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Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
Python lecture 07
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Python lecture 07

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  • 1. Python & Perl Lecture 07 Department of Computer Science Utah State University
  • 2. Outline ● Encoding and decoding with Huffman Trees ● List Comprehension ● Introduction to OOP
  • 3. Encoding & Decoding Messages with Huffman Trees
  • 4. Sample Huffman Tree {A, B, C, D, E, F, G, H}: 17 1 0 {B, C, D, E, F, G, H}: 9 A: 8 1 0 {E, F, G, H}: 4 {B, C, D}: 5 1 0 {C, D}: 2 B: 3 0 C: 1 1 0 1 D: 1 {G, H}: 2 {E, F}: 2 0 E: 1 1 F: 1 0 G: 1 1 H: 1
  • 5. Symbol Encoding 1. Given a symbol s and a Huffman tree ht, set current_node to the root node and encoding to an empty list (you can also check if s is in the root node's symbol leaf and, if not, signal error) 2. If current_node is a leaf, return encoding 3. Check if s is in current_node's left branch or right branch 4. If in the left, add 0 to encoding, set current_node to the root of the left branch, and go to step 2 5. If in the right, add 1 to encoding, set current_node to the root of the right branch, and go to step 2 6. If in neither branch, signal error
  • 6. Example ● Encode B with the sample Huffman tree ● Set current_node to the root node ● ● ● ● B is in current_node's the right branch, so add 1 to encoding & recurse into the right branch (current_node is set to the root of the right branch – {B, C, D, E, F, G, H}: 9) B is in current_node's left branch, so add 0 to encoding and recurse into the left branch (current_node is {B, C, D}: 5) B is in current_node's left branch, so add 0 to encoding & recurse into the left branch (current_node is B: 3) current_node is a leaf, so return 100 (value of encoding)
  • 7. Message Encoding ● ● ● Given a sequence of symbols message and a Huffman tree ht Concatenate the encoding of each symbol in message from left to right Return the concatenation of encodings
  • 8. Example ● Encode ABBA with the sample Huffman tree ● Encoding for A is 0 ● Encoding for B is 100 ● Encoding for B is 100 ● Encoding for A is 0 ● Concatenation of encodings is 01001000
  • 9. Message Decoding 1. Given a sequence of bits message and a Huffman tree ht, set current_node to the root and decoding to an empty list 2. If current_node is a leaf, add its symbol to decoding and set current_node to ht's root 3. If current_node is ht's root and message has no more bits, return decoding 4. If no more bits in message & current_node is not a leaf, signal error 5. If message's current bit is 0, set current_node to its left child, read the bit, & go to step 2 6. If message's current bit is 1, set current_node to its right child, read the bit, & go to step 2
  • 10. Example ● ● Decode 0100 with the sample Huffman tree Read 0, go left to A:8 & add A to decoding and reset current_node to the root ● Read 1, go right to {B, C, D, E, F, G, H}: 9 ● Read 0, go left to {B, C, D}:5 ● Read 0, go left to B:3 ● Add B to decoding & reset current_node to the root ● No more bits & current_node is the root, so return AB
  • 11. Generation of Huffman Trees
  • 12. Algorithm ● ● ● ● Basic idea: Build the tree bottom up so that symbols with the smallest frequencies are farthest from the root Given a sequence of nodes (initially single symbols and their frequencies), find two nodes with the smallest frequencies and combine them into a new node whose symbol list contains the symbols of the two nodes and whose frequency is the sum of the frequencies of the two nodes Remove the two combined nodes from the sequence and add the newly constructed node back to the sequence (note that the length of the sequence is now reduced by 1) Keep combining pairs of nodes in the above fashion until there is only one node left in the sequence: this is the root of the Huffman tree
