def fox_sample(lagged_values, lagged_times, delay, num, name, as_process=None):
" Elementary but not completely woeful sampler, used by Malaxable Fox"
dt = approx_dt(lagged_times)
lag = max(10, math.ceil(delay / dt))
print('lag = ' + str(lag))
is_proc = as_process or ('~' not in name and StatsConventions.is_process(lagged_values))
if len(lagged_values) < 250 + lag or not is_proc:
values = exponential_bootstrap(lagged=lagged_values, decay=0.1, num=num, as_process=as_process)
ret_values = StatsConventions.nudged(project_on_lagged_lattice(values=values, lagged_values=lagged_values))
else:
changes = np.diff(list(reversed(lagged_values)), n=lag)
counter = dict(Counter(changes))
d = dict(counter)
num_total = len(changes)
d1 = dict([(change, round(175 * change_count / num_total)) for change, change_count in d.items()])
values = list()
for change, rounded_count in d1.items():
values.extend([change] * rounded_count)
change_spray = list(range(-50, 50))
values.extend(change_spray)
change_values = values[:num]
abs_values = [lagged_values[0] + chg for chg in change_values]
if not len(abs_values) == num:
# Too many rounded down ... may not be discrete
abs_values = exponential_bootstrap(lagged=lagged_values, decay=0.1, num=num, as_process=True)
ret_values = StatsConventions.nudged(project_on_lagged_lattice(values=abs_values, lagged_values=lagged_values))
return ret_values
def independent_bootstrap(lagged, decay, num):
""" One parameter jiggled bootstrap favouring more recent observations
lagged [ float ] List most recent observation first
decay float Coefficient in exp(-a k) that weights samples
num int Number of scenarios requested
:returns [ float ] Statistical sample
"""
weights = list(np.exp([-decay * k for k in range(len(lagged))]))
empirical_sample = _weighted_random_sample(population=lagged, weights=weights, num=num)
return StatsConventions.nudged(empirical_sample)