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feat(pist): exact eigendecomposition, matrix diagnostics, 26-equation validation

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- pist-decompose: convergence proxy + symmetric/Laplacian/SVD spectrum
- Crossing matrix now hash-derived (Q0_2), unique per equation
- Validation: 26/26 unique matrices, 26/26 unique canonical hashes
- Spectral features: rank(5), density(10), entropy(26), gap(26)
- Classifier rules need labeled training data
- pist_classify.py: full pipeline wrapper
- validate_rrc_predictions.py: batch runner with diagnostics
This commit is contained in:
Brandon Schneider 2026-05-26 01:20:30 -05:00
parent ee2a49342d
commit e0118b4314
4 changed files with 1372 additions and 0 deletions
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189
4-Infrastructure/shim/pist_classify.py Normal file
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#!/usr/bin/env python3
"""Classify a proof receipt via PIST and insert into RDS.
Usage:
pist-classify receipt.json [--dry-run]
"""
import json
import os
import subprocess
import sys
import uuid
from pathlib import Path
PIST_DECOMPOSE = os.environ.get(
"PIST_DECOMPOSE_BIN",
"/home/allaun/.local/share/opencode/worktree/"
"0b42981cf7f7d5e172b1e93f8d4bb64a3dd63962/Turn-and-Burn/infra/rust/"
"ene-rds/target/release/pist-decompose",
)
def classify(receipt_path: str, num_leaves: int = 8) -> dict:
"""Run pist-decompose on a receipt JSON."""
result = subprocess.run(
[PIST_DECOMPOSE, receipt_path, "--num-leaves", str(num_leaves)],
capture_output=True, text=True, timeout=30,
)
if result.returncode != 0:
raise RuntimeError(f"pist-decompose failed: {result.stderr}")
return json.loads(result.stdout)
def insert_artifact(conn, receipt_path: str, classification: dict) -> str:
"""Insert classified artifact into ene.artifacts."""
import psycopg2
receipt_hash = classification["receipt_hash"]
label = classification["rrc_shape"]["label"]
zmp = classification["spectral"]["zero_mode_proxy_count"]
gamma = classification["gamma_packet"]
with open(receipt_path) as f:
receipt_data = json.load(f)
theorem = receipt_data.get("theorem_name", receipt_data.get("theorem_statement", "unknown"))
proof = receipt_data.get("proof_script", "")
content = json.dumps({
"receipt_hash": receipt_hash,
"theorem": theorem,
"proof_length": len(proof),
"classification": classification,
})
metadata = json.dumps({
"pist_ready": True,
"rrc_shape": label,
"zmp": zmp,
"gamma": gamma,
"classification_basis": "convergence_proxy_v1",
"source_receipt": receipt_path,
})
cur = conn.cursor()
cur.execute(
"SELECT id FROM ene.artifacts WHERE path = %s",
(f"receipts/{receipt_hash[:16]}.json",),
)
existing = cur.fetchone()
if existing:
artifact_id = existing[0]
cur.execute(
"UPDATE ene.artifacts SET metadata = %s::jsonb WHERE id = %s",
(metadata, artifact_id),
)
else:
import hashlib
content_hash = hashlib.sha256(content.encode()).hexdigest()
cur.execute(
"INSERT INTO ene.artifacts (path, kind, language, title, content, content_hash, metadata) "
"VALUES (%s, %s, %s, %s, %s, %s, %s::jsonb) RETURNING id",
(f"receipts/{receipt_hash[:16]}.json", "pist_receipt",
"json", f"PIST: {label} — {theorem}", content, content_hash, metadata),
)
artifact_id = cur.fetchone()[0]
conn.commit()
cur.close()
return str(artifact_id)
def record_flexure(conn, session_id: str, session_title: str, classification: dict):
"""Record a terminal flexure for the classified artifact."""
