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 <table style="width: 100%;"><tr><td>spark.gaussianMixture {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table>

 <h2>Multivariate Gaussian Mixture Model (GMM)</h2>

 <h3>Description</h3>

 <p>Fits multivariate gaussian mixture model against a SparkDataFrame, similarly to R's
 mvnormalmixEM(). Users can call <code>summary</code> to print a summary of the fitted model,
 <code>predict</code> to make predictions on new data, and <code>write.ml</code>/<code>read.ml</code>
 to save/load fitted models.
 </p>


 <h3>Usage</h3>

 <pre><code class='language-R'>spark.gaussianMixture(data, formula, ...)

 ## S4 method for signature 'SparkDataFrame,formula'
 spark.gaussianMixture(data, formula, k = 2, maxIter = 100, tol = 0.01)

 ## S4 method for signature 'GaussianMixtureModel'
 summary(object)

 ## S4 method for signature 'GaussianMixtureModel'
 predict(object, newData)

 ## S4 method for signature 'GaussianMixtureModel,character'
 write.ml(object, path, overwrite = FALSE)
 </code></pre>


 <h3>Arguments</h3>

 <table>
 <tr style="vertical-align: top;"><td><code>data</code></td>
 <td>
 <p>a SparkDataFrame for training.</p>
 </td></tr>
 <tr style="vertical-align: top;"><td><code>formula</code></td>
 <td>
 <p>a symbolic description of the model to be fitted. Currently only a few formula
 operators are supported, including '~', '.', ':', '+', and '-'.
 Note that the response variable of formula is empty in spark.gaussianMixture.</p>
 </td></tr>
 <tr style="vertical-align: top;"><td><code>...</code></td>
 <td>
 <p>additional arguments passed to the method.</p>
 </td></tr>
 <tr style="vertical-align: top;"><td><code>k</code></td>
 <td>
 <p>number of independent Gaussians in the mixture model.</p>
 </td></tr>
 <tr style="vertical-align: top;"><td><code>maxIter</code></td>
 <td>
 <p>maximum iteration number.</p>
 </td></tr>
 <tr style="vertical-align: top;"><td><code>tol</code></td>
 <td>
 <p>the convergence tolerance.</p>
 </td></tr>
 <tr style="vertical-align: top;"><td><code>object</code></td>
 <td>
 <p>a fitted gaussian mixture model.</p>
 </td></tr>
 <tr style="vertical-align: top;"><td><code>newData</code></td>
 <td>
 <p>a SparkDataFrame for testing.</p>
 </td></tr>
 <tr style="vertical-align: top;"><td><code>path</code></td>
 <td>
 <p>the directory where the model is saved.</p>
 </td></tr>
 <tr style="vertical-align: top;"><td><code>overwrite</code></td>
 <td>
 <p>overwrites or not if the output path already exists. Default is FALSE
 which means throw exception if the output path exists.</p>
 </td></tr>
 </table>


 <h3>Value</h3>

 <p><code>spark.gaussianMixture</code> returns a fitted multivariate gaussian mixture model.
 </p>
 <p><code>summary</code> returns summary of the fitted model, which is a list.
 The list includes the model's <code>lambda</code> (lambda), <code>mu</code> (mu),
 <code>sigma</code> (sigma), <code>loglik</code> (loglik), and <code>posterior</code> (posterior).
 </p>
 <p><code>predict</code> returns a SparkDataFrame containing predicted labels in a column named
 &quot;prediction&quot;.
 </p>


 <h3>Note</h3>

 <p>spark.gaussianMixture since 2.1.0
 </p>
 <p>summary(GaussianMixtureModel) since 2.1.0
 </p>
 <p>predict(GaussianMixtureModel) since 2.1.0
 </p>
 <p>write.ml(GaussianMixtureModel, character) since 2.1.0
 </p>


 <h3>See Also</h3>

 <p>mixtools: <a href="https://cran.r-project.org/package=mixtools">https://cran.r-project.org/package=mixtools</a>
 </p>
 <p><a href="../../SparkR/help/predict.html">predict</a>, <a href="../../SparkR/help/read.ml.html">read.ml</a>, <a href="../../SparkR/help/write.ml.html">write.ml</a>
 </p>


 <h3>Examples</h3>

 <pre><code class="r">## Not run:
 ##D sparkR.session()
 ##D library(mvtnorm)
 ##D set.seed(100)
 ##D a &lt;- rmvnorm(4, c(0, 0))
 ##D b &lt;- rmvnorm(6, c(3, 4))
 ##D data &lt;- rbind(a, b)
 ##D df &lt;- createDataFrame(as.data.frame(data))
 ##D model &lt;- spark.gaussianMixture(df, ~ V1 + V2, k = 2)
 ##D summary(model)
 ##D
 ##D # fitted values on training data
 ##D fitted &lt;- predict(model, df)
 ##D head(select(fitted, &quot;V1&quot;, &quot;prediction&quot;))
 ##D
 ##D # save fitted model to input path
 ##D path &lt;- &quot;path/to/model&quot;
 ##D write.ml(model, path)
 ##D
 ##D # can also read back the saved model and print
 ##D savedModel &lt;- read.ml(path)
 ##D summary(savedModel)
 ## End(Not run)
 </code></pre>


 <hr /><div style="text-align: center;">[Package <em>SparkR</em> version 3.2.2 <a href="00Index.html">Index</a>]</div>
 </div>
 </body></html>
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	<table style="width: 100%;"><tr><td>spark.gaussianMixture {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table>

	<h2>Multivariate Gaussian Mixture Model (GMM)</h2>

	<h3>Description</h3>

	<p>Fits multivariate gaussian mixture model against a SparkDataFrame, similarly to R's
	mvnormalmixEM(). Users can call <code>summary</code> to print a summary of the fitted model,
	<code>predict</code> to make predictions on new data, and <code>write.ml</code>/<code>read.ml</code>
	to save/load fitted models.
	</p>


	<h3>Usage</h3>

	<pre><code class='language-R'>spark.gaussianMixture(data, formula, ...)

