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"""Tools for min-max normalization."""
# Copyright (C) 2022-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import torch
def normalize(
targets: np.ndarray | np.float32 | torch.Tensor,
threshold: float | np.ndarray | torch.Tensor,
min_val: float | np.ndarray | torch.Tensor,
max_val: float | np.ndarray | torch.Tensor,
) -> np.ndarray | torch.Tensor:
"""Apply min-max normalization and shift the values such that the threshold value is centered at 0.5."""
normalized = ((targets - threshold) / (max_val - min_val)) + 0.5
if isinstance(targets, np.ndarray | np.float32 | np.float64):
normalized = np.minimum(normalized, 1)
normalized = np.maximum(normalized, 0)
elif isinstance(targets, torch.Tensor):
normalized = torch.minimum(normalized, torch.tensor(1)) # pylint: disable=not-callable
normalized = torch.maximum(normalized, torch.tensor(0)) # pylint: disable=not-callable
else:
msg = f"Targets must be either Tensor or Numpy array. Received {type(targets)}"
raise TypeError(msg)
return normalized
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