export function levenshtein(a, b) {
	const m = a.length, n = b.length;
	if (!m) return n;
	if (!n) return m;
	const dp = Array.from({length: m + 1}, (_, i) => [i, ...Array(n).fill(0)]);
	for (let j = 0; j <= n; j++) dp[0][j] = j;
	for (let i = 1; i <= m; i++) {
		for (let j = 1; j <= n; j++) {
			dp[i][j] = a[i - 1] === b[j - 1]
				? dp[i - 1][j - 1]
				: 1 + Math.min(dp[i - 1][j - 1], dp[i - 1][j], dp[i][j - 1]);
		}
	}
	return dp[m][n];
}

/** Normalized similarity in [0,1]: 1 - editDistance / maxLength. */
export function similarity(a, b) {
	a = a.toLowerCase(); b = b.toLowerCase();
	return 1 - levenshtein(a, b) / Math.max(a.length, b.length, 1);
}

/** Compares `target` against one or more search terms; returns avg/max/per-term similarity. */
export function fuzzyMatch(target, ...terms) {
	if (!terms.length) throw new Error('Requires at least 1 term to compare');
	const similarities = terms.map(t => similarity(target, t));
	return {
		avg: similarities.reduce((acc, s) => acc + s, 0) / similarities.length,
		max: Math.max(...similarities),
		similarities,
	};
}