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D&D: class-balance Phase 1 — full 10×30 measurement matrix
Generalizes the Phase 0 spike harness to the full build matrix the class-balance doc plans for. No tuning yet — just measurement. - classBalanceProfile gains Subclass; buildHarnessCharacter sets it on the synthetic DnDCharacter; buildHarnessPlayer now calls applySubclassPassives after class+race passives, matching live order (combat_bridge.go, combat_session_build.go). Subclass="" is a no-op, so L1–L4 pre-unlock rows are unaffected. - buildPhase1Profiles yields 190 rows: 10 classes × 4 pre-subclass levels (L1–L4) + 10 classes × 3 subclasses × 5 post-unlock checkpoints (L5/7/10/15/20). Order is registry order so output reads like the design doc / !class help. - TestClassBalance_Phase1_FullMatrix runs the matrix at 200 trials/cell (~5.5s) and logs every cell plus a per-class tier-mean summary with min/max range. Only harness-broken pathologies fail the test (0% at T1 anywhere, or 100% at T5 for an L1 build); per-tier parity bands land in Phase 2 once we have data to calibrate the tolerance. Phase-2 baseline from this run: at T4 the cross-class spread of mean win rate runs Bard 0.62 → Fighter 0.80 (~18pp); at T5 0.48 → 0.64 (~16pp); casters trail martials at the post-unlock tier (T3) by ~20pp. Phase 0 test (TestClassBalance_Phase0_FighterVsMage) still green with identical numbers — the additional applySubclassPassives call is a no-op for Subclass=="".
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prosolis
@@ -86,9 +86,16 @@ HP-remaining, and near-death-rate are logged as diagnostics, not asserted.
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- **Phase 0 — spike.** Harness skeleton; equipment + spell-selection policies;
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run *Fighter vs. Mage only* across tiers and sanity-check plausibility
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(both win something; casters not at 0%). If the numbers are implausible, fix
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the policies before trusting anything. **← current**
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the policies before trusting anything. **Done — commit 0878b4e.**
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- **Phase 1 — harness + matrix.** Generalize to all 10 classes × 30 subclasses;
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`TestClassBalance` logs the full report. No tuning yet — just measurement.
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**Done.** Subclass field plumbed through the harness, `applySubclassPassives`
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wired in to match live combat order, `buildPhase1Profiles` produces 190 rows
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(10 × 4 pre-subclass + 10 × 3 × 5 post-subclass), `TestClassBalance_Phase1_FullMatrix`
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logs the cells plus per-class tier means and ranges. Phase-2 calibration baseline:
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at T4 the cross-class spread of *mean* win rate runs Bard 0.62 → Fighter 0.80
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(~18pp); at T5 0.48 → 0.64 (~16pp); casters trail martials at the post-unlock
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tier (T3) by ~20pp. **← current**
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- **Phase 2 — tuning pass.** Adjust the levers (class passives → subclass tiers
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→ spell dice → AC floor → attack bonus, in that order) until the parity band
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holds. Lock the band into the test assertion.
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@@ -5,7 +5,10 @@ import (
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"sort"
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)
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// Phase 0 spike for the class-balance pass (gogobee_class_balance.md).
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// Measurement harness for the class-balance pass (gogobee_class_balance.md).
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// Phase 0 introduced this for a Fighter-vs-Mage spike; Phase 1 extended it
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// to drive the full 10-class × 30-subclass matrix (subclass=="" at L1–L4,
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// each of a class's three subclasses at the L5/L7/L10/L15/L20 checkpoints).
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//
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// Sibling to dnd_race_balance.go — same spirit, different method. Races
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// don't fight, so race balance had to use a hand-weighted scoring proxy.
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@@ -25,8 +28,6 @@ import (
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// - DB-touching layers: applyMagicItemEffects, applyArmedAbility, and
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// the SaveDnDCharacter inside applyPendingCast. The harness is pure
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// Go; tests run without a sqlite instance.
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// - Subclass passives: doc §2 specifies subclass = none below L5, and
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// we only need L1–L4 to sanity-check Phase 0.
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// - Race passives beyond Human (+1 all): neutral baseline, again per §2.
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// - Inventory consumables: empty.
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//
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@@ -44,6 +45,7 @@ import (
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// racial mods.
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type classBalanceProfile struct {
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Class DnDClass
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Subclass DnDSubclass // empty below L5, per doc §2
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Level int
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}
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@@ -318,6 +320,7 @@ func buildHarnessCharacter(p classBalanceProfile) *DnDCharacter {
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c := &DnDCharacter{
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Race: RaceHuman,
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Class: p.Class,
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Subclass: p.Subclass,
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Level: p.Level,
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STR: scores[0], DEX: scores[1], CON: scores[2],
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INT: scores[3], WIS: scores[4], CHA: scores[5],
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@@ -361,9 +364,13 @@ func buildHarnessPlayer(c *DnDCharacter) Combatant {
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stats.AC = computeArmorAC(armor, shield, abilityModifier(c.DEX))
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}
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// 3. Passives (no subclass in Phase 0).
