# `Latu.ML.Clustering.KMeansModel`
[🔗](https://github.com/zero-one-group/latu_ml/blob/v0.4.2/lib/latu/ml/clustering.ex#L0)

Attributes of a fitted `KMeansModel`.

Every accessor here is one name on the server's allowlist for
`org.apache.spark.ml.clustering.KMeansModel` — 5 of them — so tab completion is that
allowlist. The server refuses anything else with `CONNECT_ML.ATTRIBUTE_NOT_ALLOWED`; these
refuse a model of the wrong class before it gets that far, and name the module that would
have taken it.

Allowed by the server but not here: `toString` is `Identifiable`'s, and tells you less than
the model's own uid. `Latu.ML.attribute/2` and `Latu.ML.attribute/3` will send any of these
names; it is what comes back that has nowhere to go.

The allowlist is inherited rather than per class: this one is the union of
`org.apache.spark.ml.clustering.KMeansModel`, `org.apache.spark.ml.util.HasTrainingSummary`,
`org.apache.spark.ml.util.Identifiable`.

# `class`

```elixir
@spec class() :: String.t()
```

The JVM class these accessors belong to.

# `cluster_center_matrix`

```elixir
@spec cluster_center_matrix(Latu.ML.Model.t()) ::
  {:ok, term()} | {:error, Latu.Error.t()}
```

Spark's `clusterCenterMatrix`. PySpark exposes no attribute under that name, so there is no
description of it to quote here.

An action: it reaches the server. A `Vector` or `Matrix` comes back as an `Nx.Tensor`, or a
`Latu.ML.SparseVector` where densifying would be this package's decision rather than yours.

# `has_summary`

```elixir
@spec has_summary(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
```

Indicates whether a training summary exists for this model instance.

An action: it reaches the server. A `Vector` or `Matrix` comes back as an `Nx.Tensor`, or a
`Latu.ML.SparseVector` where densifying would be this package's decision rather than yours.

# `num_features`

```elixir
@spec num_features(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
```

Number of features, i.e., length of Vectors which this transforms.

An action: it reaches the server. A `Vector` or `Matrix` comes back as an `Nx.Tensor`, or a
`Latu.ML.SparseVector` where densifying would be this package's decision rather than yours.

# `predict`

```elixir
@spec predict(Latu.ML.Model.t(), Nx.Tensor.t() | Latu.ML.SparseVector.t()) ::
  {:ok, term()} | {:error, Latu.Error.t()}
```

Predict label for the given features.

An action: it reaches the server. A `Vector` or `Matrix` comes back as an `Nx.Tensor`, or a
`Latu.ML.SparseVector` where densifying would be this package's decision rather than yours.

## Arguments

  * `value` — a Spark `Vector`: an `Nx.Tensor` or a `Latu.ML.SparseVector`

# `summary`

```elixir
@spec summary(Latu.ML.Model.t()) :: Latu.ML.Summary.t()
```

Gets summary of the model trained on the training set. An exception is thrown if no summary
exists.

A lazy builder: Spark reaches a summary through the model that owns it, so the reference is
composed here and nothing is sent. `Latu.ML.summary/1` is the same thing without the class
check.

---

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