def stats_per_second(self, *args):
superstats = []
for game_log, matrix in args:
cstats = corestats.Stats()
stats = {}
mode = cstats.mode(matrix)
stats['mode'] = mode[0][0]
stats['modenext'] = mode[1][0]
stats['mean'] = cstats.mean(matrix)
stats['median'] = cstats.median(matrix)
#stats['harmonicmean'] = mstats.harmonicmean(matrix)
stats['variance'] = cstats.variance(matrix)
stats['stddeviation'] = stats['variance'] ** 0.5
stats['3sigma'] = 3*stats['stddeviation']
stats['cumfreq'] = mstats.cumfreq(matrix)
stats['itemfreq'] = mstats.itemfreq(matrix) # frequency of each item (each item being the count of the occurrencies for each number of lines per second)
stats['min'] = min(matrix)
stats['max'] = max(matrix)
stats['samplespace'] = stats['max'] - stats['min']
stats['count'] = len(matrix)
stats['kurtosis'] = mstats.kurtosis(matrix)
stats['perfectvalue'] = int(math.ceil(stats['3sigma'] + stats['mean']))
stats['perfectscore'] = cstats.percentileforvalue(matrix, math.ceil(stats['3sigma'] + stats['mean']))
scorepercentiles = [10, 30, 50, 70, 80, 85, 90, 95, 99, 99.9, 99.99]
stats['itemscore'] = [(percentile, cstats.valueforpercentile(matrix, percentile)) for percentile in scorepercentiles]
stats['skew'] = mstats.skew(matrix) # if positive, there are more smaller than higher values from the mean. If negative, there are more higher than smaller values from the mean.
if stats['skew'] > 0:
stats['skewmeaning'] = 'There exist more smaller values from the mean than higher'
else:
stats['skewmeaning'] = 'There exist more higher values from the mean than smaller'
superstats.append( (game_log, stats) )
return superstats
def test_cumfreq(self):
"Testing cumfreq"
data = [ self.L, self.LF, self.A, self.AF ]
results1 = ([2, 4, 6, 8, 10, 12, 14, 16, 18, 20], -0.045000050000000069, 2.0900001000000001, 0 )
results2 = ([2, 4, 6, 8, 10, 12, 14, 16, 18, 20], -0.045000050000000069, 2.0900001000000001, 0 )
results3 = (num_array([ 2., 4., 6., 8., 10., 12., 14., 16., 18., 20.]), -0.045000050000000069, 2.0900001000000001, 0 )
results4 = (num_array([ 2., 4., 6., 8., 10., 12., 14., 16., 18., 20.]), -0.045000050000000069, 2.0900001000000001, 0 )
i = 0
for d in data:
self.assertEqual( stats.cumfreq( d )[i], results1[i] )
i += 1
def test_cumfreq(self):
"Testing cumfreq"
data = [ self.L, self.LF, self.A, self.AF ]
results1 = ([2, 4, 6, 8, 10, 12, 14, 16, 18, 20], -0.045000050000000069, 2.0900001000000001, 0 )
results2 = ([2, 4, 6, 8, 10, 12, 14, 16, 18, 20], -0.045000050000000069, 2.0900001000000001, 0 )
results3 = (num_array([ 2., 4., 6., 8., 10., 12., 14., 16., 18., 20.]), -0.045000050000000069, 2.0900001000000001, 0 )
results4 = (num_array([ 2., 4., 6., 8., 10., 12., 14., 16., 18., 20.]), -0.045000050000000069, 2.0900001000000001, 0 )
i = 0
for d in data:
self.assertEqual( stats.cumfreq( d )[i], results1[i] )
i += 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
def stats_per_second(self, *args):
superstats = []
for game_log, matrix in args:
cstats = corestats.Stats()
stats = {}
mode = cstats.mode(matrix)
stats['mode'] = mode[0][0]
stats['modenext'] = mode[1][0]
stats['mean'] = cstats.mean(matrix)
stats['median'] = cstats.median(matrix)
#stats['harmonicmean'] = mstats.harmonicmean(matrix)
stats['variance'] = cstats.variance(matrix)
stats['stddeviation'] = stats['variance']**0.5
stats['3sigma'] = 3 * stats['stddeviation']
stats['cumfreq'] = mstats.cumfreq(matrix)
stats['itemfreq'] = mstats.itemfreq(
matrix
) # frequency of each item (each item being the count of the occurrencies for each number of lines per second)
stats['min'] = min(matrix)
stats['max'] = max(matrix)
stats['samplespace'] = stats['max'] - stats['min']
stats['count'] = len(matrix)
stats['kurtosis'] = mstats.kurtosis(matrix)
stats['perfectvalue'] = int(
math.ceil(stats['3sigma'] + stats['mean']))
stats['perfectscore'] = cstats.percentileforvalue(
matrix, math.ceil(stats['3sigma'] + stats['mean']))
scorepercentiles = [
10, 30, 50, 70, 80, 85, 90, 95, 99, 99.9, 99.99
]
stats['itemscore'] = [
(percentile, cstats.valueforpercentile(matrix, percentile))
for percentile in scorepercentiles
]
stats['skew'] = mstats.skew(
matrix
) # if positive, there are more smaller than higher values from the mean. If negative, there are more higher than smaller values from the mean.
