Initial commit

.cshrc

+alias a alias
+    
+a gitauto 'git commit -a -m auto'
+a pull 'git pull origin master'
+a gush 'git commit -a -m auto; git push origin master'
+
+a f 'find . -name \!* -print'
+a lth 'ls -lt \!* | head'
+a ta 'tail -2000 \!* | most'
+a pd pushd
+a lk "grep \!* [^,]*.{cc,c,go,py,pl,html,h,py,s,H,U,tex,java}"
+a lki "grep -i \!* [^,]*.{cc,c,go,py,pl,html,h,py,s,H,U,tex,java}"
+a lkw "grep -w \!* [^,]*.{cc,c,go,py,pl,html,h,py,s,H,U,tex,java}"
+a k kill -9 
+a ka killall -KILL
+a l ls -CF
+a ll "ls -alh \!*"
+a llm "ls -alh \!* | m"
+a m 'less -i -M -e -c'
+a jup 'jupyter notebook --port=8889 --no-browser --ip=0.0.0.0'
+
+bindkey "^[h" backward-delete-word
+bindkey "^[H" backward-delete-word
+bindkey "\310" backward-delete-word
+bindkey "\350" backward-delete-word
+bindkey "^R" i-search-back
+bindkey "^S" i-search-fwd
+
+setenv SPARKHOME "/vagrant/spark-2.0.1-bin-hadoop2.7"
+
+    
+set prompt_info = "vagrant %m:%~> "
+set prompt = "$prompt_info"
+

.vagrant/machines/default/virtualbox/action_provision

+1.5:6998c206-acab-4184-9a51-16d605335ecc
\ No newline at end of file

.vagrant/machines/default/virtualbox/action_set_name

+1511961833
\ No newline at end of file

.vagrant/machines/default/virtualbox/creator_uid

+501
\ No newline at end of file

.vagrant/machines/default/virtualbox/id

+6998c206-acab-4184-9a51-16d605335ecc
\ No newline at end of file

.vagrant/machines/default/virtualbox/index_uuid

+3662673237874f709a578940f16436e0
\ No newline at end of file

.vagrant/machines/default/virtualbox/private_key

+-----BEGIN RSA PRIVATE KEY-----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+DM1zLFCaOt1XbmTr2h0mjCpFSakyd2qAv1FCRx5CQihgEB+Q85AiGsu1S0JsAbAy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+-----END RSA PRIVATE KEY-----

.vagrant/machines/default/virtualbox/synced_folders

+{"virtualbox":{"/vagrant":{"guestpath":"/vagrant","hostpath":"/Users/keleher/dfs/p6","disabled":false,"__vagrantfile":true}}}
\ No newline at end of file

