andland

roll_die <- function(status, strategy) {
    roll = sample(c(names(status), "basket"), 1)
    if (roll == "basket") {
        trees = status[names(status) != "raven" & status > 0]
        if (strategy == "optimal") {
            biggest_trees = trees[trees == max(trees)]
            roll = sample(names(biggest_trees), 1)
        } else if (strategy == "random") {
            roll = sample(names(trees), 1)
        } else if (strategy == "worst") {

andland

# inspired by http://schamberlain.github.io/2012/01/logistic-regression-barplot-fig/
logithistplot <- function(data,breaks="Sturges",se=TRUE) {
    require(ggplot2);
    col_names=names(data)
    
    # get min and max axis values
    min_x <- min(data[,1])
    max_x <- max(data[,1])
    
    # get bin numbers

andland

library(XML)
library(lubridate)
library(sqldf)
library(reshape2)
library(ggplot2)
library(mgcv)

cat("loading old data...\n")
playlist=read.csv("CD101Playlist.csv",stringsAsFactors=FALSE)
colnames(playlist)[3]="Last Played"

andland

See this link for an introduction on time stacking and time slicing.

time_slice.R requires the number of pixels wide or tall the image is to be a multiple of the number of images in your timelapse.

time_slice_v2.R attempts to get around this. Some images will contribute more pixels per slice than others. This is done by making the first x% of the images cover the first x% of the pixels (with appropriate rounding). It does not deal with number of images being greater than the height or width of the images in pixels. Version 2 will probably work better for you.

For example, if the images are 150 pixels wide and your timelapse has 100 images, time_slice.R will make the first image have a slice which is 51 pixels wide. The remaining 99 images will get slices which are 1 pixel wide. time_slice_v2.R will alternate between 1 pixel per i

andland

Below are links to manuscripts or packages related to my talk at JSM on Supervised Dimensionality Reduction. Session info here.

My slides will be here.

Contact:

• My Website
• Twitter
• Github
• Yoonkyung Lee's Website

andland

Below are links to manuscripts or packages related to my talk at JSM on Generalized PCA. Session info here.

My slides are here.

• Logistic PCA manuscript
  • R package logisticPCA
• Generalized PCA manuscript
  • R package generalizedPCA

andland

I will not share anyone's information. I will only use aggregated data with no personal information.

andland

library(shiny)
library(ggplot2)
exp.age.df=read.csv("https://dl.dropboxusercontent.com/u/17648661/ExpAgeByNameYear.csv")

age.range=range(exp.age.df$Age)

unique.names=sort(unique(exp.age.df$Name))
unique.names=c("<NONE>",as.character(unique.names))

start.names=c("Andrew","Dylan","Fred","Grace","Lillian","John")

andland

# rm(list=ls())
setwd("Kaggle/Amazon Employee")

train = read.csv("train.csv")
test = read.csv("test.csv")
train$ROLE_TITLE <- NULL # Because the same as ROLE_CODE
test$ROLE_TITLE <- NULL # Because the same as ROLE_CODE

jaccard <- function(vec, matrix) {
  rowSums(as.matrix(sweep(matrix, 2, as.numeric(vec), "==")))

andland