윈도우에 git 클라이언트 설치

다운로드
http://git-scm.com/download/win

path에 설치폴더의 바이너리 경로를 등록

명령프롬프트에서 실행하면 된다

perl mechanize example

use LWP::UserAgent;
use autodie qw/ open close /;
use WWW::Mechanize;
use LWP::Simple qw($ua get);
use Encode;


$ua->agent('My agent/1.0');

# create WWW::Mechanize object
# autocheck 1 checks each request to ensure it was successful
my $mech = WWW::Mechanize->new();

$filename = "ilbe.txt";
if(-e $filename)
    {
        unlink($filename);
    }
   
$mech->get("http://www.ilbe.com/ilbe");

@link = $mech->find_all_links( url_regex => qr/\d{10}/);

for $new_link (@link){

$url = $new_link->url_abs();

$no = $url;
$no =~ m/\d{10}/;

$no = $&;

$mech->get($url);
$cont = $mech->content();

print $no." article is extracted...\n";
@files=();

# paste from url of webbrowser manually later modify

#@temp = split(/og:url/, $cont);
#@temp = split(/science&no=/, $temp[1]);
#@temp = split(/&page/, $temp[1]);


#$no = $temp[0];

$cont=encode("euc-kr", $cont);

@temp = split(/author/, $cont);
@temp = split(/<\/div>/, $temp[1]);
@temp = split(/<\/div>/, $temp[0]);
@temp = split(/margin-right:3px;/, $temp[0]);
$name = $temp[1];
$name =~ s/"//;
$name =~ s/\/>//;
$name =~ s/^\s+//;
@temp = split(/<\/span>/, $name);
$name =~ s/\s+$//;
$name = $temp[0];
#print $name;

@temp = split(/기사 제목/, $cont);
@temp = split(/-->/, $temp[1]);
@temp = split(/<!--/, $temp[1]);

$subject = $temp[0];

print $subject;


@temp = split(/기사 본문/, $cont);
@temp = split(/serverLog/, $temp[1]);

$memo = $temp[0];
$memo =~ s/-->//;
$memo =~ s/^\s+//;

#print $memo;



@img = $mech->find_all_links( text_regex => qr/png/i );

$i = 0;
for my $img_url (@img) {
    $i++;
    print $img_url->url_abs;
    $mech->mirror($img_url->url_abs, $no."-".$i.".png");
    push (@files, $no."-".$i.".png");
   
}

@img = $mech->find_all_links( text_regex => qr/jpg/i );
for my $img_url (@img) {
    $i++;
    print $img_url->url_abs;
    $mech->mirror($img_url->url_abs, $no."-".$i.".jpg");
    push (@files, $no."-".$i.".jpg");
   
    #Opens FROM_DIR, ao I can read from it
   
       
}

$attached = "file:";
for ($i=0; $i<=$#files; $i++){
$attached = $attached.$files[$i]." ";

}

$name = "name: ".$name;
$subject = "subject: ".$subject;
$memo = "memo: ".$memo;


open MYFILE, ">>$filename" or die ;
 print MYFILE "$no";
 print MYFILE "------------------------------------------------------------------------------\n";
 print MYFILE "$name\n";
 print MYFILE "$subject\n";
 print MYFILE "$memo\n";
 print MYFILE "$attached\n";
}

펄 모듈 설치 모듈이 서브 타입으로 되어 있을 때

모듈이름이
WWW::Mechanize::Firefox

다 써주어야 한다.

cpan install WWW::Mechanize::Firefox

대소문자도 구분하니까 유의

하위 폴더의 모든 파일에서 특정 문자열을 검색하는 펄스크립트

use strict;
use warnings;
use Cwd;

use File::Find;

my $search_pattern=$ARGV[0];
my $file_pattern  =$ARGV[1];

if (@ARGV <2){
print "Useage: $0 [string to search] [file type]\n";
exit 1;
}

find(\&d, cwd);

sub d {

  my $file = $File::Find::name;

  $file =~ s,/,\\,g;

  return unless -f $file;
  return unless $file =~ /$file_pattern/;

  open F, $file or print "couldn't open $file\n" && return;

  while (<F>) {
    if (my ($found) = m/($search_pattern)/o) {
      print "found $found in $file\n";
      last;
    }
  }

  close F;
}

파이어폭스 버전 28 기본설정 복원

[사용자지정]-[기본값복원]

마르코브

library(stringr)

setwd("C:/rdata")
source("DTMC.R")

###here kospi data
dat <- read.csv('port3.csv', header=TRUE, na="")

odat<-dat[c(1:nrow(dat)),]

attach(odat)

x<-odat[order(일자),]


price <- as.numeric(str_replace_all(x$리홈쿠첸, ",", ""))


d <- diff(price)

up2up <-0
up2down <-0
down2up <-0
down2down <-0

for (i in 2:length(d)){
    if (d[i-1]> 0 && d[i]>0){
    up2up <- up2up+1
    }
    if (d[i-1]> 0 && d[i]<0){
    up2down <- up2down +1
    }
    if (d[i-1]< 0 && d[i]>0){
    down2up <- down2up+1
    }
    if (d[i-1]< 0 && d[i]<0){
    down2down <- down2down+1
    }

