| spark.gaussianMixture {SparkR} | R Documentation |
Fits multivariate gaussian mixture model against a SparkDataFrame, similarly to R's
mvnormalmixEM(). Users can call summary to print a summary of the fitted model,
predict to make predictions on new data, and write.ml/read.ml
to save/load fitted models.
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)
data |
a SparkDataFrame for training. |
formula |
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. |
... |
additional arguments passed to the method. |
k |
number of independent Gaussians in the mixture model. |
maxIter |
maximum iteration number. |
tol |
the convergence tolerance. |
object |
a fitted gaussian mixture model. |
newData |
a SparkDataFrame for testing. |
path |
the directory where the model is saved. |
overwrite |
overwrites or not if the output path already exists. Default is FALSE which means throw exception if the output path exists. |
spark.gaussianMixture returns a fitted multivariate gaussian mixture model.
summary returns summary of the fitted model, which is a list.
The list includes the model's lambda (lambda), mu (mu),
sigma (sigma), loglik (loglik), and posterior (posterior).
predict returns a SparkDataFrame containing predicted labels in a column named
"prediction".
spark.gaussianMixture since 2.1.0
summary(GaussianMixtureModel) since 2.1.0
predict(GaussianMixtureModel) since 2.1.0
write.ml(GaussianMixtureModel, character) since 2.1.0
mixtools: https://cran.r-project.org/package=mixtools
## 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)