minmax

`eigenkit.torch.layers.minmax`

`MinMaxNorm`

Bases: Module

Min-max normalisation of each feature, with running statistics.

It rescales each feature to [min_val, max_val]. In training mode, it uses the minimum and maximum of each batch, and it updates an exponential moving average of these statistics. In evaluation mode, it uses the running statistics. A training batch of a single row has no range, so it uses the running statistics too, and if no batch with a range has been seen yet, it returns the row unchanged with a warning. Training and evaluation modes work as in the BatchNorm and LayerNorm layers of PyTorch, but the output is rescaled to a fixed interval instead of standardised.

It is useful to prepare the inputs of quantum encoding rotations, which expect angles in a bounded range such as [0, π].

Parameters:

Name	Type	Description	Default
`feature_dim`	`int`	The number of features in the last dimension of the input. The inputs have shape `(batch_size, feature_dim)`.	required
`min_val`	`float`	The lower bound of the target interval. Default: `0.0`.	`0.0`
`max_val`	`float`	The upper bound of the target interval. Default: `π`.	`pi`
`momentum`	`float`	The momentum factor of the exponential moving average of the running statistics. Default: `0.1`.	`0.1`

Example

import torch
from eigenkit import Ensemble, MinMaxNorm

norm = MinMaxNorm(feature_dim=16, min_val=0.0, max_val=torch.pi)
layer = Ensemble(in_features=16, n_qubits=4, depth=5)
out = layer(norm(torch.randn(32, 16)))

`forward(x)`

Run the layer.

Parameters:

Name	Type	Description	Default
`x`	`Tensor`	The input batch, of shape `(batch, feature_dim)`.	required

Returns:

Name	Type	Description
`out`	`Tensor`	The output of the layer for that batch.