def score(splits, sigma):
splits = sorted(splits)
result = []
# result = sum( sigma(a,b) for (a,b) in tools.seg_iter(splits) )
print("SCORING:", splits)
for (a, b) in tools.seg_iter(splits):
result.append(sigma(a, b))
print("SCORING SUM:", splits, sum(result))
return sum(result)
def c99score(distsmat, hyp, minlength=1, maxlength=None):
""" Do the choi c99 scoring for a hypothesis splitting """
N = distsmat.shape[0]
beta = 0.0
alpha = 0.0
for (a, b) in tools.seg_iter(hyp):
beta += distsmat[a:b, a:b].sum()
alpha += (b - a)**2
if minlength:
if (b - a) < minlength: beta += -np.inf
if maxlength:
if (b - a) > maxlength: beta += -np.inf
return -beta / (alpha + 0.)
def score(splits, sigma):
splits = sorted(splits)
return sum(sigma(a, b) for (a, b) in tools.seg_iter(splits))
def score(splits, sigma):
splits = sorted(splits)
return key(
reduce(combine,
(sigma(a, b) for (a, b) in tools.seg_iter(splits))))
