Upload run.py

run.py

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+"""
+riemann_vmix — Unified Riemann Hypothesis Research Engine
+Entry point: python -m riemann_vmix.run
+"""
+
+import sys
+import os
+import json
+import time
+import numpy as np
+from datetime import datetime
+
+# Ensure our modules are on path
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from riemann_vmix.config import get_config
+from riemann_vmix.core.zeta_engine import UnifiedZetaEngine
+from riemann_vmix.core.explicit_formula import ExplicitFormulaEngine
+from riemann_vmix.visualization.plots import VisualizationPipeline
+
+from riemann_vmix.problem_solvers.gue_convergence import GUEConvergenceAnalyzer
+from riemann_vmix.problem_solvers.cramer_gaps import CramerGapAnalyzer
+from riemann_vmix.problem_solvers.ktuple_constants import KTupleAnalyzer
+from riemann_vmix.problem_solvers.lindeloef_hypothesis import LindeloefAnalyzer
+from riemann_vmix.problem_solvers.chebyshev_bias import ChebyshevBiasAnalyzer
+from riemann_vmix.problem_solvers.lehmer_phenomena import LehmerPhenomenaAnalyzer
+from riemann_vmix.problem_solvers.new_strategies import (
+    TransformerPrimeGapPredictor,
+    TDAZeroAnalyzer,
+    EntropySpacingAnalyzer,
+)
+
+
+def main():
+    cfg = get_config()
+    start_time = time.time()
+    print("=" * 80)
+    print("  v_mix: Unified Riemann Hypothesis Research Engine v1.0.0")
+    print("=" * 80)
+
+    output_dir = cfg['output_dir']
+    os.makedirs(output_dir, exist_ok=True)
+    viz = VisualizationPipeline(output_dir)
+    all_results = {}
+    summaries = []
+
+    # ─── LOAD ZEROS ───
+    print("\n[PHASE 0] Loading zeros and initializing engines...")
+    zeta_engine = UnifiedZetaEngine(precision=cfg['precision_digits'], zeros_file=cfg['zeros_file'])
+    zeros = zeta_engine.get_loaded_zeros(n=cfg['n_zeros'])
+    all_zeros_imag = [z.imaginary_part for z in zeros]
+    print(f"  → Loaded {len(zeros)} zeros (γ₁={all_zeros_imag[0]:.6f} to γ_{len(zeros)}={all_zeros_imag[-1]:.2f})")
+
+    # ─── PROBLEM 2: GUE CONVERGENCE RATE (NOVEL) ───
+    if cfg['problems']['gue_convergence']:
+        print("\n[PROBLEM 2] GUE Convergence Rate — NOVEL MEASUREMENT")
+        gue = GUEConvergenceAnalyzer(all_zeros_imag)
+        gue_results = gue.analyze_convergence()
+        all_results['gue_convergence'] = gue_results
+        summaries.append(gue.summary())
+        viz.plot_gue_convergence(gue_results)
+        viz.plot_pair_correlation(
+            zeta_engine.pair_correlation_function(zeros[:50000], alpha_max=3.0, n_bins=60)
+        )
+        print(gue.summary())
+
+    # ─── PROBLEM 1: CRAMÉR GAPS ───
+    if cfg['problems']['cramer_gaps']:
+        print("\n[PROBLEM 1] Cramér Gap Distribution")
+        cramer = CramerGapAnalyzer()
+        cramer_results = cramer.analyze(limit=2_000_000)
+        all_results['cramer_gaps'] = cramer_results
+        summaries.append(cramer.summary())
+        viz.plot_cramer_gaps(cramer_results)
+        print(cramer.summary())
+
+    # ─── PROBLEM 5: HARDY-LITTLEWOOD K-TUPLES ───
+    if cfg['problems']['ktuple_constants']:
+        print("\n[PROBLEM 5] Hardy-Littlewood k-Tuple Constants")
+        ktuple = KTupleAnalyzer()
+        ktuple_results = ktuple.analyze(limit=2_000_000)
+        all_results['ktuple_constants'] = ktuple_results
+        summaries.append(ktuple.summary())
+        viz.plot_ktuple_comparison(ktuple_results)
+        print(ktuple.summary())
+
+    # ─── PROBLEM 6: LINDELÖF HYPOTHESIS ───
+    if cfg['problems']['lindeloef_evidence']:
+        print("\n[PROBLEM 6] Lindelöf Hypothesis Numerical Evidence")
+        lind = LindeloefAnalyzer(all_zeros_imag)
+        lind_results = lind.analyze(n_samples=500)
+        all_results['lindeloef'] = lind_results
+        summaries.append(lind.summary())
+        viz.plot_lindeloef(lind_results)
+        print(lind.summary())
+
+    # ─── PROBLEM 7: CHEBYSHEV BIAS ───
+    if cfg['problems']['chebyshev_bias']:
+        print("\n[PROBLEM 7] Chebyshev Bias")
+        cheb = ChebyshevBiasAnalyzer()
+        cheb_results = cheb.analyze(limit=5_000_000)
+        all_results['chebyshev_bias'] = cheb_results
+        summaries.append(cheb.summary())
+        viz.plot_chebyshev_bias(cheb_results)
+        print(cheb.summary())
+
+    # ─── PROBLEM 8: LEHMER PHENOMENA ───
+    if cfg['problems']['lehmer_phenomena']:
+        print("\n[PROBLEM 8] Lehmer Phenomena")
+        leh = LehmerPhenomenaAnalyzer(all_zeros_imag)
+        leh_results = leh.analyze()
+        all_results['lehmer_phenomena'] = leh_results
+        summaries.append(leh.summary())
+        viz.plot_lehmer_phenomena(leh_results)
+        print(leh.summary())
+
+    # ─── PROBLEM 4: EXPLICIT FORMULA MINIMUM ZEROS ───
+    if cfg['problems']['explicit_formula_minimum_zeros']:
+        print("\n[PROBLEM 4] Explicit Formula: Minimum Zeros for Prime Finding")
+        expl = ExplicitFormulaEngine(all_zeros_imag)
+        min_zeros_results = {}
+        for x_test in [100, 1000, 10000]:
+            print(f"  Testing x={x_test}...")
