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)
# 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)