WilsonMongwe

# Load packages ----
library(shiny)
library(quantmod)
library(randomForest)

### The simulation functions

simulateJump=function(mu_,ss_,lambda_,mu2_,sigma_,TotalTime,delta,compare,size)
{
  Sn=0

WilsonMongwe

library(shiny)
library(RQuantLib)

shinyServer(function(input, output) {
  

  
## The model class is the backbone of thsi application  
  

WilsonMongwe

#### Author: Wilson Mongwe
#### Date: 05/10/2015
#### Website: www.wilsonmongwe.co.za
#### Title: Training the nueral network for the jump test


#### This code tests fro the presence of jumps using nueral networks


#### Training the neural network #####

WilsonMongwe

#### Author: Wilson Mongwe
#### Date: 21/06/2015
#### Website: www.wilsonmongwe.co.za
#### Title: This code is for implementing the EM algorithm  to estimate paramters from a mixture 
     #### of three normal distributions.

########################### Simulating the population data #############################

N= 10000 ### Number of draws from the mixture
normals= 3 ### number of normals in the mixture. We have chosen to use 3 in this example.

WilsonMongwe

#### Author: Wilson Mongwe
#### Date: 21/06/2015
#### Website: www.wilsonmongwe.co.za
#### Title: Implementing the EM algorithm to the coin tossing example

####################### Simulating the population data #########################
N= 10000
numberOfCOins=2
components = sample(1:numberOfCOins,prob=c(0.45,0.55),size=N,replace=TRUE) 
thetas= c(0.7,0.2)  ### probablity of obtaining  a head on Coin 2 and Coin 3 respectively