def functionFromMarginalsTo_t_Distribution(eta,N,gamma,k,l):
statsDistFactory = sdf.statsDistributionFactory(gamma)
statsDistFactory.set_eta(eta)
statsDistFactory.set_N(N)
uniformMarginals = [1.0/k,1.0/l]
statsDistFactory.CalculateAndSetScaleRatioRobustToGammaExceedingEta(uniformMarginals)
return statsDistFactory.GaussianCenteredAtMinOftGammaPlusOrEtafromMarginalsScaledFromScaleRatio
def functionFromMarginalsTo_t_Distribution(eta, N, gamma, k, l):
statsDistFactory = sdf.statsDistributionFactory(gamma)
statsDistFactory.set_eta(eta)
statsDistFactory.set_N(N)
uniformMarginals = [1.0 / k, 1.0 / l]
statsDistFactory.CalculateAndSetScaleRatioRobustToGammaExceedingEta(
uniformMarginals)
return statsDistFactory.GaussianCenteredAtMinOftGammaPlusOrEtafromMarginalsScaledFromScaleRatio
def _testIntegratetoFlatFile(self):
#load in benchmark data
benchmarks = genfromtxt('cdf_for_valsets_of_variables_of_size2and2', delimiter=',', skip_header=4)
benchmarks_without_stepsize = benchmarks[:,1:]
lineInBenchMarkFileList = [0, 9]
#prepare the csv file
f=open('/home/eliot/Documents/sontag/benchmarkcomparisons/N@100_N@[email protected]', 'wt')
writer = csv.writer(f)
for lineInBenchMarkFile in lineInBenchMarkFileList:
benchmark_N = (lineInBenchMarkFile+1)*benchMarkInterval_N
benchmarksForThisN = benchmarks_without_stepsize[lineInBenchMarkFile]
stepSizeInBenchmarks = benchmarks[0,0]
numStepsInBenchmarks = len(benchmarks[0])
logging.info("Read in %s lines of benchmarks with stepsize=%s and %s steps"%(len(benchmarks_without_stepsize), stepSizeInBenchmarks, numStepsInBenchmarks))
#constants common to all of the runs
k, l = 2,2
eta = 0.01
N = benchmark_N
maxIterations = 10000
dictionaryFromNameOfMethodToResults = {}
#build the inputs to IntegrateWithStoppingCriterion
#the integrand, namely probability-of-emission calculator from p-eta, which is common to ALL methods
probabilityCalculatorAssociatedToEta = epc.emissionProbabilityCalculator(eta, k, l, N)
for gammaNumber in range(numStepsInBenchmarks-1):
gamma = (gammaNumber+1)*stepSizeInBenchmarks
benchMarkBeta = benchmarksForThisN[gammaNumber]
probabilityCalculatorAssociatedToEta.setGamma(gamma)
functionProbabilityOfEmissionByP_eta_Robbins = probabilityCalculatorAssociatedToEta.RobbinsEstimateOfEmissionProbabilityTimesCharFunctionOfTauMinusGamma
#the probability distribution used to choose marginals distribution, also common to ALL methods (except for completely-uniform, if implemented)
Gaussianscale = it.ChernoffRadius(CentralProbability, N)
normalDistLocUniformScaleDepOnChernoff = stats.norm(loc=0.5,scale=Gaussianscale)
#e.g. 5, .95 means "seeking 95 percent certainty answer is within 5 percent of truth , typically also common to all runs
accuracy_percent = 10
probability_accuracy_achieved = 0.95
#the method for picking the parameter-distribution based on the marginals, specific to this run
#
statsDistFactory = sdf.statsDistributionFactory(gamma)
dictionaryFromNameOfMethodToMethod = {}
dictionaryFromNameOfMethodToMethod['GaussianScaleAdaptiveTo_l_gamma']=statsDistFactory.GaussianCenteredAttGammaPlusfromMarginals
