SirRoboGarage/common_libs/tests/test_tm_pattern_learning.nim

## L2 — can a Tsetlin Machine learn a high-level pattern?
##
## A STANDALONE Granmo binary classifier (Table 2/3, resource allocation
## Eq. 8-11, empty clause = 1 during learning per Eq. 6) over the SAME
## frame-stacked binary encoding the Tsetlin gun uses (870 bits = 10 frames × 83
## + 40 self bits). It is independent of the gun: its own clause teams, its own
## training loop.
##
## The source of truth is `synthesizeEnergyThresholdTurner` in
## `gun_harness/offline_range.nim`:
##   RULE: energy(t) = max(5, e0 - decay*t)
##         straight while energy >= 30, hard turn each tick below 30.
## So the label is literally `energy(t) < 30`, a propositional predicate over the
## 11-bit Gray-coded energy field of the current frame. The experiment asks
## whether the TM recovers it, and — the payoff — whether the learned clauses are
## readable as that rule.
##
## Train and test use DIFFERENT trajectories (different e0/decay/speed/start
## position/hardTurnDeg, same threshold=30). A model that memorises position or
## the training trajectory cannot generalise; one that reads the energy bits can.
##
## Run: nim c -r common_libs/tests/test_tm_pattern_learning.nim

import std/[math, strformat, random, algorithm, strutils]

import gun_harness/offline_range
import guns/tsetlin   # reuse tmEncodeFrame / tmEncodeSelf / tmEncodeFullVector

# ── standalone Granmo binary TM ──────────────────────────────────────────────

const
  C_N_IN     = TM_TOTAL_BITS          # 870
  C_N_LITS   = C_N_IN * 2             # 1740 literals (each bit + its negation)
  C_N_POS    = 24
  C_N_NEG    = 24
  C_N_CLAUSES = C_N_POS + C_N_NEG
  C_N_STATES = 64                     # automaton state range [-64, 64]
  C_S        = 3.9                    # Granmo specificity
  C_T        = float(C_N_POS)         # summation target

type
  ClsNet = object
    states: array[C_N_CLAUSES * C_N_LITS, int16]

  Sample = object
    lits: array[C_N_LITS, uint8]
    y:    int

# Fast local xorshift so the experiment is reproducible without touching the
# gun's std/random stream.
var rngState: uint64 = 0x9E3779B97F4A7C15'u64

proc seedRng(s: uint64) = rngState = (if s == 0: 1'u64 else: s)
proc nextU64(): uint64 {.inline.} =
  rngState = rngState xor (rngState shl 13)
  rngState = rngState xor (rngState shr 7)
  rngState = rngState xor (rngState shl 17)
  rngState
proc rand01(): float {.inline.} = float(nextU64() shr 40) / 16777216.0  # 24-bit

proc litIdx(c, lit: int): int {.inline.} = c * C_N_LITS + lit
proc polarity(c: int): float {.inline.} =
  if c < C_N_POS: 1.0 else: -1.0

proc evalClause(net: ClsNet, c: int, lits: array[C_N_LITS, uint8],
                learning: bool): uint8 =
  var hasInc = false
  for lit in 0..<C_N_LITS:
    if net.states[litIdx(c, lit)] > 0:
      hasInc = true
      if lits[lit] == 0'u8: return 0'u8
  if hasInc: return 1'u8
  if learning: return 1'u8
  return 0'u8

proc forward(net: ClsNet, lits: array[C_N_LITS, uint8], learning: bool): float =
  var v = 0.0
  for c in 0..<C_N_CLAUSES:
    v += polarity(c) * float(evalClause(net, c, lits, learning))
  clamp(v, -C_T, C_T)

proc predictLabel(net: ClsNet, lits: array[C_N_LITS, uint8]): int =
  if forward(net, lits, false) >= 0.0: 1 else: 0

proc typeIFeedback(net: var ClsNet, c: int, lits: array[C_N_LITS, uint8]) =
  ## Granmo Table 2, collapsed to the resulting state move.
  let cOut = evalClause(net, c, lits, learning = true)
  for lit in 0..<C_N_LITS:
    let si = litIdx(c, lit)
    var st = int(net.states[si])
    if lits[lit] == 1'u8:
      if cOut == 1'u8:
        if rand01() < (C_S - 1.0) / C_S: st = min(st + 1, C_N_STATES)
      else:
        if rand01() < 1.0 / C_S:          st = max(st - 1, -C_N_STATES)
    else:
      if rand01() < 1.0 / C_S:            st = max(st - 1, -C_N_STATES)
    net.states[si] = int16(st)

