maxent-class given a matrix or matrix.csr with training data, and a vector or factor with corresponding labels. Additional parameters such as feature_cutoff, gaussian_prior, inequality_constraints, and set_heldout help prevent model overfitting.
maxent(feature_matrix, code_vector, l1_regularizer=0.0, l2_regularizer=0.0, use_sgd=FALSE, set_heldout=0, verbose=FALSE)factor or vector of labels corresponding to each document in the feature_matrix.
numeric turning on L1 regularization and setting the regularization parameter. A value of 0 will disable L1 regularization.
numeric turning on L2 regularization and setting the regularization parameter. A value of 0 will disable L2 regularization.
logical indicating that SGD parameter estimation should be used. Defaults to FALSE.
integer specifying the number of documents to hold out. Sets a held-out subset of your data to test against and prevent overfitting.
logical specifying whether to provide descriptive output about the training process. Defaults to FALSE, or no output.
maxent-class with two slots.character vector containing the trained maximum entropy model.data.frame listing all the weights in three columns: Weight, Label, and Feature. 1. Set the l1_regularizer parameter to 1.0, leaving l2_regularizer and set_heldout as default.
2. Set the l2_regularizer parameter to 1.0, leaving l1_regularizer and set_heldout as default.
3. Set the set_heldout parameter to hold-out a portion of your data, leaving l1_regularizer and l2_regularizer as default.
If you are using a large number of training samples, try setting the use_sgd parameter to TRUE.
# 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$Title[1:150]))
matrix <- DocumentTermMatrix(corpus)
# TRAIN USING SPARSEM REPRESENTATION
sparse <- as.compressed.matrix(matrix)
model <- maxent(sparse[1:100,],as.factor(data$Topic.Code)[1:100])
# A DIFFERENT EXAMPLE (taken from package e10711)
# CREATE DATA
x <- seq(0.1, 5, by = 0.05)
y <- log(x) + rnorm(x, sd = 0.2)
# ESTIMATE MODEL AND PREDICT INPUT VALUES
m <- maxent(x, y)
new <- predict(m, x)
# VISUALIZE
plot(x, y)
points(x, log(x), col = 2)
points(x, new[,1], col = 4)
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