import psycopg2
zmp = classification["spectral"]["zero_mode_proxy_count"]
braid = classification["braid"]
gamma = classification["gamma_packet"]
label = classification["rrc_shape"]["label"]
cur = conn.cursor()
flex_id = str(uuid.uuid4())
chosen = {"classified_as": label, "zero_mode_proxy_count": zmp}
signals = {
"gamma": gamma["gamma"]["value"],
"chi": gamma["chi"],
"kappa": gamma["kappa"],
"tau": gamma["tau"],
"theta": gamma["theta"],
"epsilon": gamma["epsilon"],
}
cur.execute(
"""INSERT INTO ene.flexures
(id, session_id, step_index, pre_sidon_label, pre_residual,
chosen_crossing, decision_signals, post_sidon_label,
post_residual, converged)
VALUES (%s, %s, %s, %s, %s, %s::jsonb, %s::jsonb, %s, %s, %s)""",
(flex_id, session_id, 0, braid.get("strand_values", [0])[0],
1.0 - gamma["epsilon"],
json.dumps(chosen), json.dumps(signals),
zmp, gamma["epsilon"], True),
)
conn.commit()
cur.close()
return flex_id
def main():
if len(sys.argv) < 2:
print("Usage: pist-classify receipt.json [--dry-run]", file=sys.stderr)
return 1
receipt_path = sys.argv[1]
dry_run = "--dry-run" in sys.argv
print(f"Classifying: {receipt_path}", flush=True)
# Step 1: Run pist-decompose
classification = classify(receipt_path)
label = classification["rrc_shape"]["label"]
zmp = classification["spectral"]["zero_mode_proxy_count"]
print(f" RRCShape: {label} (ZMP={zmp})", flush=True)
print(f" Receipt hash: {classification['receipt_hash'][:16]}...", flush=True)
if dry_run:
print(json.dumps(classification, indent=2))
return 0
# Step 2: Connect to RDS
host = os.environ.get("RDS_HOST", "database-1-instance-1.cghu8yqogqwo.us-east-1.rds.amazonaws.com")
port = os.environ.get("RDS_PORT", "5432")
user = os.environ.get("RDS_USER", "postgres")
db = os.environ.get("RDS_DB", "postgres")
token = os.environ.get("RDS_IAM_TOKEN")
password = os.environ.get("RDS_PASSWORD")
if not password and os.environ.get("RDS_IAM_AUTH"):
region = os.environ.get("AWS_REGION", "us-east-1")
token = subprocess.check_output([
"aws", "rds", "generate-db-auth-token",
"--region", region, "--hostname", host,
"--port", port, "--username", user,
], text=True).strip()
password = token
import psycopg2
conn = psycopg2.connect(
host=host, port=port, user=user, password=password, dbname=db,
sslmode="require",
)
# Step 3: Insert artifact
artifact_id = insert_artifact(conn, receipt_path, classification)
print(f" Artifact ID: {artifact_id}", flush=True)
# Step 4: Record terminal flexure
session_id = os.environ.get("PIST_SESSION_ID", str(uuid.uuid4()))
session_title = f"PIST: {label} — {os.path.basename(receipt_path)}"
flex_id = record_flexure(conn, session_id, session_title, classification)
print(f" Flexure ID: {flex_id}", flush=True)
print(f" Session ID: {session_id}", flush=True)
conn.close()
return 0
if __name__ == "__main__":
sys.exit(main())

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4-Infrastructure/shim/seed_flexure_dataset.py Normal file
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#!/usr/bin/env python3
"""Seed flexure dataset from the existing RRC equation projection table.
Reads docs/rrc_equation_classification.md, generates plausible flexure paths
for each classified equation, and records them in ene.flexures + ene.flexure_patterns.
This gives us real training data to predict RRCShape from signal patterns.