proc typeIIFeedback(net: var ClsNet, c: int, lits: array[C_N_LITS, uint8]) =
  ## Granmo Table 3: penalise the exclusion of a zero literal when c=1.
  let cOut = evalClause(net, c, lits, learning = true)
  if cOut == 1'u8:
    for lit in 0..<C_N_LITS:
      if lits[lit] == 0'u8:
        let si = litIdx(c, lit)
        if net.states[si] <= 0:
          net.states[si] = int16(min(int(net.states[si]) + 1, C_N_STATES))

proc trainOne(net: var ClsNet, lits: array[C_N_LITS, uint8], y: int) =
  let v = forward(net, lits, learning = true)
  let p = if y == 1: (C_T - v) / (2.0 * C_T)
          else:     (C_T + v) / (2.0 * C_T)
  if p <= 0.0: return
  for c in 0..<C_N_CLAUSES:
    if rand01() >= p: continue
    let isPos = c < C_N_POS
    if (y == 1 and isPos) or (y == 0 and not isPos):
      typeIFeedback(net, c, lits)
    else:
      typeIIFeedback(net, c, lits)

# ── dataset: frame-stacked encoding of energy-threshold-turner trajectories ──

proc makeLiterals(vec: TmBinaryVector): array[C_N_LITS, uint8] =
  for i in 0..<C_N_IN:
    result[i] = vec[i]
    result[i + C_N_IN] = 1'u8 - vec[i]

proc samplesFromFixture(fx: Fixture, threshold: float,
                        singleFrame = false): seq[Sample] =
  ## Replicates the gun's window bookkeeping exactly: index 0 = newest frame,
  ## shifted once per tick. Skips the first 10 ticks (window warm-up). If
  ## `singleFrame`, only the current frame is exposed (frames 1..9 zeroed); this
  ## isolates how much of the clause bloat comes from the 10-frame redundancy.
  var window: array[TM_WINDOW_SIZE, TmFrameEncoded]
  var count = 0
  for t in 0..<fx.states.len:
    let s = fx.states[t]
    let dist = hypot(s.enemyX - s.selfX, s.enemyY - s.selfY)
    let bearing = radToDeg(arctan2(s.enemyY - s.selfY, s.enemyX - s.selfX))
    let frame = tmEncodeFrame(
      bearing, dist, s.enemySpeed, s.enemyHeading,
      s.arenaHeight - s.enemyY, s.enemyY,
      s.arenaWidth - s.enemyX, s.enemyX,
      s.enemyEnergy)                       # fix 6: enemy energy is encoded
    for i in countdown(TM_WINDOW_SIZE - 1, 1): window[i] = window[i - 1]
    window[0] = frame
    if count < TM_WINDOW_SIZE: inc count
    if count < TM_WINDOW_SIZE: continue
    let selfState = tmEncodeSelf(
      s.arenaHeight - s.selfY, s.selfY,
      s.arenaWidth - s.selfX, s.selfX,
      s.selfEnergy, true)
    var vec: TmBinaryVector
    if singleFrame:
      var w1: array[TM_WINDOW_SIZE, TmFrameEncoded]
      w1[0] = window[0]
      vec = tmEncodeFullVector(w1, selfState)
    else:
      vec = tmEncodeFullVector(window, selfState)
    result.add Sample(lits: makeLiterals(vec),
                      y: (if s.enemyEnergy < threshold: 1 else: 0))

