102 lines
4.4 KiB
Python
102 lines
4.4 KiB
Python
from __future__ import annotations
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import numpy as np
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from algebra.cga import cga_inner
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from algebra.holonomy import holonomy_encode, holonomy_similarity
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from language_packs import load_pack
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from language_packs.compiler import compile_entries_to_manifold, load_mounted_packs, load_pack_entries
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from morphology.registry import load_morphology
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def _encode(manifold, tokens: list[str]) -> np.ndarray:
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return holonomy_encode([manifold.get_versor(t) for t in tokens])
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def test_aligned_clauses_have_higher_similarity_than_unrelated():
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_, en = load_pack("en_minimal_v1")
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_, he = load_pack("he_logos_micro_v1")
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_, grc = load_pack("grc_logos_micro_v1")
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en_clause = _encode(en, ["word", "beginning", "with", "truth"])
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he_clause = _encode(he, ["\u05d3\u05d1\u05e8", "\u05e8\u05d0\u05e9\u05d9\u05ea", "\u05d0\u05de\u05ea"])
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grc_clause = _encode(grc, ["\u03bb\u03cc\u03b3\u03bf\u03c2", "\u1f00\u03c1\u03c7\u03ae", "\u1f00\u03bb\u03ae\u03b8\u03b5\u03b9\u03b1"])
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grc_unrelated = _encode(grc, ["\u03bb\u03cc\u03b3\u03bf\u03c2", "\u1f00\u03c1\u03c7\u03ae", "\u03b6\u03c9\u03ae"])
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aligned = (np.linalg.norm(en_clause - he_clause) + np.linalg.norm(en_clause - grc_clause)) / 2.0
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unrelated = np.linalg.norm(en_clause - grc_unrelated)
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assert aligned < unrelated
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def test_triple_alignment_closer_than_other_triples():
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_, en = load_pack("en_minimal_v1")
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_, he = load_pack("he_logos_micro_v1")
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_, grc = load_pack("grc_logos_micro_v1")
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aligned_score = np.mean(
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[
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cga_inner(en.get_versor("word"), he.get_versor("\u05d3\u05d1\u05e8")),
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cga_inner(en.get_versor("word"), grc.get_versor("\u03bb\u03cc\u03b3\u03bf\u03c2")),
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cga_inner(he.get_versor("\u05d3\u05d1\u05e8"), grc.get_versor("\u03bb\u03cc\u03b3\u03bf\u03c2")),
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]
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)
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misaligned_score = np.mean(
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[
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cga_inner(en.get_versor("word"), he.get_versor("\u05e8\u05d0\u05e9\u05d9\u05ea")),
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cga_inner(en.get_versor("word"), grc.get_versor("\u03c0\u03bd\u03b5\u1fe6\u03bc\u03b1")),
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cga_inner(he.get_versor("\u05d3\u05d1\u05e8"), grc.get_versor("\u1f00\u03c1\u03c7\u03ae")),
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]
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)
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assert aligned_score > misaligned_score
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def test_light_alignment_clusters_across_mounted_trilingual_field():
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manifold = load_mounted_packs(("en_minimal_v1", "he_logos_micro_v1", "grc_logos_micro_v1"))
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aligned_score = np.mean(
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[
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cga_inner(manifold.get_versor("light"), manifold.get_versor("אוֹר")),
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cga_inner(manifold.get_versor("light"), manifold.get_versor("φῶς")),
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cga_inner(manifold.get_versor("אוֹר"), manifold.get_versor("φῶς")),
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]
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)
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unrelated_score = np.mean(
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[
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cga_inner(manifold.get_versor("light"), manifold.get_versor("דבר")),
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cga_inner(manifold.get_versor("light"), manifold.get_versor("ἀρχή")),
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cga_inner(manifold.get_versor("אוֹר"), manifold.get_versor("ζωή")),
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]
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)
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assert aligned_score > unrelated_score
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def test_word_order_permutation_changes_holonomy():
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_, en = load_pack("en_minimal_v1")
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a = _encode(en, ["word", "truth", "light", "life"])
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b = _encode(en, ["life", "word", "truth", "light"])
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assert abs(holonomy_similarity(a, b) - holonomy_similarity(a, a)) > 1e-4
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def test_same_root_hebrew_forms_land_closer_than_unrelated_noun():
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_, he = load_pack("he_logos_micro_v1")
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singular = he.get_versor("\u05d3\u05d1\u05e8")
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plural = he.get_versor("\u05d3\u05d1\u05e8\u05d9\u05dd")
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unrelated = he.get_versor("\u05e8\u05d0\u05e9\u05d9\u05ea")
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assert cga_inner(singular, plural) > cga_inner(singular, unrelated)
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def test_structured_morphology_improves_same_root_hebrew_resonance():
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entries = load_pack_entries("he_logos_micro_v1")
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no_morphology, _ = compile_entries_to_manifold(entries)
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structured, _ = compile_entries_to_manifold(entries, load_morphology("he_logos_micro_v1"))
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no_morph_score = cga_inner(no_morphology.get_versor("\u05d3\u05d1\u05e8"), no_morphology.get_versor("\u05d3\u05d1\u05e8\u05d9\u05dd"))
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structured_score = cga_inner(structured.get_versor("\u05d3\u05d1\u05e8"), structured.get_versor("\u05d3\u05d1\u05e8\u05d9\u05dd"))
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assert structured_score > no_morph_score, (
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f"Structured morphology should bring same-root forms closer: "
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f"structured={structured_score:.6f}, no_morphology={no_morph_score:.6f}"
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)
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