SSGD: Semi Stochastic Gradient Descent (SSGD) Method Learning...
In gradDescent: Gradient Descent for Regression Tasks

Description Usage Arguments Details Value References See Also Examples

A function to build prediction model using SSGD method.

1 2	SSGD(dataTrain, alpha = 0.1, maxIter = 10, lamda = 0, innerIter = 10, 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 in outerloop.
`lamda`	a float value to generate random value from innerIter with probability for innerloop.
`innerIter`	the maximal number of iterations in innerloop.
`seed`	a integer value for static random. Default value is NULL, which means the function will not do static random.

This function combines elements from both GD and SGD. SSGD starts by computing the full gradient once and then proceeds with stochastic updates by choosing one of the gradients at a time.

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

George Papamakarios Comparison of Modern Stochastic Optimization Algorithms, (2014)

SVRG, SARAH, SARAHPlus

##################################
## Learning and Build Model with SSGD
## load R Package data
data(gradDescentRData)
## get z-factor data
dataSet <- gradDescentRData$CompressilbilityFactor
## split dataset
splitedDataSet <- splitData(dataSet)
## build model with SSGD
SSGDmodel <- SSGD(splitedDataSet$dataTrain)
#show result
print(SSGDmodel)