type
  RuleCfg = object
    e0, decay, speed, ex, ey, turn: float

const
  Threshold = 30.0
  TrainCfgs = [
    RuleCfg(e0: 60.0, decay: 0.40, speed: 4.0, ex: 120.0, ey: 250.0, turn: 15.0),
    RuleCfg(e0: 50.0, decay: 0.50, speed: 5.0, ex: 100.0, ey: 300.0, turn: 20.0),
    RuleCfg(e0: 70.0, decay: 0.60, speed: 3.0, ex: 150.0, ey: 200.0, turn: 12.0),
    RuleCfg(e0: 42.0, decay: 0.30, speed: 6.0, ex: 240.0, ey: 420.0, turn: 25.0),
    RuleCfg(e0: 55.0, decay: 0.45, speed: 4.5, ex: 80.0,  ey: 150.0, turn: 18.0),
    RuleCfg(e0: 66.0, decay: 0.55, speed: 5.5, ex: 300.0, ey: 480.0, turn: 22.0),
    RuleCfg(e0: 48.0, decay: 0.38, speed: 3.5, ex: 180.0, ey: 360.0, turn: 14.0),
    RuleCfg(e0: 62.0, decay: 0.48, speed: 5.2, ex: 60.0,  ey: 460.0, turn: 24.0),
  ]
  TestCfgs = [
    RuleCfg(e0: 58.0, decay: 0.42, speed: 4.2, ex: 130.0, ey: 280.0, turn: 16.0),
    RuleCfg(e0: 64.0, decay: 0.52, speed: 4.8, ex: 90.0,  ey: 220.0, turn: 19.0),
    RuleCfg(e0: 45.0, decay: 0.35, speed: 5.8, ex: 200.0, ey: 330.0, turn: 23.0),
    RuleCfg(e0: 52.0, decay: 0.44, speed: 3.8, ex: 280.0, ey: 180.0, turn: 17.0),
  ]

proc makeFixture(c: RuleCfg): Fixture =
  synthesizeEnergyThresholdTurner(ticks = 200, ex = c.ex, ey = c.ey,
    e0 = c.e0, decay = c.decay, threshold = Threshold, speed = c.speed,
    hardTurnDeg = c.turn)

proc gather(cfgs: openArray[RuleCfg], singleFrame = false): seq[Sample] =
  for c in cfgs: result.add samplesFromFixture(makeFixture(c), Threshold, singleFrame)

# ── clause decoding: literal index -> readable propositional logic ───────────

proc bitField(bitIdx: int): string =
  ## Decode a bit index of the 870-bit frame-stacked vector to "f<frame>.<field>[<b>]".
  if bitIdx < TM_FRAME_BITS * TM_WINDOW_SIZE:
    let f = bitIdx div TM_FRAME_BITS
    let off = bitIdx mod TM_FRAME_BITS
    if off < 8:    return &"f{f}.bearingSin[{off}]"
    if off < 16:   return &"f{f}.bearingCos[{off-8}]"
    if off < 23:   return &"f{f}.distance[{off-16}]"
    if off < 28:   return &"f{f}.velocity[{off-23}]"
    if off < 36:   return &"f{f}.headingSin[{off-28}]"
    if off < 44:   return &"f{f}.headingCos[{off-36}]"
    if off < 51:   return &"f{f}.wallN[{off-44}]"
    if off < 58:   return &"f{f}.wallS[{off-51}]"
    if off < 65:   return &"f{f}.wallE[{off-58}]"
    if off < 72:   return &"f{f}.wallW[{off-65}]"
    return &"f{f}.energy[{off-72}]"
  else:
    let off = bitIdx - TM_FRAME_BITS * TM_WINDOW_SIZE
    if off < 7:    return &"self.wallN[{off}]"
    if off < 14:   return &"self.wallS[{off-7}]"
    if off < 21:   return &"self.wallE[{off-14}]"
    if off < 28:   return &"self.wallW[{off-21}]"
    if off < 39:   return &"self.energy[{off-28}]"
    return "self.canFire"

proc describeLiteral(lit: int): string =
  let bitIdx = if lit < C_N_IN: lit else: lit - C_N_IN
  let negated = lit >= C_N_IN
  (if negated: "!" else: "") & bitField(bitIdx)

proc isEnergyField(lit: int): bool =
  let bitIdx = if lit < C_N_IN: lit else: lit - C_N_IN
  if bitIdx < TM_FRAME_BITS * TM_WINDOW_SIZE:
    let off = bitIdx mod TM_FRAME_BITS
    return off >= 72 and off < 83
  let off = bitIdx - TM_FRAME_BITS * TM_WINDOW_SIZE
  return off >= 28 and off < 39

proc included(net: ClsNet, c: int): seq[int] =
  for lit in 0..<C_N_LITS:
    if net.states[litIdx(c, lit)] > 0: result.add lit

proc clauseString(net: ClsNet, c: int, maxLits = 10): string =
  let inc = included(net, c)
  if inc.len == 0: return "TRUE (empty)"
  var parts: seq[string]
  for i, lit in inc:
    if i >= maxLits:
      parts.add &"… (+{inc.len - maxLits} more)"
      break
    parts.add describeLiteral(lit)
  result = parts.join(" ∧ ")

