package search

import (
	"math/rand/v2"
	"time"

	"git.vakhrushev.me/av/remembos/internal/memos"
)

// Memory is the result of the search algorithm.
type Memory struct {
	Memo      *memos.Memo
	Tier      int
	YearsAgo  int
	ShowCount int
	Date      time.Time
}

// candidate is an intermediate struct during scoring.
type candidate struct {
	memo      *memos.Memo
	yearsAgo  int
	showCount int
}

// weightedSelect picks one candidate using weighted random selection.
// preferOlder gives higher weight to older memos. maxYearsBack normalizes recency.
func weightedSelect(candidates []candidate, preferOlder bool, maxYearsBack int) *candidate {
	if len(candidates) == 0 {
		return nil
	}
	if len(candidates) == 1 {
		return &candidates[0]
	}

	scores := make([]float64, len(candidates))
	var total float64

	for i, c := range candidates {
		var recencyFactor float64
		if preferOlder && maxYearsBack > 0 && c.yearsAgo > 0 {
			recencyFactor = float64(c.yearsAgo) / float64(maxYearsBack)
		} else {
			recencyFactor = 1.0
		}

		showCountPenalty := 1.0 / (1.0 + float64(c.showCount))

		score := recencyFactor * showCountPenalty
		if score < 0.01 {
			score = 0.01 // minimum weight
		}
		scores[i] = score
		total += score
	}

	r := rand.Float64() * total //nolint:gosec // non-cryptographic use
	var cumulative float64
	for i, s := range scores {
		cumulative += s
		if r <= cumulative {
			return &candidates[i]
		}
	}

	return &candidates[len(candidates)-1]
}