def extendLvls(self, run, lvls):
if len(lvls) == 0:
# First run, add min_lvls on each dimension
d = run.params.min_dim
eye = np.eye(d, dtype=int)
new_lvls = np.vstack([np.zeros(d, dtype=int)] +
[i*eye for i in range(1, run.params.min_lvls)])
lvls.add_from_list(new_lvls)
return
mimc.extend_prof_lvls(lvls, self.profCalc, run.params.min_lvls)
return
def extendLvls(self, run, lvls):
if len(lvls) == 0:
# First run, add min_lvls on each dimension
d = run.params.min_dim
eye = np.eye(d, dtype=int)
new_lvls = np.vstack(
[np.zeros(d, dtype=int)] +
[i * eye for i in range(1, run.params.min_lvls)])
lvls.add_from_list(new_lvls)
return
mimc.extend_prof_lvls(lvls, self.profCalc, run.params.min_lvls)
return
def extendLvls(self, run, lvls):
if len(lvls) == 0:
# First run, add min_lvls on each dimension
d = run.params.min_dim + self.extrapolate_s_dims
eye = np.eye(d, dtype=int)
new_lvls = np.vstack(
[np.zeros(d, dtype=int)] +
[i * eye for i in range(1, run.params.min_lvl)])
lvls.add_from_list(new_lvls)
return
import time
tStart = time.process_time()
# estimate rates
self.d_err_rates, \
s_fit_rates = misc.estimate_misc_error_rates(d=run.params.min_dim,
lvls=lvls,
errs=run.last_itr.calcDeltaEl(),
d_err_rates=self.d_err_rates,
lev2knots=lambda beta:misc.lev2knots_doubling(1+beta))
#################### extrapolate error rates
if s_fit_rates is not None:
valid = np.nonzero(
s_fit_rates > 1e-15
)[0] # rates that are negative or close to zero are not accurate.
N = len(s_fit_rates) + self.extrapolate_s_dims
k_of_N = self.transNK(run.params.qoi_dim, N,
run.params.qoi_problem)
K = np.max(k_of_N)
c = np.polyfit(np.log(1 + k_of_N[valid]), s_fit_rates[valid], 1)
k_rates_stoch = c[0] * np.log(1 + np.arange(0, K + 1)) + c[1]
s_err_rates = np.maximum(k_rates_stoch[k_of_N[:N]],
np.min(s_fit_rates[valid]))
s_err_rates[valid] = s_fit_rates[
valid] # The fitted rates should remain the same
else:
s_err_rates = []
tEnd_rates = time.process_time() - tStart
######### Update
tStart = time.process_time()
self.profCalc = setutil.MISCProfCalculator(
self.d_err_rates + self.d_work_rates, s_err_rates)
mimc.extend_prof_lvls(lvls, self.profCalc, run.params.min_lvl)
def extendLvls(self, run, lvls):
if len(lvls) == 0:
# First run, add min_lvls on each dimension
d = run.params.min_dim + self.extrapolate_s_dims
eye = np.eye(d, dtype=int)
new_lvls = np.vstack([np.zeros(d, dtype=int)] +
[i*eye for i in range(1, run.params.min_lvl)])
lvls.add_from_list(new_lvls)
return
import time
tStart = time.clock()
# estimate rates
self.d_err_rates, \
s_fit_rates = misc.estimate_misc_error_rates(d=run.params.min_dim,
lvls=lvls,
errs=run.last_itr.calcDeltaEl(),
d_err_rates=self.d_err_rates,
lev2knots=lambda beta:misc.lev2knots_doubling(1+beta))
#################### extrapolate error rates
if s_fit_rates is not None:
valid = np.nonzero(s_fit_rates > 1e-15)[0] # rates that are negative or close to zero are not accurate.
N = len(s_fit_rates) + self.extrapolate_s_dims
k_of_N = self.transNK(run.params.qoi_dim, N, run.params.qoi_problem)
K = np.max(k_of_N)
c = np.polyfit(np.log(1+k_of_N[valid]), s_fit_rates[valid], 1)
k_rates_stoch = c[0]*np.log(1+np.arange(0, K+1)) + c[1]
s_err_rates = np.maximum(k_rates_stoch[k_of_N[:N]],
np.min(s_fit_rates[valid]))
s_err_rates[valid] = s_fit_rates[valid] # The fitted rates should remain the same
else:
s_err_rates = []
tEnd_rates = time.clock() - tStart
######### Update
tStart = time.clock()
self.profCalc = setutil.MISCProfCalculator(self.d_err_rates +
self.d_work_rates,
s_err_rates)
mimc.extend_prof_lvls(lvls, self.profCalc, run.params.min_lvl)
