def legacy(): patts = stats.ltrimboth(patts, 0.1) # statlib. #patts = statlib.stats.ltrimboth(patts, 0.1) # statlib. print 'len(patts)', len(patts) #stats.writecc([patts,patts],'test.txt') alist = range(1,6) print 'sum(alist)', sum(alist) print 'cumsum(alist)', stats.cumsum(alist) print 'geometricmean(alist)', stats.geometricmean(alist)
def testFiltre(): fTilObj = FiltreTil('lf') fTilObj.getPatternsDictFor(5) fTilObj.analyze() patts = fTilObj.getPatternsFor(5) print 'len(patts)', len(patts) patts = stats.ltrimboth(patts, 0.1) # statlib. #patts = statlib.stats.ltrimboth(patts, 0.1) # statlib. print 'len(patts)', len(patts) #stats.writecc([patts,patts],'test.txt') alist = range(1,6) print 'sum(alist)', sum(alist) print 'cumsum(alist)', stats.cumsum(alist) print 'geometricmean(alist)', stats.geometricmean(alist)
from statlib import stats, pstat from six.moves import range reload(stats) import numpy N = numpy l = list(range(1, 21)) lf = list(range(1, 21)) lf[2] = 3.0 a = N.array(l) af = N.array(lf) ll = [l] * 5 aa = N.array(ll) print('\nCENTRAL TENDENCY') print('geometricmean:', stats.geometricmean(l), stats.geometricmean(lf), stats.geometricmean(a), stats.geometricmean(af)) print('harmonicmean:', stats.harmonicmean(l), stats.harmonicmean(lf), stats.harmonicmean(a), stats.harmonicmean(af)) print('mean:', stats.mean(l), stats.mean(lf), stats.mean(a), stats.mean(af)) print('median:', stats.median(l), stats.median(lf), stats.median(a), stats.median(af)) print('medianscore:', stats.medianscore(l), stats.medianscore(lf), stats.medianscore(a), stats.medianscore(af)) print('mode:', stats.mode(l), stats.mode(a)) print('\nMOMENTS') print('moment:', stats.moment(l), stats.moment(lf), stats.moment(a), stats.moment(af)) print('variation:', stats.variation(l), stats.variation(a), stats.variation(lf), stats.variation(af))
import os from statlib import stats, pstat reload(stats) import numpy N=numpy l = range(1,21) lf = range(1,21) lf[2] = 3.0 a = N.array(l) af = N.array(lf) ll = [l]*5 aa = N.array(ll) print '\nCENTRAL TENDENCY' print 'geometricmean:',stats.geometricmean(l), stats.geometricmean(lf), stats.geometricmean(a), stats.geometricmean(af) print 'harmonicmean:',stats.harmonicmean(l), stats.harmonicmean(lf), stats.harmonicmean(a), stats.harmonicmean(af) print 'mean:',stats.mean(l), stats.mean(lf), stats.mean(a), stats.mean(af) print 'median:',stats.median(l),stats.median(lf),stats.median(a),stats.median(af) print 'medianscore:',stats.medianscore(l),stats.medianscore(lf),stats.medianscore(a),stats.medianscore(af) print 'mode:',stats.mode(l),stats.mode(a) print '\nMOMENTS' print 'moment:',stats.moment(l),stats.moment(lf),stats.moment(a),stats.moment(af) print 'variation:',stats.variation(l),stats.variation(a),stats.variation(lf),stats.variation(af) print 'skew:',stats.skew(l),stats.skew(lf),stats.skew(a),stats.skew(af) print 'kurtosis:',stats.kurtosis(l),stats.kurtosis(lf),stats.kurtosis(a),stats.kurtosis(af) print 'tmean:',stats.tmean(a,(5,17)),stats.tmean(af,(5,17)) print 'tvar:',stats.tvar(a,(5,17)),stats.tvar(af,(5,17)) print 'tstdev:',stats.tstdev(a,(5,17)),stats.tstdev(af,(5,17)) print 'tsem:',stats.tsem(a,(5,17)),stats.tsem(af,(5,17))
def test_geometricmean(self): "Testing geometric mean" data = [self.L, self.LF, self.A, self.AF] for d in data: self.EQ(stats.geometricmean(d), 8.304, 3)
def evaluate(self, *args, **params): return _stats.geometricmean(*args, **params)
def test_geometricmean(self): "Testing geometric mean" data = [ self.L, self.LF, self.A, self.AF ] for d in data : self.EQ( stats.geometricmean( d ), 8.304, 3)
import os from statlib import stats, pstat reload(stats) import numpy N = numpy l = range(1, 21) lf = range(1, 21) lf[2] = 3.0 a = N.array(l) af = N.array(lf) ll = [l] * 5 aa = N.array(ll) print '\nCENTRAL TENDENCY' print 'geometricmean:', stats.geometricmean(l), stats.geometricmean( lf), stats.geometricmean(a), stats.geometricmean(af) print 'harmonicmean:', stats.harmonicmean(l), stats.harmonicmean( lf), stats.harmonicmean(a), stats.harmonicmean(af) print 'mean:', stats.mean(l), stats.mean(lf), stats.mean(a), stats.mean(af) print 'median:', stats.median(l), stats.median(lf), stats.median( a), stats.median(af) print 'medianscore:', stats.medianscore(l), stats.medianscore( lf), stats.medianscore(a), stats.medianscore(af) print 'mode:', stats.mode(l), stats.mode(a) print '\nMOMENTS' print 'moment:', stats.moment(l), stats.moment(lf), stats.moment( a), stats.moment(af) print 'variation:', stats.variation(l), stats.variation(a), stats.variation( lf), stats.variation(af)