GD: Gradient Descent (GD) 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 Gradient Descent method.

1	GD(dataTrain, alpha = 0.1, maxIter = 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.
`seed`	a integer value for static random. Default value is NULL, which means the function will not do static random.

This function build a prediction model using Gradient Descent (GD) method. Gradient Descent is a first order optimization algorithm to find a local minimum of an objective function by searching along the steepest descent direction. In machine learning, it is mostly used for dealing with supervised learning, which is regression task. By using GD, we construct a model represented in a linear equation that maps the relationship between input variables and the output one. In other words, GD determines suitable coefficient of each variables. So, that the equation can express the mapping correctly.

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

L.A. Cauchy, "Methode generale pour la resolution des systemes d equations", Compte Rendu a l Academie des Sciences 25, pp. 536-538 (1847)

MBGD

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