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#!/bin/sh
 
# one way (older scala version will be installed)
# sudo apt-get install scala
 
#2nd way
sudo apt-get remove scala-library scala
wget http://www.scala-lang.org/files/archive/scala-2.11.2.deb
sudo dpkg -i scala-2.11.2.deb
sudo apt-get update

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#!coding:utf-8
#!coding:/usr/bin/python 
"""
dependency:
	you have to install  beautifulsoup4 modulex
	sudo pip install beautifulsoup4

description:
 ximalaya downloading program made by Meyou(Wuhan) --2015.4.27

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import requests
from bs4 import BeautifulSoup
from multiprocessing.dummy import Pool
import os

os.chdir(r'E:\python\2017_4_20')

mp3_url = 'http://www.ximalaya.com/2452186/album/4015467'

headers = {

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#!/bin/bash
# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.

set -e

if [[ ! -z "${JUPYTERHUB_API_TOKEN}" ]]; then
  # launched by JupyterHub, use single-user entrypoint
  exec /usr/local/bin/start-singleuser.sh $*
else

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// see https://gist.github.com/2382341

// scalaz for only solution3
import scalaz._
import Scalaz._

object SolutionForMultiNestedMatchforMyStudy {

  def f(num: Int): Option[Int] = {
    num match {

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// this flavour is pure magic...
def toDouble: (Any) => Double = { case i: Int => i case f: Float => f case d: Double => d }
  
// whilst this flavour is longer but you are in full control...
object any2Double extends Function[Any,Double] {
  def apply(any: Any): Double = 
    any match { case i: Int => i case f: Float => f case d: Double => d }
}

// like when you can invoke any2Double from another similar conversion...  

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    def averageTime[R](block: => R, numIter: Int = 10): Unit = {
     val t0 = System.nanoTime()
     (1 to numIter).foreach( _ => block)
     val t1 = System.nanoTime()
     val averageTimeTaken = (t1 - t0) / numIter
     val timeTakenMs = averageTimeTaken / 1000000
     println("Elapsed time: " + timeTakenMs + "ms")
   }

 val testDf = spark.range(10000000).toDF.cache

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import org.apache.spark.ml._
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.sql.types._

val df = Seq(
  (0, "a"), (1, "b"),
  (2, "c"), (3, "a"),
  (4, "a"), (5, "c"))
  .toDF("label", "category")

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    df.collect().foreach(row => row.toSeq.foreach(col => {

      col match {
        case n: Number => println(col)
        case _ => None
      }
    }))

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    df.collect().foreach(row => row.toSeq.foreach(col => {

      col match {
        case n: Number => println(col)
        case _ => None
      }
    }))