def independent_gaussian_samples(lagged, num):
shrunk_std = np.nanstd(list(lagged) + [0.01, -0.01])
shrunk_mean = np.nanmean(lagged + [0.0])
return [
shrunk_mean + shrunk_std * StatsConventions.norminv(p)
for p in evenly_spaced_percentiles(num)
]
def normal_sample(prediction_mean: float, prediction_std: float,
num: int) -> [float]:
""" Returns normally distributed samples evenly spaced in probability """
return [
prediction_mean + prediction_std * StatsConventions.norminv(p)
for p in evenly_spaced_percentiles(num=num)
]
def to_zcurve(self, prctls: List[float]):
""" A mapping from I^n -> R based on the Morton z-curve """
SAFE = False
dim = len(prctls)
if dim == 1:
return self.to_zscores(prctls)[0]
else:
zpercentile = self.from_cube(prctls=prctls)
return StatsConventions.norminv(zpercentile)
def to_zcurve(self, prctls: List[float] ):
""" A mapping from R^n -> R based on the Morton z-curve """
SAFE = False
dim = len(prctls)
if dim==1:
return self.to_zscores(prctls)[0]
else:
SCALE = self.morton_scale(dim)
int_prctls = [ int(math.floor(p*SCALE)) for p in prctls ]
m1 = pymorton.interleave(*int_prctls)
if SAFE:
int_prctls_back = pymorton.deinterleave2(m1) if dim==2 else pymorton.deinterleave3(m1)
assert all( i1==i2 for i1,i2 in zip(int_prctls, int_prctls_back))
m2 = pymorton.interleave(*[ SCALE-1 for _ in range(dim) ])
zpercentile = m1/m2
return StatsConventions.norminv(zpercentile)
def univariate_trivariate(trial):
u = trial.suggest_float('u', 1e-6, 1 - 1e-6)
z = StatsConventions.norminv(u)
u2 = zc.from_zcurve(zvalue=z, dim=3)
return objective(scale_me(u2, scale))[0]
def univariate(u):
z = StatsConventions.norminv(u)
u2 = zc.from_zcurve(zvalue=z, dim=3)
return objective(scale_me(u2,scale))[0]
def inv_cdf(self, p: float) -> float:
return self.mean + StatsConventions.norminv(p) * self.std()