SparseWake/sparsewake
SparseWake SparseWake is a synthetic benchmark for sparse temporal hydrodynamic sensing. ICLR 2027 release The expanded release adds controlled multi-source mixtures and common-prior nearest-source tasks, with complete core data banks, reference checkpoints, a small review supplement, and reproduction code with a frozen wake-library input. Download release iclr2027-v1.0rc2 The version page lists the three archives, exact sizes, checksums, extraction instructions… See the full description on the dataset page: https://huggingface.co/datasets/SparseWake/sparsewake.
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1from __future__ import annotations2 3import numpy as np4import torch5 6from .metrics import summarize7 8 9def predict(model: torch.nn.Module, x: np.ndarray, batch_size: int = 4096) -> np.ndarray:10 model.eval()11 out = []12 with torch.no_grad():13 for start in range(0, len(x), batch_size):14 xb = torch.from_numpy(x[start : start + batch_size].astype(np.float32))15 out.append(model(xb).cpu().numpy())16 return np.concatenate(out, axis=0)17 18 19def evaluate_predictions(y_true: np.ndarray, y_pred: np.ndarray) -> dict[str, float]:20 return summarize(y_true, y_pred)21 22