primaryobjects

# Example of training a glm model on a spam data-set, using the caret library.
library(caret)
library(kernlab)

# Load spam dataset.
data(spam)

# Split the data into a training/test set by 60% training/40% test.
inTrain <- createDataPartition(y = spam$type, p=0.6, list=FALSE)
training <- spam[inTrain,]

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library(AppliedPredictiveModeling)
library(caret)

data(AlzheimerDisease)

adData = data.frame(diagnosis,predictors)
trainIndex = createDataPartition(diagnosis, p = 0.60,list=FALSE)
training = adData[trainIndex,]
testing = adData[-trainIndex,]

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library(caret)
library(doParallel)

# Enable parallel processing.
cl <- makeCluster(detectCores())
registerDoParallel(cl)

# Load the MNIST digit recognition dataset into R
# http://yann.lecun.com/exdb/mnist/
# assume you have all 4 files and gunzip'd them

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library(caret)
library(doParallel)
library(reshape2)
library(ggplot2)

# Enable parallel processing.
cl <- makeCluster(detectCores())
registerDoParallel(cl)

# Load the MNIST digit recognition dataset into R

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library(caret)

data(faithful)

set.seed(333)

# Plot data.
plot(x=faithful$waiting, faithful$eruptions)

# Calculate linear model.

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# Question 1
library(AppliedPredictiveModeling)
library(caret)

data(segmentationOriginal)

set.seed(125)

#inTrain <- createDataPartition(segmentationOriginal$Case, list=FALSE)
inTrain <- data$Case == "Train"

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# Quiz 4

# Question 1.
library(ElemStatLearn)
library(randomForest)
library(caret)

data(vowel.train)
data(vowel.test)

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# Simple implementation of Selection Sort and Quicksort in R.
# Quick sort algorithm:
# 1. Select a random value from the array.
# 2. Put all values less than the random in arrayLeft.
# 3. Put all values greater than the random in arrayRight.
# 4. If arrayLeft or arrayRight has more than 1 value, repeat the above steps on it.
# 5. The sorted result is arrayLeft, random, arrayRight.
#
# Selection sort algorithm:
# 1. Find the smallest value in the array and move it to a result array.

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is.prime <- function(num) {
  if (num == 2) {
    TRUE
  } else if (any(num %% 2:(num-1) == 0)) {
    FALSE
  } else { 
    TRUE
  }
}