gradDescent (version 3.0)

SGD: Stochastic Gradient Descent (SGD) Method Learning Function

Description

A function to build prediction model using Stochastic Gradient Descent (SGD) method.

Usage

SGD(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 GD method with optimization to use only one instance of training data stochasticaly. So, SGD will perform fast computation and the learning. However, the learning to reach minimum cost will become more unstable.

References

N. Le Roux, M. Schmidt, F. Bach A Stochastic Gradient Method with an Exceptional Convergence Rate for Finite Training Sets, Advances in Neural Information Processing Systems, (2011)

See Also

SAGD

Examples

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

# }