README.md

+# Project 5
+**(Due Dec 10, midnight, *no late submissions*)**
+
+Project 5 focuses on using Apache Spark for doing large-scale data analysis tasks. For this assignment, we will use relatively small datasets and  we won't run anything in distributed mode; however Spark can be easily used to run the same programs on much larger datasets.
+
+## Getting Started with Spark
+
+This guide is basically a summary of the excellent tutorials that can be found at the [Spark website](http://spark.apache.org).
+
+[Apache Spark](https://spark.apache.org) is a relatively new cluster computing framework, developed originally at UC Berkeley. It significantly generalizes
+the 2-stage Map-Reduce paradigm (originally proposed by Google and popularized by open-source Hadoop system); Spark is instead based on the abstraction of **resilient distributed datasets (RDDs)**. An RDD is basically a distributed collection 
+of items, that can be created in a variety of ways. Spark provides a set of operations to transform one or more RDDs into an output RDD, and analysis tasks are written as
+chains of these operations.
+
+Spark can be used with the Hadoop ecosystem, including the HDFS file system and the YARN resource manager. 
+
+### Installing Spark
+
+As before, we have provided a VagrantFile in the `p5` directory. We have included the tarball, but you will have to expand it.
+
+2. Uncompress with `tar zxvf spark-2.0.1-bin-hadoop2.7.tar.gz`
+3. This will create a new directory: `spark-2.0.1-bin-hadoop2.7`. 
+4. Set the SPARKHOME variable: `export SPARKHOME=/vagrant/spark-2.0.1-bin-hadoop2.7`
+
+We are ready to use Spark. 
+
+### Spark and Python
+
+Spark primarily supports three languages: Scala (Spark is written in Scala), Java, and Python. We will use Python here -- you can follow the instructions at the tutorial
+and quick start (http://spark.apache.org/docs/latest/quick-start.html) for other languages. The Java equivalent code can be very verbose and hard to follow. The below
+shows a way to use the Python interface through the standard Python shell.
+
+### Jupyter Notebook
+
+To use Spark within the Jupyter Notebook (and to play with the Notebook we have provided), you can do (from the `/vagrant` directory on the VM):
+	```
+	PYSPARK_DRIVER_PYTHON="jupyter" PYSPARK_DRIVER_PYTHON_OPTS="notebook --no-browser --ip=0.0.0.0 --port=8881" $SPARKHOME/bin/pyspark
+	```
+### PySpark Shell
+
+You can also use the PySpark Shell directly.
+
+1. `$SPARKHOME/bin/pyspark`: This will start a Python shell (it will also output a bunch of stuff about what Spark is doing). The relevant variables are initialized in this python
+shell, but otherwise it is just a standard Python shell.
+
+2. `>>> textfile = sc.textFile("README.md")`: This creates a new RDD, called `textfile`, by reading data from a local file. The `sc.textFile` commands create an RDD
+containing one entry per line in the file.
+
+3. You can see some information about the RDD by doing `textfile.count()` or `textfile.first()`, or `textfile.take(5)` (which prints an array containing 5 items from the RDD).
+
+4. We recommend you follow the rest of the commands in the quick start guide (http://spark.apache.org/docs/latest/quick-start.html). Here we will simply do the Word Count
+application.
+
+#### Word Count Application
+
+The following command (in the pyspark shell) does a word count, i.e., it counts the number of times each word appears in the file `README.md`. Use `counts.take(5)` to see the output.
+
+`>>> counts = textfile.flatMap(lambda line: line.split(" ")).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a + b)`
+
+Here is the same code without the use of `lambda` functions.
+
+```
+def split(line): 
+    return line.split(" ")
+def generateone(word): 
+    return (word, 1)
+def sum(a, b):
+    return a + b
+
+count = textfile.flatMap(split).map(generateone).reduceByKey(sum)
+```
+
+The `flatmap` splits each line into words, and the following `map` and `reduce` do the counting (we will discuss this in the class, but here is an excellent and detailed
+description: [Hadoop Map-Reduce Tutorial](http://hadoop.apache.org/docs/r1.2.1/mapred_tutorial.html#Source+Code) (look for Walk-Through).
+
+The `lambda` representation is more compact and preferable, especially for small functions, but for large functions, it is better to separate out the definitions.
+
+### Running it as an Application
+
+Instead of using a shell, you can also write your code as a python file, and *submit* that to the spark cluster. The `project5` directory contains a python file `wordcount.py`,
+which runs the program in a local mode. To run the program, do:
+`$SPARKHOME/bin/spark-submit wordcount.py`
+
+### More...
+
+We encourage you to look at the [Spark Programming Guide](https://spark.apache.org/docs/latest/programming-guide.html) and play with the other RDD manipulation commands. 
+You should also try out the Scala and Java interfaces.
+
+## Assignment Details
+
+We have provided a Python file: `assignment.py`, that initializes the folllowing RDDs:
+* An RDD consisting of lines from a Shakespeare play (`play.txt`)
+* An RDD consisting of lines from a log file (`NASA_logs_sample.txt`)
+* An RDD consisting of 2-tuples indicating user-product ratings from Amazon Dataset (`amazon-ratings.txt`)
+* An RDD consisting of JSON documents pertaining to all the Noble Laureates over last few years (`prize.json`)
+
+The file also contains some examples of operations on these RDDs. 
+
+Your tasks are to fill out the 8 functions that are defined in the `functions.py` file (starting with `task`). The amount of code that you 
+write would typically be small (several would be one-liners), with the exception of the last one. 
+
+- **Task 1 (4pt)**: This takes as input the playRDD and for each line, finds the first word in the line, and also counts the number of words. It should then filter the RDD by only selecting the lines where the count of words in the line is > 10. The output will be an RDD where the key is the first word in the line, and the value is a 2-tuple, the first being the line and the second being the number of words (which must be >10). Simplest way to do it is probably a `map` followed by a `filter`.
+
+- **Task 2 (4pt)**: Write just the flatmap function (`task2_flatmap`) that takes in a parsed JSON document (from `prize.json`) and returns the surnames of the Nobel Laureates. In other words, the following command should create an RDD with all the surnames. We will use `json.loads` to parse the JSONs (this is already done). Make sure to look at what it returns so you know how to access the information inside the parsed JSONs (these are basically nested dictionaries). (https://docs.python.org/2/library/json.html)
+```
+     	task2_result = nobelRDD.map(json.loads).flatMap(task2_flatmap)
+```
+
+- **Task 3 (4pt)**: Write a sequence of transformations starting from prizeRDD that returns an PairRDD where the key is the `category` (`physics` etc), and the value is a list of all Nobel Laureates for that category (just their surnames). Make sure the final values are `list`s, and not some other class objects (if you do a `take(5)`, it should print out the lists).
+
+- **Task 4 (4pt)**: This function operates on the `logsRDD`. It takes as input a list of *dates* and returns an RDD with "hosts" that were present in the log on all of 
+those dates. The dates would be provided as strings, in the same format that they appear in the logs (e.g., '01/Jul/1995' and '02/Jul/1995').
+The format of the log entries should be self-explanatory, but here are more details if you need: [NASA Logs](http://ita.ee.lbl.gov/html/contrib/NASA-HTTP.html)
+Try to minimize the number of RDDs you end up creating.
+
+- **Task 5 (4pt)**: Complete a function to calculate the degree distribution of user nodes in the Amazon graph (i.e., `amazonBipartiteRDD`). In other words, calculate 
+the degree of 
+each user node (i.e., number of products each user has rated), and then use a reduceByKey (or aggregateByKey) to find the number of nodes with a given degree. The output 
+should be a PairRDD where the key is the degree, and the value is the number of nodes in the graph with that degree.
+
+- **Task 6 (4pt)**: On the `logsRDD`, for two given days (provided as input analogous to Task 4 above), use a 'cogroup' to create the following RDD: the key of 
+the RDD will be a host, and the value will be a 2-tuple, where the first element is a list of all URLs fetched from that host on the first day, and the second element
+is the list of all URLs fetched from that host on the second day. Use `filter` to first create two RDDs from the input `logsRDD`.
+
+- **Task 7 (8pt)**: [Bigrams](http://en.wikipedia.org/wiki/Bigram) are sequences of two consecutive words. For example, the previous sentence contains the following bigrams: "Bigrams are", "are simply", "simply sequences", "sequences of", etc. Your task is to write a bigram counting application for counting the bigrams in the `motivation`s of the Nobel Prizes (i.e., the reason they were given the Nobel Prize). The return value should be a PairRDD where the key is a bigram, and the value is its count, i.e., in how many different `motivations` did it appear. Don't assume 'motivation' is always present.
+
+- **Task 8 (8pt)**: [Maximal Matching] `task8` should implement one iteration of a greedy algorithm for finding a maximal matching in a bipartite graph. 
+A *matching* in a graph is a subset of the edges such that no two edges share a vertex (i.e., every vertex is part of at most 1 edge in the matching). A *maximal* matching
+is such that, we cannot add any more edges to it (in other words, there is no remaining edge in the graph both of whose endpoints are unmatched). Here is a simple greedy
+algorithm for finding a maximal matching using map-reduce; note that this is not a particularly good algorithm for solving this problem, but it is easy to parallelize.
+We maintain the current state of the program in a PairRDD called currentMatching, where we note all the user-product relationships
+that have already been chosen. Initially this RDD is set to be empty (for making it easy to debug, we have added one entry to it).
+The following is then executed repeatadly till currentMatching does not change.
+  * For each user who is currently unmatched (i.e., does not have an entry in currentMatching), find the group of products connected to it that are also unmatched.
+  * For each such user, among the group of unmatched products it is connected to, pick the `min` (it is better to pick this randomly but then the output is not deterministic and will make testing/debugging difficult)
+  * It is possible that we have picked the same product two different unmatched users, which would violate the matching constraint.
+  * In another step, repeat the same process from the products' perspective, i.e., for each product that has been picked as potential match for multiple user nodes, pick the minimum user node (again doing this randomly is better).
+  * Now we are left with a set of user-product relationships that we can add to currentMatching and iterate
+
+### Sample results.txt File
+You can use spark-submit to run the `assignment.py` file, but it would be easier to develop with pyspark (by copying the commands over). We will also shortly post iPython instructions.
+
+**results.txt** shows the results of running assignment.py on our code using: `$SPARKHOME/bin/spark-submit assignment.py`
+
+### Submission
+
+Submit the `functions.py` file.
+