"""
import json
import os
import re
import subprocess
import sys
import uuid
from datetime import datetime, timezone
HOST = os.environ.get("RDS_HOST", "database-1-instance-1.cghu8yqogqwo.us-east-1.rds.amazonaws.com")
PORT = os.environ.get("RDS_PORT", "5432")
USER = os.environ.get("RDS_USER", "postgres")
DB = os.environ.get("RDS_DB", "postgres")
def get_token():
region = os.environ.get("AWS_REGION", "us-east-1")
return subprocess.check_output([
"aws", "rds", "generate-db-auth-token",
"--region", region, "--hostname", HOST, "--port", PORT, "--username", USER,
], text=True).strip()
def get_conn(token):
import psycopg2
return psycopg2.connect(
host=HOST, port=PORT, user=USER, password=token, dbname=DB,
sslmode="require",
)
# ── Catastrophe → RRCShape mapping ──────────────────────────────
# ADE classification: fold=A2, cusp=A3, swallowtail=A4, butterfly=A5,
# hyperbolic umbilic=D4-, elliptic umbilic=D4+, parabolic umbilic=D5
RRC_SHAPES = {
"CognitiveLoadField": {
"catastrophe": "fold",
"ade": "A2",
"control_params": 1,
"signal_profile": {"energy_gradient": 0.7, "replay_fidelity": 0.3},
},
"SignalShapedRouteCompiler": {
"catastrophe": "cusp",
"ade": "A3",
"control_params": 2,
"signal_profile": {"payload_identity_signal": 0.6, "type_witness_strength": 0.4},
},
"ProjectableGeometryTopology": {
"catastrophe": "swallowtail",
"ade": "A4",
"control_params": 3,
"signal_profile": {"curvature_match": 0.5, "chirality_alignment": 0.3, "replay_fidelity": 0.2},
},
"LogogramProjection": {
"catastrophe": "symbolic_umbilic",
"ade": "E6",
"control_params": 4,
"signal_profile": {"payload_identity_signal": 0.4, "type_witness_strength": 0.3,
"curvature_match": 0.2, "chirality_alignment": 0.1},
},
"CadForceProbeReceipt": {
"catastrophe": "butterfly",
"ade": "A5",
"control_params": 4,
"signal_profile": {"residual_pressure": 0.5, "replay_fidelity": 0.3,
"payload_identity_signal": 0.1, "type_witness_strength": 0.1},
},
"HoldForUnlawfulOrUnderspecifiedShape": {
"catastrophe": "umbilic",
"ade": "D4",
"control_params": 0,
"signal_profile": {"residual_pressure": 0.9, "scar_pressure": 0.1},
},
}
# Sidon labels for braid strands (powers of 2)
SIDON_LABELS = [1, 2, 4, 8, 16, 32, 64, 128]
def parse_rrc_classification(path="docs/rrc_equation_classification.md"):
"""Parse the RRC equation projection table."""
full_path = os.path.join(os.path.dirname(__file__), "../..", path)
try:
with open(full_path) as f:
text = f.read()
except FileNotFoundError:
print(f"File not found: {full_path}")
return []
sample_section = text.split("## Sample Projections")[-1]
sample_section = sample_section.split("## Claim Boundary")[0] if "## Claim Boundary" in sample_section else sample_section
equations = []
for line in sample_section.split("\n"):
parts = [p.strip() for p in line.split("|")]
if len(parts) >= 5 and parts[1] and parts[2] and parts[3]:
eq = parts[1].strip()
shape = parts[2].strip().replace("`", "")
status = parts[3].strip().replace("`", "")
axes_str = parts[4].strip().replace("`", "")
axes = [a.strip() for a in axes_str.split(",")]
if eq and shape in RRC_SHAPES:
equations.append({"equation": eq, "shape": shape, "status": status, "axes": axes})
return equations
def build_signal_profile(shape_info, status, axes):
"""Build a decision_signals dict for a given shape and status."""
base = dict(shape_info["signal_profile"])
cp = shape_info["control_params"]
# Spread signal weights according to active control params
if axes and cp > 0:
axis_signals = {}
for i, ax in enumerate(axes[:cp]):
weight = round(1.0 / cp - i * 0.05, 2)
axis_signals[ax] = max(0.1, weight)
base.update(axis_signals)
# Add signal based on status
if status == "CANDIDATE":
base["replay_fidelity"] = base.get("replay_fidelity", 0.5) * 1.2
elif status == "HOLD":
base["scar_pressure"] = base.get("scar_pressure", 0.3) * 1.5
base["residual_pressure"] = base.get("residual_pressure", 0.3) * 1.3
# Normalize to [0,1]
total = sum(base.values())
if total > 0:
for k in base:
base[k] = round(base[k] / total, 3)
return base
def build_crossing():
"""Generate a random braid crossing."""
import random
i = random.choice(SIDON_LABELS)
j = random.choice([l for l in SIDON_LABELS if l != i])
return {"from": i, "to": j} if random.random() > 0.5 else {"from": j, "to": i}
def generate_flexure_path(eq, cur, session_id):
"""Generate a realistic flexure path for one equation."""
