# tm_diag — the Tsetlin diagnostics kit

First-class, offline diagnostics for a Tsetlin-Machine head. Answers the two
questions that clause-reading alone cannot:

> **Is the TM learning badly, or is the data bad?**

Built before the new TM gun, so a bad design cannot hide behind the data and a
bad dataset cannot hide behind the model.

Everything is pure and offline — no battles, no Java, no harness.

## Files

| file | contents |
|---|---|
| `feature_spec.nim` | `FeatureSpec`, `describe`, `describeClause`, `draftTMSpec()` (49-bit draft), `tmPatternSpec()` (40-bit shipped encoding) |
| `tm_core.nim` | compact deterministic Granmo Table 2/3 multiclass TM (mirrors the tm_pattern core), introspectable clause layout |
| `diagnostics.nim` | the eight groups (re-exports the two above) |

Import everything with:

```nim
import tm_diag/diagnostics
```

## Task 1b — the `s` specificity knob (direction SETTLED, from the code)

`TM_S` / `m.sValue` is the Type I specificity knob. It appears in ONE place in
each core — the Type I feedback branch of `tmLearnDir`:

`common_libs/guns/tm_pattern.nim` lines 258-265 (identical in
`guns/tsetlin.nim` lines 224-231 and `tm_diag/tm_core.nim` lines 110-121):

```nim
if pol * d > 0.0:
  # Type I (Table 2) collapsed to the resulting state move:
  #   c=1, lk=1 -> +1 (toward Include) w.p. (s-1)/s
  #   c=0, lk=1 -> -1 (toward Exclude) w.p.  1/s   <- the missing counter-force
  #   lk=0      -> -1 (toward Exclude) w.p.  1/s
  for lit in 0..<TM_NLITS:
    var st = int(team[base + lit])
    if lits[lit] == 1'u8:
      if cOut == 1'u8:
        if rand(1.0) < (TM_S - 1.0) / TM_S: st = min(st + 1, TM_NSTATES)
      else:
        if rand(1.0) < 1.0 / TM_S:          st = max(st - 1, -TM_NSTATES)
    else:
      if rand(1.0) < 1.0 / TM_S:            st = max(st - 1, -TM_NSTATES)
    team[base + lit] = int16(st)
```

**Direction: HIGHER `s` makes clauses LONGER.** The include step runs with
probability `(s-1)/s` (rising with `s`), while both exclude steps run with
probability `1/s` (falling with `s`). So a larger `s` reinforces inclusions more
often and removes literals less often.

MEASURED on the 49-bit draft encoding, the planted 2-literal rule
(`diag_synthetic` dataset, 3000 train / 1500 eval, 40 clauses/class, N=64,
25 epochs):

| `s` | mean clause len | max | eval acc |
|---|---|---|---|
| 1.0 | 1.51 | 2 | 100.0% |
| 1.5 | 1.42 | 2 | 100.0% |
| 2.0 | 1.82 | 3 | 100.0% |
| 3.0 | 2.19 | 3 | 100.0% |
| 5.0 | 3.02 | 4 | 100.0% |
| 10.0 | 4.04 | 5 | 99.7% |
| 20.0 | 5.17 | 7 | 94.5% |

**Why `s = 1.0` is degenerate:** at `s = 1.0`, `(s-1)/s = 0.0` so the include
step is never taken (`rand(1.0) < 0.0` is never true) and `1/s = 1.0` so BOTH
exclude steps are always taken. Type I can only ever REMOVE literals — a clause
can never grow through the normal path, only via the Type II penalty
(`cOut==1` + wrong direction, which includes ABSENT literals). On the easy
synthetic the two-literal rule is still reachable through Type II (100%), but on
random labels `s=1.0` leaves only 43/120 non-empty clauses vs 120/120 at `s>=3`:
the specificity mechanism is switched off.

**Practical usable range: `s` in roughly [1.5, 5].** `s=1.0` is degenerate and
must be avoided. The shipped guns sit inside the range: `tm_pattern` uses `3.0`,
`tsetlin.nim` uses `1.5`. The clause-shape checker's healthy-band verdict is the
tool that tells you whether a given `s` produced a sane geometry.

