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#!/usr/bin/env python
#-*-coding:utf-8-*-

import argparse

# args
parser = argparse.ArgumentParser()
parser.add_argument('text')

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data(JohnsonJohnson)
p=40

# y = α + βx + γ(x > t)(x - t + 1) + ε
t = c(1:84)
JJ2 = as.data.frame(cbind(JohnsonJohnson, t, (t>p)*(t-p)))
colnames(JJ2) = c('earnings', 't', 't2')
JJ2.lm <- lm(earnings~t+t2, data=JJ2)
summary(JJ2.lm)

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library(randomForest)

# load data
data = read.delim("data/sample.tsv", sep="\t")

# create data for k-fold cross validation
cv = function(d, k) {
  n = sample(nrow(d), nrow(d))
  d.randomized = data[n,] # randomize data
  n.residual = k-nrow(d)%%k

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library(xgboost)
library(Matrix)

# load data
data = read.delim("data/sample.tsv", sep="\t")
data$v6 = NULL

# create data for k-fold cross validation
cv = function(d, k) {
  n = sample(nrow(d), nrow(d))

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<?php
$hosts = array('slave_host01', 'slave_host02', 'slave_host03');
foreach ($hosts as $host) {
  exec("ssh $host /usr/java/default/bin/jps | /bin/grep 'DataNode\|TaskTracker' | /bin/sed 's/\([0-9]\) .*/\1/'", $pids);
  foreach ($pids as $pid) {
    exec("ssh $host kill $pid);
  }
}

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# import data
data <- read.delim(testadata.txt")

# execute regression and plot confidence/predict interval 
predict_continue <- function(target_method, target_cpa, interval) {
  d <- subset(data, method==target)
  # regression using log() transformation
  d.lm <- lm(continue~log(month)+cpa, data=d)
  summary(d.lm)
  

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source("http://aoki2.si.gunma-u.ac.jp/R/src/pairwise.prop2.test.R", encoding="euc-jp")
x <- matrix(c(18, 82, 40, 76, 32, 52), ncol=2, byrow=T)
x
chisq.test(x)
fisher.test(x)
pairwise.prop2.test(x, p.adjust.method="holm", test.function=chisq.test)

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x <- matrix(c(1, 1, 1, 1, 1, 1, 3, 7, 1, 5, 4, 2), 6, 2) #素性ベクトル
l <- c(-1, 1, -1, 1, 1, -1) #ラベル
w <- c(0, 0) #重みベクトル
r <- 0.5 #学習係数

#重みベクトルの更新メソッド
update <- function(x, l, w) {
  if (sign(x %*% w) == sign(l)) {
      return(w)
  }

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#パラメタ
x <- matrix(c(1, 1, 1, 1, 1, 1, 3, 7, 1, 5, 4, 2), 6, 2) #素性ベクトル
l <- c(-1, 1, -1, 1, 1, -1) #ラベル
w <- c(0, 0) #重みベクトル
r <- 0.5 #学習係数

#重みベクトルの更新メソッド
update <- function(x, l, w) {
  if (sign(x %*% w) == sign(l)) {
    return(w)

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team team_no number era game win lose winning_rate WHIP DIPS
giants 1 19 3.12 27 13 6 0.684 1.15 2.81
giants 1 26 3.31 25 13 6 0.684 1.27 3.65
giants 1 15 3.13 34 5 10 0.333 1.14 3.64
giants 1 18 3.35 24 11 6 0.647 1.12 3.87
tigers 2 54 2.89 30 12 8 0.6 1.17 3.03
tigers 2 14 2.69 25 11 7 0.611 1.08 3.67
tigers 2 55 2.74 26 8 12 0.4 1.29 3.46
curp 3 18 2.1 26 15 7 0.682 0.96 2.98
curp 3 42 3.23 28 11 9 0.55 1.13 3.97