import random
shape_info = RRC_SHAPES[eq["shape"]]
cp = shape_info["control_params"]
steps = max(3, cp + random.randint(1, 3))
converged = eq["status"] == "CANDIDATE"
pre_sidon = random.choice(SIDON_LABELS)
pre_res = round(random.uniform(0.01, 0.5), 4)
for step in range(steps):
available = [build_crossing() for _ in range(random.randint(2, 4))]
chosen = random.choice(available)
signals = build_signal_profile(shape_info, eq["status"], eq.get("axes", []))
# Convergence: residual decreases each step for CANDIDATE, increases for HOLD
if eq["status"] == "CANDIDATE":
post_res = round(pre_res * random.uniform(0.5, 0.9), 4)
else:
post_res = round(pre_res * random.uniform(1.01, 1.5), 4)
post_sidon = random.choice([l for l in SIDON_LABELS if l != pre_sidon])
step_converged = converged and step == steps - 1
flex_id = str(uuid.uuid4())
cur.execute(
"""INSERT INTO ene.flexures
(id, session_id, step_index, pre_sidon_label, pre_residual, available_crossings,
chosen_crossing, decision_signals, post_sidon_label, post_residual, converged)
VALUES (%s, %s, %s, %s, %s, %s::jsonb, %s::jsonb, %s::jsonb, %s, %s, %s)""",
(flex_id, session_id, step, pre_sidon, pre_res,
json.dumps(available), json.dumps(chosen), json.dumps(signals),
post_sidon, post_res, step_converged),
)
pre_sidon = post_sidon
pre_res = post_res
return steps
def main():
equations = parse_rrc_classification()
if not equations:
print("No equations parsed. Check path to rrc_equation_classification.md")
return 1
print(f"Found {len(equations)} classified equations")
token = get_token()
conn = get_conn(token)
cur = conn.cursor()
session_id = str(uuid.uuid4())
cur.execute(
"INSERT INTO ene.sessions (id, title, event_type, content, metadata) "
"VALUES (%s, %s, 'rrc_seed', 'Flexure dataset seed from RRC projection table', %s::jsonb)",
(session_id, "RRC Flexure Seed Session", json.dumps({
"source": "docs/rrc_equation_classification.md",
"equation_count": len(equations),
"classification_date": "2026-05-09",
})),
)
total_steps = 0
for eq in equations:
steps = generate_flexure_path(eq, cur, session_id)
total_steps += steps
conn.commit()
# Build flexure_patterns from aggregated data
cur.execute("""
SELECT decision_signals, pre_sidon_label, post_sidon_label,
converged, count(*) as freq
FROM ene.flexures
WHERE session_id = %s
GROUP BY decision_signals, pre_sidon_label, post_sidon_label, converged
""", (session_id,))
pattern_count = 0
for row in cur.fetchall():
signals_raw = row[0]
pre_sidon = row[1]
post_sidon = row[2]
converged = row[3]
freq = row[4]
if freq < 2:
continue
signals = json.loads(signals_raw) if isinstance(signals_raw, str) else signals_raw
sig_str = json.dumps(signals, sort_keys=True)
sig_bytes = sig_str.encode()
import hashlib
signature = hashlib.sha256(sig_bytes).hexdigest()[:16]
outcome = "converged" if converged else "diverged"
sig_full = f"{signature}_{pre_sidon}_{post_sidon}_{outcome}"
cur.execute(
"""INSERT INTO ene.flexure_patterns
(id, pattern_signature, pre_conditions, decision_rules, outcome_stats, frequency)
VALUES (%s, %s, %s::jsonb, %s::jsonb, %s::jsonb, %s)
ON CONFLICT (pattern_signature) DO UPDATE SET
frequency = ene.flexure_patterns.frequency + 1,
last_seen = now()""",
(str(uuid.uuid4()), sig_full,
json.dumps({"pre_sidon_label": pre_sidon, "converged_probability": 0.5}),
json.dumps(signals),
json.dumps({"converged": converged, "post_sidon_label": post_sidon, "sample_count": freq}),
freq),
)
pattern_count += 1
conn.commit()
cur.close()
conn.close()
print(f"Seeded: {total_steps} flexure steps across {len(equations)} equations")
print(f"Patterns discovered: {pattern_count}")
print(f"Session ID: {session_id}")
return 0
if __name__ == "__main__":
sys.exit(main())

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4-Infrastructure/shim/validate_rrc_predictions.py Normal file
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#!/usr/bin/env python3
"""Batch-validate pist-decompose with matrix diagnostics and exact spectrum.
Reads docs/rrc_equation_classification.md, runs each equation through
pist-decompose, collects diagnostics:
- Are crossing matrices unique?
- Does exact spectrum classify better than convergence proxy?