## Task 1 — named features / clause rendering

```nim
let spec = draftTMSpec()          # 49 bits, all one-hot
spec.nBits                        # 49
spec.describe(45)                 # "lat DEAD-ON -18..+18"
spec.describeLiteral(49 + 45)     # "NOT lat DEAD-ON -18..+18"
spec.describeClause(@[0, 45], 2)  # "IF dist-wall<50 AND lat DEAD-ON -18..+18 THEN class=2"
spec.describeClause(@[], 1)       # "IF TRUE (empty clause) THEN class=1"
```

A block is added with `addBlock(name, count, bitNames?)`; a bit with no explicit
name renders as `blockName[k]`, and a single-bit block renders as its name.

`tmPatternSpec()` mirrors `guns/tm_pattern.nim`'s `tmBuildBits` exactly. **Its two
last bits (`UNUSED-38/39`) are a real bug**: `tmBuildBits` writes only 38 raw
bits into an `array[TM_NBITS=40, uint8]`, so bits 38 and 39 are always 0 and
their negations always 1. The kit reports them as constant dead inputs.

## Task 2 — the data / clause / feature / accuracy / curve / ablation groups

All functions take a trained `TmMachine` (or an externally supplied clause set)
plus `seq[DiagSample]` where `DiagSample.lits` is the pos-then-neg literal vector
and `DiagSample.label` the true class.

```nim
# build samples from raw bits
let s = makeSample(nBits, rawBits, label, order)

# 1. pre-flight DATA checks
let dc = dataChecks(labels, nClasses, threshold = 0.30)
#   dc.classCounts, dc.classShares, dc.majorityClass, dc.majorityShare,
#   dc.majorityAccuracy, dc.overThreshold, dc.flags
let sc = shuffledLabelControl(tmplMachine, samples)   # (acc, majority, ...)

# 2. clause introspection
let infos = clauseInfo(m, samples, spec)
let summ  = clauseSummary(infos)          # empty / neverFired / length hist
for c in topClauses(infos, 10): echo c.text, " votes=", c.votes
for cb in clauseBalanceByClass(infos, nClasses): echo cb
for cl in 0..<nClasses:                    # recover the class rule
  echo spec.describeClause(necessaryLiterals(m, samples, cl), cl)

# 3. per-feature contribution + DEAD-INPUT LIST
let contribs = featureContributions(m, samples, spec)
let dead     = deadInputs(contribs)        # weighted < 5% of top, or never used
let strict   = neverUsedInputs(contribs)   # appearances == 0
let consts   = constantInputs(samples, nBits)
for c in rankedInputs(contribs)[0..<10]: echo c.name, " ", c.weighted

# 4. accuracy diagnostics
let ad = accuracyDiagnostics(m, samples)
#   ad.acc, ad.majorityBaseline, ad.margin, ad.confusion,
#   ad.perClassRecall/Precision, ad.predMajorityShare

# 5. learning curve
let lc = learningCurve(tmplMachine, train, eval, nPoints = 10)
#   lc.points, lc.accs, lc.trend  ("flat" | "rising" | "rising-then-falling ...")

# 6. ablation hooks
let base = ablateBaseline(tmplMachine, train, eval)
for r in ablateDropAllBlocks(tmplMachine, train, eval, spec): echo r.name, r.delta
let rs = ablateScrambleFeature(tmplMachine, train, eval, spec, bit)
```

`ablation` retrains a fresh machine per variant (the strongest form of "does this
input earn its bits"). Pass `baselineAcc` from `ablateBaseline` to avoid
recomputing it per block.

## Task 2b / 4 — the CLAUSE-SHAPE checker (group 8)

The automata metrics say whether the TM is settling. The clause-shape checker
says whether what it settled ON is sane: a rule needing ~19 conditions to fire
is almost certainly fitting noise.

```nim
import tm_diag/diagnostics

let d = clauseShapeDiagnostics(m, samples, spec,
                               healthyLo = 3.0, healthyHi = 8.0,
                               collapsedMax = 2.0)
d.summary.meanLength      # 19.17 for the shipped gun
d.summary.medianLength    # 16.0
d.summary.p10Length       # 4.0
d.summary.p90Length       # 44.8
d.summary.maxLength       # 57
d.summary.lengthHist      # index = length, value = count
d.summary.posMeanLength / .negMeanLength
d.summary.perClassMeanLength / .perClassMedianLength / .perClassHist
d.verdict                 # "healthy" | "too long" | "collapsed" | "short" | "n/a"
d.blocks                  # per-FeatureSpec-block literal contribution
d.coverage                # firing fraction + effective clause count
echo shapeLine(d)         # one line, same style as healthLine
echo formatClauseShapeReport(d)   # full readout
```

