MBGD: Mini-Batch Gradient Descent (MBGD) Method Learning Function
In gradDescent: Gradient Descent for Regression Tasks

Description Usage Arguments Details Value References See Also Examples

A function to build prediction model using Mini-Batch Gradient Descent (MBGD) method.

1	MBGD(dataTrain, alpha = 0.1, maxIter = 10, nBatch = 2, seed = NULL)

`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.
`nBatch`	a integer value representing the training data batch.
`seed`	a integer value for static random. Default value is NULL, which means the function will not do static random.

This function based on GD method with optimization to use the training data partially. MBGD has a parameter named batchRate that represent the instances percentage of training data.

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

A. Cotter, O. Shamir, N. Srebro, K. Sridharan Better Mini-Batch Algoritms via Accelerated Gradient Methods, NIPS, pp. 1647- (2011)

GD

##################################
## Learning and Build Model with MBGD
## load R Package data
data(gradDescentRData)
## get z-factor data
dataSet <- gradDescentRData$CompressilbilityFactor
## split dataset
splitedDataSet <- splitData(dataSet)
## build model with 0.8 batch rate MBGD
MBGDmodel <- MBGD(splitedDataSet$dataTrain, nBatch=2)
#show result
print(MBGDmodel)