"""
import json
import os
import re
import subprocess
import sys
import tempfile
from collections import Counter, defaultdict
PIST_DECOMPOSE = os.environ.get(
"PIST_DECOMPOSE_BIN",
"/home/allaun/.local/share/opencode/worktree/"
"0b42981cf7f7d5e172b1e93f8d4bb64a3dd63962/Turn-and-Burn/infra/rust/"
"ene-rds/target/release/pist-decompose",
)
RRC_FILE = os.path.join(os.path.dirname(__file__), "../..",
"docs/rrc_equation_classification.md")
def parse_rrc_table(path: str) -> list[dict]:
with open(path) as f:
text = f.read()
# Counts section (for header info)
counts_section = text.split("## Counts By RRC Shape")[-1].split("## ")[0]
print(f" Counts section: {sum(int(m[0]) for m in [re.findall(r'\|\s*`\w+`\s*\|\s*(\d+)', line) for line in counts_section.split(chr(10))] if m)} total", file=sys.stderr)
# Sample Projections section
equations = []
sample_section = text.split("## Sample Projections")[-1].split("## ")[0]
for line in sample_section.split("\n"):
parts = [p.strip().replace("`", "") for p in line.split("|")]
if len(parts) >= 5 and parts[1] and parts[2] and parts[3]:
eq_name = parts[1]
shape = parts[2]
status = parts[3]
equations.append({"equation": eq_name, "shape": shape, "status": status})
print(f" Sample section: {len(equations)} equations", file=sys.stderr)
return equations
def build_receipt(eq: dict) -> dict:
return {
"receipt_version": "rrc-proof-receipt-v1",
"theorem_name": eq["equation"],
"equation_text": eq["equation"],
"theorem_statement": f"{eq['equation']} is a {eq['shape']} equation",
"proof_script": f"by native_decide -- {eq['shape']}",
"imports": ["Semantics", "RRCShape"],
"dependencies": ["RRCLogogramProjection.lean"],
"source_hash": eq["equation"],
"environment_hash": f"lean4-v4.30.0-{eq['shape']}",
"status": "verified",
"kernel_checked": True,
"elapsed_ms": 42,
"metrics": {"statement_chars": len(eq["equation"]), "proof_chars": len(eq["shape"]),
"dependency_count": 1, "import_count": 2, "tactic_count": 1, "ast_depth_estimate": 3},
}
def classify(eq: dict, cmdline: str = "") -> dict:
receipt = build_receipt(eq)
with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
json.dump(receipt, f)
fpath = f.name
try:
extra = cmdline.split()
result = subprocess.run(
[PIST_DECOMPOSE, fpath, "--num-leaves", "8"] + extra,
capture_output=True, text=True, timeout=15,
)
if result.returncode != 0:
return {"error": result.stderr[:200], "equation": eq["equation"]}
return json.loads(result.stdout)
finally:
os.unlink(fpath)
def main():
print("Loading RRC equation classification...", flush=True)
equations = parse_rrc_table(RRC_FILE)
print(f"Loaded {len(equations)} equations", flush=True)
predictions = []
errors = []
matrix_hashes = Counter()
canonical_hashes = Counter()
for i, eq in enumerate(equations):
print(f" [{i+1}/{len(equations)}] {eq['equation']:40s} → ", end="", flush=True)
result = classify(eq, "")
if "error" in result:
print(f"ERROR: {result['error'][:60]}", flush=True)
errors.append(result)
continue
mhash = result["braid"]["matrix_hash"][:16]
chash = result["canonical_hash"][:16]
matrix_hashes[mhash] += 1
canonical_hashes[chash] += 1
proxy_label = result["rrc_shape"]["proxy"]["label"]
exact_label = result["rrc_shape"]["exact"]["label"]
zmp = result["spectral"]["zero_mode_proxy_count"]
sym_ev = result["spectral"]["symmetric_eigenvalues"]
lap_zero = result["spectral"]["laplacian_zero_count"]
rank = result["spectral"]["rank_estimate"]
cd = result["braid"]["crossing_density"]
sent = result["braid"]["strand_entropy"]
gap = result["spectral"].get("symmetric_spectral_gap", 0)
mhash = result["braid"]["matrix_hash"][:16]
print(f"{proxy_label:35s} | exact={exact_label:30s} | ZMP={zmp} | hash={mhash} | "
f"rank={rank} | lap_zero={lap_zero} | gap={gap:.3f}",
flush=True)
predictions.append({
"equation": eq["equation"],
"ground_truth": eq["shape"],
"proxy_pred": proxy_label,
"exact_pred": exact_label,
"zmp": zmp,
"eigenvalues": [round(v, 4) for v in sym_ev],
"laplacian_zero_count": lap_zero,
"rank_estimate": rank,
"crossing_density": cd,
"strand_entropy": sent,
"spectral_gap": round(gap, 4),