  • 13. Example ● ● Initial sequence: [A:4, B:2, C:1, D:1] Find two nodes with the smallest frequencies and combine them into a new node whose symbol list contains the symbols of the two nodes and whose frequency is the sum of the frequencies of the two nodes ● The nodes are C:1 and D:1 ● The new node is {C, D}:2 ● After removing C:1 and D:1 and adding {C, D}:2, the sequence becomes [A:4, B:2, {C, D}:2]
  • 14. Example ● The Huffman tree so far: {C,D}:2 C:1 D:1
  • 15. Example ● ● Current sequence: [A:4, B:2, {C,D}:2] Find two nodes with the smallest frequencies and combine them into a new node whose symbol list contains the symbols of the two nodes and whose frequency is the sum of the frequencies of the two nodes ● The nodes are B:2 and {C, D}:2 ● The new node is {B, C, D}:4 ● After removing B:2 and {C, D}:2 and adding {B, C, D}:4, the sequence becomes [A:4, {B, C, D}:4]
  • 16. Example ● The Huffman tree so far: {B,C,D}:4 {C,D}:2 B:2 C:1 D:1
  • 17. Example ● ● Current sequence: [A:4, {B,C,D}:4] Find two nodes with the smallest frequencies and combine them into a new node whose symbol list contains the symbols of the two nodes and whose frequency is the sum of the frequencies of the two nodes ● The nodes are A:4 and {B,C, D}:4 ● The new node is {A,B, C, D}:4 ● ● After removing A:4 and {B,C, D}:4 and adding {A,B, C, D}:8, the sequence becomes [{A,B, C, D}:8] We are done, because the sequence has only one node
  • 18. Example ● The final Huffman tree: {A, B,C,D}:8 {B,C,D}:4 A:4 {C,D}:2 B:2 C:1 D:1
  • 19. This is a programming assignment
  • 20. Remarks on the Algorithm ● ● ● The algorithm does not specify a unique Huffman tree, because there may be more than two nodes in the sequence with the same frequencies How these nodes are combined at each step (e.g., two rightmost nodes, two leftmost nodes, two middle nodes) is arbitrary, and is left for the programmer to decide The algorithm does guarantee the same code lengths regardless of which combination method is used
  • 21. List Comprehension
  • 22. List Comprehension ● ● List comprehension is a syntactic construct in some programming languages for building lists from list specifications List comprehension derives its conceptual roots from the set-former (set-builder) notation in mathematics [Y for X in LIST] ● List comprehension is available in other programming languages such as Common Lisp, Haskell, and Ocaml
  • 23. Set-Former Notation Example 4  x | x  N , x   100  4  x is the output function  x is the variable  N is the input set 2  x  100 is the predicate 2
  • 24. For-Loop Implementation ### building the list of the set-former example with forloop >>> rslt = [] >>> for x in xrange(201): if x ** 2 < 100: rslt.append(4 * x) >>> rslt [0, 4, 8, 12, 16, 20, 24, 28, 32, 36]
  • 25. List Comprehension Equivalent ### building the same list with list comprehension >>> s = [ 4 * x for x in xrange(201) if x ** 2 < 100] >>> s [0, 4, 8, 12, 16, 20, 24, 28, 32, 36]
  • 26. For-Loop ### building list of squares of even numbers in [0, 10] ### with for-loop >>> rslt = [] >>> for x in xrange(11): if x % 2 == 0: rslt.append(x**2) >>> rslt [0, 4, 16, 36, 64, 100]
  • 27. List Comprehension Equivalent ### building the same list with list comprehension >>> [x ** 2 for x in xrange(11) if x % 2 == 0] [0, 4, 16, 36, 64, 100]
  • 28. For-Loop ## building list of squares of odd numbers in [0, 10] >>> rslt = [] >>> for x in xrange(11): if x % 2 != 0: rslt.append(x**2) >>> rslt [1, 9, 25, 49, 81]
  • 29. List Comprehension Equivalent ## building list of squares of odd numbers [0, 10] ## with list comprehension >>> [x ** 2 for x in xrange(11) if x % 2 != 0] [1, 9, 25, 49, 81]
  • 30. List Comprehension with For-Loops
