Class 31:Deanonymizing

cs1120 Fall 2011David Evans4 November 2011

2

Plan

Dictionaries in PythonHistory of Object-Oriented Programming

Python Dictionary

Dictionary abstraction provides a lookup table. Each entry in a dictionary is a

<key, value>pair. The key must be an immutable object. The value can be anything.

dictionary[key] evaluates to the value associated with key. Running time is approximately constant! Recall: Class 9 on consistent hashing. Python

dictionaries use (inconsistent) hashing to make lookups nearly constant time.

Dictionary Example>>> d = {}>>> d['UVa'] = 1818>>> d['UVa'] = 1819>>> d['Cambridge'] = 1209>>> d['UVa']1819>>> d['Oxford']

Traceback (most recent call last): File "<pyshell#93>", line 1, in <module> d['Oxford']KeyError: 'Oxford'

Create a new, empty dictionary

Add an entry: key ‘UVa’, value 1818

Update the value: key ‘UVa’, value 1819

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HistogrammingDefine a procedure histogram that takes a text string as its input, and returns a dictionary that maps each word in the input text to the number of occurrences in the text.

Useful string method: split([separator])outputs a list of the words in the string

>>> 'here we go'.split() ['here', 'we', 'go']>>> "Simula, Nygaard and Dahl, Norway, 1962".split(",")['Simula', ' Nygaard and Dahl', ' Norway', ' 1962']

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def histogram(text): d = {} words = text.split()

>>> histogram(""""Mathematicians stand on each others' shoulders and computer scientists stand on each others' toes."Richard Hamming"""){'and': 1, 'on': 2, 'shoulders': 1, 'computer': 1, 'Richard': 1, 'scientists': 1, "others'": 2, 'stand': 2, 'Hamming': 1, 'each': 2, '"Mathematicians': 1, 'toes."': 1}

def histogram(text): d = {} words = text.split() for w in words: if w in d: d[w] = d[w] + 1 else: d[w] = 1 return d

>>> declaration = urllib.urlopen('http://www.cs.virginia.edu/cs11

20/readings/declaration.html').read() >>> histogram(declaration){'government,': 1, 'all': 11, 'forbidden': 1, '</title>': 1, '1776</b>': 1, 'hath': 1, 'Caesar': 1, 'invariably': 1, 'settlement': 1, 'Lee,': 2, 'causes': 1, 'whose': 2, 'hold': 3, 'duty,': 1, 'ages,': 2, 'Object': 1, 'suspending': 1, 'to': 66, 'present': 1, 'Providence,': 1, 'under': 1, '<dd>For': 9, 'should.': 1, 'sent': 1, 'Stone,': 1, 'paralleled': 1, …

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Sorting the Histogramsorted(collection, cmp)Returns a new sorted list of the elements in collection ordered by cmp.

cmp specifies a comparison function of two arguments which should return a negative, zero or positive number depending on whether the first argument is considered smaller than, equal to, or larger than the second argument.

>>> sorted([1,5,3,2,4],<)SyntaxError: invalid syntax>>> <SyntaxError: invalid syntax>>> sorted([1,5,3,2,4], lambda a, b: a > b)[1, 5, 3, 2, 4]>>> sorted([1,5,3,2,4], lambda a, b: a - b)[1, 2, 3, 4, 5]

Expression ::= lambda Parameters : Expression

Makes a procedure, just like Scheme’s lambda (instead of listing parameters in (), separate with :)

Showing the Histogram

def show_histogram(d): keys = d.keys() okeys = sorted(keys,

for k in okeys: print str(k) + ": " + str(d[k])

Showing the Histogram

def show_histogram(d): keys = d.keys() okeys = sorted(keys,

lambda k1, k2: d[k2] - d[k1]) for k in okeys: print str(k) + ": " + str(d[k])

Author Fingerprinting(aka Plagarism Detection)

“The program identifies phrases of three words or more in an author’s known work and searches for them in unattributed plays. In tests where authors are known to be different, there are up to 20 matches because some phrases are in common usage. When Edward III was tested against Shakespeare’s works published before 1596 there were 200 matches.”