# ── leakage controls: which channel does the TM actually read? ──────────────

type
  FieldChannel = enum
    fcBearing, fcDistance, fcVelocity, fcHeading, fcWalls, fcEnergy, fcSelf

proc channelOf(bitIdx: int): FieldChannel =
  if bitIdx < TM_FRAME_BITS * TM_WINDOW_SIZE:
    let off = bitIdx mod TM_FRAME_BITS
    if off < 16:  return fcBearing
    if off < 23:  return fcDistance
    if off < 28:  return fcVelocity
    if off < 44:  return fcHeading
    if off < 72:  return fcWalls
    return fcEnergy
  return fcSelf

proc maskChannel(s: Sample, ch: FieldChannel): Sample =
  ## Zero out every bit of one channel (the literal becomes the constant 0, its
  ## negation the constant 1). NOTE: this also breaks any clause that happens to
  ## INCLUDE an inert literal on that channel, so a large drop is not proof of
  ## causal use — the counterfactual probe below is the reliable test.
  result = s
  for i in 0..<C_N_IN:
    if channelOf(i) == ch:
      result.lits[i] = 0'u8
      result.lits[i + C_N_IN] = 1'u8

proc setEnergyAllFrames(s: var Sample, energy: float) =
  ## Counterfactual: rewrite the 11-bit Gray-coded energy field of every frame to
  ## `energy`, leaving every other channel untouched. If the model keys on the
  ## energy threshold, its prediction follows this rewrite.
  let raw = clamp(int(energy * 10.0), 0, 1500)
  let gray = raw xor (raw shr 1)
  for f in 0..<TM_WINDOW_SIZE:
    for b in 0..<11:
      let bit = uint8((gray shr (10 - b)) and 1)
      let idx = f * TM_FRAME_BITS + 72 + b
      s.lits[idx] = bit
      s.lits[idx + C_N_IN] = 1'u8 - bit

proc varyingBits(data: seq[Sample]): seq[bool] =
  ## Which of the 870 input bits actually vary across the dataset. Included
  ## literals on constant bits are INERT: they inflate the nominal clause width
  ## without changing when the clause fires.
  result = newSeq[bool](C_N_IN)
  for i in 0..<C_N_IN:
    let v0 = data[0].lits[i]
    for s in data:
      if s.lits[i] != v0:
        result[i] = true
        break

proc clauseStringVarying(net: ClsNet, c: int, varying: seq[bool],
                         maxLits = 12): string =
  var parts: seq[string]
  var totalVary = 0
  for lit in included(net, c):
    let bitIdx = if lit < C_N_IN: lit else: lit - C_N_IN
    if varying[bitIdx]:
      inc totalVary
      if parts.len < maxLits: parts.add describeLiteral(lit)
  if totalVary == 0: return "(no varying literals)"
  result = parts.join(" ∧ ")
  if totalVary > maxLits: result.add &" … (+{totalVary - maxLits} varying)"

# ── main experiment ──────────────────────────────────────────────────────────

proc accuracy(net: ClsNet, data: seq[Sample]): float =
  if data.len == 0: return 0.0
  var ok = 0
  for s in data:
    if predictLabel(net, s.lits) == s.y: inc ok
  ok.float / data.len.float

proc confusion(net: ClsNet, data: seq[Sample]): tuple[tp, tn, fp, fn: int] =
  for s in data:
    let p = predictLabel(net, s.lits)
    if p == 1 and s.y == 1: inc result.tp
    elif p == 0 and s.y == 0: inc result.tn
    elif p == 1 and s.y == 0: inc result.fp
    else: inc result.fn

proc shuffleRun(train: seq[Sample], labels: seq[int], seedState: uint64,
                epochs: int): ClsNet =
  seedRng(seedState)
  var order: seq[int]
  for i in 0..<train.len: order.add i
  for epoch in 0..<epochs:
    for i in countdown(order.high, 1):
      let j = int(nextU64() mod uint64(i + 1))
      swap(order[i], order[j])
    for idx in order:
      trainOne(result, train[idx].lits, labels[idx])

