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Create lucidia_codex/test_harness/equation_validator.py which imports the validated coherence equation and provides functions to compute sample values, check monotonicity, and generate a JSON report summarizing boundedness and monotonicity results.
97 lines
3.5 KiB
Python
97 lines
3.5 KiB
Python
"""
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equation_validator.py
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Test harness for validating the coherence equation within the Lucidia codex.
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This script imports the validated coherence equation implementation and
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performs numerical checks on its boundedness and monotonicity. The harness
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is designed to be simple and independent: run it directly to see a JSON
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report summarizing the validation results.
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Functions:
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test_coherence_samples() -> list[float]:
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Compute coherence for several representative input triples.
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validate_monotonicity(psi_m: float, alpha: float, deltas: list[float]) -> bool:
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Check that coherence is non-increasing as the magnitude of delta grows.
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generate_validation_report() -> dict[str, bool]:
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Run all tests and return a dictionary summarizing pass/fail.
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Run this file as a script to print the report to stdout.
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"""
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from __future__ import annotations
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from typing import List, Dict
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import json
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import sys
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from pathlib import Path
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# Attempt a relative import of the coherence equation.
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try:
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from ..validated.coherence_equation import calculate_coherence
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except ImportError:
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# Fallback: add the parent of this file's parent to sys.path.
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current_path = Path(__file__).resolve()
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# lucidia_codex/test_harness/equation_validator.py -> parents[2] == lucidia_codex
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parent_root = current_path.parents[2]
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sys.path.insert(0, str(parent_root))
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try:
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from validated.coherence_equation import calculate_coherence # type: ignore
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except Exception as exc:
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raise ImportError("Unable to import calculate_coherence from validated.coherence_equation") from exc
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def test_coherence_samples() -> List[float]:
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"""Compute coherence for a set of representative input triples.
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Returns:
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A list of computed coherence values.
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"""
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samples = [
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(0.5, 0.0, 0.3),
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(0.5, -2.0, 0.3),
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(1.0, 10.0, 1.0),
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(-1.0, 5.0, 1.0),
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]
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return [calculate_coherence(psi_m, delta, alpha) for psi_m, delta, alpha in samples]
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def validate_monotonicity(psi_m: float, alpha: float, deltas: List[float]) -> bool:
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"""Check that coherence decreases or stays constant as |delta| increases.
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Args:
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psi_m: Truth value of memory (within [-1, 1]).
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alpha: Constructive contradiction weight (within [-1, 1]).
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deltas: A list of contradiction magnitudes in non-decreasing order.
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Returns:
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True if coherence is non-increasing across the provided deltas; False otherwise.
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"""
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values = [calculate_coherence(psi_m, d, alpha) for d in deltas]
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# Verify each successive value is less than or equal to the previous.
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return all(earlier >= later for earlier, later in zip(values, values[1:]))
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def generate_validation_report() -> Dict[str, bool]:
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"""Generate a validation report for the coherence equation.
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The report currently includes boundedness and monotonicity checks.
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Returns:
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A dictionary with boolean results for each validation test.
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"""
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report: Dict[str, bool] = {}
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# Boundedness: all sample results should lie in [0, 1]
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sample_results = test_coherence_samples()
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report["coherence_bounded"] = all(0.0 <= v <= 1.0 for v in sample_results)
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# Monotonicity: test with increasing absolute delta values.
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delta_values = [0, 1, 2, 3, 4, 5]
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report["coherence_monotonic"] = validate_monotonicity(0.5, 0.3, delta_values)
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return report
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def main() -> None:
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"""Run the validation tests and print a JSON report."""
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report = generate_validation_report()
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print(json.dumps(report, indent=2))
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if __name__ == "__main__":
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main()
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