The Times, 12 October 2009

def phrase_collector(text, plen): d = {} words = text.split() words = map(lambda s: s.lower(), words) for windex in range(0, len(words) - plen): phrase = tuple(words[windex:windex+plen]) if phrase in d: d[phrase] = d[phrase] + 1 else: d[phrase]= 1 return d

Dictionary keys must beimmutable: convert the (mutable) list to an immutable tuple.

def histogram(text): d = {} words = text.split() for w in words: if w in d: d[w] = d[w] + 1 else: d[w] = 1 return d

def common_phrases(d1, d2): keys = d1.keys() common = {} for k in keys: if k in d2: common[k] = (d1[k], d2[k]) return common

>>> ptj = phrase_collector(declaration, 3)>>> ptj{('samuel', 'adams,', 'john'): 1, ('to', 'pass', 'others'): 1, ('absolute', 'despotism,', 'it'): 1, ('a', 'firm', 'reliance'): 1, ('with', 'his', 'measures.'): 1, ('are', 'his.', '<p>'): 1, ('the', 'ruler', 'of'): 1, …>>> pde = phrase_collector(myhomepage, 3)>>> common_phrases(ptj, pde){('from', 'the', '<a'): (1, 1)}

myhomepage = urllib.urlopen('http://www.cs.virginia.edu/evans/index.html').read()declaration = urllib.urlopen('http://www.cs.virginia.edu/cs1120/readings/declaration.html').read()

>>> pde = phrase_collector(myhomepage, 2)>>> ptj = phrase_collector(declaration, 2)>>> show_phrases(common_phrases(ptj, pde))('of', 'the'): (12, 5)('in', 'the'): (7, 7)('the', '<a'): (1, 10)('for', 'the'): (6, 5)('to', 'the'): (7, 3)('from', 'the'): (3, 5)('to', 'be'): (6, 1)('<p>', 'i'): (1, 6)('on', 'the'): (5, 2)('we', 'have'): (5, 1)('of', 'their'): (4, 1)('and', 'the'): (3, 1)('to', 'provide'): (1, 3)('of', 'a'): (2, 2)('the', 'state'): (2, 2)('by', 'their'): (3, 1)('the', 'same'): (2, 1)…

History ofObject-Oriented

Programming

Computing in World War II

Cryptanalysis (Lorenz: Collossus at Bletchley Park, Enigma: Bombes at Bletchley, NCR in US)

Ballistics Tables, calculations for Hydrogen bomb (ENIAC at U. Pennsylvania)

Batch processing: submit a program and its data, wait your turn, get a result

Building a flight simulator required a different type of computing: interactive computing

Pre-History:MIT’s Project Whirlwind (1947-1960s)

Jay Forrester

Whirlwind Innovations

Magnetic Core Memory(first version used vacuum tubes) IBM 704 (used by John McCarthy to

create LISP) commercialized this

August 29, 1949:

First Soviet Atomic Test

Short or Endless Golden Age of Nuclear Weapons?

0

10000

20000

30000

40000

50000

60000

1940 1950 1960 1970 1980 1990 2000 2010 2020

Hiroshima (12kt), Nagasaki (20kt)

First H-Bomb (10Mt)

Tsar Bomba (50 Mt, largest ever = 10x all of WWII)

B83 (1.2Mt), largestin currently active arsenal

kilo

tons

Semi-Automatic Ground Environment (SAGE)MIT/IBM, 1950-1982Coordinate radar

stations in real-time to track incoming bombers

Total cost: $55B (more than Manhattan Project)

R-7 Semyorka

First intercontinental ballistic missileFirst successful test: August 21, 1957

Sputnik: launched by R-7, October 4, 1957

SketchpadIvan Sutherland’s 1963 PhD thesis

(supervised by Claude Shannon)

Interactive drawing programLight pen

Components in Sketchpad

Objects in SketchpadIn the process of making the Sketchpad system operate, a few very general functions were developed which make no reference at all to the specific types of entities on which they operate. These general functions give the Sketchpad system the ability to operate on a wide range of problems. The motivation for making the functions as general as possible came from the desire to get as much result as possible from the programming effort involved. For example, the general function for expanding instances makes it possible for Sketchpad to handle any fixed geometry subpicture. The rewards that come from implementing general functions are so great that the author has become reluctant to write any programs for specific jobs. Each of the general functions implemented in the Sketchpad system abstracts, in some sense, some common property of pictures independent of the specific subject matter of the pictures themselves.

Ivan Sutherland, Sketchpad: a Man-Machine Graphical Communication System, 1963

Simula• Considered the first

“object-oriented” programming language

• Language designed for simulation by Kristen Nygaard and Ole-Johan Dahl (Norway, 1962)

• Had special syntax for defining classes that packages state and procedures together

Counter in Simula

class counter; integer count; begin procedure reset(); count := 0; end; procedure next(); count := count + 1; end; integer procedure current(); current := count; end; end

Does this have everything we need for “object-oriented programming”?

Object-Oriented Programming

Object-Oriented Programming is a state of mind where you program by thinking about objects

It is difficult to reach that state of mind if your language doesn’t have mechanisms for packaging state and procedures (Python has class, Scheme has lambda expressions)

Other things can help: dynamic dispatch, inheritance, automatic memory management, mixins, good donuts, etc.

31

Charge

Monday: continue OOP historyPS6 Due MondayNext week:

PS7 Due next Monday: only one week!building a (mini) Scheme interpreter

(in Python and Java)

Reminder: Peter has office hours now! (going over to Rice)