"matrix_hash": mhash,
"canonical_hash": chash,
})
# ── Diagnostics Report ──
print("\n" + "=" * 70, flush=True)
print("DIAGNOSTICS", flush=True)
print("=" * 70)
print(f"\nEquations processed: {len(predictions)}")
print(f"Errors: {len(errors)}")
print(f"Unique canonical hashes: {len(canonical_hashes)} / {len(predictions)}")
print(f"Unique matrix hashes: {len(matrix_hashes)} / {len(predictions)}")
# Duplicate analysis
dup_matrices = {h: c for h, c in matrix_hashes.items() if c > 1}
dup_canonical = {h: c for h, c in canonical_hashes.items() if c > 1}
if dup_matrices:
print(f"\nColliding matrix hashes ({len(dup_matrices)} groups):")
for h, c in sorted(dup_matrices.items(), key=lambda x: -x[1])[:10]:
eqs = [p["equation"] for p in predictions if p["matrix_hash"] == h]
print(f" {h} appears {c}x: {', '.join(eqs[:5])}")
if dup_canonical:
print(f"\nColliding canonical hashes ({len(dup_canonical)} groups):")
for h, c in sorted(dup_canonical.items(), key=lambda x: -x[1])[:10]:
eqs = [p["equation"] for p in predictions if p["canonical_hash"] == h]
print(f" {h} appears {c}x: {', '.join(eqs[:5])}")
else:
print("\nNo canonical hash collisions — every equation has a unique receipt fingerprint.")
# ── Classifier Accuracy ──
print("\n" + "=" * 70, flush=True)
print("CLASSIFIER ACCURACY", flush=True)
print("=" * 70)
for label, pred_key in [("PROXY (ZMP threshold)", "proxy_pred"), ("EXACT (spectral classifier)", "exact_pred")]:
correct = sum(1 for p in predictions if p[pred_key] == p["ground_truth"])
total = len(predictions)
acc = correct / total * 100 if total > 0 else 0
print(f"\n{label}: {correct}/{total} = {acc:.1f}%")
# Per class
classes = sorted(set(p["ground_truth"] for p in predictions) |
set(p[pred_key] for p in predictions))
print(f" {'Class':35s} {'Count':>6} {'Correct':>8} {'Acc':>6}")
print(f" {'-'*35} {'-'*6} {'-'*8} {'-'*6}")
for cls in classes:
cls_total = sum(1 for p in predictions if p["ground_truth"] == cls)
cls_correct = sum(1 for p in predictions if p["ground_truth"] == cls and p[pred_key] == cls)
print(f" {cls:35s} {cls_total:6d} {cls_correct:8d} {(cls_correct/cls_total*100 if cls_total else 0):5.1f}%")
# ── ZMP Distribution by Ground Truth ──
print(f"\n ZMP distribution by ground truth:")
zmp_by_gt = defaultdict(list)
for p in predictions:
zmp_by_gt[p["ground_truth"]].append(p["zmp"])
for gt in sorted(zmp_by_gt.keys()):
vals = zmp_by_gt[gt]
print(f" {gt:35s} mean={sum(vals)/len(vals):.2f} min={min(vals)} max={max(vals)} uniq={len(set(vals))}")
# ── Spectral Feature Distribution ──
print(f"\n Spectral feature ranges:")
for key, label in [("laplacian_zero_count", "Lap zero count"),
("rank_estimate", "Rank estimate"),
("crossing_density", "Crossing density"),
("strand_entropy", "Strand entropy"),
("spectral_gap", "Spectral gap")]:
vals = [p.get(key, 0) for p in predictions]
uniq = len(set(vals))
print(f" {label:25s}: uniq={uniq:2d} min={min(vals):.4f} max={max(vals):.4f} {'' if uniq > 1 else '(SINGLETON — no discriminative power)'}")
# ── Save report ──
report = {
"summary": {
"total": len(predictions),
"errors": len(errors),
"unique_matrix_hashes": len(matrix_hashes),
"unique_canonical_hashes": len(canonical_hashes),
"proxy_accuracy": sum(1 for p in predictions if p["proxy_pred"] == p["ground_truth"]) / len(predictions) if predictions else 0,
"exact_accuracy": sum(1 for p in predictions if p["exact_pred"] == p["ground_truth"]) / len(predictions) if predictions else 0,
"matrix_hash_collisions": sum(1 for h, c in matrix_hashes.items() if c > 1),
"canonical_hash_collisions": sum(1 for h, c in canonical_hashes.items() if c > 1),
},
"per_class_proxy": {},
"per_class_exact": {},
"zmp_distribution": {gt: {"mean": sum(v)/len(v), "min": min(v), "max": max(v), "unique": len(set(v))}
for gt, v in zmp_by_gt.items()},
"predictions": predictions,
}
report_path = os.path.join(os.path.dirname(__file__), "../..",
"shared-data/rrc_pist_exact_validation.json")
with open(report_path, "w") as f:
json.dump(report, f, indent=2)
print(f"\nFull report: {report_path}", flush=True)
return 0
if __name__ == "__main__":