	## S4 method for signature 'SparkDataFrame,formula'
	spark.gaussianMixture(data, formula, k = 2, maxIter = 100, tol = 0.01)

	## S4 method for signature 'GaussianMixtureModel'
	summary(object)

	## S4 method for signature 'GaussianMixtureModel'
	predict(object, newData)

	## S4 method for signature 'GaussianMixtureModel,character'
	write.ml(object, path, overwrite = FALSE)
	</code></pre>


	<h3>Arguments</h3>

	<table>
	<tr style="vertical-align: top;"><td><code>data</code></td>
	<td>
	<p>a SparkDataFrame for training.</p>
	</td></tr>
	<tr style="vertical-align: top;"><td><code>formula</code></td>
	<td>
	<p>a symbolic description of the model to be fitted. Currently only a few formula
	operators are supported, including '~', '.', ':', '+', and '-'.
	Note that the response variable of formula is empty in spark.gaussianMixture.</p>
	</td></tr>
	<tr style="vertical-align: top;"><td><code>...</code></td>
	<td>
	<p>additional arguments passed to the method.</p>
	</td></tr>
	<tr style="vertical-align: top;"><td><code>k</code></td>
	<td>
	<p>number of independent Gaussians in the mixture model.</p>
	</td></tr>
	<tr style="vertical-align: top;"><td><code>maxIter</code></td>
	<td>
	<p>maximum iteration number.</p>
	</td></tr>
	<tr style="vertical-align: top;"><td><code>tol</code></td>
	<td>
	<p>the convergence tolerance.</p>
	</td></tr>
	<tr style="vertical-align: top;"><td><code>object</code></td>
	<td>
	<p>a fitted gaussian mixture model.</p>
	</td></tr>
	<tr style="vertical-align: top;"><td><code>newData</code></td>
	<td>
	<p>a SparkDataFrame for testing.</p>
	</td></tr>
	<tr style="vertical-align: top;"><td><code>path</code></td>
	<td>
	<p>the directory where the model is saved.</p>
	</td></tr>
	<tr style="vertical-align: top;"><td><code>overwrite</code></td>
	<td>
	<p>overwrites or not if the output path already exists. Default is FALSE
	which means throw exception if the output path exists.</p>
	</td></tr>
	</table>


	<h3>Value</h3>

	<p><code>spark.gaussianMixture</code> returns a fitted multivariate gaussian mixture model.
	</p>
	<p><code>summary</code> returns summary of the fitted model, which is a list.
	The list includes the model's <code>lambda</code> (lambda), <code>mu</code> (mu),
	<code>sigma</code> (sigma), <code>loglik</code> (loglik), and <code>posterior</code> (posterior).
	</p>
	<p><code>predict</code> returns a SparkDataFrame containing predicted labels in a column named
	"prediction".
	</p>


	<h3>Note</h3>

	<p>spark.gaussianMixture since 2.1.0
	</p>
	<p>summary(GaussianMixtureModel) since 2.1.0
	</p>
	<p>predict(GaussianMixtureModel) since 2.1.0
	</p>
	<p>write.ml(GaussianMixtureModel, character) since 2.1.0
	</p>


	<h3>See Also</h3>

	<p>mixtools: <a href="https://cran.r-project.org/package=mixtools">https://cran.r-project.org/package=mixtools</a>
	</p>
	<p><a href="../../SparkR/help/predict.html">predict</a>, <a href="../../SparkR/help/read.ml.html">read.ml</a>, <a href="../../SparkR/help/write.ml.html">write.ml</a>
	</p>


	<h3>Examples</h3>

	<pre><code class="r">## Not run:
	##D sparkR.session()
	##D library(mvtnorm)
	##D set.seed(100)
	##D a <- rmvnorm(4, c(0, 0))
	##D b <- rmvnorm(6, c(3, 4))
	##D data <- rbind(a, b)
	##D df <- createDataFrame(as.data.frame(data))
	##D model <- spark.gaussianMixture(df, ~ V1 + V2, k = 2)
	##D summary(model)
	##D
	##D # fitted values on training data
	##D fitted <- predict(model, df)
	##D head(select(fitted, "V1", "prediction"))
	##D
	##D # save fitted model to input path
	##D path <- "path/to/model"
	##D write.ml(model, path)
	##D
	##D # can also read back the saved model and print
	##D savedModel <- read.ml(path)
	##D summary(savedModel)
	## End(Not run)
	</code></pre>


	<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 3.2.2 <a href="00Index.html">Index</a>]</div>
	</div>
	</body></html>