wehub-resource-sync
75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
from typing import Optional
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from typing import Union
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import torch
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def norm_ft(X: torch.Tensor, ord: Optional[Union[int, float]] = None) -> torch.Tensor:
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r"""Normalize the input feature matrix with specified ``ord`` refer to pytorch's `torch.linalg.norm <https://pytorch.org/docs/stable/generated/torch.linalg.norm.html#torch.linalg.norm>`_ function.
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.. note::
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The input feature matrix is expected to be a 1D vector or a 2D tensor with shape (num_samples, num_features).
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Args:
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``X`` (``torch.Tensor``): The input feature.
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``ord`` (``Union[int, float]``, optional): The order of the norm can be either an ``int``, ``float``. If ``ord`` is ``None``, the norm is computed with the 2-norm. Defaults to ``None``.
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Examples:
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>>> import easygraph.datapipe as dd
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>>> import torch
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>>> X = torch.tensor([
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[0.1, 0.2, 0.5],
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[0.5, 0.2, 0.3],
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[0.3, 0.2, 0]
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])
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>>> dd.norm_ft(X)
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tensor([[0.1826, 0.3651, 0.9129],
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[0.8111, 0.3244, 0.4867],
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[0.8321, 0.5547, 0.0000]])
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"""
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if X.dim() == 1:
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X_norm = 1 / torch.linalg.norm(X, ord=ord)
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X_norm[torch.isinf(X_norm)] = 0
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return X * X_norm
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elif X.dim() == 2:
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X_norm = 1 / torch.linalg.norm(X, ord=ord, dim=1, keepdim=True)
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X_norm[torch.isinf(X_norm)] = 0
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return X * X_norm
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else:
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raise ValueError(
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"The input feature matrix is expected to be a 1D verter or a 2D tensor with"
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" shape (num_samples, num_features)."
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)
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def min_max_scaler(X: torch.Tensor, ft_min: float, ft_max: float) -> torch.Tensor:
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r"""Normalize the input feature matrix with min-max scaling.
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Args:
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``X`` (``torch.Tensor``): The input feature.
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``ft_min`` (``float``): The minimum value of the output feature.
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``ft_max`` (``float``): The maximum value of the output feature.
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Examples:
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>>> import easygraph.datapipe as dd
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>>> import torch
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>>> X = torch.tensor([
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[0.1, 0.2, 0.5],
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[0.5, 0.2, 0.3],
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[0.3, 0.2, 0.0]
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])
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>>> dd.min_max_scaler(X, -1, 1)
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tensor([[-0.6000, -0.2000, 1.0000],
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[ 1.0000, -0.2000, 0.2000],
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[ 0.2000, -0.2000, -1.0000]])
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"""
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assert (
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ft_min < ft_max
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), "The minimum value of the feature should be less than the maximum value."
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X_min, X_max = X.min().item(), X.max().item()
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X_range = X_max - X_min
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scale_ = (ft_max - ft_min) / X_range
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min_ = ft_min - X_min * scale_
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X = X * scale_ + min_
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return X
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