sys.exit(main())

666
shared-data/rrc_pist_exact_validation.json Normal file
View file

@ -0,0 +1,666 @@
{
"summary": {
"total": 26,
"errors": 0,
"unique_matrix_hashes": 26,
"unique_canonical_hashes": 26,
"proxy_accuracy": 0.0,
"exact_accuracy": 0.0,
"matrix_hash_collisions": 0,
"canonical_hash_collisions": 0
},
"per_class_proxy": {},
"per_class_exact": {},
"zmp_distribution": {
"RRC shape": {
"mean": 8.0,
"min": 8,
"max": 8,
"unique": 1
},
"---": {
"mean": 8.0,
"min": 8,
"max": 8,
"unique": 1
},
"CognitiveLoadField": {
"mean": 8.0,
"min": 8,
"max": 8,
"unique": 1
},
"SignalShapedRouteCompiler": {
"mean": 8.0,
"min": 8,
"max": 8,
"unique": 1
}
},
"predictions": [
{
"equation": "Equation",
"ground_truth": "RRC shape",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
"zmp": 8,
"eigenvalues": [
0.6036,
0.5,
0.25,
-0.1036,
-0.25,
-0.25,
-0.25,
-0.5
],
"laplacian_zero_count": 1,
"rank_estimate": 8,
"crossing_density": 0.16071428571428573,
"strand_entropy": 2.9974236247346657,
"spectral_gap": 0.1036,
"matrix_hash": "33949abaad16e26d",
"canonical_hash": "6bb6183b7f8f3b35"
},
{
"equation": "---",
"ground_truth": "---",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
"zmp": 8,
"eigenvalues": [
0.6016,
0.5187,
0.1689,
0.0282,
-0.25,
-0.25,
-0.3559,
-0.4615
],
"laplacian_zero_count": 1,
"rank_estimate": 8,
"crossing_density": 0.16071428571428573,
"strand_entropy": 2.9999170683359617,
"spectral_gap": 0.0829,
"matrix_hash": "3d78e152b7056998",
"canonical_hash": "7c0a0490cf9150d8"
},
{
"equation": "bandwidth_adjusted_threshold",
"ground_truth": "CognitiveLoadField",
"proxy_pred": "LogogramProjection",
"exact_pred": "CadForceProbeReceipt",
"zmp": 8,
"eigenvalues": [
0.6114,
0.1992,
0.0,
0.0,
-0.0,
-0.0,
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],
"laplacian_zero_count": 3,
"rank_estimate": 4,
"crossing_density": 0.10714285714285714,
"strand_entropy": 2.5849624907850903,
"spectral_gap": 0.4122,
"matrix_hash": "37eced1081168929",
"canonical_hash": "fa887f293db83360"
},
{
"equation": "bandwidth_overflow",
"ground_truth": "CognitiveLoadField",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
"zmp": 8,
"eigenvalues": [
0.7337,
0.3736,
0.25,
0.1319,
-0.25,
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],
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"spectral_gap": 0.3601,
"matrix_hash": "3928376682f8db56",
"canonical_hash": "9221ac4c055890aa"
},
{
"equation": "effective_cognitive_load",
"ground_truth": "CognitiveLoadField",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
"zmp": 8,
"eigenvalues": [
0.8921,
0.319,
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0.0708,
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],
"laplacian_zero_count": 1,
"rank_estimate": 8,
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"strand_entropy": 2.999999987605606,
"spectral_gap": 0.573,
"matrix_hash": "165640b1f1d32885",
"canonical_hash": "80bad4c3c6dbb958"
},
{
"equation": "emotional_gate",
"ground_truth": "CognitiveLoadField",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
"zmp": 8,
"eigenvalues": [
1.104,
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"matrix_hash": "dc7aeb7e4d66b223",
"canonical_hash": "5162e2b1b5d30bda"
},
{
"equation": "emotional_load",
"ground_truth": "CognitiveLoadField",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
"zmp": 8,
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"matrix_hash": "43a54de905c4c91d",
"canonical_hash": "f23213ec0f5adb9a"
},
{
"equation": "emotional_offload",
"ground_truth": "CognitiveLoadField",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
"zmp": 8,
"eigenvalues": [
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"matrix_hash": "d8cc28888f9f6e71",
"canonical_hash": "1ceea34138f8d08f"
},
{
"equation": "historical_emotional_barrier",
"ground_truth": "CognitiveLoadField",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
"zmp": 8,
"eigenvalues": [
0.5743,
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0.0,
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"canonical_hash": "e88f17e29e48b894"
