learnware/learnware/specification/utils.py

import numpy as np

from .base import BaseStatSpecification
from .rkme import RKMESpecification


def generate_rkme_spec(
    X: np.ndarray,
    gamma: float = 0.1,
    K: int = 100,
    step_size: float = 0.1,
    steps: int = 3,
    nonnegative_beta: bool = True,
    reduce: bool = True,
    cuda_idx: int = -1,
) -> RKMESpecification:
    """
		Interface for users to generate Reduced-set Kernel Mean Embedding (RKME) specification.
		Return a RKMESpecification object, use .save() method to save as json file.


	Parameters
	----------
	X : np.ndarray
		Raw data in np.ndarray format.
		Size of array: (n*d)
	gamma : float
            Bandwidth in gaussian kernel, by default 0.1.
	K : int
		Size of the construced reduced set.
	step_size : float
		Step size for gradient descent in the iterative optimization.
	steps : int
		Total rounds in the iterative optimization.
	nonnegative_beta : bool, optional
		True if weights for the reduced set are intended to be kept non-negative, by default False.
	reduce : bool, optional
		Whether shrink original data to a smaller set, by default True	
	cuda_idx : int
		A flag indicating whether use CUDA during RKME computation. -1 indicates CUDA not used.

	Returns
	-------
	RKMESpecification
		A RKMESpecification object
	"""
    rkme_spec = RKMESpecification(gamma=gamma, cuda_idx=cuda_idx)
    rkme_spec.generate_stat_spec_from_data(X, K, step_size, steps, nonnegative_beta, reduce)
    return rkme_spec


def generate_stat_spec(X: np.ndarray) -> BaseStatSpecification:
    """
		Interface for users to generate statistical specification.
		Return a StatSpecification object, use .save() method to save as npy file.


	Parameters
	----------
	X : np.ndarray
		Raw data in np.ndarray format.
		Size of array: (n*d)

	Returns
	-------
	StatSpecification
		A StatSpecification object
	"""
    return None