Jānis Kacēns
968479ff51
- internal/sampling: ComputeWeight (Laplace-smoothed error rate + recency
multiplier, floor 0.15) and SelectWeighted (A-Res reservoir algorithm).
10k-run statistical test verifies weak questions appear >3x more often
than mastered, and mastered questions still appear (floor exercised).
- GET/POST /test/new: source filter with live available-count JS update,
n-questions input, weighted vs uniform mode radio.
- GET /test/{id}/q/{n}: deterministic answer shuffle per (test_id,
question_id), progress bar, mobile-friendly large tap targets.
- POST /test/{id}/q/{n}: records answer + upserts stat; advances to next
question or finishes test and redirects to results stub.
- GET /test/{id}/results: stub (Phase 7 will add full review).
- Ownership enforced: all test routes 404 for wrong user.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
47 lines
1006 B
Go
47 lines
1006 B
Go
package sampling
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import (
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"math"
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"math/rand"
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"sort"
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)
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// Candidate is a question ID paired with its sampling weight.
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type Candidate struct {
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ID string
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Weight float64
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}
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// SelectWeighted picks n distinct candidates using the A-Res weighted
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// reservoir algorithm (Efraimidis–Spirakis). Each item's selection
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// probability is proportional to its weight. O(m log m) time.
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func SelectWeighted(candidates []Candidate, n int, rng *rand.Rand) []Candidate {
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if n >= len(candidates) {
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out := make([]Candidate, len(candidates))
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copy(out, candidates)
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return out
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}
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type keyed struct {
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c Candidate
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key float64
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}
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keys := make([]keyed, len(candidates))
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for i, c := range candidates {
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u := rng.Float64()
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if u == 0 {
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u = 1e-12 // avoid log(0) / pow weirdness
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}
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keys[i] = keyed{c, math.Pow(u, 1.0/c.Weight)}
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}
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sort.Slice(keys, func(i, j int) bool { return keys[i].key > keys[j].key })
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out := make([]Candidate, n)
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for i := range out {
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out[i] = keys[i].c
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}
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return out
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}
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