# ml_gaussian_mixture

##### Spark ML -- Gaussian Mixture clustering.

This class performs expectation maximization for multivariate Gaussian Mixture Models (GMMs). A GMM represents a composite distribution of independent Gaussian distributions with associated "mixing" weights specifying each's contribution to the composite. Given a set of sample points, this class will maximize the log-likelihood for a mixture of k Gaussians, iterating until the log-likelihood changes by less than `tol`

, or until it has reached the max number of iterations. While this process is generally guaranteed to converge, it is not guaranteed to find a global optimum.

##### Usage

```
ml_gaussian_mixture(x, formula = NULL, k = 2, max_iter = 100,
tol = 0.01, seed = NULL, features_col = "features",
prediction_col = "prediction", probability_col = "probability",
uid = random_string("gaussian_mixture_"), ...)
```

##### Arguments

- x
A

`spark_connection`

,`ml_pipeline`

, or a`tbl_spark`

.- formula
Used when

`x`

is a`tbl_spark`

. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details.- k
The number of clusters to create

- max_iter
The maximum number of iterations to use.

- tol
Param for the convergence tolerance for iterative algorithms.

- seed
A random seed. Set this value if you need your results to be reproducible across repeated calls.

- features_col
Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by

`ft_r_formula`

.- prediction_col
Prediction column name.

- probability_col
Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

- uid
A character string used to uniquely identify the ML estimator.

- ...
Optional arguments, see Details.

##### Value

The object returned depends on the class of `x`

.

`spark_connection`

: When`x`

is a`spark_connection`

, the function returns an instance of a`ml_estimator`

object. The object contains a pointer to a Spark`Estimator`

object and can be used to compose`Pipeline`

objects.`ml_pipeline`

: When`x`

is a`ml_pipeline`

, the function returns a`ml_pipeline`

with the clustering estimator appended to the pipeline.`tbl_spark`

: When`x`

is a`tbl_spark`

, an estimator is constructed then immediately fit with the input`tbl_spark`

, returning a clustering model.`tbl_spark`

, with`formula`

or`features`

specified: When`formula`

is specified, the input`tbl_spark`

is first transformed using a`RFormula`

transformer before being fit by the estimator. The object returned in this case is a`ml_model`

which is a wrapper of a`ml_pipeline_model`

. This signature does not apply to`ml_lda()`

.

##### See Also

See http://spark.apache.org/docs/latest/ml-clustering.html for more information on the set of clustering algorithms.

Other ml clustering algorithms: `ml_bisecting_kmeans`

,
`ml_kmeans`

, `ml_lda`

##### Examples

```
# NOT RUN {
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
gmm_model <- ml_gaussian_mixture(iris_tbl, Species ~ .)
pred <- sdf_predict(iris_tbl, gmm_model)
ml_clustering_evaluator(pred)
# }
```

*Documentation reproduced from package sparklyr, version 1.1.0, License: Apache License 2.0 | file LICENSE*