def testAccountForType(self):
result = iP.firstIndexHash(self.entrySet, self.increasingList)
self.assertEqual(result, {
100: 1,
200: 3,
300: 6,
400: 7,
1000: 7,
2000: 10,
10000: 11
})
def unSerialize(self, pickleFile): #TODO: refactor to name "deSerialize"
#Load in results stored in serialized pickle format
#auxiliary function
def N_KLDiv_Pts(arrayOfResultsForThisEta,p_eta):
N_KLDiv_Pts = [tuple([alist['N'], alist['gamma'], alist['beta']]) for alist in arrayOfResultsForThisEta]
N_KLDiv_Pts = np.array( N_KLDiv_Pts, dtype=[('N', '<i8'), ('KL_Div','<f8'), ('beta', '<f8')])
#import ipdb; ipdb.set_trace()
for row in N_KLDiv_Pts:
row['KL_Div'] = p_eta.KLDivergenceOfP_gammaFromDist(row['KL_Div'])
N_KLDiv_Pts.sort(order=['N','KL_Div'])
return N_KLDiv_Pts
#main body:
if not self.eta:
raise Exception("Eta must be defined prior to unserializing results of beta computation")
probabilityDistFactoryUniformMarginals = pdf.probabilityDistributionFactory(self.k, self.l)
p_eta = probabilityDistFactoryUniformMarginals.get_p_eta(self.eta)
listOfResults= pickle.load(open(pickleFile,'rb'))
print pickleFile
listOfResultsAsTuples = [tuple(alist) for alist in listOfResults]
arrayOfResults = np.array(listOfResultsAsTuples, dtype=[('time', '<f8'),('eta', '<f8'), ('N', '<i8'), ('gamma', '<f8'), ('beta', '<f8')])
arrayOfResultsForThisEtaGammaLessThanEta = arrayOfResults[ arrayOfResults['eta'] == self.eta] #and arrayOfResults['gamma'] < self.eta]
arrayOfResultsForThisEtaGammaLessThanEta = arrayOfResultsForThisEtaGammaLessThanEta[arrayOfResultsForThisEtaGammaLessThanEta['gamma'] < self.eta]
arrayOfResultsForThisEtaGammaAtLeastEta = arrayOfResults[ arrayOfResults['eta'] == self.eta]
arrayOfResultsForThisEtaGammaAtLeastEta = arrayOfResultsForThisEtaGammaAtLeastEta[ arrayOfResultsForThisEtaGammaAtLeastEta ['gamma'] >= self.eta ]
self.betasByAscendingNAscendingGamma = np.array(listOfResultsAsTuples, dtype=[('time', '<f8'),('eta', '<f8'), ('N', '<i8'), ('gamma', '<f8'), ('beta', '<f8')])
self.betasByAscendingNAscendingGamma.sort(order = ['N', 'gamma'])
self.N_KLDivPtsForInterpolationGammaLessThanEta = N_KLDiv_Pts(arrayOfResultsForThisEtaGammaLessThanEta,p_eta)
self.N_KLDivPtsForInterpolationGammaAtLeastEta = N_KLDiv_Pts(arrayOfResultsForThisEtaGammaAtLeastEta,p_eta)
#import ipdb; ipdb.set_trace()
N_KLDivPtsForInterpolation_nd_GammaLessThanEta = self.N_KLDivPtsForInterpolationGammaLessThanEta[['N', 'KL_Div']].view(np.ndarray).reshape(len(self.N_KLDivPtsForInterpolationGammaLessThanEta), -1)
self.points_GammaLessThanEta = np.array([list(arow[0].view(np.ndarray)) for arow in N_KLDivPtsForInterpolation_nd_GammaLessThanEta])
N_KLDivPtsForInterpolation_nd_GammaAtLeastEta = self.N_KLDivPtsForInterpolationGammaAtLeastEta[['N', 'KL_Div']].view(np.ndarray).reshape(len(self.N_KLDivPtsForInterpolationGammaAtLeastEta), -1)
self.points_GammaAtLeastEta = np.array([list(arow[0].view(np.ndarray)) for arow in N_KLDivPtsForInterpolation_nd_GammaAtLeastEta])
self.valuesForInterpolationGammaLessthanEta = [np.log(row['beta']) for row in self.N_KLDivPtsForInterpolationGammaLessThanEta]
self.valuesForInterpolationGammaAtLeastEta = [np.log(row['beta']) for row in self.N_KLDivPtsForInterpolationGammaAtLeastEta]
self.largestN = self.betasByAscendingNAscendingGamma['N'][-1]