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// 3. Passives. Live order is class → race → subclass (see
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// combat_bridge.go and combat_session_build.go). Subclass passives are
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// a no-op when c.Subclass == "" — the harness uses that for the L1–L4
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// pre-unlock rows.
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applyClassPassives(&stats, &mods, c)
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applyRacePassives(&stats, &mods, c)
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applySubclassPassives(&stats, &mods, c)
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return Combatant{
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Name: string(c.Class),
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@@ -462,6 +469,43 @@ func runClassBalanceMatrix(profiles []classBalanceProfile, trials int) []classBa
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return out
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}
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// ── Phase 1 matrix builder ───────────────────────────────────────────────────
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// phase1SubclassLevels is the post-unlock checkpoint ladder from doc §2.
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// L5/L7/L10/L15/L20 line up with the subclass tier-unlock structure in
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// dnd_subclass_combat.go — each row reads a class's behaviour at one more
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// unlocked tier than the row above it.
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var phase1SubclassLevels = []int{5, 7, 10, 15, 20}
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// phase1PreSubclassLevels is the L1–L4 ladder run with Subclass=="". Doc §2
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// notes that subclasses aren't selected until L5, so these rows measure the
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// raw class chassis.
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var phase1PreSubclassLevels = []int{1, 2, 3, 4}
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// buildPhase1Profiles assembles the full Phase 1 build matrix: every class
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// at L1–L4 (no subclass), then each of that class's three subclasses at
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// each of the five tier-unlock checkpoints. 10 × 4 + 10 × 3 × 5 = 190 rows.
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// Order is registry order (dndClasses, then subclassesForClass) so the
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// matrix log reads the same way as the design doc and the !class help.
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func buildPhase1Profiles() []classBalanceProfile {
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out := make([]classBalanceProfile, 0, 10*4+10*3*5)
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for _, ci := range dndClasses {
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for _, lvl := range phase1PreSubclassLevels {
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out = append(out, classBalanceProfile{Class: ci.Key, Level: lvl})
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}
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for _, si := range subclassesForClass(ci.Key) {
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for _, lvl := range phase1SubclassLevels {
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out = append(out, classBalanceProfile{
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Class: ci.Key,
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Subclass: si.ID,
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Level: lvl,
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})
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}
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}
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}
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return out
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}
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// _ keeps the math/rand/v2 import live in case future iterations of this
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// file want to draw directly (e.g. for harness-level RNG control). Today
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// every randomized step is inside production helpers.
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@@ -1,6 +1,7 @@
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package plugin
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import (
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"sort"
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"testing"
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)
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@@ -84,3 +85,149 @@ func TestClassBalance_Phase0_FighterVsMage(t *testing.T) {
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mageT1, fighterT1)
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}
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}
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// Phase 1 — full matrix measurement. Per gogobee_class_balance.md §5
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// Phase 1: "Generalize to all 10 classes × 30 subclasses; TestClassBalance
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// logs the full report. No tuning yet — just measurement."
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//
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// This test does not assert balance. The only failures it catches are
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// harness-broken pathologies — a profile that's 0% at T1 across the board
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// (build can't damage anything), or an L1-pre-subclass build that's 100%
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// at T5 (monster scaling collapsed). Per-tier parity bands land in Phase 2
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// once we have data to calibrate the tolerance.
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//
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// Skipped under -short. 190 profiles × 5 tiers × 200 trials = 190k
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// simulated fights; runs in a few seconds.
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func TestClassBalance_Phase1_FullMatrix(t *testing.T) {
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if testing.Short() {
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t.Skip("phase-1 matrix — measurement only")
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}
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profiles := buildPhase1Profiles()
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const trials = 200
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results := runClassBalanceMatrix(profiles, trials)
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// Index results for table layout: rows = (class, subclass, level),
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// columns = tier. Group by class so the log reads class-by-class.
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type rowKey struct {
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Class DnDClass
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Subclass DnDSubclass
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Level int
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}
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rows := make(map[rowKey]map[int]classBalanceResult, len(profiles))
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for _, r := range results {
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k := rowKey{r.Profile.Class, r.Profile.Subclass, r.Profile.Level}
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if rows[k] == nil {
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rows[k] = make(map[int]classBalanceResult, 5)
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}
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rows[k][r.Tier] = r
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}
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t.Logf("class-balance Phase 1 — full matrix, %d trials/cell", trials)
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t.Logf("%-10s %-18s %-3s T1 T2 T3 T4 T5", "class", "subclass", "lvl")
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// Per-tier accumulators for a tail summary — mean win rate by class
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// across all of its rows at each tier, plus the cross-class spread.
