A PyTorch layer for expanding input features into Legendre polynomial bases. Useful for building models with polynomial feature expansions while keeping the workflow compatible with standard PyTorch nn.Module layers.

• Computes Legendre polynomial terms P₀(x), P₁(x), …, Pₙ₋₁(x) for each input feature
• Supports arbitrary input dimensionality
• Optional trainable linear projection after expansion
• Drop-in compatible with torch.nn.Sequential
• Efficient recurrence-based computation (no loops over batches)

pip install -e .

You can see a full demonstration of the LegendreLayer in the provided example.ipynb notebook.

import torch
from torch_legendre import LegendreLayer

# Example: 2 input features, expand to degree 4 (P₀...P₃)
layer = LegendreLayer(in_features=2, degree=4)

x = torch.tensor([[0.1, -0.3],
                  [0.5,  0.2]])  # shape (batch=2, in_features=2)

y = layer(x)
print(y.shape)  # (2, 2 * 4) = (2, 8)

# Expand then project down to 3 outputs
layer = LegendreLayer(in_features=2, degree=4, out_features=3)

x = torch.rand(5, 2)
y = layer(x)
print(y.shape)  # (5, 3)

import torch.nn as nn

model = nn.Sequential(
    LegendreLayer(in_features=1, degree=5, out_features=10),
    LegendreLayer(in_features=10, degree=3, out_features=1)
)

Expands each input feature into its Legendre polynomial basis and optionally applies a trainable linear projection.

Parameters

• in_features (int): Number of input features.

• degree (int): Number of polynomial degrees to compute per input feature. degree = 1 means only the constant term P₀(x) = 1.

• out_features (int, optional): If provided, a final nn.Linear layer maps from (in_features * degree) → out_features. If None, returns the raw expanded features.

• bias (bool, default=True): Whether to include a bias term in the optional linear mapping.

Shapes

• Input: (batch_size, in_features)
• Output:
  • If out_features is None: (batch_size, in_features * degree)
  • Else: (batch_size, out_features)

This project is licensed under the MIT License — see the LICENSE file for details.