}

u2u <- up2up/(up2up+up2down)
u2d <- up2down/(up2up+up2down)
d2u <- down2up/(down2up+down2down)
d2d <- down2down/(down2up+down2down)

mattrans <- matrix(c(u2u, d2u, u2d, d2d),nrow=2,ncol=2)

colnames(mattrans) <- c("U","D")
row.names(mattrans) <- c("U","D")
rm(temp)
rm(sim)
temp<-0
sim <-data.frame(vector())

#now set states
state<- c(1,2,3,4,5)
nstate<-c(8000,9000,10000,11000,12000)

#current state
temp[1]<- 5

for (i in 1:100){
ins<- simula(mattrans,10)
 for (j in 2:20){
if (ins[j] == "U"){
    temp[j] <- temp[j-1]+1
}
else { temp[j] <- temp[j-1]-1}
}

sim <-rbind(sim, temp)
}

p1<-sum(rowSums(sim <=1))/(10*100)

p2<-sum(rowSums(sim == 2))/(10*100)

p3<-sum(rowSums(sim == 3))/(10*100)
p4<-sum(rowSums(sim == 4))/(10*100)
p5<-sum(rowSums(sim >= 5))/(10*100)

p <- c(p1,p2,p3,p4,p5)

cat("tansition matex is:\n")
matrix(mattrans, nrow = 2,ncol = 2)

f <- data.frame(nstate, p)
colnames(f)<-c("주가", "확률")

f[order(-f[,1]),]

cat("current state is:",temp[1], sep="\n")

markov chain for stock price

## NAME:  markovchain.py
##
## USAGE:  From shell prompt: python -i markovchain.py
##         or within interactive python environment, scrape and paste
## REQUIRED ARGUMENTS: none
##
## OPTIONS: none
## DESCRIPTION: Simulation of the cash management model as a Markov chain
## DIAGNOSTICS: None
## CONFIGURATION AND ENVIRONMENT: None
## DEPENDENCIES: None
## INCOMPATIBILITIES: None known
## PROVENANCE: Tue Aug 28, 2012  5:38 AM
## BUGS AND LIMITATIONS:  None Known
## FEATURES AND POTENTIAL IMPROVEMENTS:
## For the innermost for-loop, you can simply use
## newState = scipy.random.choice(1, 1, prob=T[state,])
## That shows more clearly what's going on
## On the other hand, if other languages don't have a function comparable to sample, then
## the current formulation is easier to adapt and make the algorithms similar.

## AUTHOR:  Steve Dunbar
## VERSION: 1.0 Tue Aug 28, 2012  5:38 AM
##          1.1 added some ouput formatting Mon Nov 12, 2012  6:03 AM
## KEYWORDS:   Markov chain, simulation, reflecting boundary
#! /usr/bin/env python

import scipy

n = 6;   # Top boundary, number of states 0..n is n+1
s = 4;   # Start and Reset state number 1 <= s <= n-1
p = 0.8;
steps = 1000;

T = scipy.diag( p*scipy.ones( n ), 1) + scipy.diag( (1-p)*scipy.ones( n ), -1);
T[0,1] = 0; T[0,s] = 1;
T[n,n-1] = 0; T[n,s] = 1;

# vector to hold the count of visits to each state during a cycle
count = scipy.zeros(n+1, float);
# Initialize the cyclelength
numberCycles = 0;
# Start in the state s
state = s;
# Make steps through the markov chain
for i in range(1, steps+1):
    x = 0;
    u = scipy.random.random(1);
    newState = state;
    for j in range(n+1):
    x = x + T[state, j];
    if (x >= u):
        newState = j;
        break;
    ## newState = scipy.random.choice(1, 1, prob=T[state,])
    state = newState;
    count[state] = count[state] + 1;
   
    if (state == n or state == 0):
        numberCycles = numberCycles + 1;
        avgVisits = count/numberCycles;
        avgCycleLength = i/numberCycles;

Wsk = avgVisits

print Wsk

theoreticalWsk = 2*(  s*(1-scipy.arange(0,n+1,dtype=float)/n) - scipy.maximum(s-scipy.arange(0,n+1), scipy.zeros(n+1, int) ) )

prob = [0] * (n+1)    # initialize probility as list data type
for i in range(1, n+1):
    prob[i] = Wsk[i]/sum(Wsk);

price = [2000, 2900, 3500, 4500, 5100, 5300, 5500]   # this is list not array
print "each price state in a cycle:\n", price
print "Average number of visits to each state in a cycle:\n ",  [round(n, 2) for n in prob]