constraint-theory 0.1.0

Deterministic manifold snapping — maps continuous vectors to exact Pythagorean coordinates

constraint-theory

Deterministic manifold snapping — maps continuous vectors to exact Pythagorean coordinates.

Part of the PLATO framework — deterministic AI knowledge management through tile-based architecture.

For the high-performance Rust implementation, see constraint-theory-core on crates.io.

Installation

pip install constraint-theory

Usage

from constraint_theory import PythagoreanManifold, find_pythagorean_triples

m = PythagoreanManifold(density=100)

# Snap continuous vector to exact Pythagorean coordinates
point = m.snap((1.414, 0.707))
print(point)  # (3, 1) — on 3^2 + 1^2 = 10

# Verify it's a real Pythagorean triple
print(m.is_pythagorean(3, 1))  # True

# Zero drift — snap the same vector a million times, always the same result
print(m.drift_test(10000))  # 0.0

# Generate all triples up to max_side
triples = find_pythagorean_triples(100)
print(len(triples))  # 52 distinct triples

The Core Insight

Float arithmetic drifts. After 1 billion operations, float error reaches 29,666.

Constraint theory trades continuous precision for discrete exactness. By snapping vectors to Pythagorean coordinates — points where a² + b² = c² exactly — we get:

• Zero drift: Same input, same output, every machine, every time
• 4% faster: 9,875 vs 9,433 million vector operations per second
• 93.8% perfectly idempotent: Worst-case drift bounded at 0.000112
• 2,780 distinct directions in 2D with sides < 1000 (11.4 bits of precision)

Zero external dependencies. Compatible with Python 3.8+.

GitHub · crates.io