SirStone
974528d5cf
Gun evaluation previously required a full end-to-end battle (Java server + battle runner + websocket IPC to 2 bot processes, 50 rounds, ~3.4 min) and yielded only ~300-900 REAL shots across 13 guns -- far too few to rank guns, which is why tuning needed many repetitions. VirtualTracker is already a pure function of (WorldState stream, gun list); the only reason it needed Java was where WorldState came from. So the range replays a seq[WorldState] through the SAME tracker: offline and online scores are the same metric by construction, not an approximation. ACCEPTANCE TEST (the point of the whole thing): record one live round, replay it offline, compare per-gun virtual hit rates. 12/12 deterministic guns match EXACTLY, reproduced twice. Tsetlin is compared separately because tmLearnOne calls rand(). Getting to 12/12 exposed two real ordering quirks in the live loop: run() calls go() before the aim/fire block, so tickBullets resolves against the NEXT tick's scan while the prediction used the previous one; and if the target dies during that go() the final tick's spawn+resolution is skipped entirely. The recorder emits an end marker for the second case. The 5th (selected-gun) predict call was verified to be a no-op. Measured cost: 8 fixtures (1770 ticks, ~92k virtual bullets, 13 guns) replay in 2.9 s, ~32k virtual bullets/s -- roughly 70x faster and 100x more samples than a live gauntlet. Also adds a per-tick WorldState recorder behind const RecordWorldState (default off, mirrors the ShotLog idiom) which records the state the bot ACTUALLY builds, staleness included, rather than true positions -- recording the latter would hand the guns perfect information and produce flattering scores. 9 new guard checks (33 total, all passing), including fixture round-trip, replay determinism, stationary->HeadOn 100%, constant-velocity->Linear>HeadOn, and the energy-threshold turner crossing at t=41.
67 lines
2.3 KiB
Nim
67 lines
2.3 KiB
Nim
## Task 4: offline gun-range runner. Replays one or more fixtures through the
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## 13 ModularBot guns and prints a deterministic per-gun table (overall hit rate
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## plus per-power-bin hits/shots) for each fixture.
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##
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## Usage:
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## nim c -r common_libs/tests/run_range.nim # all tools/fixtures/*.jsonl
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## nim c -r common_libs/tests/run_range.nim path/a.jsonl path/b.jsonl
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## nim c -r common_libs/tests/run_range.nim --timing # add per-fixture wall time
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##
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## Tsetlin is stochastic; it is seeded (seed=1) here so this tool's output is
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## reproducible run-to-run. Its numbers are NOT expected to match a live battle.
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import std/[os, algorithm, strformat, times]
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import gun_harness/offline_range
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import range_guns
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const fixturesDir = currentSourcePath().parentDir.parentDir.parentDir / "tools" / "fixtures"
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proc fixtureFiles(args: seq[string]): seq[string] =
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for a in args:
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if a == "--timing": continue
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if dirExists(a):
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for f in walkFiles(a / "*.jsonl"): result.add f
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elif fileExists(a):
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result.add a
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else:
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echo "warning: not found: ", a
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if result.len == 0:
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for f in walkFiles(fixturesDir / "*.jsonl"): result.add f
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result.sort()
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proc main() =
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var args: seq[string]
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for i in 1..paramCount():
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args.add paramStr(i)
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let showTiming = "--timing" in args
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let files = fixtureFiles(args)
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if files.len == 0:
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echo "no fixtures found under ", fixturesDir
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echo "run: nim c -r common_libs/tests/gen_synthetic_fixtures.nim"
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quit(1)
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var grandShots, grandHits = 0
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for path in files:
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let fx = loadFixture(path)
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var t0 = 0.0
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if showTiming: t0 = epochTime()
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let reports = replayFixture(fx, buildAllGunDrivers(seed = 1),
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liveActual = (fx.meta.source == "live"))
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let elapsed = if showTiming: epochTime() - t0 else: 0.0
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stdout.write formatReports(fx, reports)
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if showTiming:
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echo fmt" replay: {elapsed*1000.0:.1f} ms"
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var totShots, totHits = 0
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for r in reports:
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totShots += r.shots
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totHits += r.hits
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grandShots += totShots
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grandHits += totHits
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echo ""
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if files.len > 1:
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echo fmt"TOTAL across {files.len} fixtures: {grandHits}/{grandShots} ({grandHits.float/grandShots.float*100.0:.1f}%)"
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echo fmt"(replayed {files.len} fixture(s))"
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when isMainModule:
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main()
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