#dictionaryFromNameOfMethodToMethod['GaussianScaleAdaptiveTo_N']=statsDistFactory.GaussianCenteredAttGammaPlusfromMarginalsScaledFromScaleRatio
#functionFromMarginalsTo_t_Distribution = statsDistFactory.GaussianCenteredAttGammaPlusfromMarginals
uniformMarginals = [1.0/k, 1.0/l]
for nameOfMethod in dictionaryFromNameOfMethodToMethod.keys():
if dictionaryFromNameOfMethodToResults.has_key( nameOfMethod):
resultsDictionary = dictionaryFromNameOfMethodToResults[nameOfMethod]
else:
resultsDictionary = {}
statsDistFactory.set_eta(eta)
statsDistFactory.set_N(N)
statsDistFactory.CalculateAndSetScaleRatio(uniformMarginals)
#functionFromMarginalsTo_t_Distribution = statsDistFactory.GaussianCenteredAttGammaPlusfromMarginalsScaledFromScaleRatio
functionFromMarginalsTo_t_Distribution = dictionaryFromNameOfMethodToMethod[nameOfMethod]
#start the calculation proper
with Timer.Timer() as t:
iteration_values, rho_hat_values = iwsc.IntegrateWithStoppingCriterion(k,l,
functionProbabilityOfEmissionByP_eta_Robbins,
normalDistLocUniformScaleDepOnChernoff, functionFromMarginalsTo_t_Distribution, #!!!MAIN THING TO CHANGE!!!
maxIterations, 100, 100, #frequencyOfRecodingreslt, frequencyOfApplicationOfStoppingCriterion
accuracy_percent, probability_accuracy_achieved)
logging.info("Time elapsed is %.03f sec." % t.interval)
logging.info("Iteration_values for the method %s for eta=%s, gamma=%s, N=%s, accpercent=%s, prob_achieved=%s"%(nameOfMethod, eta,gamma,N,accuracy_percent, probability_accuracy_achieved))
logging.info(iteration_values)
logging.info("rho_hat_values for the method %s for eta=%s, gamma=%s, N=%s, accpercent=%s, prob_achieved=%s"%(nameOfMethod, eta,gamma,N,accuracy_percent, probability_accuracy_achieved))
logging.info( rho_hat_values)
betaResult = rho_hat_values[-1]*N**3
if resultsDictionary.has_key('time'):
resultsDictionary['time'].append(t.interval)
else:
resultsDictionary['time'] = [t.interval]
if resultsDictionary.has_key('CalculatedResult'):
resultsDictionary['CalculatedResult'].append(betaResult)
else:
resultsDictionary['CalculatedResult'] = [betaResult]
if resultsDictionary.has_key('BenchmarkBeta'):
resultsDictionary['BenchmarkBeta'].append(benchMarkBeta)
else:
resultsDictionary['BenchmarkBeta'] = [benchMarkBeta]
dictionaryFromNameOfMethodToResults[nameOfMethod] = resultsDictionary
fileNameForPickleDump = "/home/eliot/Documents/sontag/benchmarkcomparisons/testRun" + str(N)
pickle.dump( dictionaryFromNameOfMethodToResults, open( fileNameForPickleDump, "wb" ) )
#write to the CSV file
N_header = 'N=%s'%(N)
writer.writerow( [N_header])
for resultsDictionary in dictionaryFromNameOfMethodToResults.values():
for key in resultsDictionary.keys():
print key
print resultsDictionary[key]
rowList = resultsDictionary[key]
rowList.insert(0,key)
print rowList
writer.writerow(rowList)
f.close()
def setUp(self):
gamma, eta, N = 0.01, 0.1, 30
self.statsDistFact = sdf.statsDistributionFactory(gamma)
self.statsDistFact.set_eta(eta)
self.statsDistFact.set_N(N)
def setUp(self):
gamma, eta, N = 0.01, 0.1, 30
self.statsDistFact = sdf.statsDistributionFactory(gamma)
self.statsDistFact.set_eta(eta)
self.statsDistFact.set_N(N)