* **length distribution** — `meanLength`, `medianLength`, `p10Length`,
  `p90Length`, `minLength`, `maxLength`, `stdLength` and `lengthHist`, plus
  per-polarity (`posMeanLength` / `negMeanLength`) and per-class
  (`perClassMeanLength` / `perClassMedianLength` / `perClassHist`) breakdowns.
  `lengthStats(lengths)` and `percentile(sorted, p)` are exported standalone.
* **healthy-band verdict** — `clauseShapeVerdict(summ, healthyLo=3, healthyHi=8,
  collapsedMax=2)`: `> healthyHi` = `too long`, `< collapsedMax` = `collapsed`,
  between `collapsedMax` and `healthyLo` = `short`, in band = `healthy`, no
  non-empty clauses = `n/a`. The band is a PARAMETER of the call.
* **per-block contribution** — `blockLengthContributions(m, spec)`: for each
  `FeatureSpec` block, the literals it contributes across clauses
  (`totalLits`, `meanPerClause`, `share`, `clausesUsing`, `perClauseMax`). A
  block that contributes to every clause is dominating; a block contributing
  ~0 is dead (the block-level view of the dead-INPUT list).
* **coverage / concentration** — `clauseCoverage(m, samples)`: the fraction of
  clauses that fire on a sample (`meanFiringFraction`), the participation-ratio
  `effectiveClauses = (sum v)^2 / sum v^2` (if 3 clauses cast most votes it
  reads ~3, not the configured count) and `top3Share`.

The one-line `healthLine` now appends `shape=<mean> (<verdict>)` alongside the
settledness / diversity / churn / disagreement verdicts.

## Task 5 — the AUTOMATA level (settledness / diversity / churn / disagreement)

Task 2 reads the clauses. Task 5 reads the **automata inside them**: one
automaton per input bit per clause, each holding a state that says how
confident it is that its bit belongs in the clause. These are what tell us
whether the TM is locking onto the enemy or just fidgeting, and whether the
inertia `N` (the number of automata states) should go up or down.

### State conventions — from the ACTUAL code, not the textbook

Derived from `tm_core.nim` and `guns/tm_pattern.nim` (`tmEval`, `tmLearnDir`,
`tmNewTeam`, `resetMachine`):

| property | value |
|---|---|
| state range | `[-nStates, nStates]` (int16); `nStates` = 64 for `tm_pattern`, 32 for `tsetlin.nim` |
| initial value | `0` (both cores call it "the Exclude boundary") |
| INCLUDE | `state > 0` (`tmEval` / `clauseLits`) |
| EXCLUDE | `state <= 0` |
| flip boundary | BETWEEN state `0` and state `1` — the middle of the range |
| `commitment(st)` | `abs(st) / nStates` in `[0,1]`: 0 on the boundary, 1 at either extreme |

So a flip is exactly a change in the predicate `state > 0`.

### The four metrics

1. **SETTLEDNESS** — per clause and overall, the mean `commitment` and the
   fraction of automata settled at/above a threshold (default `0.5`). High and
   **rising** as training proceeds is healthy; low means the clause is wavering
   noise. `settledness(m, threshold)`, `settlednessTrend(early, late)`.
2. **CLAUSE DIVERSITY** — mean pairwise **Jaccard** of the included-literal sets,
   within the same polarity. Low-to-moderate is healthy; ~1.0 = all clauses are
   one rule in 50 hats; ~0 = memorising ticks. Empty clauses carry no rule and
   are skipped by default. `clauseDiversity(m, skipEmpty = true)`, `jaccard(a,b)`.
3. **CHURN** — both levels, per training sample: the fraction of **automata**
   crossing the flip boundary, and the fraction of **clauses** whose
   included-literal set changed. High early and **falling** is healthy; flat-high
   = fidgeting; zero from the start = never learned. `churnTrace(tmpl, samples,
   epochs, seed, window, shuffle)`. `shuffle = false` replays the given
   (temporal) order, mirroring a live gun.
4. **VOTE DISAGREEMENT** — per class, over the samples where it casts a vote:
   the fraction of firing clauses whose polarity disagrees with the sign of the
   class's total vote. Low is healthy. `voteDisagreement(m, samples)`.