Vagrantfile

+# -*- mode: ruby -*-
+# vi: set ft=ruby :
+
+# All Vagrant configuration is done below. The "2" in Vagrant.configure
+# configures the configuration version (we support older styles for
+# backwards compatibility). Please don't change it unless you know what
+# you're doing.
+Vagrant.configure("2") do |config|
+  # The most common configuration options are documented and commented below.
+  # For a complete reference, please see the online documentation at
+  # https://docs.vagrantup.com.
+
+  # Every Vagrant development environment requires a box. You can search for
+  # boxes at https://atlas.hashicorp.com/search.
+  config.vm.box = "ubuntu/xenial64"
+
+  # Disable automatic box update checking. If you disable this, then
+  # boxes will only be checked for updates when the user runs
+  # `vagrant box outdated`. This is not recommended.
+  # config.vm.box_check_update = false
+
+  # Create a forwarded port mapping which allows access to a specific port
+  # within the machine from a port on the host machine. In the example below,
+  # accessing "localhost:8080" will access port 80 on the guest machine.
+  config.vm.network "forwarded_port", guest: 8881, host: 8881
+  config.vm.network "forwarded_port", guest: 8882, host: 8882
+  config.vm.network "forwarded_port", guest: 8883, host: 8883
+
+  # Create a private network, which allows host-only access to the machine
+  # using a specific IP.
+  # config.vm.network "private_network", ip: "192.168.33.10"
+
+  # Create a public network, which generally matched to bridged network.
+  # Bridged networks make the machine appear as another physical device on
+  # your network.
+  # config.vm.network "public_network"
+
+  # Share an additional folder to the guest VM. The first argument is
+  # the path on the host to the actual folder. The second argument is
+  # the path on the guest to mount the folder. And the optional third
+  # argument is a set of non-required options.
+  # config.vm.synced_folder "../data", "/vagrant_data"
+
+  # Provider-specific configuration so you can fine-tune various
+  # backing providers for Vagrant. These expose provider-specific options.
+  # Example for VirtualBox:
+  #
+  # config.vm.provider "virtualbox" do |vb|
+  #   # Display the VirtualBox GUI when booting the machine
+  #   vb.gui = true
+  #
+  #   # Customize the amount of memory on the VM:
+  #   vb.memory = "1024"
+  # end
+  #
+  # View the documentation for the provider you are using for more
+  # information on available options.
+
+  # Define a Vagrant Push strategy for pushing to Atlas. Other push strategies
+  # such as FTP and Heroku are also available. See the documentation at
+  # https://docs.vagrantup.com/v2/push/atlas.html for more information.
+  # config.push.define "atlas" do |push|
+  #   push.app = "YOUR_ATLAS_USERNAME/YOUR_APPLICATION_NAME"
+  # end
+
+  # Enable provisioning with a shell script. Additional provisioners such as
+  # Puppet, Chef, Ansible, Salt, and Docker are also available. Please see the
+  # documentation for more information about their specific syntax and use.
+  config.vm.provision "shell", inline: <<-SHELL
+     apt-get update
+
+     apt-get install -y python-pip
+     pip install --upgrade pip
+     pip install jupyter
+     pip install ipython-sql
+
+     apt-get install -y postgresql postgresql-contrib python build-essential python-dev python-psycopg2 emacs tcsh default-jre
+
+     cp /vagrant/.cshrc /home/ubuntu/
+     cp -r /vagrant/.jupyter /home/ubuntu/
+     chown -R ubuntu ~ubuntu/.jupyter
+     echo "export SPARKHOME=/vagrant/spark-2.0.1-bin-hadoop2.7" >> .bashrc
+  SHELL
+end

