def fTransition(s, sI, s2, sI2, i, j, t, de, x2, y2, x1, y1):
l = s[i] + t + de
s2[j] = Maths.logAdd(s2[j], l)
l += bMatrix[x1][y1][j] - total
#print x1, x2, "boo"
if x2 >= x1:
raise IndexError()
for k in xrange(0, x1-x2):
pPA[(x1-k, y1-k)] = Maths.logAdd(pPA[(x1-k, y1-k)], l)
def bTransition(self, s, sI, s2, sI2, i, j, t, de, x1, y1, x2, y2):
sD = x1 + y1
k = s2[sI2 + j] + t + de
for m in xrange(len(self.__list)-1, -1, -1):
l = self.__list[m]
if sD < l[0]:
if x2 + y2 < l[0]:
self.__list.pop()
#print "new total ", l[1]
self.__totalFn(l[1])
else:
l[1] = Maths.logAdd(l[1], s[sI + i + self.__stateNo] + k)
else:
break
s[sI + i] = Maths.logAdd(s[sI + i], k)
def bTransition(self, s, sI, s2, sI2, i, j, t, de, x1, y1, x2, y2):
sD = x1 + y1
k = s2[sI2 + j] + t + de
for m in xrange(len(self.__list) - 1, -1, -1):
l = self.__list[m]
if sD < l[0]:
if x2 + y2 < l[0]:
self.__list.pop()
#print "new total ", l[1]
self.__totalFn(l[1])
else:
l[1] = Maths.logAdd(l[1], s[sI + i + self.__stateNo] + k)
else:
break
s[sI + i] = Maths.logAdd(s[sI + i], k)
def addNewScale(self, states, adP, ad):
scale = Maths.NEG_INFINITY
for i in xrange(adP.yS(ad), adP.yS(ad + 1)):
s = adP.e[i]
scale = Maths.logAdd(scale, \
logSum(states[s+self.__stateNo:s+2*self.__stateNo]))
i = (adP.yS(ad + 1) - adP.yS(ad)) * self.__stateNo
if i is 0:
i = 1
i = Maths.log(i)
if scale is Maths.NEG_INFINITY:
scale = i
#print "________________________________________scale ", i, scale, int(scale - i)
#scale = i
self.__list.append(self.__list[-1] + int(scale - i))
def addNewScale(self, states, adP, ad):
scale = Maths.NEG_INFINITY
for i in xrange(adP.yS(ad), adP.yS(ad+1)):
s = adP.e[i]
scale = Maths.logAdd(scale, \
logSum(states[s+self.__stateNo:s+2*self.__stateNo]))
i = (adP.yS(ad+1) - adP.yS(ad))*self.__stateNo
if i is 0:
i = 1
i = Maths.log(i)
if scale is Maths.NEG_INFINITY:
scale = i
#print "________________________________________scale ", i, scale, int(scale - i)
#scale = i
self.__list.append(self.__list[-1] + int(scale - i))
def bTransition(s, sI, s2, sI2, i, j, t, de, *args):
s[sI + i] = Maths.logAdd(s[sI + i], s2[sI2 + j] + t + de)
def fTransition(s, sI, s2, sI2, i, j, t, de, *args):
s2[sI2 + j + stateNo] = Maths.logAdd(s2[sI2 + j + stateNo], s[sI + i + stateNo] + t + de)
def logSum(s):
f = s[0]
for i in s[1:]:
f = Maths.logAdd(f, i)
return f
def bTransition(self, s, sI, s2, sI2, i, j, t, de, *args):
self.__current = \
Maths.logAdd(self.__current,
s2[sI2 + j] + t + de + s[sI + self.__stateNo + i] - self.__total)
def fTransition2(s, sI, s2, sI2, i, j, t, de, x2, y2, x1, y1):
l = s[i] + t + de
s2[j] = Maths.logAdd(s2[j], l)
def bTransition(s, sI, s2, sI2, i, j, t, de, *args):
s2[i] = Maths.logAdd(s2[i], s[j] + t + de)
def bTransition(s, sI, s2, sI2, i, j, t, de, *args):
s[sI + i] = Maths.logAdd(s[sI + i], s2[sI2 + j] + t + de)
def fTransition(s, sI, s2, sI2, i, j, t, de, *args):
s2[sI2 + j + stateNo] = Maths.logAdd(s2[sI2 + j + stateNo],
s[sI + i + stateNo] + t + de)
def logSum(s):
f = s[0]
for i in s[1:]:
f = Maths.logAdd(f, i)
return f
def bTransition(self, s, sI, s2, sI2, i, j, t, de, *args):
self.__current = \
Maths.logAdd(self.__current,
s2[sI2 + j] + t + de + s[sI + self.__stateNo + i] - self.__total)
def gap_de(self, x1, x2, y2):
i = x2 - x1
return Maths.logAdd(
GAP_OPEN + i * self.COMBINED - GAP_EXTEND + GAP_CLOSE,
JUNK_OPEN + i * self.COMBINED2 - JUNK_EXTEND + JUNK_CLOSE)
def gap_de(self, x1, x2, y2):
i = x2 - x1
return Maths.logAdd(GAP_OPEN + i*self.COMBINED - GAP_EXTEND + GAP_CLOSE,
JUNK_OPEN + i*self.COMBINED2 - JUNK_EXTEND + JUNK_CLOSE)