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def hack1(n, k):
  def f(s):
    return s.count('1')
  binaries = []
  for x in range(2**n):
    binaries.append(bin(x))
  binaries.sort(key=f, reverse = True)
  return binaries[k - 1]

def hack(n, k):

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import os
import sys
import site

# Calculates the path based on location of WSGI script.
apache_configuration = os.path.realpath(os.path.dirname(__file__))
project = os.path.join(apache_configuration, 'nomedoproto')

sys.path.append(apache_configuration)
sys.path.append(project)

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import os
import sys

# Install venv by `virtualenv --distribute venv`
# Then install depedencies: `source venv/bin/active`
# `pip install -r requirements.txt`
activate_this = '/var/www/apache/csshat.com/csshat.com/venv/bin/activate_this.py'
execfile(activate_this, dict(__file__=activate_this))

path = os.path.join(os.path.dirname(__file__), os.pardir)

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package br.com.toptarget.topvagas.helper;

import android.content.Context;
import android.graphics.drawable.Drawable;
import android.os.AsyncTask;
import android.text.Html;
import android.util.Log;
import android.view.View;

import org.apache.http.HttpResponse;

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#!/bin/bash
if [ "$EUID" -ne 0 ]
  then echo "Please run as root"
  exit
fi

apt-get install pkg-config libmagickwand-dev -y
cd /tmp
wget https://pecl.php.net/get/imagick-3.4.0.tgz
tar xvzf imagick-3.4.0.tgz

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package codepath.com.recyclerviewfun;

import java.util.ArrayList;
import java.util.List;

public class Contact {
    private String mName;
    private boolean mOnline;

    public Contact(String name, boolean online) {

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Sentiment analysis experiment using scikit-learn
================================================

The script sentiment.py reproduces the sentiment analysis approach from Pang,
Lee and Vaithyanathan (2002), who tried to classify movie reviews as positive
or negative, with three differences:

* tf-idf weighting is applied to terms
* the three-fold cross validation split is different
* regularization is tuned by cross validation

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******************************************************************** Set up Django, Nginx and Gunicorn in a Virtualenv controled by Supervisor********************************************************************

Steps with explanations to set up a server using:

• Virtualenv
• Virtualenvwrapper
• Django
• Gunicorn

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class AttentionWithContext(Layer):
    """
        Attention operation, with a context/query vector, for temporal data.
        Supports Masking.
        Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf]
        "Hierarchical Attention Networks for Document Classification"
        by using a context vector to assist the attention
        # Input shape
            3D tensor with shape: `(samples, steps, features)`.
        # Output shape

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#! encoding=UTF-8
"""
kernel canonical correlation analysis
"""

import numpy as np
from scipy.linalg import svd
from sklearn.metrics.pairwise import pairwise_kernels, euclidean_distances

class KCCA(object):