assignment.py

+from pyspark import SparkContext
+from functions import *
+import re
+
+sc = SparkContext("local", "Simple App")
+setDefaultAnswer(sc.parallelize([0]))
+
+## Load data into RDDs
+playRDD = sc.textFile("datafiles/play.txt")
+logsRDD = sc.textFile("datafiles/NASA_logs_sample.txt")
+amazonInputRDD = sc.textFile("datafiles/amazon-ratings.txt")
+nobelRDD = sc.textFile("datafiles/prize.json")
+
+## The following converts the amazonInputRDD into 2-tuples with integers
+amazonBipartiteRDD = amazonInputRDD.map(lambda x: x.split(" ")).map(lambda x: (x[0], x[1])).distinct()
+
+
+### Task 1
+print "=========================== Task 1"
+task1_result = task1(playRDD)
+for x in task1_result.takeOrdered(10):
+	print x
+
+### Task 2
+print "=========================== Task 2"
+task2_result = nobelRDD.map(json.loads).flatMap(task2_flatmap).distinct()
+print task2_result.takeOrdered(10)
+
+#### Task 3
+print "=========================== Task 3"
+task3_result = task3(nobelRDD)
+for x in task3_result.takeOrdered(10):
+	print x
+
+#### Task 4
+print "=========================== Task 4"
+task4_result = task4(logsRDD, ['01/Jul/1995', '02/Jul/1995'])
+for x in task4_result.takeOrdered(10):
+	print x
+
+#### Task 5
+print "=========================== Task 5"
+task5_result = task5(amazonBipartiteRDD)
+print task5_result.collect()
+
+#### Task 6
+print "=========================== Task 6"
+task6_result = task6(logsRDD, '01/Jul/1995', '02/Jul/1995')
+for x in task6_result.takeOrdered(10):
+	print x
+
+#### Task 7
+print "=========================== Task 7"
+task7_result = task7(nobelRDD)
+for x in task7_result.takeOrdered(10):
+	print x
+
+#### Task 8 -- we will start with a non-empty currentMatching and do a few iterations
+print "=========================== Task 8"
+currentMatching = sc.parallelize([('user1', 'product8')])
+res1 = task8(amazonBipartiteRDD, currentMatching)
+print "Found {} edges to add to the matching".format(res1.count())
+print res1.takeOrdered(100)
+currentMatching = currentMatching.union(res1)
+res2 = task8(amazonBipartiteRDD, currentMatching)
+print "Found {} edges to add to the matching".format(res2.count())
+print res2.takeOrdered(100)
+currentMatching = currentMatching.union(res2)

correctanswers/correct1

+=========================== Task 1
+(u'--O', (u'--O God, that I were a man! I would eat his heart', 12))
+(u'/Enter', (u'/Enter DOGBERRY, VERGES, and Sexton, in gowns; and the Watch, with', 11))
+(u'A', (u'A bird of my tongue is better than a beast of yours.', 12))
+(u'A', (u"A commodity in question, I warrant you. Come, we'll obey you.", 11))
+(u'A', (u'A good old man, sir; he will be talking: as they', 11))
+(u'A', (u'A lord to a lord, a man to a man; stuffed with all', 13))
+(u'Against', (u'Against my will I am sent to bid you come in to dinner.', 13))
+(u'Against', (u'Against that power that bred it: there will she hide her,', 11))
+(u'Ah,', (u'Ah, how much might the man deserve of me that would right her!', 13))
+(u'All', (u'All this I see; and I see that the fashion wears', 11))
+(u'Amen,', (u'Amen, if you love her; for the lady is very well worthy.', 12))
+(u'An', (u'An he had been a dog that should have howled thus,', 11))
+(u'An', (u"An he should, it were an alms to hang him. She's an", 12))
+(u'And', (u'And God keep him out of my sight when the dance is', 12))
+(u'And', (u'And a good soldier to a lady: but what is he to a lord?', 14))
+(u'And', (u"And one Deformed is one of them: I know him; a'", 11))
+(u'And', (u'And so will he do; for the man doth fear God,', 11))
+(u'And,', (u'And, as I wooed for thee to obtain her, I will join', 12))
+(u'And,', (u'And, by my two faiths and troths, my lord, I spoke mine.', 12))
+(u'As', (u'As I am an honest man, he looks pale. Art thou', 11))
+(u'As', (u'As strange as the thing I know not. It were as', 11))
+(u'Brief,', (u'Brief, I pray you; for you see it is a busy time with me.', 14))
+(u'But,', (u'But, soft you, let me be: pluck up, my heart, and', 11))
+(u'By', (u"By my troth, 's not so good; and I warrant your", 11))
+(u'By', (u'By my troth, my lord, I cannot tell what to think', 11))
+(u'Come,', (u'Come, I will have thee; but, by this light, I take', 11))
+(u'Come,', (u'Come, come, do you think I do not know you by your', 12))
+(u'Cousins,', (u'Cousins, you know what you have to do. O, I cry you', 12))
+(u'Cupid', (u'Cupid is no longer an archer: hi s glory shall be', 11))
+(u'Do', (u'Do you question me, as an honest man should do, for', 11))
+(u'Do,', (u"Do, do: he'll but break a comparison or two on me;", 11))
+(u'Even', (u'Even since you left it. Doth not my wit become me rarely?', 12))
+(u'Fare', (u'Fare you well, boy: you know my mind. I will leave', 11))
+(u'Foul', (u'Foul words is but foul wind, and foul wind is but', 11))
+(u'Go', (u"Go to, i' faith; an thou wilt needs thrust thy neck", 11))
+(u'Go,', (u'Go, I discharge thee of thy prisoner, and I thank thee.', 11))
+(u'Go,', (u'Go, then; find me a meet hour to draw Don Pedro and', 12))
+(u'God', (u'God give me joy to wear it! for my heart is', 11))
+(u'Grow', (u'Grow this to what adverse issue it can, I will put', 11))
+(u'Ha!', (u"Ha! 'Against my will I am sent to bid you come in", 12))
+(u'Ha,', (u'Ha, no, no, faith; thou singest well enough for a shift.', 11))
+(u'He', (u'He hath an uncle here in Messina will be very much', 11))
+(u'He', (u'He is most in the company of the right noble Claudio.', 11))
+(u'He', (u'He is then a giant to an ape; but then is an ape a', 14))
+(u'He', (u'He is very busy about it. But, brother, I can tell', 11))
+(u'He', (u'He is very near by this: he was not three leagues off', 12))
+(u'He', (u'He were an excellent man that were made just in the', 11))
+(u'How', (u'How if the nurse be asleep and will not hear us?', 11))
+(u'How,', (u'How, how, pray you? You amaze me: I would have I', 11))
+(u'I', (u'I am a wise fellow, and, which is more, an officer,', 11))
+(u'I', (u'I am gone, though I am here: there is no love in', 12))
+(u'I', (u'I am yours for the walk; and especially when I walk away.', 12))
+(u'I', (u'I can see yet without spectacles and I see no such', 11))
+(u'I', (u'I do love nothing in the world so well as you: is', 12))
+(u'I', (u'I found him here as melancholy as a lodge in a', 11))
+(u'I', (u'I go, let me go with that I came; which is, with', 12))
+(u'I', (u'I had rather be a canker in a hedge than a rose in', 13))
+(u'I', (u'I have a good eye, uncle; I can see a church by daylight.', 13))
+(u'I', (u'I hear as good exclamation on your worship as of any', 11))
+(u'I', (u'I know none of that name, lady: there was none such', 11))
+(u'I', (u'I know that; but I would have thee hence, and here again.', 12))
+(u'I', (u'I know what you would say: if I have known her,', 11))
+(u'I', (u'I know. Thus far can I praise him; he is of a noble', 13))
+(u'I', (u'I love you with so much of my heart that none is', 12))
+(u'I', (u'I make all use of it, for I use it only.', 11))
+(u'I', (u'I shall see thee, ere I die, look pale with love.', 11))
+(u'I', (u'I thank you: I am not of many words, but I thank', 12))
+(u'I', (u'I think I told your lordship a year since, how much', 11))
+(u'I', (u'I took no more pains for those thanks than you take', 11))
+(u'I', (u'I up my apes, and away to Saint Peter for the', 11))
+(u'I', (u'I will but teach them to sing, and restore them to', 11))
+(u'I', (u'I will do any modest office, my lord, to help my', 11))
+(u'I', (u'I will live in thy heart, die in thy lap, and be', 12))
+(u'I', (u'I will make it good how you dare, with what you', 11))
+(u'I', (u'I will owe thee an answer for that: and now forward', 11))
+(u'I', (u'I will swear by it that you love me; and I will make', 13))
+(u'I', (u'I would my horse had the speed of your tongue, and', 11))
+(u'I', (u'I would not deny you; but, by this good day, I yield', 12))
+(u'I', (u'I would she had bestowed this dotage on me: I would', 11))
+(u"I'", (u"I' faith, I thank him; he hath bid me to a calf's", 12))
+(u"I'll", (u"I'll take my oath on it, till he have made an oyster", 12))
+(u'If', (u'If I do, hang me in a bottle like a cat and shoot', 13))
+(u'If', (u'If I see any thing to-night why I should not marry', 11))
+(u'If', (u'If he be not in love with some woman, there is no', 12))
+(u'If', (u'If he do not dote on her upon this, I will never', 12))
+(u'If', (u'If he will not stand when he is bidden, he is none', 12))
+(u'If', (u'If we know him to be a thief, shall we not lay', 12))
+(u'If', (u'If you hear a child cry in the night, you must call', 12))
+(u'If', (u'If you meet a thief, you may suspect him, by virtue', 11))
+(u'If', (u'If you spite it for my sake, I will spite it for', 12))
+(u'In', (u'In a false quarrel there is no true valour. I came', 11))
+(u'In', (u'In mine eye she is the sweetest lady that ever I', 11))
+(u'Indeed,', (u'Indeed, he looks younger than he did, by the loss of a beard.', 13))
+(u'Indeed,', (u'Indeed, my lord, he lent it me awhile; and I gave', 11))
+(u'It', (u'It is not seen enough, you should wear it in your', 11))
+(u'It', (u'It is so, indeed; he is no less than a stuffed man:', 12))
+(u'It', (u'It pleases your worship to say so, but we are the', 11))
+(u'Let', (u'Let there be the same net spread for her; and that', 11))
+(u'Like', (u"Like the old tale, my lord: 'it is not so, nor", 11))
+(u'Messina,', (u'Messina, and one that knows the law, go to; and a', 11))

