedData.append(purchases[i])
trimmedTimes.append(times[i])
for i in range(max(trimmedTimes)):
data.append(0)
flag = 0
for w in trimmedTimes:
data[w - 1] += trimmedData[flag]
flag = flag + 1
for w in range(max(trimmedTimes) - 1):
data[w + 1] += data[w]
for i in outliers:
for w in range(len(data)):
data[w] += i
return data
开发者ID:qazwsxcde125,项目名称:DaemonDashFall2015-1,代码行数:29,代码来源:dataAnalysis.py
示例19: main
def main(total_rolls=20000):
rolls_list = rolls(total_rolls, 1, 6)
sliced_sum20 = sliced_sums(20, rolls_list)
sums20 = sums(sliced_sum20, -20)
roll_count20 = lens(sliced_sum20)
sliced_sum10k = sliced_sums(10000, rolls_list)
sums10k = sums(sliced_sum10k, -10000)
roll_count10k = lens(sliced_sum10k)
paired_sums = [(20, sums20), (10000, sums10k)]
paired_rolls = [(20, roll_count20), (10000, roll_count10k)]
answers("Mean of the sum - {0} when M is {0}:",
paired_sums, lambda s: statistics.mean(s))
answers("Mean of the number of rolls when M is {0}:",
paired_rolls, lambda s: statistics.mean(s))
answers("Standard deviation of the sum - {0} when M is {0}:",
paired_sums, lambda s: statistics.stdev(s))
answers("Standard deviation of the number of rolls when M is {0}:",
paired_rolls, lambda s: statistics.stdev(s))
answers("\nView of the rolls summing to {0}\n" +
format("Count", ">7") + " " + format("Sum", ">7") + " Rolls\n",
[(20, sliced_sum20), (10000, sliced_sum10k)],
lambda ss: ''.join(
format(len(s[1]), ">7") + " " + format(s[0], ">7") + " " +
format(s[1]) + "\n" for s in ss)
, sep=''
)
开发者ID:dlamblin,项目名称:NYC-taxi-Data-Incubator-challenge,代码行数:27,代码来源:rolls.py
示例20: run_simulation
def run_simulation(init_duration, init_stake, samples, player):
""" Run simulation, print the result to stdout
"""
wheel = create_wheel()
table = Table(wheel)
game = RouletteGame(wheel, table)
simulator = Simulator(game, player,
init_duration=init_duration, samples=samples,
init_stake=init_stake)
simulator.gather()
durations = simulator.durations
maxima = simulator.maxima
print(player)
print()
print("Durations")
print(" min :", min(durations))
print(" max :", max(durations))
print(" mean: %.2f" % statistics.mean(durations))
print(" dev : %.2f" % statistics.stdev(durations))
print("Maxima")
print(" min :", min(maxima))
print(" max :", max(maxima))
print(" mean: %.2f" % statistics.mean(maxima))
print(" dev : %.2f" % statistics.stdev(maxima))
开发者ID:yannicklm,项目名称:pyroulette,代码行数:25,代码来源:simulator.py
注:本文中的statistics.mean函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。 |
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