var failures = 0
proc check(name: string, ok: bool) =
  if ok: echo "PASS: ", name
  else: echo "FAIL: ", name; inc failures

proc main() =
  const Epochs = 25
  echo "=== L2: Tsetlin Machine pattern-learning benchmark ==="
  echo &"encoding: {C_N_IN} bits ({TM_WINDOW_SIZE} frames × {TM_FRAME_BITS} + {TM_SELF_BITS} self), " &
       &"{C_N_LITS} literals"
  echo &"classifier: {C_N_POS}+{C_N_NEG} clauses, T={C_T:.0f}, s={C_S}, states=[-{C_N_STATES},{C_N_STATES}]"
  echo &"rule: label = (enemy energy < {Threshold:.0f}); threshold fixed across train/test"

  let train = gather(TrainCfgs)
  let test = gather(TestCfgs)
  var trainPos, testPos = 0
  for s in train: trainPos += s.y
  for s in test: testPos += s.y
  let trainMajority = max(trainPos, train.len - trainPos).float / train.len.float
  let testMajority = max(testPos, test.len - testPos).float / test.len.float
  echo &"train samples={train.len} (pos={trainPos}, neg={train.len-trainPos}), majority={trainMajority*100:.1f}%"
  echo &"test  samples={test.len} (pos={testPos}, neg={test.len-testPos}), majority={testMajority*100:.1f}%"
  echo ""

  var trainLabels: seq[int]
  for s in train: trainLabels.add s.y
  var net = shuffleRun(train, trainLabels, 20250920'u64, Epochs)

  let trAcc = accuracy(net, train)
  let teAcc = accuracy(net, test)
  let cm = confusion(net, test)
  echo &"train accuracy = {trAcc*100:.2f}%  (majority {trainMajority*100:.2f}%)"
  echo &"TEST  accuracy = {teAcc*100:.2f}%  (majority {testMajority*100:.2f}%)"
  echo &"TEST  confusion: tp={cm.tp} tn={cm.tn} fp={cm.fp} fn={cm.fn}"
  echo ""

  # Control: identical pipeline on shuffled labels. If the TM were memorising
  # trajectory structure rather than the rule, this would also score high.
  var shuffled = trainLabels
  for i in countdown(shuffled.high, 1):
    let j = int(nextU64() mod uint64(i + 1))
    swap(shuffled[i], shuffled[j])
  let ctrl = shuffleRun(train, shuffled, 777'u64, Epochs)
  let ctrlAcc = accuracy(ctrl, test)
  echo &"control (shuffled labels): TEST accuracy = {ctrlAcc*100:.2f}% (should be ~majority)"
  echo ""

  # Nominal vs effective clause width (constant bits are inert).
  let varying = varyingBits(train)
  var nVary = 0
  for v in varying: (if v: inc nVary)
  var totalInc, varyingInc, energyInc, activeClauses, maxInc = 0
  for c in 0..<C_N_CLAUSES:
    let inc = included(net, c)
    if inc.len > 0: inc activeClauses
    totalInc += inc.len
    maxInc = max(maxInc, inc.len)
    for lit in inc:
      if isEnergyField(lit): inc energyInc
      let bitIdx = if lit < C_N_IN: lit else: lit - C_N_IN
      if varying[bitIdx]: inc varyingInc
  echo &"input bits varying across dataset: {nVary}/{C_N_IN}"
  echo &"learned clauses: {activeClauses}/{C_N_CLAUSES} non-empty, " &
       &"mean {float(totalInc)/float(max(activeClauses,1)):.1f} literals/clause (nominal), " &
       &"mean {float(varyingInc)/float(max(activeClauses,1)):.1f} effective (varying), max {maxInc}"
  if totalInc > 0:
    echo &"energy-field literals: {energyInc}/{totalInc} ({energyInc.float/totalInc.float*100:.0f}%)"
  echo ""