correctanswers/correct2

+=========================== Task 2
+
+'t Hooft
+(John William Strutt)
+(Lord Edgar Algernon Robert Gascoyne Cecil)
+Abrikosov
+Addams
+Adrian
+Agnon
+Agre
+Ahtisaari
+Akasaki
+Akerlof
+Alder
+Aleixandre
+Alexievich
+Alferov
+Alfvén
+Allais
+Altman
+Alvarez
+Amano
+Anderson
+Andric
+Anfinsen
+Angell (Ralph Lane)
+Annan
+Appleton
+Arafat
+Arber
+Arias Sánchez
+Arnoldson
+Arrhenius
+Arrow
+Asser
+Aston
+Asturias
+Aumann
+Axel
+Axelrod
+Bajer
+Balch
+Baltimore
+Banting
+Bardeen
+Barkla
+Barré-Sinoussi
+Barton
+Basov
+Beadle
+Becker
+Beckett
+Becquerel
+Bednorz
+Beernaert
+Begin
+Bellow
+Belo
+Benacerraf
+Benavente
+Berg
+Bergius
+Bergson
+Bergström
+Bethe
+Betzig
+Beutler
+Binnig
+Bishop
+Bjørnson
+Black
+Blackburn
+Blackett
+Blobel
+Bloch
+Bloembergen
+Blumberg
+Bohr
+Bordet
+Borlaug
+Born
+Bosch
+Bothe
+Bourgeois
+Bovet
+Boyd Orr of Brechin
+Boyer
+Boyle
+Bragg
+Brandt
+Branting
+Brattain
+Braun
+Brenner
+Briand
+Bridgman
+Brockhouse
+Brodsky
+Brown
+Buchanan Jr.
+Buchner

