Source code for aepsych.models.inducing_points.fixed

from typing import Any, Dict, Optional

import torch
from aepsych.config import Config
from aepsych.models.inducing_points.base import BaseAllocator


[docs]class FixedAllocator(BaseAllocator):
    def __init__(
        self,
        dim: int,
        points: torch.Tensor,
    ) -> None:
        """Initialize the FixedAllocator with inducing points to use and bounds.

        Args:
            dim (int): Dimensionality of the search space.
            points (torch.Tensor): Inducing points to use (should be n, d).
        """
        super().__init__(dim=dim)
        self.points = points

[docs]    def allocate_inducing_points(
        self,
        inputs: Optional[torch.Tensor] = None,
        covar_module: Optional[torch.nn.Module] = None,
        num_inducing: int = 100,
        input_batch_shape: torch.Size = torch.Size([]),
    ) -> torch.Tensor:
        """Allocate inducing points by returning the fixed inducing points.

        Args:
            inputs (torch.Tensor): Input tensor, not required for FixedAllocator.
            covar_module (torch.nn.Module, optional): Kernel covariance module; included for API compatibility, but not used here.
            num_inducing (int, optional): The number of inducing points to generate. Defaults to 10.
            input_batch_shape (torch.Size, optional): Batch shape, defaults to an empty size; included for API compatibility, but not used here.

        Returns:
            torch.Tensor: The fixed inducing points.
        """
        # TODO: Usually, these are initialized such that the transforms are applied to
        # points already, this means that if the transforms change over training, the inducing
        # points aren't in the space. However, we don't have any changing transforms
        # right now.

        self.last_allocator_used = self.__class__
        return self.points