map_inference_from_stream: Runs MAP inference from an AMIDST data stream

In ramidst: An Interface to the AMIDST Toolbox for Data Stream Processing

Description

Runs MAP inference from an AMIDST data stream

Usage

map_inference_from_stream(network, map_variables, evidence_variables,
  input_stream, sample_size, parallel = T, seed = 3L)

Arguments

network	a java object of class BayesianNetwork over which the computations will be carried out
map_variables	a vector with the name of the variables over which the MAP configuration will be computed
evidence_variables	a vector with the names of the observed variables
input_stream	and AMIDST data stream
sample_size	the sample size to be used for estimating marginals
parallel	a boolean indicating whether or not the items in the sample will be generated in parallel (when allowed by the system)
seed	the seed for the genertion of random numbers

Value

a data.frame with the MAP configuration of the variables of interest for each item in the stream

Note

The function computes the MAP configuration of the variables of interest given some evidence for all the items in the input stream.

References

D. Ramos-Lopez, A. Salmeron, R. Rumi, A.M. Martinez, T.D. Nielsen, A.R. Masegosa, H. Langseth, A.L. Madsen (2016) Scalable MAP inference in Bayesian networks based on a Map-Reduce approach. PGM'2016. JMLR: Workshop and Conference Proceedings, vol. 52: 415-425.

Examples

## Not run: 
network <- load_amidst_bn(system.file("extdata","WasteIncinerator.bn",
package="ramidst"))
sample_stream <- amidst_data_stream(system.file("extdata",
"WasteIncineratorSample.arff",package="ramidst"))
map_configurations <- map_inference_from_stream(network,c("D","B"),c("W"),
sample_stream,5L)
map_configurations

## End(Not run)

ramidst documentation built on May 30, 2017, 5:27 a.m.