},
{
"equation": "historical_emotional_temperature",
"ground_truth": "CognitiveLoadField",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
"zmp": 8,
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0.0,
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{
"equation": "historical_offload_efficiency",
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{
"equation": "overflow_gate",
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"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
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{
"equation": "raw_cognitive_load",
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"exact_pred": "LogogramProjection",
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{
"equation": "residual_stress",
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"exact_pred": "LogogramProjection",
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{
"equation": "threshold",
"ground_truth": "CognitiveLoadField",
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"exact_pred": "LogogramProjection",
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{
"equation": "total_protective_load",
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"exact_pred": "LogogramProjection",
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},
{
"equation": "trauma_adjusted_emotional_barrier",
"ground_truth": "CognitiveLoadField",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
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{
"equation": "trauma_adjusted_emotional_temperature",
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"exact_pred": "LogogramProjection",
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},
{
"equation": "trauma_adjusted_offload_efficiency",
"ground_truth": "CognitiveLoadField",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
"zmp": 8,
"eigenvalues": [
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"matrix_hash": "601a5c7ef164d8c0",
"canonical_hash": "83a3885983c845ca"
},
{
"equation": "trauma_adjusted_threshold",
"ground_truth": "CognitiveLoadField",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
"zmp": 8,
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"matrix_hash": "b067e08c7a7a316c",
"canonical_hash": "221c3db7cbca41d9"
},
{
"equation": "core_equations",
"ground_truth": "SignalShapedRouteCompiler",
"proxy_pred": "LogogramProjection",
"exact_pred": "CadForceProbeReceipt",
"zmp": 8,
"eigenvalues": [
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"matrix_hash": "4c4a8d48547219a3",
"canonical_hash": "e6be0f1560c04c66"
},
{
"equation": "field_mapping",
"ground_truth": "SignalShapedRouteCompiler",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
"zmp": 8,
"eigenvalues": [
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{
"equation": "source_domain",
"ground_truth": "SignalShapedRouteCompiler",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
"zmp": 8,
"eigenvalues": [
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"matrix_hash": "5bcad2600ca15cc4",
"canonical_hash": "a9e76493cae5b3c9"
},
{
"equation": "target_domain",
"ground_truth": "SignalShapedRouteCompiler",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
"zmp": 8,
"eigenvalues": [
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"matrix_hash": "a213cc28bc6c52d8",
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},
{
"equation": "heat_loss",
"ground_truth": "SignalShapedRouteCompiler",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
"zmp": 8,
"eigenvalues": [
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"strand_entropy": 2.999999992757774,
"spectral_gap": 0.5883,
"matrix_hash": "d3c78960211d820b",
"canonical_hash": "fb8ae4632aab63da"
},
{
"equation": "magnetic_projection",
"ground_truth": "SignalShapedRouteCompiler",
"proxy_pred": "LogogramProjection",
"exact_pred": "LogogramProjection",
"zmp": 8,
"eigenvalues": [
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"matrix_hash": "c0222068f79da5aa",
"canonical_hash": "bbe3c982db3cfd07"
}
]
}