# `Monty.Experiment.RecognitionStrategy.MinEvalMaxTotalSteps`
[🔗](https://github.com/d-led/ex.tbp.monty/blob/v0.1.0/packages/ex_tbp_monty_core/lib/monty/experiment/recognition_strategy/min_eval_max_total_steps.ex#L1)

A wrapper that refuses to stop too early, and stops anyway at a ceiling.

Mirrors `tbp/monty/experiment/recognition_strategy.py::MinEvalMaxTotalSteps`. It
exists because a model can recognise an object in three steps, which makes an
evaluation measure nothing: this holds the episode open until
`min_eval_steps` matching steps have happened, while still ending it at
`max_total_steps` — which is also the guard against a policy that stalls
because matching steps never increase.

The wrapped policy's `time_out_lms` and `start_exploring` are carried through
in every branch, so wrapping changes *when* an episode stops and nothing else.

# `call`

```elixir
@spec call(map(), Monty.Experiment.RecognitionStrategy.RecognitionCounter.t(), map()) ::
  Monty.Experiment.RecognitionStrategy.RecognitionResult.t()
```

The wrapped answer, held back while too little has been seen, or ended at the
ceiling.

# `new`

```elixir
@spec new(keyword()) :: {:ok, {module(), map()}} | {:error, String.t()}
```

A wrapper around `:wrapped`, which is a policy in the same `{module, state}`
shape.

Monty raises `ValueError` for a negative minimum, a non-positive ceiling, or a
minimum above the ceiling; here that is `{:error, message}` with Monty's own
words, checked in the same order.

---

*Consult [api-reference.md](api-reference.md) for complete listing*