pfa.kmeans: PFA Formatting of Fitted K-means Models

In aurelius: Generates PFA Documents from R Code and Optionally Runs Them

Description

This function takes a K-means model fit using kmeans and returns a list-of-lists representing in valid PFA document that could be used for scoring

Usage

## S3 method for class 'kmeans'
pfa(object, name = NULL, version = NULL, doc = NULL,
  metadata = NULL, randseed = NULL, options = NULL,
  cluster_names = NULL, ...)

Arguments

object	an object of class "kmeans"
name	a character which is an optional name for the scoring engine
version	an integer which is sequential version number for the model
doc	a character which is documentation string for archival purposes
metadata	a list of strings that is computer-readable documentation for archival purposes
randseed	a integer which is a global seed used to generate all random numbers. Multiple scoring engines derived from the same PFA file have different seeds generated from the global one
options	a list with value types depending on option name Initialization or runtime options to customize implementation (e.g. optimization switches). May be overridden or ignored by PFA consumer
cluster_names	a character vector of length k to name the values relating to each cluster instead of just an integer. If not specified, then the predicted cluster will be the string representation of the cluster index.
...	additional arguments affecting the PFA produced

Value

a list of lists that compose valid PFA document

Source

pfa_config.R avro_typemap.R avro.R pfa_cellpool.R pfa_expr.R pfa_utils.R kcca.R

See Also

kmeans pfa.kcca

Examples

model <- kmeans(x=iris[, 1:2], centers=3)
model_as_pfa <- pfa(model)

Example output

aurelius documentation built on May 2, 2019, 3:43 a.m.