correctanswers/correct3

+=========================== Task 3
+(u'chemistry', [u'Sauvage', u'Stoddart', u'Feringa', u'Lindahl', u'Modrich', u'Sancar', u'Betzig', u'Hell', u'Moerner', u'Karplus', u'Levitt', u'Warshel', u'Lefkowitz', u'Kobilka', u'Shechtman', u'Heck', u'Negishi', u'Suzuki', u'Ramakrishnan', u'Steitz', u'Yonath', u'Shimomura', u'Chalfie', u'Tsien', u'Ertl', u'Kornberg', u'Chauvin', u'Grubbs', u'Schrock', u'Ciechanover', u'Hershko', u'Rose', u'Agre', u'MacKinnon', u'Fenn', u'Tanaka', u'W\xfcthrich', u'Knowles', u'Noyori', u'Sharpless', u'Heeger', u'MacDiarmid', u'Shirakawa', u'Zewail', u'Kohn', u'Pople', u'Boyer', u'Walker', u'Skou', u'Curl Jr.', u'Kroto', u'Smalley', u'Crutzen', u'Molina', u'Rowland', u'Olah', u'Mullis', u'Smith', u'Marcus', u'Ernst', u'Corey', u'Altman', u'Cech', u'Deisenhofer', u'Huber', u'Michel', u'Cram', u'Lehn', u'Pedersen', u'Herschbach', u'Lee', u'Polanyi', u'Hauptman', u'Karle', u'Merrifield', u'Taube', u'Klug', u'Fukui', u'Hoffmann', u'Berg', u'Gilbert', u'Sanger', u'Brown', u'Wittig', u'Mitchell', u'Prigogine', u'Lipscomb', u'Cornforth', u'Prelog', u'Flory', u'Fischer', u'Wilkinson', u'Anfinsen', u'Moore', u'Stein', u'Herzberg', u'Leloir', u'Barton', u'Hassel', u'Onsager', u'Eigen', u'Norrish', u'Porter', u'Mulliken', u'Woodward', u'Hodgkin', u'Ziegler', u'Natta', u'Perutz', u'Kendrew', u'Calvin', u'Libby', u'Heyrovsky', u'Sanger', u'Todd', u'Hinshelwood', u'Semenov', u'du Vigneaud', u'Pauling', u'Staudinger', u'Martin', u'Synge', u'McMillan', u'Seaborg', u'Diels', u'Alder', u'Giauque', u'Tiselius', u'Robinson', u'Sumner', u'Northrop', u'Stanley', u'Virtanen', u'Hahn', u'de Hevesy', u'Butenandt', u'Ruzicka', u'Kuhn', u'Haworth', u'Karrer', u'Debye', u'Joliot', u'Joliot-Curie', u'Urey', u'Langmuir', u'Bosch', u'Bergius', u'Fischer', u'Harden', u'von Euler-Chelpin', u'Windaus', u'Wieland', u'Svedberg', u'Zsigmondy', u'Pregl', u'Aston', u'Soddy', u'Nernst', u'Haber', u'Willst\xe4tter', u'Richards', u'Werner', u'Grignard', u'Sabatier', u'Curie, n\xe9e Sklodowska', u'Wallach', u'Ostwald', u'Rutherford', u'Buchner', u'Moissan', u'von Baeyer', u'Ramsay', u'Arrhenius', u'Fischer', u"van 't Hoff"])
+(u'economics', [u'Hart', u'Holmstr\xf6m', u'Deaton', u'Tirole', u'Fama', u'Hansen', u'Shiller', u'Roth', u'Shapley', u'Sargent', u'Sims', u'Diamond', u'Mortensen', u'Pissarides', u'Ostrom', u'Williamson', u'Krugman', u'Hurwicz', u'Maskin', u'Myerson', u'Phelps', u'Aumann', u'Schelling', u'Kydland', u'Prescott', u'Engle III', u'Granger', u'Kahneman', u'Smith', u'Akerlof', u'Spence', u'Stiglitz', u'Heckman', u'McFadden', u'Mundell', u'Sen', u'Merton', u'Scholes', u'Mirrlees', u'Vickrey', u'Lucas Jr.', u'Harsanyi', u'Nash Jr.', u'Selten', u'Fogel', u'North', u'Becker', u'Coase', u'Markowitz', u'Miller', u'Sharpe', u'Haavelmo', u'Allais', u'Solow', u'Buchanan Jr.', u'Modigliani', u'Stone', u'Debreu', u'Stigler', u'Tobin', u'Klein', u'Schultz', u'Lewis', u'Simon', u'Ohlin', u'Meade', u'Friedman', u'Kantorovich', u'Koopmans', u'Myrdal', u'von Hayek', u'Leontief', u'Hicks', u'Arrow', u'Kuznets', u'Samuelson', u'Frisch', u'Tinbergen'])
+(u'literature', [u'Dylan', u'Alexievich', u'Modiano', u'Munro', u'Yan', u'Transtr\xf6mer', u'Vargas Llosa', u'M\xfcller', u'Le Cl\xe9zio', u'Lessing', u'Pamuk', u'Pinter', u'Jelinek', u'Coetzee', u'Kert\xe9sz', u'Naipaul', u'Xingjian', u'Grass', u'Saramago', u'Fo', u'Szymborska', u'Heaney', u'Oe', u'Morrison', u'Walcott', u'Gordimer', u'Paz', u'Cela', u'Mahfouz', u'Brodsky', u'Soyinka', u'Simon', u'Seifert', u'Golding', u'Garc\xeda M\xe1rquez', u'Canetti', u'Milosz', u'Elytis', u'Singer', u'Aleixandre', u'Bellow', u'Montale', u'Johnson', u'Martinson', u'White', u'B\xf6ll', u'Neruda', u'Solzhenitsyn', u'Beckett', u'Kawabata', u'Asturias', u'Agnon', u'Sachs', u'Sholokhov', u'Sartre', u'Seferis', u'Steinbeck', u'Andric', u'Perse', u'Quasimodo', u'Pasternak', u'Camus', u'Jim\xe9nez', u'Laxness', u'Hemingway', u'Churchill', u'Mauriac', u'Lagerkvist', u'Russell', u'Faulkner', u'Eliot', u'Gide', u'Hesse', u'Mistral', u'Jensen', u'Sillanp\xe4\xe4', u'Buck', u'Martin du Gard', u"O'Neill", u'Pirandello', u'Bunin', u'Galsworthy', u'Karlfeldt', u'Lewis', u'Mann', u'Undset', u'Bergson', u'Deledda', u'Shaw', u'Reymont', u'Yeats', u'Benavente', u'France', u'Hamsun', u'Spitteler', u'Gjellerup', u'Pontoppidan', u'von Heidenstam', u'Rolland', u'Tagore', u'Hauptmann', u'Maeterlinck', u'Heyse', u'Lagerl\xf6f', u'Eucken', u'Kipling', u'Carducci', u'Sienkiewicz', u'Mistral', u'Echegaray y Eizaguirre', u'Bj\xf8rnson', u'Mommsen', u'Prudhomme'])
