def wilcoxontest(ranks_a, ranks_b, expects):
""" Return wilcoxon p values of ranks_a and ranks_b
@arg ranks_a a list() of ranks(dataset(){'label', 'score'})
@arg ranks_b a list() of ranks(dataset(){'label', 'score'})
@arg expects a list() of testitems(dataset(){'label', ...})
@return p-value of two tailed wilcoxon teset
"""
rak_x = list()
rak_y = list()
for i in range(expects.size()):
for j in range(ranks_a[i].size()):
if ranks_a[i]['label'][j] == expects['label'][i]:
rak_x.append(j)
break
for j in range(ranks_b[i].size()):
if ranks_b[i]['label'][j] == expects['label'][i]:
rak_y.append(j)
break
sval, pval = lwilcoxont(rak_x, rak_y)
#sval, pval = wilcoxon(rak_x, rak_y)
return pval
def wilcoxontest(ranks_a, ranks_b, expects):
""" Return wilcoxon p values of ranks_a and ranks_b
@arg ranks_a a list() of ranks(dataset(){'label', 'score'})
@arg ranks_b a list() of ranks(dataset(){'label', 'score'})
@arg expects a list() of testitems(dataset(){'label', ...})
@return p-value of two tailed wilcoxon teset
"""
rak_x = list()
rak_y = list()
for i in range(expects.size()):
for j in range(ranks_a[i].size()):
if ranks_a[i]['label'][j] == expects['label'][i]:
rak_x.append(j)
break
for j in range(ranks_b[i].size()):
if ranks_b[i]['label'][j] == expects['label'][i]:
rak_y.append(j)
break
sval, pval = lwilcoxont(rak_x, rak_y)
#sval, pval = wilcoxon(rak_x, rak_y)
return pval
sign = [rank[i]/abs(rank[i]) for i in range(len(rank))]
val = rank[0]
st = 0
normrank = list()
for i in range(1, len(rank)):
if abs(rank[i]) == val:
cumrank += float(i + 1)
else:
normrank.extend([sign[j] * cumrank/(i-st) for j in range(st, i)])
st = i
val = abs(rank[i])
cumrank = float(i + 1)
normrank.extend([sign[j] * cumrank/(len(rank)-st) for j in range(st, len(rank))])
print normrank
wpos, wnag = 0.0, 0.0
wpos = sum(filter(lambda x: x>0, normrank))
wnag = - sum(filter(lambda x: x<0, normrank))
return len(rate_a), min(wpos, wnag)
if __name__ == '__main__':
print lwilcoxont([125, 115, 130, 140, 140, 115, 140, 125, 140, 135],
[110, 122, 125, 120, 140, 124, 123, 137, 135, 145])
if len(rank) == 0:
return 0.0
cumrank = 1.0
sign = [rank[i] / abs(rank[i]) for i in range(len(rank))]
val = rank[0]
st = 0
normrank = list()
for i in range(1, len(rank)):
if abs(rank[i]) == val:
cumrank += float(i + 1)
else:
normrank.extend(
[sign[j] * cumrank / (i - st) for j in range(st, i)])
st = i
val = abs(rank[i])
cumrank = float(i + 1)
normrank.extend(
[sign[j] * cumrank / (len(rank) - st) for j in range(st, len(rank))])
print normrank
wpos, wnag = 0.0, 0.0
wpos = sum(filter(lambda x: x > 0, normrank))
wnag = -sum(filter(lambda x: x < 0, normrank))
return len(rate_a), min(wpos, wnag)
if __name__ == '__main__':
print lwilcoxont([125, 115, 130, 140, 140, 115, 140, 125, 140, 135],
[110, 122, 125, 120, 140, 124, 123, 137, 135, 145])
