def test_mode(self): "Testing mode" L1 = [1, 1, 1, 2, 3, 4, 5] L2 = [1, 1, 1, 2, 3, 4, 5, 6] A1 = num_array(L1) A2 = num_array(L2) data = [L1, L2, A1, A2] for d in data: self.assertEqual(stats.mode(d), (3, [1]))
def test_mode(self): "Testing mode" L1 = [1,1,1,2,3,4,5 ] L2 = [1,1,1,2,3,4,5,6 ] A1 = num_array( L1 ) A2 = num_array( L2 ) data = [ L1, L2, A1, A2 ] for d in data : self.assertEqual( stats.mode( d ), (3, [1]) )
def _format_ratings(self, output): ratings = [] for result in self.data: try: ratings.append( float(result[0].toPython()) ) except ValueError: pass output['results'] = {} if ratings: output['results']['median'] = stats.medianscore(ratings) output['results']['mode'] = stats.mode(ratings) output['results']['mean'] = stats.mean(ratings) output['results']['histogram'] = stats.histogram(ratings,6) output['results']['cumfreq'] = stats.cumfreq(ratings,6) output['results']['count'] = len(ratings) return output
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))) print('describe:') print(stats.describe(l))
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)) print 'describe:' print stats.describe(l) print stats.describe(lf) print stats.describe(a) print stats.describe(af)
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)) print 'describe:' print stats.describe(l)
def evaluate(self, *args, **params): return _stats.mode(*args, **params)