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Merge branch 'preferential-gp3' of github.com:optuna/optuna-dashboard into preferential-gp3

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Contramundum
2023-09-08 14:11:30 +09:00
parent 91edb0fc7c 348d92e012
commit edd03589b1
1 changed files with 3 additions and 3 deletions
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optuna_dashboard/preferential/samplers/gp.py
+3 -3
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@@ -25,7 +25,7 @@ def _orthants_MVN_Gibbs_sampling(cov_inv: Tensor, cycles: int, initial_sample: T
assert cov_inv.shape == (dim, dim)
sample_chain = initial_sample
conditional_std = 1 / torch.sqrt(torch.diag(cov_inv))
conditional_std = torch.rsqrt(torch.diag(cov_inv))
scaled_cov_inv = cov_inv / torch.diag(cov_inv)[:, None]
out = torch.empty((cycles + 1, dim), dtype=torch.float64)
@@ -150,7 +150,7 @@ def _truncnorm_mean_var_logz(alpha: Tensor) -> tuple[Tensor, Tensor, Tensor]:
logz = torch.special.log_ndtr(-alpha)
mean = 1 / (SQRT_HALF_PI * torch.special.erfcx(alpha * SQRT_HALF))
var = 1 - mean * (mean - alpha)
return (mean, var, logz)
return mean, var, logz
def _orthants_MVN_EP(
@@ -194,7 +194,7 @@ def _orthants_MVN_EP(
mu = mu - Sxy * ((db + mean1 * da) * dr)
cov = cov - (Sxy[:, None] * (da * dr)) @ Sxy[None, :]
log_zs[i] = logz
return (mu, cov, torch.sum(log_zs))
return mu, cov, torch.sum(log_zs)
_orthants_MVN_EP_jit = torch.jit.script(_orthants_MVN_EP)