# if N is greater than the last (largest) N in the N_KLDivPtsForInterpolation['N'], compute value of function
# on this largest N and the given gamma, then second largest N and given gamma, and extrapolate from those two.
self.nextToLargestN, self.nextToLargestNLastIndex = self.nextLargestNAndLastIndex(self.largestN)
self.largestNFirstIndex = self.nextToLargestNLastIndex
thirdLargestN,thirdLargestNLastIndex = self.nextLargestNAndLastIndex(self.nextToLargestN, self.nextToLargestNLastIndex)
self.nextToLargestNFirstIndex = thirdLargestNLastIndex
self.NList = sorted(set(self.betasByAscendingNAscendingGamma['N']))
self.NFirstIndexHash = iP.firstIndexHash(set(self.NList),list(self.betasByAscendingNAscendingGamma['N']))
def testAccountForType(self):
result = iP.firstIndexHash(self.entrySet,self.increasingList)
self.assertEqual(result,{100: 1, 200: 3, 300: 6, 400: 7, 1000: 7, 2000: 10, 10000: 11})
def unSerialize(self, pickleFile): #TODO: refactor to name "deSerialize"
#Load in results stored in serialized pickle format
#auxiliary function
def N_KLDiv_Pts(arrayOfResultsForThisEta, p_eta):
N_KLDiv_Pts = [
tuple([alist['N'], alist['gamma'], alist['beta']])
for alist in arrayOfResultsForThisEta
]
N_KLDiv_Pts = np.array(N_KLDiv_Pts,
dtype=[('N', '<i8'), ('KL_Div', '<f8'),
('beta', '<f8')])
#import ipdb; ipdb.set_trace()
for row in N_KLDiv_Pts:
row['KL_Div'] = p_eta.KLDivergenceOfP_gammaFromDist(
row['KL_Div'])
N_KLDiv_Pts.sort(order=['N', 'KL_Div'])
return N_KLDiv_Pts
#main body:
if not self.eta:
raise Exception(
"Eta must be defined prior to unserializing results of beta computation"
)
probabilityDistFactoryUniformMarginals = pdf.probabilityDistributionFactory(
self.k, self.l)
p_eta = probabilityDistFactoryUniformMarginals.get_p_eta(self.eta)
listOfResults = pickle.load(open(pickleFile, 'rb'))
print pickleFile
listOfResultsAsTuples = [tuple(alist) for alist in listOfResults]
arrayOfResults = np.array(listOfResultsAsTuples,
dtype=[('time', '<f8'), ('eta', '<f8'),
('N', '<i8'), ('gamma', '<f8'),
('beta', '<f8')])
arrayOfResultsForThisEtaGammaLessThanEta = arrayOfResults[
arrayOfResults['eta'] ==
self.eta] #and arrayOfResults['gamma'] < self.eta]
arrayOfResultsForThisEtaGammaLessThanEta = arrayOfResultsForThisEtaGammaLessThanEta[
arrayOfResultsForThisEtaGammaLessThanEta['gamma'] < self.eta]
arrayOfResultsForThisEtaGammaAtLeastEta = arrayOfResults[
arrayOfResults['eta'] == self.eta]
arrayOfResultsForThisEtaGammaAtLeastEta = arrayOfResultsForThisEtaGammaAtLeastEta[
arrayOfResultsForThisEtaGammaAtLeastEta['gamma'] >= self.eta]
self.betasByAscendingNAscendingGamma = np.array(listOfResultsAsTuples,
dtype=[
('time', '<f8'),
('eta', '<f8'),
('N', '<i8'),
('gamma', '<f8'),
('beta', '<f8')
])
self.betasByAscendingNAscendingGamma.sort(order=['N', 'gamma'])
self.N_KLDivPtsForInterpolationGammaLessThanEta = N_KLDiv_Pts(
arrayOfResultsForThisEtaGammaLessThanEta, p_eta)
self.N_KLDivPtsForInterpolationGammaAtLeastEta = N_KLDiv_Pts(
arrayOfResultsForThisEtaGammaAtLeastEta, p_eta)
#import ipdb; ipdb.set_trace()
N_KLDivPtsForInterpolation_nd_GammaLessThanEta = self.N_KLDivPtsForInterpolationGammaLessThanEta[
['N', 'KL_Div']].view(np.ndarray).reshape(
len(self.N_KLDivPtsForInterpolationGammaLessThanEta), -1)
self.points_GammaLessThanEta = np.array([
list(arow[0].view(np.ndarray))
for arow in N_KLDivPtsForInterpolation_nd_GammaLessThanEta
])
N_KLDivPtsForInterpolation_nd_GammaAtLeastEta = self.N_KLDivPtsForInterpolationGammaAtLeastEta[
['N', 'KL_Div']].view(np.ndarray).reshape(
len(self.N_KLDivPtsForInterpolationGammaAtLeastEta), -1)
self.points_GammaAtLeastEta = np.array([
list(arow[0].view(np.ndarray))
for arow in N_KLDivPtsForInterpolation_nd_GammaAtLeastEta
])
self.valuesForInterpolationGammaLessthanEta = [
np.log(row['beta'])
for row in self.N_KLDivPtsForInterpolationGammaLessThanEta
]
self.valuesForInterpolationGammaAtLeastEta = [
np.log(row['beta'])
for row in self.N_KLDivPtsForInterpolationGammaAtLeastEta
]
self.largestN = self.betasByAscendingNAscendingGamma['N'][-1]
# if N is greater than the last (largest) N in the N_KLDivPtsForInterpolation['N'], compute value of function
# on this largest N and the given gamma, then second largest N and given gamma, and extrapolate from those two.
self.nextToLargestN, self.nextToLargestNLastIndex = self.nextLargestNAndLastIndex(
self.largestN)
self.largestNFirstIndex = self.nextToLargestNLastIndex
thirdLargestN, thirdLargestNLastIndex = self.nextLargestNAndLastIndex(
self.nextToLargestN, self.nextToLargestNLastIndex)
self.nextToLargestNFirstIndex = thirdLargestNLastIndex
self.NList = sorted(set(self.betasByAscendingNAscendingGamma['N']))
self.NFirstIndexHash = iP.firstIndexHash(
set(self.NList), list(self.betasByAscendingNAscendingGamma['N']))