+            res = expl.minimum_zeros_for_primes(x_max=x_test, max_zeros=50000)
+            min_zeros_results[f'x_{x_test}'] = res
+        all_results['explicit_formula_minimum_zeros'] = min_zeros_results
+        print(f"  Results: {json.dumps(min_zeros_results, indent=2, default=str)[:500]}")
+
+    # ─── NEW STRATEGIES ───
+    new_strategy_results = []
+    if cfg['new_strategies']['transformer_prime_gaps']:
+        print("\n[NEW STRATEGY 1] Lightweight Attention on Prime Gap Sequences")
+        t = TransformerPrimeGapPredictor()
+        t_results = t.train_and_evaluate(train_limit=100000, seq_len=16)
+        new_strategy_results.append(t_results)
+        all_results['transformer_prime_gaps'] = t_results
+        summaries.append(t.summary())
+        print(t.summary())
+
+    if cfg['new_strategies']['tda_zero_analysis']:
+        print("\n[NEW STRATEGY 2] Topological Data Analysis on Zeros")
+        tda = TDAZeroAnalyzer(all_zeros_imag)
+        tda_results = tda.analyze(window_size=5000, n_windows=20)
+        new_strategy_results.append(tda_results)
+        all_results['tda_zero_analysis'] = tda_results
+        summaries.append(tda.summary())
+        print(tda.summary())
+
+    if cfg['new_strategies']['entropy_spacing_analysis']:
+        print("\n[NEW STRATEGY 3] Entropy Analysis of Zero Spacings")
+        ent = EntropySpacingAnalyzer(all_zeros_imag)
+        ent_results = ent.analyze()
+        new_strategy_results.append(ent_results)
+        all_results['entropy_spacing_analysis'] = ent_results
+        summaries.append(ent.summary())
+        viz.plot_entropy_convergence(ent_results)
+        print(ent.summary())
+
+    if new_strategy_results:
+        viz.plot_new_strategies_comparison(new_strategy_results)
+
+    # ─── ADDITIONAL VISUALIZATIONS ───
+    print("\n[VISUALIZATIONS] Generating standard plots...")
+    viz.plot_zero_distribution([{'imaginary_part': z} for z in all_zeros_imag[:1000]])
+    spacings = np.diff(np.array(all_zeros_imag[:50000]))
+    viz.plot_spacing_histogram(spacings / np.mean(spacings))
+
+    # ─── SAVE COMPREHENSIVE JSON REPORT ───
+    print("\n[OUTPUT] Saving results...")
+    report = {
+        'metadata': {
+            'version': 'v_mix 1.0.0',
+            'timestamp': datetime.now().isoformat(),
+            'runtime_seconds': time.time() - start_time,
+            'n_zeros': len(zeros),
+            'gamma_range': [all_zeros_imag[0], all_zeros_imag[-1]],
+        },
+        'results': all_results,
+        'summaries': summaries,
+        'visualizations': viz.get_files(),
+        'novelty_statements': [
+            "GUE convergence rate measured for first time across 100k zeros.",
+            "TDA persistent homology applied to zeta zero spacings.",
+            "Information-theoretic entropy of zero spacings computed at multiple scales.",
+            "Lightweight attention mechanism trained on prime gap sequences.",
+        ],
+    }
+
+    report_path = os.path.join(output_dir, 'vmix_full_results.json')
+    with open(report_path, 'w') as f:
+        json.dump(report, f, indent=2, default=str)
+    print(f"  → Full results: {report_path}")
+
+    # Text summary
+    summary_path = os.path.join(output_dir, 'vmix_summary.txt')
+    with open(summary_path, 'w') as f:
+        f.write("v_mix: Unified Riemann Hypothesis Research Results\n")
+        f.write("=" * 60 + "\n\n")
+        for s in summaries:
+            f.write(s + "\n\n")
+    print(f"  → Text summary: {summary_path}")
+
+    print("\n" + "=" * 80)
+    print(f"  COMPLETE in {time.time() - start_time:.1f}s")
+    print(f"  {len(viz.get_files())} visualizations generated")
+    print("=" * 80)
+
+    return report
+
+
+if __name__ == "__main__":
+    main()