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// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

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library(nnet)
even.n = 2*(1:75)
iris.train = iris[even.n,]
iris.test = iris[-even.n,]

iris.nnet = nnet(Species~.,size=3, decay=0.1, data = iris.train)
iris.nnetp = predict(iris.nnet, iris.test[,-5], type="class")
table(iris.test[,5], iris.nnetp)

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# -*- coding: utf-8 -*- 


def chap1_0():
  targ = "stressed"
  print targ[::-1]

def chap1_1():
  targ = u"パタトクカシーー"
  print targ[0::2]

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# coding:utf8

import MeCab

mt = MeCab.Tagger("-Ochasen")
print mt.parse("隣の客はよく柿食う客だ")

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# coding:utf-8

import CaboCha

mt = CaboCha.Parser()
targ = "太郎はこの本を次郎を見た女性に渡した"

print mt.parseToString(targ)

tree = mt.parse(targ)

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import pulp as lp
from pulp import lpSum
from pulp import LpVariable


def mixture():
	prob = lp.LpProblem("example mixture", lp.LpMinimize)
	
	# Parameters
	elements = ["Lead", "Zinc", "Tin"]

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library(ggplot2)

g = ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, colour=Species))
g + geom_point() + geom_smooth(method=glm)

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library(ggplot2)
library(reshape2)

airq.df = data.frame(airquality)
airq.df = cbind(1:nrow(airq.df), airq.df)
colnames(airq.df)[1] = "id"

airq.melt = melt(airq.df, id = "id", measure = c("Ozone", "Solar.R", "Wind", "Temp"))

airq_g = ggplot(airq.melt, aes(x=id, y=value, group=variable, colour=variable))

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library(forecast)

# データ読み込み
d = read.csv("temp.csv")

# unixtimeをPOSIXctに変換
tsPOSIX = as.POSIXct(d[,1], origin="1970-01-01")
d = cbind(tsPOSIX, d)
colnames(d)[1] = "ts"
head(d)

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"+" = function(e1, e2){
  if(is.character(c(e1, e2))){
    paste(e1, e2, sep="")
  }else{
    base::"+"(e1, e2)
  }
}