nishidy

#/bin/bash

set -e

[ ${#@} -ne 2 ] && {
    echo "Usage : $0 measurement_time(sec) bandwidth[|K|M](bps)"
    echo "---"
    echo "You also need to put 'hosts' file which contains target IPs."
    exit 1
}

nishidy

#include <opencv2/core/core.hpp>
#include <opencv2/opencv.hpp>
#include <iostream>

using namespace std;
using namespace cv;

void show(Mat *img){
    cout << "Width " << img->cols << " / Height " << img->rows << endl;
    imshow("Image", *img);

nishidy

import os,re
from functools import reduce

def stroll(topdir,no_root=[],yes_root=[],no_path=[],yes_path=[],no_file=[],yes_file=[],sortby=os.path.basename,reverse=False,ignorecase=True):

    T=True
    F=False
    I=ignorecase

    no_root.append("\..")

nishidy

#include <iostream>
#include <algorithm>
#include <iterator>
#include <sys/stat.h>
#include <dirent.h>
#include <queue>
#include <regex>

namespace stroll {

nishidy

from collections import defaultdict

def init(data):

    if isinstance(data, dict):
        for key1, val1 in data.items():
            if isinstance(val1, dict):
                for key2, val2 in val1.items():
                    if isinstance(val2, dict):
                        for key3, val3 in val2.items():

nishidy

# coding: utf-8

import csv
import urllib3

import matplotlib
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties

import pandas as pd

nishidy

import java.lang.reflect.Constructor;
import java.lang.reflect.Method;
import java.lang.reflect.Field;
import sun.misc.Unsafe;

class S {
	private void privatePrintS () { System.out.println("This is in S."); };
	public void publicPrintS () { System.out.println("This is in S."); };
}

nishidy

git clone https://github.com/python/cpython
cd cpython
git pull
export $(grep ^PACKAGE_VERSION= configure))
./configure  --prefix=$HOME/.pyenv/versions/$PACKAGE_VERSION
make
make test
make install
ln -s $HOME/.pyenv/versions/$PACKAGE_VERSION/bin/python3 $HOME/.pyenv/versions/$PACKAGE_VERSION/bin/python
pyenv rehash

nishidy

import pandas as pd
import numpy as np
from collections import defaultdict

df1 = pd.DataFrame({"col1":[0,1,2,3],"col2":[4,5,6,7],"col3":[8,9,10,11]},index=["idx1","idx2","idx3","idx4"])

print("df1")
print(df1,end="\n\n")

df2 = pd.DataFrame([[0,4,8],[1,5,9],[2,6,10],[3,7,11]],index=["idx1","idx2","idx3","idx4"],columns=["col1","col2","col3"])

nishidy

from tensorflow.examples.tutorials.mnist import input_data
# one_hot is for multiclass classification
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
import tensorflow as tf

# Input
# 784 = 28 x 28
# Mini batch size is not fixed here
x = tf.placeholder(tf.float32, [None,784])