+(u'medicine', [u'Ohsumi', u'Campbell', u'\u014cmura', u'Tu', u"O'Keefe", u'Moser', u'Moser', u'Rothman', u'Schekman', u'S\xfcdhof', u'Gurdon', u'Yamanaka', u'Beutler', u'Hoffmann', u'Steinman', u'Edwards', u'Blackburn', u'Greider', u'Szostak', u'zur Hausen', u'Barr\xe9-Sinoussi', u'Montagnier', u'Capecchi', u'Evans', u'Smithies', u'Fire', u'Mello', u'Marshall', u'Warren', u'Axel', u'Buck', u'Lauterbur', u'Mansfield', u'Brenner', u'Horvitz', u'Sulston', u'Hartwell', u'Hunt', u'Nurse', u'Carlsson', u'Greengard', u'Kandel', u'Blobel', u'Furchgott', u'Ignarro', u'Murad', u'Prusiner', u'Doherty', u'Zinkernagel', u'Lewis', u'N\xfcsslein-Volhard', u'Wieschaus', u'Gilman', u'Rodbell', u'Roberts', u'Sharp', u'Fischer', u'Krebs', u'Neher', u'Sakmann', u'Murray', u'Thomas', u'Bishop', u'Varmus', u'Black', u'Elion', u'Hitchings', u'Tonegawa', u'Cohen', u'Levi-Montalcini', u'Brown', u'Goldstein', u'Jerne', u'K\xf6hler', u'Milstein', u'McClintock', u'Bergstr\xf6m', u'Samuelsson', u'Vane', u'Sperry', u'Hubel', u'Wiesel', u'Benacerraf', u'Dausset', u'Snell', u'Cormack', u'Hounsfield', u'Arber', u'Nathans', u'Smith', u'Guillemin', u'Schally', u'Yalow', u'Blumberg', u'Gajdusek', u'Baltimore', u'Dulbecco', u'Temin', u'Claude', u'de Duve', u'Palade', u'von Frisch', u'Lorenz', u'Tinbergen', u'Edelman', u'Porter', u'Sutherland, Jr.', u'Katz', u'von Euler', u'Axelrod', u'Delbr\xfcck', u'Hershey', u'Luria', u'Holley', u'Khorana', u'Nirenberg', u'Granit', u'Hartline', u'Wald', u'Rous', u'Huggins', u'Jacob', u'Lwoff', u'Monod', u'Bloch', u'Lynen', u'Eccles', u'Hodgkin', u'Huxley', u'Crick', u'Watson', u'Wilkins', u'von B\xe9k\xe9sy', u'Burnet', u'Medawar', u'Ochoa', u'Kornberg', u'Beadle', u'Tatum', u'Lederberg', u'Bovet', u'Cournand', u'Forssmann', u'Richards', u'Theorell', u'Enders', u'Weller', u'Robbins', u'Krebs', u'Lipmann', u'Waksman', u'Theiler', u'Kendall', u'Reichstein', u'Hench', u'Hess', u'Moniz', u'M\xfcller', u'Cori', u'Cori, n\xe9e Radnitz', u'Houssay', u'Muller', u'Fleming', u'Chain', u'Florey', u'Erlanger', u'Gasser', u'Dam', u'Doisy', u'Domagk', u'Heymans', u'von Szent-Gy\xf6rgyi Nagyr\xe1polt', u'Dale', u'Loewi', u'Spemann', u'Whipple', u'Minot', u'Murphy', u'Morgan', u'Sherrington', u'Adrian', u'Warburg', u'Landsteiner', u'Eijkman', u'Hopkins', u'Nicolle', u'Wagner-Jauregg', u'Fibiger', u'Einthoven', u'Banting', u'Macleod', u'Hill', u'Meyerhof', u'Krogh', u'Bordet', u'B\xe1r\xe1ny', u'Richet', u'Carrel', u'Gullstrand', u'Kossel', u'Kocher', u'Mechnikov', u'Ehrlich', u'Laveran', u'Golgi', u'Ram\xf3n y Cajal', u'Koch', u'Pavlov', u'Finsen', u'Ross', u'von Behring'])
+(u'peace', [u'Santos', u'', u'Satyarthi', u'Yousafzai', u'', u'', u'Johnson Sirleaf', u'Gbowee', u'Karman', u'Xiaobo', u'Obama', u'Ahtisaari', u'', u'Gore Jr.', u'Yunus', u'', u'', u'ElBaradei', u'Maathai', u'Ebadi', u'Carter', u'', u'Annan', u'Dae-jung', u'', u'Hume', u'Trimble', u'', u'Williams', u'Belo', u'Ramos-Horta', u'Rotblat', u'', u'Arafat', u'Peres', u'Rabin', u'Mandela', u'de Klerk', u'Mench\xfa Tum', u'', u'Gorbachev', u'', u'', u'Arias S\xe1nchez', u'Wiesel', u'', u'Tutu', u'Walesa', u'Myrdal', u'Garc\xeda Robles', u'', u'P\xe9rez Esquivel', u'', u'al-Sadat', u'Begin', u'', u'Williams', u'Corrigan', u'Sakharov', u'MacBride', u'Sato', u'Kissinger', u'', u'Brandt', u'Borlaug', u'', u'Cassin', u'', u'King