def integrated_mean(f, lb, ub):
"""
calculate the integrated mean of a function f
Inputs:
f -- a function that takes a list and returns a number
lb -- a list of lower bounds
ub -- a list of upper bounds
"""
expectation = integrate(f, lb, ub)
from mystic.math.stats import mean, volume
vol = volume(lb, ub)
return mean(expectation, vol)
def integrated_mean(f, lb, ub):
"""
calculate the integrated mean of a function f
Inputs:
f -- a function that takes a list and returns a number
lb -- a list of lower bounds
ub -- a list of upper bounds
"""
expectation = integrate(f, lb, ub)
from mystic.math.stats import mean,volume
vol = volume(lb, ub)
return mean(expectation, vol)
failure, success = sample(model, lb[0], ub[0])
pof = float(failure) / float(failure + success)
print("Exact PoF: %s" % pof)
for i in range(len(lb)):
print("\n")
print(" lower bounds: %s" % lb[i])
print(" upper bounds: %s" % ub[i])
for solved in params0[0]:
print("solved: %s" % solved)
print("subdiameters (squared): %s" % subdiams0[0])
print("diameter (squared): %s" % diam0[0])
print(" probability mass: %s" % probmass0[0])
expectation = expectation_value(model, lower_bounds, upper_bounds)
print(" expectation: %s" % expectation)
mean_value = mean(expectation, cuboid_volume)
print(" mean value: %s" % mean_value)
mcdiarmid = mcdiarmid_bound(mean_value, sqrt(diam0[0]))
print("McDiarmid bound: %s" % mcdiarmid)
# determine 'best' cuts to cuboid
for cut in range(max_number_of_cuts):
print("\n..... cut #%s ....." % (cut + 1))
lb, ub = make_cut(lb, ub, RVstart, RVend, cuboid_volume)
if DEBUG:
print("\n..... cut #%s ....." % (max_number_of_cuts + 1))
# get diameter for each subcuboid
params, subdiams, diam, probmass = test_cuboids(lb,ub,RVstart,RVend,\
cuboid_volume)
SOLVED_PARAMETERS, SUB_DIAMETERS = params, subdiams
failure, success = sample(model, lb[0], ub[0])
pof = float(failure) / float(failure + success)
print "Exact PoF: %s" % pof
for i in range(len(lb)):
print "\n"
print " lower bounds: %s" % lb[i]
print " upper bounds: %s" % ub[i]
for solved in params0[0]:
print "solved: %s" % solved
print "subdiameters (squared): %s" % subdiams0[0]
print "diameter (squared): %s" % diam0[0]
print " probability mass: %s" % probmass0[0]
expectation = expectation_value(model, lower_bounds, upper_bounds)
print " expectation: %s" % expectation
mean_value = mean(expectation, cuboid_volume)
print " mean value: %s" % mean_value
mcdiarmid = mcdiarmid_bound(mean_value, sqrt(diam0[0]))
print "McDiarmid bound: %s" % mcdiarmid
# determine 'best' cuts to cuboid
for cut in range(max_number_of_cuts):
print "\n..... cut #%s ....." % (cut + 1)
lb, ub = make_cut(lb, ub, RVstart, RVend, cuboid_volume)
if DEBUG:
print "\n..... cut #%s ....." % (max_number_of_cuts + 1)
# get diameter for each subcuboid
params, subdiams, diam, probmass = test_cuboids(lb, ub, RVstart, RVend, cuboid_volume)
SOLVED_PARAMETERS, SUB_DIAMETERS = params, subdiams
TOTAL_DIAMETERS, PROBABILITY_MASS = diam, probmass
