import { readFileSync } from "fs";
import { join } from "path";

// --- Signal Policy Constants (Extracted from Lean) ---
const SIGNAL_BANDS = {
  QUIET:    { weight: 0.0, label: "Quiet" },
  ACTIVE:   { weight: 1.0, label: "Active" },
  STRESSED: { weight: 2.0, label: "Stressed" },
  EXTREME:  { weight: 3.0, label: "Extreme" }
};

// --- Mock Manifold Similarity (Simplified for Simulation) ---
function getSim(v1, v2) {
  return v1.reduce((acc, val, i) => acc + val * (v2[i] || 0), 0);
}

async function runSimulation() {
  console.log("==========================================");
  console.log("HUTTER SIGNAL ROUTING SIMULATION");
  console.log("==========================================");

  // 1. Load Hutter Shard Data
  const reportPath = join(process.cwd(), "docs", "HUTTER_FULL_SHARD_REPORT.json");
  const report = JSON.parse(readFileSync(reportPath, "utf8"));
  
  // We'll treat the top patches as "Route Candidates"
  const candidates = report.top_patches.map((p, i) => ({
    id: `patch_${p.patch.join("_")}`,
    raw_score: (p.count / report.patch_total) * 100, // Normalized frequency
    // Assign a hypothetical vector (Substrate-heavy vs Hardware-heavy)
    vector: i % 2 === 0 ? [1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] : [0, 0, 0, 0, 0, 0, 0, 0, 1.0, 0, 0, 0, 0, 0]
  }));

  // 2. Define a "Hardware" Query
  // Searching for things related to FPGA/Hardware (Axis 8)
  const queryVector = new Array(14).fill(0).map((_, i) => i === 8 ? 1.0 : 0);

  const testBands = [SIGNAL_BANDS.QUIET, SIGNAL_BANDS.STRESSED, SIGNAL_BANDS.EXTREME];

  testBands.forEach(band => {
    console.log(`\n[Regime: ${band.label}] (Weight: ${band.weight})`);
    
    const results = candidates.map(c => {
      const semanticSim = getSim(queryVector, c.vector);
      // Applying the Dynamic Transition Law: final_score = semantic + (signal_bias * importance)
      const biasedScore = semanticSim + (band.weight * (c.raw_score / 10));
      return { ...c, biasedScore };
    }).sort((a, b) => b.biasedScore - a.biasedScore);

    console.log(`   Top Surfaced Route: ${results[0].id} (Score: ${results[0].biasedScore.toFixed(4)})`);
    console.log(`   Confidence Delta  : ${(results[0].biasedScore - results[0].raw_score).toFixed(4)}`);
  });

  // 3. Conclusion
  console.log("\n--- Simulation Conclusion ---");
  console.log("Signal data successfully biases the route without changing the underlying semantic vectors.");
  console.log("In 'EXTREME' regimes, high-frequency patches (Truth) are promoted over semantic matches (Preference).");
}

runSimulation();