Jr.', u'', u'', u'Pauling', u'Hammarskj\xf6ld', u'Lutuli', u'Noel-Baker', u'Pire', u'Pearson', u'', u'Marshall', u'Schweitzer', u'Jouhaux', u'Bunche', u'Boyd Orr of Brechin', u'', u'', u'Balch', u'Mott', u'Hull', u'', u'', u'(Lord Edgar Algernon Robert Gascoyne Cecil)', u'Saavedra Lamas', u'von Ossietzky', u'Henderson', u'Angell (Ralph Lane)', u'Addams', u'Butler', u'S\xf6derblom', u'Kellogg', u'Buisson', u'Quidde', u'Briand', u'Stresemann', u'Chamberlain', u'Dawes', u'Nansen', u'Branting', u'Lange', u'Bourgeois', u'Wilson', u'', u'La Fontaine', u'Root', u'Asser', u'Fried', u'', u'Beernaert', u"d'Estournelles de Constant, Baron de Constant de Rebecque", u'Arnoldson', u'Bajer', u'Moneta', u'Renault', u'Roosevelt', u'von Suttner, n\xe9e Countess Kinsky von Chinic und Tettau', u'', u'Cremer', u'Ducommun', u'Gobat', u'Dunant', u'Passy'])
+(u'physics', [u'Thouless', u'Haldane', u'Kosterlitz', u'Kajita', u'McDonald', u'Akasaki', u'Amano', u'Nakamura', u'Englert', u'Higgs', u'Haroche', u'Wineland', u'Perlmutter', u'Schmidt', u'Riess', u'Geim', u'Novoselov', u'Kao', u'Boyle', u'Smith', u'Nambu', u'Kobayashi', u'Maskawa', u'Fert', u'Gr\xfcnberg', u'Mather', u'Smoot', u'Glauber', u'Hall', u'H\xe4nsch', u'Gross', u'Politzer', u'Wilczek', u'Abrikosov', u'Ginzburg', u'Leggett', u'Davis Jr.', u'Koshiba', u'Giacconi', u'Cornell', u'Ketterle', u'Wieman', u'Alferov', u'Kroemer', u'Kilby', u"'t Hooft", u'Veltman', u'Laughlin', u'St\xf6rmer', u'Tsui', u'Chu', u'Cohen-Tannoudji', u'Phillips', u'Lee', u'Osheroff', u'Richardson', u'Perl', u'Reines', u'Brockhouse', u'Shull', u'Hulse', u'Taylor Jr.', u'Charpak', u'de Gennes', u'Friedman', u'Kendall', u'Taylor', u'Ramsey', u'Dehmelt', u'Paul', u'Lederman', u'Schwartz', u'Steinberger', u'Bednorz', u'M\xfcller', u'Ruska', u'Binnig', u'Rohrer', u'von Klitzing', u'Rubbia', u'van der Meer', u'Chandrasekhar', u'Fowler', u'Wilson', u'Bloembergen', u'Schawlow', u'Siegbahn', u'Cronin', u'Fitch', u'Glashow', u'Salam', u'Weinberg', u'Kapitsa', u'Penzias', u'Wilson', u'Anderson', u'Mott', u'van Vleck', u'Richter', u'Ting', u'Bohr', u'Mottelson', u'Rainwater', u'Ryle', u'Hewish', u'Esaki', u'Giaever', u'Josephson', u'Bardeen', u'Cooper', u'Schrieffer', u'Gabor', u'Alfv\xe9n', u'N\xe9el', u'Gell-Mann', u'Alvarez', u'Bethe', u'Kastler', u'Tomonaga', u'Schwinger', u'Feynman', u'Townes', u'Basov', u'Prokhorov', u'Wigner', u'Goeppert Mayer', u'Jensen', u'Landau', u'Hofstadter', u'M\xf6ssbauer', u'Glaser', u'Segr\xe8', u'Chamberlain', u'Cherenkov', u'Frank', u'Tamm', u'Yang', u'Lee', u'Shockley', u'Bardeen', u'Brattain', u'Lamb', u'Kusch', u'Born', u'Bothe', u'Zernike', u'Bloch', u'Purcell', u'Cockcroft', u'Walton', u'Powell', u'Yukawa', u'Blackett', u'Appleton', u'Bridgman', u'Pauli', u'Rabi', u'Stern', u'Lawrence', u'Fermi', u'Davisson', u'Thomson', u'Hess', u'Anderson', u'Chadwick', u'Schr\xf6dinger', u'Dirac', u'Heisenberg', u'Raman', u'de Broglie', u'Richardson', u'Compton', u'Wilson', u'Perrin', u'Franck', u'Hertz', u'Siegbahn', u'Millikan', u'Bohr', u'Einstein', u'Guillaume', u'Stark', u'Planck', u'Barkla', u'Bragg', u'Bragg', u'von Laue', u'Kamerlingh Onnes', u'Dal\xe9n', u'Wien', u'van der Waals', u'Marconi', u'Braun', u'Lippmann', u'Michelson', u'Thomson', u'von Lenard', u'(John William Strutt)', u'Becquerel', u'Curie', u'Curie, n\xe9e Sklodowska', u'Lorentz', u'Zeeman', u'R\xf6ntgen'])

correctanswers/correct4

+=========================== Task 4
+03-dynamic-c.wokingham.luna.net
+04-dynamic-c.rotterdam.luna.net
+04-dynamic-c.wokingham.luna.net
+06-dynamic-c.rotterdam.luna.net
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correctanswers/correct5

+=========================== Task 5
+(1, 2)
+(2, 1)
+(3, 3)
+(4, 4)
+(5, 9)
+(6, 12)
+(7, 18)
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+(114, 2)

correctanswers/correct6

+=========================== Task 6
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correctanswers/correct7

+=========================== Task 7
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