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 randomEntropyData(seed,N,dag,falsePos=False,falseNeg=False,maxCount=10):
random.seed(seed)
loc=random.randint(0,N-1)
locList=[False for x in range(N)]
locList[loc]=True
detectable=listOr(dag.anyUpto(locList),locList)
counts=[(random.randint(0,maxCount),random.randint(0,maxCount)) for x in range(N)]
# eliminate impossible counts for this test
if not falsePos:
counts=[ cond(not detectable[x],(counts[x][0],0),counts[x]) for x in range(N)]
if not falseNeg:
counts=[ cond(detectable[x],(0,counts[x][1]),counts[x]) for x in range(N)]
UlocPrior=[random.random() for x in range(N)]
norm=sum(UlocPrior)
locPrior=[numberType.const(i/norm) for i in UlocPrior]
return (counts,locPrior)
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 entropies(counts,locPrior,likelihoodsObj,dag):
(locProbs,evProb)=likelihoodsObj.probs(counts,locPrior,dag)
deb_ef=[]
deb_enf=[]
findProbs=[]
entropyResults=[]
entropyFunc=entropy.renyi
currEntropy=entropyFunc(locProbs)
if debug: print "ac",counts
for i in xrange(len(locProbs)):
testFound=copy.copy(counts)
testNotFound=copy.copy(counts)
(t,d)=counts[i]
testFound[i]=(t,d+1)
testNotFound[i]=(t+1,d)
try:
(probsIfFound,evDProb)=likelihoodsObj.probs(testFound,locPrior,dag)
eFound=entropyFunc(probsIfFound)
except Impossible:
eFound=numberType.zero
evDProb=numberType.zero
try:
(probsIfNotFound,junk)=likelihoodsObj.probs(testNotFound,locPrior,dag)
eNotFound=entropyFunc(probsIfNotFound)
except Impossible:
eNotFound=numberType.zero
# probability of finding at i:
findProb=evDProb/evProb
findProbs.append(findProb)
if debug: print "a",eFound,eNotFound,evDProb,probsIfFound
# expected entropy after testing at i:
eResult=eFound*findProb+eNotFound*(numberType.const(1)-findProb)
entropyResults.append(eResult)
return (currEntropy,entropyResults,findProbs)
def __init__(self,
locPrior,
certainty,
interactor,
likelihoodsObj,
dag,
strategy=greedyStrat,
skipProbsFunc=skipProbability.skipProbsSimple):
self.locPrior=numberType.copyList(locPrior)
self.certainty=numberType.const(certainty)
self.counts=[(0,0) for p in locPrior]
self.skipProbsFunc=skipProbsFunc
self.skipped=[False for p in locPrior]
self.dag=dag
self.skipProbs = self.skipProbsFunc(self.skipped,self.dag)
self.interactor=interactor
self.total=0
self.likelihoodsObj=likelihoodsObj
self.strategy=strategy
return 1
return fact(x-1)
# logGamma
def logGamma(x):
if(x==0):
raise "Gamma called on zero!"
if(x==1):
return 0
return logFact(x-1)
# standard factorial function
factMemo=[numberType.const('1')]
# standard factorial function
def fact(x):
while(x>=len(factMemo)):
factMemo.append(factMemo[-1]*len(factMemo))
return factMemo[x]
# log-factorial function
logFactMemo=[numberType.const('0')]
def logFact(x):
while(x>=len(logFactMemo)):
logFactMemo.append(logFactMemo[-1]+numberType.log(len(logFactMemo)))
return logFactMemo[x]
def __init__(self,decayFactor):
self.decayFactor=decayFactor/numberType.const(2)
def __init__(self,decayFactor):
self.decayFactor=numberType.pow(decayFactor,numberType.const(0.5))
#
# BBChop is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BBChop. If not, see <http://www.gnu.org/licenses/>.
import evidence
import random
import dag
import testCases
import numberType
epsilon=numberType.const(0.000000001)
epCoeff=1+epsilon
epDelta=numberType.const(0.00000000000001)
def rough_eq(a,b):
r=True
minS_a=a-epDelta
maxS_a=a+epDelta
if a<0:
minP_a=a*epCoeff
maxP_a=a/epCoeff
else:
minP_a=a/epCoeff
maxP_a=a*epCoeff
return 1
return fact(x - 1)
# logGamma
def logGamma(x):
if (x == 0):
raise "Gamma called on zero!"
if (x == 1):
return 0
return logFact(x - 1)
# standard factorial function
factMemo = [numberType.const('1')]
# standard factorial function
def fact(x):
while (x >= len(factMemo)):
factMemo.append(factMemo[-1] * len(factMemo))
return factMemo[x]
# log-factorial function
logFactMemo = [numberType.const('0')]
def logFact(x):
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# BBChop is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BBChop. If not, see <http://www.gnu.org/licenses/>.
import numberType
def shannon(probs):
e=0
for p in probs:
if(p>0):
e-=p*numberType.log(p)
return e
alpha=numberType.const('1.2')
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 prod(l):
return reduce(operator.mul,l,numberType.const(1.0))