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require(mlbench)
require(caret)
require(MLmetrics)
require(plyr)
require(ade4)

data(Satellite)
summary(Satellite)
dim(Satellite)
my_data <- Satellite

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library(shiny)

shinyUI(fluidPage(
  titlePanel("Self-Organized-Maps"),
  fluidRow(
    sidebarPanel(
      fileInput("file1", "Select CSV:", accept = c("text/csv", "text/comma-separated-values,text/plain", ".csv")),
      selectInput("sep", label = "Separator", 
                  choices = list("Coma" = ",", "Semicolon" = ";", "Tab" = "\t"),
                  selected = 1),

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# based on Manuel Bernal's code

######## SOM GENERATOR ###########
#Loading Packages needed
library(png) # For writePNG function
library(kohonen)
library(shiny)
library(ggplot2)
library(jsonlite)
library(Hmisc)

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# based on Manuel Bernal's code

######## SOM GENERATOR ###########
#Loading Packages needed
library(png) # For writePNG function
library(kohonen)
library(shiny)
library(ggplot2)
library(jsonlite)
library(Hmisc)

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library(kohonen)
library(Hmisc)    # useful for multi histogram

#loading client data
client <- read.csv(file = "C:/Users/eye1/Desktop/Customer Data.csv")

#Feature selection
data_train <- client[, c("NO_OF_PRODUCTS_6","Balance","LOGIN_DAYS_L3M", "ONLINE_TRANSACTION_L3M")]#data_train <- iris[c(1,2,3,4)]

#Explore the data

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library(Hmisc)    # useful for multi histogram
hist.data.frame(full)  # histogram of each variable
pairs(full)            # pairwise scatter plots


data = mx.symbol.Variable('data')
fc1 = mx.symbol.FullyConnected(data=data, num_hidden=7)
sigm1 = mx.symbol.Activation(data=fc1, act_type="relu")
fc2 = mx.symbol.FullyConnected(data=sigm1, num_hidden=2)
net = mx.symbol.SoftmaxOutput(data=fc2)

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library(xgboost)
library(Matrix)
library(data.table)
library(vcd)

data(Arthritis)
df <- data.table(Arthritis, keep.rownames = F)

head(df[,AgeDiscret := as.factor(round(Age/10,0))])

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library(caret)

#looking at missing value
options(digits=2)
stats <- data.frame(missing = sapply(iris, function(x) sum(is.na(x))),
           mean = sapply(iris, function(x) if(is.numeric(x)) {mean(x, na.rm = T)} else names(table(x)[order(table(x), decreasing =  T)])[1])
)

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library(rpart)

#Generate binary Target
iris$isSetosa <- "N"
iris[iris$Species == "setosa",]$isSetosa <- "Y"
iris$isSetosa <- as.factor((iris$isSetosa))
levels(iris$isSetosa) <- c("N","Y")
head(iris)

#Create function

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# Basic scatter plot of vocabulary (y) against education (x). Use geom_point()
ggplot(Vocab, aes(education, vocabulary))+geom_point()


# Use geom_jitter() instead of geom_point()
ggplot(Vocab, aes(education, vocabulary))+geom_jitter()


# Using the above plotting command, set alpha to a very low 0.2
ggplot(Vocab, aes(education, vocabulary))+geom_jitter(alpha = 0.2)