covsW = dict( [ ( name1, dict( [ ( name2, 0. ) for name2 in funcNames ] ) ) for name1 in funcNames ] )
# generate data and compute moments in loop
import sys
from ROOT import RooRandom
for it in range(nIters) :
if it % 100 == 0 :
print 'iteration %d' % it
sys.stdout.flush()
# generate data with (1 + x) / 2
RooRandom.randomGenerator().SetSeed( 100000 + it )
noWeightData.reset()
weightData.reset()
for evIt in range(nEvents) :
weight = RooRandom.uniform()
obs.setVal( 2. * weight - 1. )
if RooRandom.uniform() <= weight :
noWeightData.add(obsSet)
weightData.add( obsSet, weight )
# compute moments of functions
moms.compute( noWeightData, ResetFirst = True, Verbose = False )
momsW.compute( weightData, ResetFirst = True, Verbose = False )
# update covariance sums
momCoefs = moms.coefficients()
momCoefsW = momsW.coefficients()
for name1 in funcNames :
for name2 in funcNames :
covs[name1][name2] += ( momCoefs[name1][0] - funcMeans[name1] ) * ( momCoefs[name2][0] - funcMeans[name2] )
