def testChop(likelihoodObj, randomDag, falseNeg, multi):
tests = 0
right = 0
wrong = []
totalLooks = 0.0
for k in range(trials):
random.seed(k + 1)
dagObj = testCases.testDag(N, randomDag)
if falseNeg:
rate = random.random()
else:
rate = 1
tester = detect(N, rate, dagObj, multi)
finder = BBChop.BBChop(prior, cert, tester, likelihoodObj, dagObj)
loc = tester.loc
guess = finder.search()
totalLooks += finder.total
tests += tester.tests
if loc == guess:
right = right + 1
else:
wrong.append(guess)
# if((k+1)%10 == 0):
print "test ", k, " right%=", 100.0 * right / (k + 1), "right=", loc == guess, "totallooks=", totalLooks / (
k + 1
), "looks=", finder.total, "rate=", rate
# if((k+1)%100 ==0):
# tester.printLocs()
print "right:", right, " out of", trials, tests / trials
return (float(right) / trials) < cert
def testChop(likelihoodObj, randomDag, falseNeg, multi):
tests = 0
right = 0
wrong = []
totalLooks = 0.0
for k in range(trials):
random.seed(k + 1)
dagObj = testCases.testDag(N, randomDag)
if falseNeg:
rate = random.random()
else:
rate = 1
tester = detect(N, rate, dagObj, multi)
finder = BBChop.BBChop(prior, cert, tester, likelihoodObj, dagObj)
loc = tester.loc
guess = finder.search()
totalLooks += finder.total
tests += tester.tests
if (loc == guess):
right = right + 1
else:
wrong.append(guess)
#if((k+1)%10 == 0):
print "test ", k, " right%=", 100.0 * right / (
k + 1), "right=", loc == guess, "totallooks=", totalLooks / (
k + 1), "looks=", finder.total, "rate=", rate
#if((k+1)%100 ==0):
# tester.printLocs()
print "right:", right, " out of", trials, tests / trials
return (float(right) / trials) < cert
def testEntropyFunc(entropyFunc1,entropyFunc2,likelihoodArg1,likelihoodArg2,falsePos=False,falseNeg=False,maxCount=10,randomDag=False):
print "testing " ,entropyFunc1.func_name,entropyFunc2.func_name,likelihoodArg1.name(),likelihoodArg2.name(),"maxCount=",maxCount,"randomDag=",randomDag
fail=False
for t in range(tests):
d=testCases.testDag(n,randomDag)
(counts,locPrior)=testCases.randomEntropyData(t+1,n,d,falsePos,falseNeg,maxCount)
# pdb.set_trace()
(c1,e1,f1)=entropyFunc1(counts,locPrior,likelihoodArg1,d)
# pdb.set_trace()
(c2,e2,f2)=entropyFunc2(counts,locPrior,likelihoodArg2,d)
thisFail=False
if not rough_eq(c1,c2):
print "Error:\t",c1,c2
thisFail=True
for j in range(n):
# print "entropy:\t ",j,e1[j],e2[j] #,max(e1[j]/e2[j],e2[j]/e1[j])
if not rough_eq(e1[j],e2[j]):
print "Error:\t",e1[j],e2[j]
thisFail=True
if thisFail:
fail=True
print "test %d failed!" % t
else:
print "test %d passed" % t
if fail:
print "FAILED!"
else:
print "PASSED!"
return fail
import testCases
random.seed(1)
mult=100
out=file("data/N_T.singleRate.csv","w")
for n in range(50,1000,50):
d=testCases.testDag(n,False)
for i in range(mult):
(counts,locPrior)=testCases.randomEntropyData(i+1,n,d,False,True,10)
start=time.clock()
junk=evidence.entropiesFast(counts,locPrior,likelihoods.singleRateCalc,d)
end=time.clock()
out.write("%d,%f\n" % (n,(end-start)))
print n
out.close()
#
# 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 time
import random
import likelihoods
import testCases
random.seed(1)
mult = 100
out = file("data/N_T.singleRate.csv", "w")
for n in range(50, 1000, 50):
d = testCases.testDag(n, False)
for i in range(mult):
(counts,
locPrior) = testCases.randomEntropyData(i + 1, n, d, False, True, 10)
start = time.clock()
junk = evidence.entropiesFast(counts, locPrior,
likelihoods.singleRateCalc, d)
end = time.clock()
out.write("%d,%f\n" % (n, (end - start)))
print n
out.close()