ashish01

## PerformanceBlock.rs
#![feature(time2)]
use std::time::Instant;
use std::thread;
use std::sync::mpsc;
use std::io;

struct ScopedPerformanceBlock<'a> {
    sender: &'a mpsc::Sender<u32>,
    creation_time: Instant,
}

## whats_cooking.py
import json
import argparse

import numpy as np

from sklearn.feature_extraction import DictVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder

## gist:2a4a0f9b525096633ca2
import json

from flask import Flask, url_for, redirect, session
from flask_login import (UserMixin, login_required, login_user, logout_user, current_user)
from flask_googlelogin import GoogleLogin


users = {}

app = Flask(__name__)

## gist:c9ed92e8b2747941b476
@{
    Layout = null;
}
<!DOCTYPE html>

<html>
    <head>
        <meta http-equiv="X-UA-Compatible" content="IE=9"/>
        <title>Title</title>
        <link href="@Url.Content("~/Content/bootstrap.css")" rel="stylesheet" type="text/css" />

## PriorityQueue
/// Minimal generic min heap and priority queue implementation in C#, in less than 100 lines of code

using System;
using System.Collections.Generic;

class MinHeap<T> where T : IComparable<T>
{
    private List<T> array = new List<T>();

    public void Add(T element)

## Truncated SVD
from __future__ import print_function
from scipy.sparse import *
from sklearn.decomposition import TruncatedSVD
import matplotlib.pyplot as plt
from matplotlib import cm as CM
import numpy as np

import time

class Timer(object):

## trie
class node:
  def __init__(self):
    self.is_end = False
    self.table = {}

  def add(self, c):
    if c not in self.table:
      self.table[c] = node()
    return self.table[c]

## gist:742988
<html>

<head>
  <title>Styling thing</title>
  <style type="text/css">
    .riParent {border: 1px dotted gray}
    .riTitle {font-size:larger}
    .riText {font-size:smaller; font-style:italic; color: #AAAAAA; clear: both}
    .riReject {}
    .riAccept {}
	#![feature(time2)]
	use std::time::Instant;
	use std::thread;
	use std::sync::mpsc;
	use std::io;

	struct ScopedPerformanceBlock<'a> {
	sender: &'a mpsc::Sender<u32>,
	creation_time: Instant,
	}
	import json
	import argparse

	import numpy as np

	from sklearn.feature_extraction import DictVectorizer
	from sklearn.feature_extraction.text import CountVectorizer
	from sklearn.linear_model import LogisticRegression
	from sklearn.preprocessing import LabelEncoder
	import json

	from flask import Flask, url_for, redirect, session
	from flask_login import (UserMixin, login_required, login_user, logout_user, current_user)
	from flask_googlelogin import GoogleLogin


	users = {}

	app = Flask(__name__)
	@{
	Layout = null;
	}
	<!DOCTYPE html>

	<html>
	<head>
	<meta http-equiv="X-UA-Compatible" content="IE=9"/>
	<title>Title</title>
	<link href="@Url.Content("~/Content/bootstrap.css")" rel="stylesheet" type="text/css" />
	/// Minimal generic min heap and priority queue implementation in C#, in less than 100 lines of code

	using System;
	using System.Collections.Generic;

	class MinHeap<T> where T : IComparable<T>
	{
	private List<T> array = new List<T>();

	public void Add(T element)
	from __future__ import print_function
	from scipy.sparse import *
	from sklearn.decomposition import TruncatedSVD
	import matplotlib.pyplot as plt
	from matplotlib import cm as CM
	import numpy as np

	import time

	class Timer(object):
	class node:
	def __init__(self):
	self.is_end = False
	self.table = {}

	def add(self, c):
	if c not in self.table:
	self.table[c] = node()
	return self.table[c]
	<html>

	<head>
	<title>Styling thing</title>
	<style type="text/css">
	.riParent {border: 1px dotted gray}
	.riTitle {font-size:larger}
	.riText {font-size:smaller; font-style:italic; color: #AAAAAA; clear: both}
	.riReject {}
	.riAccept {}