gradDescent (version 3.0)

ADAGRAD: ADAGRAD Method Learning Function

Description

A function to build prediction model using ADAGRAD method.

Usage

ADAGRAD(dataTrain, alpha = 0.1, maxIter = 10, seed = NULL)

Arguments

dataTrain

a data.frame that representing training data (\(m \times n\)), where \(m\) is the number of instances and \(n\) is the number of variables where the last column is the output variable. dataTrain must have at least two columns and ten rows of data that contain only numbers (integer or float).

alpha

a float value representing learning rate. Default value is 0.1

maxIter

the maximal number of iterations.

seed

a integer value for static random. Default value is NULL, which means the function will not do static random.

Value

a vector matrix of theta (coefficient) for linear model.

Details

This function based on SGD with an optimization to create an adaptive learning rate with an approach that accumulate previous cost in each iteration.

References

J. Duchi, E. Hazan, Y. Singer Adaptive Subgradient Methods for Online Learning and Stochastic Optimization, Journal of Machine Learning Research 12, pp. 2121-2159 (2011)

See Also

ADADELTA, RMSPROP, ADAM

Examples

# NOT RUN {
##################################
## Learning and Build Model with ADAGRAD
## load R Package data
data(gradDescentRData)
## get z-factor data
dataSet <- gradDescentRData$CompressilbilityFactor
## split dataset
splitedDataSet <- splitData(dataSet)
## build model with ADAGRAD
ADAGRADmodel <- ADAGRAD(splitedDataSet$dataTrain)
#show result
print(ADAGRADmodel)

# }