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Last data update: 2014.03.03

R: predicts the expected label of a document given a trained...

predict.maxent

R Documentation

predicts the expected label of a document given a trained model.

Description

Predicts the expected labels and probability scores of a matrix of documents given a trained model of class maxent-class generated by function maxent.

Usage

## S3 method for class 'maxent'
predict(object, feature_matrix, ...)

Arguments

`object`	An object of class `maxent-class`, as returned by the `maxent` function.
`feature_matrix`	Either a regular `matrix` of class `DocumentTermMatrix` or `TermDocumentMatrix` from package tm, a `matrix.csr` representation generated by `as.compressed.matrix`, Matrix (package Matrix), matrix.csr (SparseM), data.frame, or matrix.
`...`	Not used but needed for compatibility with generic `predict` method.

Value

Returns a matrix with the first column containing predicted labels, and the remaining columns containing probability scores for each unique label.

Author(s)

Timothy P. Jurka <tpjurka@ucdavis.edu>

References

Y. Tsuruoka. "A simple C++ library for maximum entropy classification." University of Tokyo Department of Computer Science (Tsujii Laboratory), 2011. URL http://www-tsujii.is.s.u-tokyo.ac.jp/~tsuruoka/maxent/.

Examples

# LOAD LIBRARY
library(maxent)

# READ THE DATA, PREPARE THE CORPUS, and CREATE THE MATRIX
data <- read.csv(system.file("data/NYTimes.csv.gz",package="maxent"))
corpus <- Corpus(VectorSource(data$Title[1:150]))
matrix <- DocumentTermMatrix(corpus)

# TRAIN/PREDICT USING SPARSEM REPRESENTATION
sparse <- as.compressed.matrix(matrix)
model <- maxent(sparse[1:100,],as.factor(data$Topic.Code)[1:100])
results <- predict(model,sparse[101:150,])