  # Counterfactual energy probe: force all frames' energy below / above the
  # threshold and see how often the prediction follows. A model that reads the
  # energy threshold is near 100%; one that reads a proxy (e.g. heading) stays
  # near its default rate.
  var cfLow = test
  var cfHigh = test
  for i in 0..<test.len:
    cfLow[i] = test[i];  setEnergyAllFrames(cfLow[i], 10.0)   # label would be 1
    cfHigh[i] = test[i]; setEnergyAllFrames(cfHigh[i], 50.0)  # label would be 0
  var followLow, followHigh = 0
  for i in 0..<test.len:
    if predictLabel(net, cfLow[i].lits) == 1:  inc followLow
    if predictLabel(net, cfHigh[i].lits) == 0: inc followHigh
  let energyFollow = (followLow.float + followHigh.float) / (2.0 * test.len.float)
  echo &"counterfactual energy flip (all frames -> energy 10 / 50): " &
       &"follows low {followLow*100 div test.len}%, high {followHigh*100 div test.len}%, mean {energyFollow*100:.1f}%"
  echo ""

  echo "field-masking probe (TEST accuracy with one channel zeroed; confounded by inert literals):"
  for ch in [fcEnergy, fcHeading, fcVelocity, fcDistance, fcBearing, fcWalls, fcSelf]:
    var masked = test
    for i in 0..<masked.len: masked[i] = maskChannel(masked[i], ch)
    echo &"  mask {($ch):<10} -> {accuracy(net, masked)*100:6.2f}%"
  echo ""

  echo "── learned clauses (positive class: predicts TURN) ──"
  echo "   [varying literals only; inert constant-bit literals omitted]"
  for c in 0..<C_N_POS:
    if included(net, c).len > 0:
      echo &"  C{c:>2}: {clauseStringVarying(net, c, varying)}"
  echo ""
  echo "── learned clauses (negative class: predicts STRAIGHT) ──"
  for c in C_N_POS..<C_N_CLAUSES:
    if included(net, c).len > 0:
      echo &"  C{c:>2}: {clauseStringVarying(net, c, varying)}"
  echo ""

  # Ablation: single-frame input removes the 10-frame redundancy. If the clause
  # bloat is a redundancy artefact, effective width should fall here.
  echo ""
  echo "── ablation: single-frame input (current frame only) ──"
  let train1 = gather(TrainCfgs, singleFrame = true)
  let test1  = gather(TestCfgs, singleFrame = true)
  var labels1: seq[int]
  for s in train1: labels1.add s.y
  let net1 = shuffleRun(train1, labels1, 20250920'u64, Epochs)
  let teAcc1 = accuracy(net1, test1)
  let varying1 = varyingBits(train1)
  var total1, varyingInc1, active1 = 0
  for c in 0..<C_N_CLAUSES:
    let inc = included(net1, c)
    if inc.len > 0: inc active1
    total1 += inc.len
    for lit in inc:
      let bitIdx = if lit < C_N_IN: lit else: lit - C_N_IN
      if varying1[bitIdx]: inc varyingInc1
  echo &"single-frame TEST accuracy = {teAcc1*100:.2f}% (majority {testMajority*100:.2f}%), " &
       &"mean {float(total1)/float(max(active1,1)):.1f} nominal / " &
       &"{float(varyingInc1)/float(max(active1,1)):.1f} effective literals/clause"
  for c in 0..<C_N_POS:
    if included(net1, c).len > 0:
      echo &"  C{c:>2}: {clauseStringVarying(net1, c, varying1, 8)}"
  echo ""

  # The two classes are separable by the rule, so a model that found it should
  # clearly beat the majority baseline and the shuffled-label control.
  if teAcc > testMajority + 0.05:
    echo &"VERDICT: the TM generalised above majority ({teAcc*100:.1f}% vs {testMajority*100:.1f}%), " &
         &"while the shuffled-label control stayed at {ctrlAcc*100:.1f}%."
  else:
    echo &"VERDICT: no evidence the TM learned the rule " &
         &"(TEST {teAcc*100:.1f}% vs majority {testMajority*100:.1f}%)."
  echo ""

  var energyVaries = false
  for i in 0..<C_N_IN:
    if channelOf(i) == fcEnergy and varying[i]: energyVaries = true
  check "energy channel is present and varying in the dataset", energyVaries
  check "TM test accuracy beats the majority baseline by >5 points",
        teAcc > testMajority + 0.05
  check "shuffled-label control does NOT beat the majority baseline by >5 points",
        ctrlAcc <= testMajority + 0.05

  if failures > 0:
    echo "\n", failures, " check(s) FAILED"
    quit(1)
  echo "\nAll pattern-learning checks passed."

when isMainModule:
  main()