def mean():
"""Running mean co-routine.
mean() consumes values and returns the running average:
>>> aver = mean()
>>> aver.send(1)
1.0
>>> [aver.send(n) for n in (2, 3, 4)]
[1.5, 2.0, 2.5]
The running average, also known as the cumulative moving average,
consumes data:
a, b, c, d, ...
and returns the values:
a, (a+b)/2, (a+b+c)/3, (a+b+c+d)/4, ...
>>> aver = mean()
>>> [aver.send(n) for n in (40, 30, 50, 46, 39, 44)]
[40.0, 35.0, 40.0, 41.5, 41.0, 41.5]
"""
n = 0
running_sum = sum()
x = (yield None)
while True:
total = running_sum.send(x)
n += 1
x = (yield total/n)
def calc(x, conf): size = len(x) sum = stats.sum(x) av = stats.average(sum, size) gm = stats.gmean(x) v = stats.var(sum, stats.sqsum(x), size) med = stats.median(x) if v != 'error': sd = stats.stdv1(v) c = stats.conf(float(conf), sd, size) else: sd = 'error' c = 'none' return av, gm, v, sd, c, med
print tabjoin((hit.queryID, hit.subjectID, hit.score, hit.eValue))
else:
lastQuery = None
for hit in BLAT.Iterator(sys.stdin):
q = hit.queryID
s = hit.subjectID
qInterval = (hit.queryStart, hit.queryEnd)
sInterval = (hit.subjectStart, hit.subjectEnd)
if options.identity and q == s:
continue
elif q != lastQuery:
if lastQuery is not None:
for subject in scores:
score = stats.sum(scores[subject])
eValue = stats.product(eValues[subject])
if eValue < 1e-50:
eValue = 0
print tabjoin((lastQuery, subject, score, "%.0e" % eValue))
lastQuery = q
queryIntervals = {s: [qInterval]}
subjectIntervals = {s: [sInterval]}
scores = {s: [hit.score]}
eValues = {s: [hit.eValue]}
elif ((not overlapsWithAny(qInterval,
queryIntervals.setdefault(s, []))) and
(not overlapsWithAny(sInterval,
subjectIntervals.setdefault(s, [])))):
l = range(1,21)
a = N.array(l)
ll = [l]*5
aa = N.array(ll)
m = range(4,24)
m[10] = 34
b = N.array(m)
print('\n\nF_oneway:')
print(stats.F_oneway(l,m))
print(stats.F_oneway(a,b))
# print 'F_value:',stats.F_value(l),stats.F_value(a)
print('\nSUPPORT')
print('sum:',stats.sum(l),stats.sum(lf),stats.sum(a),stats.sum(af))
print('cumsum:')
print(stats.cumsum(l))
print(stats.cumsum(lf))
print(stats.cumsum(a))
print(stats.cumsum(af))
print('ss:',stats.ss(l),stats.ss(lf),stats.ss(a),stats.ss(af))
print('summult:',stats.summult(l,m),stats.summult(lf,m),stats.summult(a,b),stats.summult(af,b))
print('sumsquared:',stats.square_of_sums(l),stats.square_of_sums(lf),stats.square_of_sums(a),stats.square_of_sums(af))
print('sumdiffsquared:',stats.sumdiffsquared(l,m),stats.sumdiffsquared(lf,m),stats.sumdiffsquared(a,b),stats.sumdiffsquared(af,b))
print('shellsort:')
print(stats.shellsort(m))
print(stats.shellsort(b))
print('rankdata:')
print(stats.rankdata(m))
print(stats.rankdata(b))
def fixedsolve(th,xj,N):
val = stats.sum(xj)*1.0/N
tmp = exp(-xj/th)
term = sum(xj*tmp,axis=0)
term /= sum(tmp,axis=0)
return val - term
l = range(1,21) a = N.array(l) ll = [l]*5 aa = N.array(ll) m = range(4,24) m[10] = 34 b = N.array(m) print '\n\nF_oneway:' print stats.F_oneway(l,m) print stats.F_oneway(a,b) #print 'F_value:',stats.F_value(l),stats.F_value(a) print '\nSUPPORT' print 'sum:',stats.sum(l),stats.sum(lf),stats.sum(a),stats.sum(af) print 'cumsum:' print stats.cumsum(l) print stats.cumsum(lf) print stats.cumsum(a) print stats.cumsum(af) print 'ss:',stats.ss(l),stats.ss(lf),stats.ss(a),stats.ss(af) print 'summult:',stats.summult(l,m),stats.summult(lf,m),stats.summult(a,b),stats.summult(af,b) print 'sumsquared:',stats.square_of_sums(l),stats.square_of_sums(lf),stats.square_of_sums(a),stats.square_of_sums(af) print 'sumdiffsquared:',stats.sumdiffsquared(l,m),stats.sumdiffsquared(lf,m),stats.sumdiffsquared(a,b),stats.sumdiffsquared(af,b) print 'shellsort:' print stats.shellsort(m) print stats.shellsort(b) print 'rankdata:' print stats.rankdata(m) print stats.rankdata(b)
import stats
my_list = [4,1,5,7,6,8,9,10,8,3,3,8,12]
mean = stats.mean(my_list)