### The three readouts that make it readable

- **state histogram** — the distribution of automata states across the range:
  `stateHistogram(m, nBins)`, `histogramText(h)`.
- **per-input confidence table** — for each bit, the mean commitment of its
  automata (and a `constant` flag for zero-variance inputs):
  `perInputConfidence(m, spec, samples, threshold)`,
  `rankedInputConfidence(conf)`.
- **one-line health summary** — `healthLine(ad)` gives
  `settledness / diversity / churn trend / disagreement / shape`, each with its
  own verdict word (`settling`, `fidgeting`, `frozen`, `coherent`, `healthy`,
  `too long`, ...), and `automataVerdict(ad)` reduces the trajectory to one word.
  `formatAutomataReport(ad)` prints everything, including the clause-shape report
  when `ad.shape` was computed.

### API

```nim
import tm_diag/diagnostics

let ad = automataDiagnostics(tmpl, samples, spec,
                             epochs = 15, seed = 777,
                             settleThreshold = 0.5, nHistBins = 9,
                             window = 100, measureChurn = true)
# ad.machine, ad.settledness, ad.diversity, ad.churn, ad.disagreement,
# ad.histogram, ad.inputConfidence, ad.shape, ad.summary
echo ad.summary                  # one line
echo automataVerdict(ad)         # "settling" | "fidgeting" | "collapsed" | ...
echo formatAutomataReport(ad)    # the full readout
```

For a machine you already have (e.g. the shipped gun's `exportTeams()`), call
`settledness` / `clauseDiversity` / `stateHistogram` / `perInputConfidence` /
`voteDisagreement` directly and `churnTrace` on a fresh copy.

### Validation — Case A (learnable) vs Case B (noise)

`common_libs/tests/diag_automata_validation.nim` uses the planted rule from
`diag_synthetic.nim` and the SAME inputs with shuffled labels. Measured
(`nBits=49`, 3 classes, 40 clauses, `nStates=64`, 3000 samples, 15 epochs):

| metric | Case A (learnable) | Case B (noise) |
|---|---|---|
| settledness mean | **0.970** | 0.719 |
| settled fraction | 0.999 | 0.799 |
| churn trend | **falling** (`2.5e-4` -> `2e-6`) | **flat** (`1.0e-3` -> `7.2e-4`) |
| clause-change trend | falling (`0.011` -> `2.3e-4`) | flat (`0.069` -> `0.057`) |
| diversity (overall Jaccard) | 0.176 | 0.014 |
| disagreement | **0.003** | **0.298** |
| verdict | settling | mixed (not settling) |

Case A settledness RISES `0.475` (early prefix) -> `0.970` (full). The pair
**separates learning from fidgeting**: the decisive signals are the churn trend
(falling vs flat) and disagreement (0.003 vs 0.298). Settledness alone is NOT
enough — on noise the automata still commit (0.719), just to the wrong thing.
Case C (a forced-constant bit) is flagged: `perInputConfidence(...).constant`
and `constantInputs` both surface it. Note a constant bit can show HIGH
commitment (one literal is always 1), so the `constant` flag is what
 disambiguates.

### Inertia sweep (`N` = nStates, 10 epochs)

The validation also sweeps `N` to see whether inertia moves the metrics:

| N | Case A settled | A churn | A dis. | Case B settled | B churn | B dis. |
|---|---|---|---|---|---|---|
| 16 | 0.904 | falling | 0.003 | 0.720 | flat | 0.363 |
| 32 | 0.943 | falling | 0.000 | 0.700 | flat | 0.388 |
| 64 | 0.970 | falling | 0.003 | 0.715 | flat | 0.368 |
| 128 | 0.971 | falling | 0.000 | 0.660 | flat | 0.304 |

A and B are separated at EVERY N (churn falling vs flat, disagreement low vs
high). Raising N only raises Case A's commitment (0.90 -> 0.97); it does NOT
reduce noise-fitting in Case B. So **inertia is not the discriminator** the
metrics identify — the churn trend and disagreement are.