if stats['skew'] > 0:
stats[
'skewmeaning'] = 'There exist more smaller values from the mean than higher'
else:
stats[
'skewmeaning'] = 'There exist more higher values from the mean than smaller'
superstats.append((game_log, stats))
return superstats
print(stats.describe(af))
print('\nFREQUENCY')
print('freqtable:')
print('itemfreq:')
print(stats.itemfreq(l))
print(stats.itemfreq(a))
print('scoreatpercentile:', stats.scoreatpercentile(l, 40),
stats.scoreatpercentile(lf, 40), stats.scoreatpercentile(a, 40),
stats.scoreatpercentile(af, 40))
print('percentileofscore:', stats.percentileofscore(l, 12),
stats.percentileofscore(lf, 12), stats.percentileofscore(a, 12),
stats.percentileofscore(af, 12))
print('histogram:', stats.histogram(l), stats.histogram(a))
print('cumfreq:')
print(stats.cumfreq(l))
print(stats.cumfreq(lf))
print(stats.cumfreq(a))
print(stats.cumfreq(af))
print('relfreq:')
print(stats.relfreq(l))
print(stats.relfreq(lf))
print(stats.relfreq(a))
print(stats.relfreq(af))
print('\nVARIATION')
print('obrientransform:')
l = list(range(1, 21))
a = N.array(l)
ll = [l] * 5
print 'describe:' print stats.describe(l) print stats.describe(lf) print stats.describe(a) print stats.describe(af) print '\nFREQUENCY' print 'freqtable:' print 'itemfreq:' print stats.itemfreq(l) print stats.itemfreq(a) print 'scoreatpercentile:',stats.scoreatpercentile(l,40),stats.scoreatpercentile(lf,40),stats.scoreatpercentile(a,40),stats.scoreatpercentile(af,40) print 'percentileofscore:',stats.percentileofscore(l,12),stats.percentileofscore(lf,12),stats.percentileofscore(a,12),stats.percentileofscore(af,12) print 'histogram:',stats.histogram(l),stats.histogram(a) print 'cumfreq:' print stats.cumfreq(l) print stats.cumfreq(lf) print stats.cumfreq(a) print stats.cumfreq(af) print 'relfreq:' print stats.relfreq(l) print stats.relfreq(lf) print stats.relfreq(a) print stats.relfreq(af) print '\nVARIATION' print 'obrientransform:' l = range(1,21) a = N.array(l) ll = [l]*5
def evaluate(self, *args, **params):
return _stats.cumfreq(*args, **params)
print stats.describe(af)
print '\nFREQUENCY'
print 'freqtable:'
print 'itemfreq:'
print stats.itemfreq(l)
print stats.itemfreq(a)
print 'scoreatpercentile:', stats.scoreatpercentile(
l, 40), stats.scoreatpercentile(lf, 40), stats.scoreatpercentile(
a, 40), stats.scoreatpercentile(af, 40)
print 'percentileofscore:', stats.percentileofscore(
l, 12), stats.percentileofscore(lf, 12), stats.percentileofscore(
a, 12), stats.percentileofscore(af, 12)
print 'histogram:', stats.histogram(l), stats.histogram(a)
print 'cumfreq:'
print stats.cumfreq(l)
print stats.cumfreq(lf)
print stats.cumfreq(a)
print stats.cumfreq(af)
print 'relfreq:'
print stats.relfreq(l)
print stats.relfreq(lf)
print stats.relfreq(a)
print stats.relfreq(af)
print '\nVARIATION'
print 'obrientransform:'
l = range(1, 21)
a = N.array(l)
ll = [l] * 5