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type tierAgg struct {
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sum float64
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count int
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minVal float64
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maxVal float64
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}
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classTier := make(map[DnDClass]map[int]*tierAgg)
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for _, ci := range dndClasses {
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classTier[ci.Key] = map[int]*tierAgg{
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1: {minVal: 1}, 2: {minVal: 1}, 3: {minVal: 1},
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4: {minVal: 1}, 5: {minVal: 1},
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}
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}
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for _, ci := range dndClasses {
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// pre-subclass rows first, then each subclass's L5+ rows.
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for _, lvl := range phase1PreSubclassLevels {
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row := rows[rowKey{ci.Key, "", lvl}]
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t.Logf("%-10s %-18s %-3d %.3f %.3f %.3f %.3f %.3f",
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ci.Key, "—", lvl,
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row[1].WinRate(), row[2].WinRate(), row[3].WinRate(),
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row[4].WinRate(), row[5].WinRate())
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for tier := 1; tier <= 5; tier++ {
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ta := classTier[ci.Key][tier]
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wr := row[tier].WinRate()
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ta.sum += wr
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ta.count++
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if wr < ta.minVal {
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ta.minVal = wr
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}
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if wr > ta.maxVal {
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ta.maxVal = wr
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}
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}
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}
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for _, si := range subclassesForClass(ci.Key) {
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for _, lvl := range phase1SubclassLevels {
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row := rows[rowKey{ci.Key, si.ID, lvl}]
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t.Logf("%-10s %-18s %-3d %.3f %.3f %.3f %.3f %.3f",
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ci.Key, si.ID, lvl,
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row[1].WinRate(), row[2].WinRate(), row[3].WinRate(),
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row[4].WinRate(), row[5].WinRate())
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for tier := 1; tier <= 5; tier++ {
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ta := classTier[ci.Key][tier]
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wr := row[tier].WinRate()
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ta.sum += wr
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ta.count++
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if wr < ta.minVal {
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ta.minVal = wr
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}
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if wr > ta.maxVal {
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ta.maxVal = wr
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}
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}
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}
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}
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}
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// Per-class summary: mean win rate per tier, sorted by overall mean
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// (lowest first). Useful at a glance to spot the outliers Phase 2 will
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// tune.
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t.Logf("")
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t.Logf("per-class mean win rate by tier (range in brackets):")
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t.Logf("%-10s T1 T2 T3 T4 T5", "class")
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classKeys := make([]DnDClass, 0, len(dndClasses))
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for _, ci := range dndClasses {
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classKeys = append(classKeys, ci.Key)
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}
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overall := func(c DnDClass) float64 {
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var s float64
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for tier := 1; tier <= 5; tier++ {
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ta := classTier[c][tier]
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if ta.count > 0 {
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s += ta.sum / float64(ta.count)
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}
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}
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return s
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}
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sort.SliceStable(classKeys, func(i, j int) bool {
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return overall(classKeys[i]) < overall(classKeys[j])
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})
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for _, c := range classKeys {
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t.Logf("%-10s %.2f [%.2f-%.2f] %.2f [%.2f-%.2f] %.2f [%.2f-%.2f] %.2f [%.2f-%.2f] %.2f [%.2f-%.2f]",
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c,
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classTier[c][1].sum/float64(classTier[c][1].count), classTier[c][1].minVal, classTier[c][1].maxVal,
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classTier[c][2].sum/float64(classTier[c][2].count), classTier[c][2].minVal, classTier[c][2].maxVal,
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classTier[c][3].sum/float64(classTier[c][3].count), classTier[c][3].minVal, classTier[c][3].maxVal,
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classTier[c][4].sum/float64(classTier[c][4].count), classTier[c][4].minVal, classTier[c][4].maxVal,
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classTier[c][5].sum/float64(classTier[c][5].count), classTier[c][5].minVal, classTier[c][5].maxVal,
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)
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}
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// Harness-broken gates only. Tuned-balance assertions land in Phase 2.
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for _, r := range results {
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if r.Tier == 1 && r.WinRate() == 0 {
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t.Errorf("%s/%s L%d T1 win rate is 0%% — the build can't damage anything; loadout or spell policy is dead",
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r.Profile.Class, r.Profile.Subclass, r.Profile.Level)
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}
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if r.Tier == 5 && r.Profile.Level == 1 && r.WinRate() == 1 {
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t.Errorf("%s L1 T5 win rate is 100%% — monster scaling looks broken", r.Profile.Class)
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}
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}
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}
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