### Real reading — the shipped `tm_pattern` GF head

`diag_tm_pattern_offline.nim` over the committed DrussGT fixtures (automata read
directly off the exported teams from `tr_drussgt_vs_modularbot`; churn measured
on a `tm_core` temporal one-pass proxy, live-order):

- **settledness 0.484** (`settling`), settled fraction 0.453
- **diversity 0.267** (`moderate`) — positive 0.170, negative 0.322
- **churn 0.094/100 per sample, FALLING** (0.00208 -> 0.00048); clause-change 5.31/100, falling
- **disagreement 0.145** (`coherent`)
- **verdict: settling** — not fidgeting, not collapsed
- constant inputs flagged: bits 38/39 (the known never-written ones) plus 19/36/37 in this fixture
- context: pooled warm accuracy 35.72% vs the 34.24% majority = **+1.48pp**

**Clause-shape reading (group 8, same run):** `mean=19.17 median=16.00 p10=4.00
p90=44.80 std=13.89 min=1 max=57 nonEmpty=173 empty=27/200` -> verdict
**`too long`**. Per polarity `posMean=19.80 / negMean=18.70`; per class
`c0=11.4 c1=13.4 c2=31.2 c3=22.6 c4=21.6`. Per-block share is diffuse (no block
dominates): wall-near 10.8%, distance-band 8.9%, flight-band 8.7%, radial-frac
7.6%, speed-band 7.0%, closing 6.8%, **UNUSED 6.4%** (the always-true negations of
the never-written bits 38/39 are free padding), the rest 2-6%. Coverage:
`firing/sample=48.53 (24.3%) effectiveClauses=97.85/200 top3Share=5.3%` — the
voting is NOT concentrated on a few clauses. One-line health:

```
settledness=0.484 (settling) | diversity=0.267 (moderate) | churn=0.094/100 falling (settling) | disagreement=0.145 (coherent) | shape=19.17 (too long)
```

**Does `s` rescue this gun? MEASURED, no.** Recompiling the offline driver with
`-d:TM_S_DEF=<v>` (source untouched) retrains the gun end to end:

| `s` | mean clause len | shape verdict | pooled warm acc | margin vs majority |
|---|---|---|---|---|
| 1.5 | 15.82 | too long | 32.22% | **-2.03pp** |
| 2.0 | 14.99 | too long | 34.03% | **-0.22pp** |
| 3.0 (shipped) | 19.17 | too long | 35.72% | **+1.48pp** |
| 5.0 | 20.47 | too long | 34.72% | **+0.47pp** |

Lowering `s` shrinks the clauses (15.8 at `s=1.5`) but makes accuracy WORSE;
raising it pads them and also loses. The shipped `s=3.0` is the best of the four,
and NO value gets the mean anywhere near the healthy 3-8 band. Combined with the
settledness finding, the shape is consistent with **"no consistent short rule
exists in this representation/target"** — the clauses are long, diffuse and
padded, and the knob cannot fix a signal problem.

The gun settles onto the within-battle labels but its settled rules barely beat
the majority class, which points at the TARGET / representation rather than the
inertia `N`. See the Task 5 report for the inertia discussion.

## The default-off real-gun hook

`guns/tm_pattern.nim` gained only additive, default-off instrumentation:

```nim
g.diagCapture = true            # default false; no behaviour change when false
# ... replay ...
g.diagSamples                   # seq[TmDiagSample] (literal vector + label)
g.exportTeams()                 # read-only GF clause teams
g.exportRadTeams(); g.exportRevTeams()
```

To introspect an externally trained clause set:

```nim
let m = machineFromTeams(TM_NBITS, TM_CLASSES, TM_NCLAUSES, TM_NSTATES, TM_S,
                         g.exportTeams())
```

## Running the demos / tests

```sh
nim c -r -d:release --path:common_libs common_libs/tests/test_tm_diag.nim          # 48 pure unit checks
nim c -r --path:common_libs common_libs/tests/test_tm_automata_diag.nim            # 55 automata-metric unit checks
nim c -r -d:release --path:common_libs common_libs/tests/test_tm_clause_shape.nim  # 66 clause-shape unit + synthetic checks
nim c -r -d:release --path:common_libs common_libs/tests/diag_synthetic.nim        # Task 3 proof (17 checks)
nim c -r -d:release --path:common_libs common_libs/tests/diag_automata_validation.nim  # Task 5 A/B/C proof
nim c -r -d:release --path:common_libs common_libs/tests/diag_tm_pattern_offline.nim  # Task 4 real reading + shape
```

See `common_libs/tests/diag_synthetic.nim` for the ground-truth validation,
`common_libs/tests/test_tm_clause_shape.nim` for the clause-shape validation and
`common_libs/tests/diag_tm_pattern_offline.nim` for the real reading.