feat(difficulty): implement Monte Carlo simulation for accurate difficulty calculation

This commit is contained in:
2026-02-11 04:14:06 +01:00
parent a3fd11f59c
commit 74fb2beb27
6 changed files with 771 additions and 28 deletions

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@@ -53,35 +53,78 @@ export function generateRandomGrid(size, density = 0.5) {
}
export function calculateDifficulty(density, size = 10) {
// Shannon Entropy: H(x) = -x*log2(x) - (1-x)*log2(1-x)
// Normalized to 0-1 range (since max entropy at 0.5 is 1)
// Data derived from Monte Carlo Simulation (Logical Solver)
// Format: { size: [solved_pct_at_0.1, ..., solved_pct_at_0.9] }
// Densities: 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9
const SIM_DATA = {
5: [89, 74, 74, 81, 97, 98, 99, 100, 100],
10: [57, 20, 16, 54, 92, 100, 100, 100, 100],
15: [37, 10, 2, 12, 68, 100, 100, 100, 100],
20: [23, 3, 1, 2, 37, 100, 100, 100, 100],
25: [16, 0, 0, 1, 19, 99, 100, 100, 100],
30: [8, 0, 0, 0, 5, 99, 100, 100, 100],
35: [6, 0, 0, 0, 4, 91, 100, 100, 100],
40: [3, 0, 0, 0, 2, 91, 100, 100, 100],
45: [2, 0, 0, 0, 1, 82, 100, 100, 100],
50: [2, 0, 0, 0, 1, 73, 100, 100, 100],
60: [0, 0, 0, 0, 0, 35, 100, 100, 100],
70: [0, 0, 0, 0, 0, 16, 100, 100, 100],
80: [0, 0, 0, 0, 0, 1, 100, 100, 100]
};
// Helper to get interpolated value from array
const getSimulatedSolvedPct = (s, d) => {
// Find closest sizes
const sizes = Object.keys(SIM_DATA).map(Number).sort((a, b) => a - b);
let sLower = sizes[0];
let sUpper = sizes[sizes.length - 1];
for (let i = 0; i < sizes.length - 1; i++) {
if (s >= sizes[i] && s <= sizes[i+1]) {
sLower = sizes[i];
sUpper = sizes[i+1];
break;
}
}
// Clamp density to 0.1 - 0.9
const dClamped = Math.max(0.1, Math.min(0.9, d));
// Index in array: 0.1 -> 0, 0.9 -> 8
const dIndex = (dClamped - 0.1) * 10;
const dLowerIdx = Math.floor(dIndex);
const dUpperIdx = Math.ceil(dIndex);
const dFraction = dIndex - dLowerIdx;
// Bilinear Interpolation
// 1. Interpolate Density for Lower Size
const rowLower = SIM_DATA[sLower];
const valLower = rowLower[dLowerIdx] * (1 - dFraction) + (rowLower[dUpperIdx] || rowLower[dLowerIdx]) * dFraction;
// 2. Interpolate Density for Upper Size
const rowUpper = SIM_DATA[sUpper];
const valUpper = rowUpper[dLowerIdx] * (1 - dFraction) + (rowUpper[dUpperIdx] || rowUpper[dLowerIdx]) * dFraction;
// 3. Interpolate Size
if (sLower === sUpper) return valLower;
const sFraction = (s - sLower) / (sUpper - sLower);
return valLower * (1 - sFraction) + valUpper * sFraction;
};
const solvedPct = getSimulatedSolvedPct(size, density);
// Avoid log(0)
if (density <= 0 || density >= 1) return { level: 'easy', value: 0 };
const entropy = -density * Math.log2(density) - (1 - density) * Math.log2(1 - density);
// Difficulty score combines entropy (complexity) and size (scale)
// We use sqrt(size) to dampen the effect of very large grids,
// ensuring that density still plays a major role.
// Normalized against max size (80)
const sizeFactor = Math.sqrt(size / 80);
const score = entropy * sizeFactor * 100;
const value = Math.round(score);
// Difficulty Score: Inverse of Solved Percent
// 100% Solved -> 0 Difficulty
// 0% Solved -> 100 Difficulty
const value = Math.round(100 - solvedPct);
// Thresholds
let level = 'easy';
if (value >= 80) level = 'extreme';
else if (value >= 60) level = 'hardest';
else if (value >= 40) level = 'harder';
else if (value >= 20) level = 'medium'; // Using 'medium' key if available, or we need to add it?
// Wait, useI18n only has: easy, harder, hardest, extreme.
// Let's stick to those keys but adjust ranges.
if (value >= 75) level = 'extreme';
else if (value >= 50) level = 'hardest';
else if (value >= 25) level = 'harder';
else level = 'easy';
if (value >= 90) level = 'extreme'; // < 10% Solved
else if (value >= 60) level = 'hardest'; // < 40% Solved
else if (value >= 30) level = 'harder'; // < 70% Solved
else level = 'easy'; // > 70% Solved
return { level, value };
}