Helper function. Check if data directory exists. If not, create it. Parameters ---------- save_dir : Path Path t

Create and initialize a database table based on problem configuration. Parameters ---------- name: str N

Loads the data given a task or another source.

Global registry of hyperparams. Each entry is a (default, random) tuple. New algorithms / networks / etc. should add entries here.

Create the optimizer object.

Called when the surrogate is first initialized or restarted. Args: train_size: The size of the current training set.

Defines the available fidelity parameters as a "fidelity space" for each benchmark. Parameters ---------- seed: int, None

Create the scikit-learn (training-)pipeline

Helper function to obtain RandomState from int or create a new one. Sometimes a default random state (self_rng) is already available, but a

Check whether matrix `a` is positive definite via Cholesky decomposition. Args: a: Input matrix. Returns: `True` if input ma

Load the state from a previous checkpoint.

Calculate the nearest positive-definite matrix to a given symmetric matrix `a`. Nearest is defined by the Frobenius norm. Args: a: S

Samples the posterior GP at the points X. :param X: the points at which to take the samples. :type X: np.ndarray (Nnew x sel

Samples the posterior GP at the points X. :param X: the points at which to take the samples. :type X: np.ndarray (Nnew x sel

Samples the posterior GP at the points X. :param X: The points at which to take the samples. :type X: np.ndarray (Nnew x sel

Predictions with the model. Returns posterior means and standard deviations at X. Note that this is different in GPy where the variances are

预测给定 antigen 和序列的能量 :param antigen: 要预测的 antigen :param sequence: 要预测的序列 :return: 预测的能量结果

Predict the mean function for given test point(s). For `idx=None` returns the same as `self.predict(data)[0]` but avoids the overhead

return proj_{B(h, \delta)}(adv_h), Euclidean projection to Euclidean ball

Query similar vectors in the cache. Parameters: ----------- vector: tuple of (str, str, int, int) The mu

Initialize random samples. :param num_samples: Number of random samples to generate :return: List of dictionaries, each repr