Justin Visser
c40e483ee6
Two ways to spend the same education budget: a uniform random sample (spray and pray) vs the highest-degree hubs, ties broken stably towards lower node numbers so the pick is deterministic. The origin is never educated; they post the fake. rng.Shuffle does a seeded Fisher-Yates, so the random pick is reproducible too. min() is a builtin since Go 1.21.
52 lines
1.6 KiB
Go
52 lines
1.6 KiB
Go
package engine
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import (
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"math/rand/v2"
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"slices"
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)
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// Education strategies pick which students the program educates. The origin
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// is never educated (they post the fake in the first place). Returned
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// slices are sorted by node number for readable, stable output; the order
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// carries no meaning.
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// EducateRandom educates count students drawn uniformly from everyone but
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// the origin. This is the "spray and pray" baseline.
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func EducateRandom(graph *Graph, origin, count int, rng *rand.Rand) []int {
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if count <= 0 {
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return nil
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}
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candidates := make([]int, 0, graph.NumNodes()-1)
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for student := range graph.NumNodes() {
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if student != origin {
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candidates = append(candidates, student)
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}
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}
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rng.Shuffle(len(candidates), func(i, j int) {
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candidates[i], candidates[j] = candidates[j], candidates[i]
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})
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educated := candidates[:min(count, len(candidates))]
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slices.Sort(educated)
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return educated
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}
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// EducateMostConnected educates the count students with the highest degree:
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// the hubs whose forwarding keeps the network connected. Ties break towards
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// the lower node number (stable sort) so the choice is deterministic.
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func EducateMostConnected(graph *Graph, origin, count int) []int {
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if count <= 0 {
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return nil
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}
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candidates := make([]int, 0, graph.NumNodes()-1)
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for student := range graph.NumNodes() {
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if student != origin {
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candidates = append(candidates, student)
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}
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}
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slices.SortStableFunc(candidates, func(first, second int) int {
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return graph.Degree(second) - graph.Degree(first) // descending by degree
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})
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educated := candidates[:min(count, len(candidates))]
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slices.Sort(educated)
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return educated
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}
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