akiniwa

// referenced from http://docs.spring.io/spring-batch/trunk/reference/html/readersAndWriters.html
public class Foo {}

public class Bar {
    public Bar(Foo foo) {}
}

public class Foobar{
    public Foobar(Bar bar) {}
}

akiniwa

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

class Tree:
    def __init__(self, value=0):
        self.value = value
        self.child = []

    def insertChild(self, index, v):
        self.child.insert(index, Tree(v))
            

akiniwa

import glob
import os
os.chdir("/mydir")
for file in glob.glob("*.txt"):
    print file

import os
for file in os.listdir("/mydir"):
    if file.endswith(".txt"):
        print file

akiniwa

#!/bin/bash

echo -ne '[======......................................................](10%)\r'
sleep 0.1
echo -ne '[============................................................](20%)\r'
sleep 0.1
echo -ne '[==================..........................................](30%)\r'
sleep 0.1
echo -ne '[========================....................................](40%)\r'
sleep 0.1

akiniwa

#coding: utf8
"""
1. Download this gist.
2. Get the MNIST data. 
    wget http://deeplearning.net/data/mnist/mnist.pkl.gz 
3. Run this code.
    python autoencoder.py 100 -e 1 -b 20 -v 

Wait about a minute ... and get a vialization of weights.
"""

akiniwa

#coding: utf8
"""
1. Download this gist.
2. Get the MNIST data. 
    wget http://deeplearning.net/data/mnist/mnist.pkl.gz 
3. Run this code.
    python autoencoder.py 100 -e 1 -b 20 -v 

Wait about a minute ... and get a vialization of weights.
"""

akiniwa

tr -d "\r" < file.txt > modified.txt

akiniwa

#!/bin/sh
cat $1 | sed -e '1s/^/id\
/g' > _$1

akiniwa

for f in *.txt; do sed -e 's/\t\|\s/,/g' $f > "${f%.*}".csv; done

akiniwa

awk '/A/ {print >> "a.txt"} /B/ { print >> "b.txt"}'