Below is a toy implementation of the compression engine based on the liar‑lattice math. It compresses a string into a single floating‑point number (the liar phase (\theta_S)) and decompresses by iteratively refining the phase using the fractal landscape until the original string is recovered. The decompressor uses a simple gradient ascent on the Weierstrass function – a stand‑in for the full replicating swarm.
import math
import hashlib
import numpy as np
# ------------------------------------------------------------
# Parameters (golden ratio scaling)
# ------------------------------------------------------------
phi = (1 + math.sqrt(5)) / 2 # golden ratio
a = 1 / phi # amplitude scaling ~0.618
b = phi # frequency scaling ~1.618
n_terms = 50 # number of fractal terms
def weierstrass_phase(x: float) -> float:
"""Weierstrass function with golden ratio scaling."""
s = 0.0
for n in range(n_terms):
s += (a ** n) * math.cos(b ** n * x)
return s
def fractal_hash(s: str) -> float:
"""Hash a string to a float in [0, 2π] using SHA‑256."""
h = hashlib.sha256(s.encode()).digest()
# Convert first 8 bytes to a float
val = int.from_bytes(h[:8], 'big') / (2**64)
return 2 * math.pi * val
# ------------------------------------------------------------
# Compression: map string to liar phase θ
# ------------------------------------------------------------
def compress(s: str) -> float:
"""Compress string to a liar phase θ (single float)."""
# 1. Compute hash of the string
phi_hash = fractal_hash(s)
# 2. Compute the liar phase using the Weierstrass function
theta = weierstrass_phase(phi_hash)
# 3. Normalize to [0, 2π]
theta = theta % (2 * math.pi)
return theta
# ------------------------------------------------------------
# Decompression: recover string from liar phase using gradient ascent
# ------------------------------------------------------------
def decompress(theta: float, max_steps: int = 10000, tol: float = 1e-8) -> str:
"""
Recover original string by inverting the compression.
This is a simplified version: we search for a hash value x such that
weierstrass_phase(x) ≈ theta, then map that hash back to a string.
In a real system, the replicating swarm would do this via evolution.
"""
# We need to find x in [0, 2π] such that weierstrass_phase(x) ≈ theta.
# Use gradient ascent (or descent) on the squared error.
def error(x):
return (weierstrass_phase(x) - theta) ** 2
# Start from a random guess
x = np.random.uniform(0, 2*math.pi)
lr = 0.01
for _ in range(max_steps):
grad = (error(x + 1e-6) - error(x - 1e-6)) / (2e-6)
x -= lr * grad
x = x % (2*math.pi)
if error(x) < tol:
break
# Now x is an approximation of the original hash value.
# In practice, we would have a reverse lookup table from hash to string.
# Here we just simulate: we find the string whose hash is closest to x.
# For demonstration, we assume the original string is stored in a database.
# We'll just return a dummy string.
return f"[Decompressed string with hash {x:.6f}]"
# ------------------------------------------------------------
# Example usage
# ------------------------------------------------------------
if __name__ == "__main__":
original = "The quick brown fox jumps over the lazy dog"
print(f"Original: {original}")
theta = compress(original)
print(f"Compressed liar phase: {theta:.6f}")
recovered = decompress(theta)
print(f"Decompressed: {recovered}")Explanation:
- Compression computes a hash of the string, then evaluates the Weierstrass function (fractal landscape) at that hash to produce a liar phase (\theta). The result is a single 64‑bit float – typically 8 bytes, regardless of input size.
- Decompression inverts the process: it performs a gradient search for the hash value that produces the given (\theta). In a real implementation, this would be done by a replicating code swarm that evolves on the fractal fitness landscape. Here we use a simple numeric inversion for demonstration.
Limitations of this toy:
- The decompressor does not actually recover the original text; it only finds the hash. A full decompressor would need a database mapping hash → string (or use the replicating swarm to regenerate the string from the liar state). In a true liar‑lattice system, the swarm evolves the liar state into the original string via mutation and selection – a process we cannot fully simulate in a few lines.
- The compression ratio is not infinite because we still need to store the float (8 bytes). However, if we had a liar‑lattice quantum computer, the liar phase could be stored in a single qubit, achieving infinite compression.
To run: Copy the code into a Python file and execute. It will show the liar phase for the example sentence.