Md Tareque Khan mdtareque

## Test.java
public class Test {

 public static void main(String[] args){
  System.out.println("Hello, World !");
 }
}

## gist:6176284

      
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                mdtareque
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                Sample json test
              
          
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## ThreadCreationTimeIncreasesWithNumberOfThreads.java
public class ThreadCreationTimeIncreasesWithNumberOfThreads {
  public static void main(String[] args) {
    long threadCreationTime = 0, noOfThreadsCreated=1;
    while (true) {
      long time = System.currentTimeMillis();
      new Thread() {
          public void run() {}
      }.start();
      time = System.currentTimeMillis() - time;
      if (time > threadCreationTime) {

## ProblemA.java
import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;

/**
 *  @author mtk - Mohammed Tareque Khan
 *  More details: http://mtktechiecode.blogspot.in/2015/04/google-codejam-15-standing-ovation.html
 *
 *  Algo description:
 *  if number of people for next shynesslevel is not zero then:

## stdc++.h
// C++ includes used for precompiling -*- C++ -*-

// Copyright (C) 2003-2013 Free Software Foundation, Inc.
//
// This file is part of the GNU ISO C++ Library.  This library is free
// software; you can redistribute it and/or modify it under the
// terms of the GNU General Public License as published by the
// Free Software Foundation; either version 3, or (at your option)
// any later version.

## python_deep_n_shallow_copy.py

In [1]: L1=[ 1, 2, 3, 4, [11, 22, 33, 44] ]

In [2]: L2=L1

In [3]: L3=L1[:]

In [4]: L1, L2, L3
Out[4]:
([1, 2, 3, 4, [11, 22, 33, 44]],

## example1.py
import nltk

with open('sample.txt', 'r') as f:
    sample = f.read()

sentences = nltk.sent_tokenize(sample)
tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
chunked_sentences = nltk.batch_ne_chunk(tagged_sentences, binary=True)

## attribute_flattener.py
import sys

"""
usage: python attribute_flattener.py [include-keys=yes] [override filename]
generates <file_name>.out file
contents are all attributes are flattened to single line per key

NOte: Keep attributes.csv in current folder and run
python attribute_flattener.py
"""

## master.vim
" copy all this into a vim buffer, save it, then...
" source the file by typing :so %
" Now the vim buffer acts like a specialized application for mastering vim

" There are two queues, Study and Known.  Depending how confident you feel
" about the item you are currently learning, you can move it down several
" positions, all the way to the end of the Study queue, or to the Known
" queue.

" type ,, (that's comma comma)

## mtk.vimrc
set ts=4
set sw=4
set et
set ai
set hls

so ~/.vim/Align.vim


" https://github.com/tpope/vim-pathogen To install vim plugins
	public class Test {

	public static void main(String[] args){
	System.out.println("Hello, World !");
	}
	}
	public class ThreadCreationTimeIncreasesWithNumberOfThreads {
	public static void main(String[] args) {
	long threadCreationTime = 0, noOfThreadsCreated=1;
	while (true) {
	long time = System.currentTimeMillis();
	new Thread() {
	public void run() {}
	}.start();
	time = System.currentTimeMillis() - time;
	if (time > threadCreationTime) {
	import java.io.BufferedReader;
	import java.io.IOException;
	import java.io.InputStreamReader;

	/**
	* @author mtk - Mohammed Tareque Khan
	* More details: http://mtktechiecode.blogspot.in/2015/04/google-codejam-15-standing-ovation.html
	*
	* Algo description:
	* if number of people for next shynesslevel is not zero then:
	// C++ includes used for precompiling -- C++ --

	// Copyright (C) 2003-2013 Free Software Foundation, Inc.
	//
	// This file is part of the GNU ISO C++ Library. This library is free
	// software; you can redistribute it and/or modify it under the
	// terms of the GNU General Public License as published by the
	// Free Software Foundation; either version 3, or (at your option)
	// any later version.

	In [1]: L1=[ 1, 2, 3, 4, [11, 22, 33, 44] ]

	In [2]: L2=L1

	In [3]: L3=L1[:]

	In [4]: L1, L2, L3
	Out[4]:
	([1, 2, 3, 4, [11, 22, 33, 44]],
	import nltk

	with open('sample.txt', 'r') as f:
	sample = f.read()

	sentences = nltk.sent_tokenize(sample)
	tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
	tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
	chunked_sentences = nltk.batch_ne_chunk(tagged_sentences, binary=True)
	import sys

	"""
	usage: python attribute_flattener.py [include-keys=yes] [override filename]
	generates <file_name>.out file
	contents are all attributes are flattened to single line per key

	NOte: Keep attributes.csv in current folder and run
	python attribute_flattener.py
	"""
	" copy all this into a vim buffer, save it, then...
	" source the file by typing :so %
	" Now the vim buffer acts like a specialized application for mastering vim

	" There are two queues, Study and Known. Depending how confident you feel
	" about the item you are currently learning, you can move it down several
	" positions, all the way to the end of the Study queue, or to the Known
	" queue.

	" type ,, (that's comma comma)
	set ts=4
	set sw=4
	set et
	set ai
	set hls

	so ~/.vim/Align.vim


	" https://github.com/tpope/vim-pathogen To install vim plugins