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 'mean:', stats.mean(a), stats.mean(af)
print 'var:', stats.var(a), stats.var(af)
print 'stdev:', stats.stdev(a), stats.stdev(af)
print 'sem:', stats.sem(a), stats.sem(af)
pass
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('mean:',stats.mean(a),stats.mean(af))
print('var:',stats.var(a),stats.var(af))
print('stdev:',stats.stdev(a),stats.stdev(af))
print('sem:',stats.sem(a),stats.sem(af))
print('describe:')
print(stats.describe(l))
print(stats.describe(lf))
print(stats.describe(a))
print(stats.describe(af))
loc2 = reverse_geocode.county_lookup(lat2, long2, strict=True)
state2 = None
if loc2 is None:
print>>sys.stderr, "no county for prediction %s,%s" % (lat2,long2)
state2 = "WTF"
import state_codes
state1 = state_codes.fips2postal[loc1.statefp]
state2 = state2 or state_codes.fips2postal[loc2.statefp]
region1 = regions.StateRegion.get(state1, "WTF")
region2 = regions.StateRegion.get(state2, "WTF")
corrects['state'].append( int(state1 == state2) )
#corrects['div'].append( int(div1 == div2) )
corrects['region'].append( int(region1 == region2) )
#corrects['metro'].append( int(metro1==metro2) )
print "\t".join(str(x) for x in [km,state1,state2, region1, region2])
print "--"
print "mean_dist_km %f" % mean(dists)
print "med_dist_km %f" % medianscore(dists)
print "state_acc %f" % mean(corrects['state'])
#print "div_acc %f" % mean(corrects['div'])
print "region_acc %f" % mean(corrects['region'])
# print "metro_acc %f" % mean(corrects['metro'])
# vim:foldmethod=marker