print('The mean is: ' + str(mean))
median = stats.median(my_list)
print('The median is: ' + str(median))
range = stats.range(my_list)
print('The range is: ' + str(range))
sum = stats.sum(my_list)
print('The sum of all numbers is: ' + str(su
if options.useFloatValues:
d = map(float, sys.stdin.xreadlines())
else:
d = map(long, sys.stdin.xreadlines())
except ValueError, err:
sys.stderr.write("Bad datum: %s\n" % str(err))
sys.exit(1)
if len(d) == 0:
sys.stderr.write("No data given\n")
sys.exit(1)
d.sort()
print " N =", len(d)
print " SUM =", stats.sum(d)
print " MIN =", min(d)
print "1ST-QUARTILE =", stats.firstquartile(d)
print " MEDIAN =", stats.median(d)
print "3RD-QUARTILE =", stats.thirdquartile(d)
print " MAX =", max(d)
print " MEAN =", stats.mean(d)
if d[0] < 0:
print " N50 = NA"
else:
print " N50 =", stats.n50(d)
if options.showMode:
modeinfo = stats.mode(d)
print " MODE(S) =", ','.join(map(str, modeinfo[0])), "(%d)" % modeinfo[1]
l = range(1, 21)
a = N.array(l)
ll = [l] * 5
aa = N.array(ll)
m = range(4, 24)
m[10] = 34
b = N.array(m)
print '\n\nF_oneway:'
print stats.F_oneway(l, m)
print stats.F_oneway(a, b)
# print 'F_value:',stats.F_value(l),stats.F_value(a)
print '\nSUPPORT'
print 'sum:', stats.sum(l), stats.sum(lf), stats.sum(a), stats.sum(af)
print 'cumsum:'
print stats.cumsum(l)
print stats.cumsum(lf)
print stats.cumsum(a)
print stats.cumsum(af)
print 'ss:', stats.ss(l), stats.ss(lf), stats.ss(a), stats.ss(af)
print 'summult:', stats.summult(l, m), stats.summult(lf, m), stats.summult(
a, b), stats.summult(af, b)
print 'sumsquared:', stats.square_of_sums(l), stats.square_of_sums(
lf), stats.square_of_sums(a), stats.square_of_sums(af)
print 'sumdiffsquared:', stats.sumdiffsquared(l, m), stats.sumdiffsquared(
lf, m), stats.sumdiffsquared(a, b), stats.sumdiffsquared(af, b)
print 'shellsort:'
print stats.shellsort(m)
print stats.shellsort(b)
def fixedsolve(th,xj,N):
val = stats.sum(xj)*1.0/N
tmp = exp(-xj/th)
term = sum(xj*tmp,axis=0)
term /= sum(tmp,axis=0)
return val - term
# Compute the people that only played once
for i in range(len(table[TABLE_LOGINS])):
if table[TABLE_LOGINS][i] == 1:
playTime = table[TABLE_TIME][i]
onlySessions.append(playTime)
print
print("ACCOUNTS")
print("--------------------------------------------------")
print("Number of accounts: %s" % (len(accounts)))
print
print("HOURS OF PLAY")
print("--------------------------------------------------")
print("Total play time: %s" % getHourString(stats.sum(table[TABLE_TIME])))
print("Largest play time for a single account: %s" %
getHourString(max(table[TABLE_TIME])))
print("Median total play time for all accounts: %s" %
getHourString(stats.median(table[TABLE_TIME])))
print(
"Macro Histogram play time: \n%s" %
getHistogramString(stats.histogram(table[TABLE_TIME], 10, [0, 100 * 3600]),
getHourString))
print(
"Micro Histogram play time: \n%s" % getHistogramString(
stats.histogram(table[TABLE_TIME], 10, [0, 10 * 3600]), getHourString))
print(
"Pico Histogram play time: \n%s" % getHistogramString(
stats.histogram(table[TABLE_TIME], 12, [0, 1 * 3600]), getHourString))
def sumvar(l, m):
return stats.sum(map(lambda x: (x - m)**2, l))
def sumvar(l,m):
return stats.sum(map(lambda x:(x-m)**2,l))
def testSum(self):
result = stats.sum(self.data)
expected = self.expected["sum"]
n = int(math.log(result, 10)) # Yuck.
self.assertAlmostEqual(result, expected, places=self.places - n)
def testSumSqs(self):
Sx2 = stats.sum(x ** 2 for x in self.xdata)
Sy2 = stats.sum(x ** 2 for x in self.ydata)
self.assertAlmostEqual(Sx2, 1366357 / 4096, places=self.places)
self.assertAlmostEqual(Sy2, 1117 / 16, places=self.places)
def testSums(self):
Sx = stats.sum(self.xdata)
Sy = stats.sum(self.ydata)
self.assertAlmostEqual(Sx, 3295 / 64, places=self.places)
self.assertAlmostEqual(Sy, 115 / 4, places=self.places)
