def entropiesFast(counts, locPrior, likelihoodsObj, d):
# d=dag.linearTestDag(len(locPrior))
one = numberType.const(1.0)
renyiFactor = one / (one - entropy.alpha)
lk = likelihoodsObj.calc(counts, locPrior, entropy.alpha, d)
(rsum, osum) = lk.orig()
currEntropy = rsum / numberType.pow(osum, entropy.alpha)
currEntropy = numberType.log(currEntropy) * renyiFactor
entropyResults = []
findProbs = []
for i in xrange(len(locPrior)):
(findProb, renyLksFoundTot, renyLksNFoundTot, evDProb,
NfoundNorm) = lk[i]
findProbs.append(findProb)
#entropyFound:normalise
try:
eFound = renyLksFoundTot / numberType.pow(evDProb, entropy.alpha)
eFound = numberType.log(eFound) * renyiFactor
except numberType.zeroDivisionError:
eFound = 0
except numberType.overflowError:
eFound = 0
#entropyNotFound:normalise
try:
eNotFound = renyLksNFoundTot / numberType.pow(
NfoundNorm, entropy.alpha)
eNotFound = numberType.log(eNotFound) * renyiFactor
except numberType.zeroDivisionError:
eNotFound = 0
except numberType.overflowError:
eNotFound = 0
# expected entropy after testing at i:
if debug: print "b", eFound, eNotFound, findProb
eResult = eFound * findProb + eNotFound * (1 - findProb)
entropyResults.append(eResult)
return (currEntropy, entropyResults, findProbs)
def entropiesFast(counts,locPrior,likelihoodsObj,d):
# d=dag.linearTestDag(len(locPrior))
one=numberType.const(1.0)
renyiFactor=one/(one-entropy.alpha)
lk=likelihoodsObj.calc(counts,locPrior,entropy.alpha,d)
(rsum,osum)=lk.orig()
currEntropy=rsum/numberType.pow(osum,entropy.alpha)
currEntropy =numberType.log(currEntropy)*renyiFactor
entropyResults=[]
findProbs=[]
for i in xrange(len(locPrior)):
(findProb,renyLksFoundTot,renyLksNFoundTot,evDProb,NfoundNorm)=lk[i]
findProbs.append(findProb)
#entropyFound:normalise
try:
eFound=renyLksFoundTot/numberType.pow(evDProb,entropy.alpha)
eFound=numberType.log(eFound)*renyiFactor
except numberType.zeroDivisionError:
eFound=0
except numberType.overflowError:
eFound=0
#entropyNotFound:normalise
try:
eNotFound=renyLksNFoundTot/numberType.pow(NfoundNorm,entropy.alpha)
eNotFound=numberType.log(eNotFound)*renyiFactor
except numberType.zeroDivisionError:
eNotFound=0
except numberType.overflowError:
eNotFound=0
# expected entropy after testing at i:
if debug: print "b",eFound,eNotFound,findProb
eResult=eFound*findProb+eNotFound*(1-findProb)
entropyResults.append(eResult)
return (currEntropy,entropyResults,findProbs)
def renyi(probs):
e=0
one=numberType.const(1.0)
d=one/(one-alpha)
for p in probs:
e=e+numberType.pow(p,alpha)
return numberType.log(e)*d
def logFact(x):
while(x>=len(logFactMemo)):
logFactMemo.append(logFactMemo[-1]+numberType.log(len(logFactMemo)))
return logFactMemo[x]
def logFact(x):
while (x >= len(logFactMemo)):
logFactMemo.append(logFactMemo[-1] + numberType.log(len(logFactMemo)))
return logFactMemo[x]
def shannon(probs):
e=0
for p in probs:
if(p>0):
e-=p*numberType.log(p)
return e
