tensor-spline 1.0.0

Compressed neural network layers — Eisenstein lattice splines and low-rank factorization

tensor-spline

Compressed neural network layers — Eisenstein lattice splines and low-rank factorization.

What's Novel

SplineLinear: Weights parameterized by control points on an Eisenstein (hexagonal) lattice, interpolated to create the full weight matrix. Built-in compression, regularization, and constraint-native structure.

Key result: drift-detect task hits 100% accuracy at 20× compression with SplineLinear.

Install

pip install tensor-spline

Requires PyTorch ≥ 2.0.

Quick Start

import torch.nn as nn
from tensor_spline import SplineLinear, inject_spline, LowRankLinear, recommend_variant

# Option 1: Direct construction
layer = SplineLinear(512, 512, n_control_points=16)
# 262,144 params → 16 params (16,384:1 compression)

# Option 2: Inject into any model
model = nn.Sequential(nn.Linear(256, 128), nn.ReLU(), nn.Linear(128, 10))
inject_spline(model, n_control_points=16)

# Option 3: Low-rank for sharp classification tasks
layer = LowRankLinear(256, 128, rank=16)
# 32,768 params → 6,144 params (5.3× compression, 80% accuracy retention)

# Auto-select the right compression for your task
variant = recommend_variant("detect drift in sensor data")  # → "spline"
variant = recommend_variant("classify documents by topic")   # → "lowrank"

Compression Strategies

Method	Best For	Compression	Accuracy
SplineLinear	Smooth/continuous tasks	20-40×	95-100% retention
LowRankLinear	Classification, sharp boundaries	5-16×	80% retention
HierarchicalSpline	Multi-scale continuous	10-20×	Experimental

Honest Findings

SplineLinear achieves 100% accuracy at 20× compression on drift-detect (smooth task). But only 31% on topic-classify (sharp boundaries) — IDW interpolation is too smooth for classification. Use LowRankLinear for classification tasks instead.

Use the right tool for the right task.

3 Basis Functions (SplineLinear)

• eisenstein — Inverse distance weighting (default, best for smooth)
• gaussian — Gaussian RBF
• bspline — Cubic B-spline kernel

Related

• plato-training — Micro model training (primary consumer of SplineLinear)
• eisenstein-embed — 5-layer semantic matching cascade (uses SplineLinear for 20× embedding quantization)
• eisenstein — Eisenstein integer arithmetic (mathematical foundation)
• plato-types — Tile lifecycle and provenance
• ASSEMBLY-GUIDE — Full ecosystem assembly guide

Mesh Protocol

tensor-spline supports the SuperInstance mesh protocol. When co-installed with plato-core, compression capabilities auto-register with the shared registry.

pip install tensor-spline[mesh]

This enables:

• spline-linear and low-rank compressors discoverable via the mesh registry
• inject, measure, and recommend utilities available to other mesh services
• Entry-point based plugin loading (superinstance.plugins)

Works standalone without plato-core — graceful degradation via try/except.

License

MIT