phelrine

#!/usr/bin/env python
import sys
import csv
import os
from optparse import OptionParser
import numpy as np

if __name__ == '__main__':
    parser = OptionParser()
    parser.add_option("-d", "--delimiter", default = ",", dest = "delimiter")

phelrine

phelrine

#!/usr/bin/env ruby
# -*- coding: utf-8 -*-
require 'RMagick'

FONTPATH = "/usr/share/fonts/takao-fonts/TakaoPMincho.ttf"

draw = Magick::Draw.new{
  self.font = FONTPATH
  self.fill = "black"
  self.gravity = Magick::CenterGravity

phelrine

# -*- coding: utf-8 -*-
require 'twitter/json_stream'
require 'twitter_oauth'
require 'json'
require 'pp'

CONSUMER_KEY = "consumer key"
CONSUMER_SECRET = "consumer secret"
ACCESS_KEY = "access key"
ACCESS_SECRET = "access secret"

phelrine

#!/usr/bin/env gosh
(use gauche.collection)

(define (qsort ls)
  (if (null? ls)
      '()
      (receive (left right) (partition (cut < <> (car ls)) (cdr ls))
               (append (qsort left) (list (car ls)) (qsort right)))))

(define (main args)

phelrine

import numpy as np
import numpy.random as nprand
import matplotlib.pyplot as plt

def dnorm(x, m, s):
    return np.exp(-((x - m) ** 2)/(2 * s)) / np.sqrt(2 * np.pi * s)

def EM(data, init, iter):
    params = np.array(init)

phelrine

# -*- coding: utf-8 -*-
require 'MeCab'
require 'json'
require 'readline'

class NaiveBayesClassifier
  @@FEATURES = ["名詞", "形容詞", "動詞"]

  def initialize(train)
    @users = {}

phelrine

#!/usr/bin/env python

import numpy as np
import numpy.random
import numpy.linalg
import matplotlib.pyplot as pyplot

RANGE = 2 * np.pi

def noise_sin(x):

phelrine

#!/bin/sh

## usage
# sh notify.sh REPOSITORY_PATH FORMAT_STRING

TWEET_PROGRAM="ruby tweet.rb"
pushd `dirname $0`

# update repository
cd $1

phelrine

#!/bin/sh

new_icon=$1
old_icon=$2
composite_icon_name=$3
percentage=35

size=`identify $new_icon | cut -d' ' -f3 | cut -d'x' -f1`
resize_size=$(($size * $percentage / 100))
geometry=$(($size - $resize_size))