  • 31. For-Loop >>> rslt = [] >>> for x in xrange(6): if x % 2 == 0: for y in xrange(6): if y % 2 != 0: rslt.append((x, y)) >>> rslt [(0, 1), (0, 3), (0, 5), (2, 1), (2, 3), (2, 5), (4, 1), (4, 3), (4, 5)]
  • 32. List Comprehension Equivalent >>> [(x, y) for x in xrange(6) if x % 2 == 0 for y in xrange(6) if y % 2 != 0] [(0, 1), (0, 3), (0, 5), (2, 1), (2, 3), (2, 5), (4, 1), (4, 3), (4, 5)]
  • 33. List Comprehension with Matrices
  • 34. List Comprehension with Matrices ● List comprehension can be used to scan rows and columns in matrices >>> matrix = [ [10, 20, 30], [40, 50, 60], [70, 80, 90] ] ### extract all rows >>> [r for r in matrix] [[10, 20, 30], [40, 50, 60], [70, 80, 90]]
  • 35. List Comprehension with Matrices >>> matrix = [ [10, 20, 30], [40, 50, 60], [70, 80, 90] ] ### extract column 0 >>> [r[0] for r in matrix] [10, 40, 70]
  • 36. List Comprehension with Matrices >>> matrix = [ [10, 20, 30], [40, 50, 60], [70, 80, 90] ] ### extract column 1 >>> [r[1] for r in matrix] [20, 50, 80]
  • 37. List Comprehension with Matrices >>> matrix = [ [10, 20, 30], [40, 50, 60], [70, 80, 90] ] ### extract column 2 >>> [r[2] for r in matrix] [30, 60, 90]
  • 38. List Comprehension with Matrices ### turn matrix columns into rows >>> rslt = [] >>> for c in xrange(len(matrix)): rslt.append([matrix[r][c] xrange(len(matrix))]) for >>> rslt [[10, 40, 70], [20, 50, 80], [30, 60, 90]] r in
  • 39. List Comprehension with Matrices ● List comprehension can work with iterables (e.g., dictionaries) >>> dict = {'a' : 'A', 'bb' : 'BB', 'ccc' : 'CCC'} >>> [(item[0], item[1], len(item[0]+item[1])) for item in dict.items()] [('a', 'A', 2), ('ccc', 'CCC', 6), ('bb', 'BB', 4)]
  • 40. List Comprehension ● If the expression inside [ ] is a tuple, parentheses are a must >>> cubes = [(x, x**3) for x in xrange(5)] >>> cubes [(0, 0), (1, 1), (2, 8), (3, 27), (4, 64)] ● Sequences can be unpacked in list comprehension >>> sums = [x + y for x, y in cubes] >>> sums [0, 2, 10, 30, 68]
  • 41. List Comprehension ● for-clauses in list comprehensions can iterate over any sequences: >>> rslt = [ c * n for c in 'math' for n in (1, 2, 3)] >>> rslt ['m', 'mm', 'mmm', 'a', 'aa', 'aaa', 't', 'tt','ttt', 'h', 'hh', 'hhh']
  • 42. List Comprehension & Loop Variables ● The loop variables used in the list comprehension for-loops (and in regular for-loops) stay after the execution. >>> for i in [1, 2, 3]: print i 1 2 3 >>> i + 4 7 >>> [j for j in xrange(10) if j % 2 == 0] [0, 2, 4, 6, 8] >>> j * 2 18
  • 43. When To Use List Comprehension ● For-loops are easier to understand and debug ● List comprehensions may be harder to understand ● ● ● List comprehensions are faster than for-loops in the interpreter List comprehensions are worth using to speed up simpler tasks For-loops are worth using when logic gets complex
  • 44. OOP in Python
  • 45. Classes vs. Object ● ● ● A class is a definition (blueprint, description) of states and behaviors of objects that belong to it An object is a member of its class that behaves according to its class blueprint Objects of a class are also called instances of that class
  • 46. Older Python: Classes vs. Types ● ● ● ● In older versions of Python, there was a difference between classes and types The programmer could create classes but not types In newer versions of Python, the distinction between types and classes is disappearing The programmer can now make subclasses of built-in types and the types are behaving like classes
  • 47. Older Python: Classes vs. Types ● ● In Python versions prior to Python 3.0, old style classes are default To get the new style classes, place __metaclass__ = type at the beginning of a script or a module ● ● There is no reason to use old style classes any more (unless there is a serious backward compatibility issue). Python 3.0 and higher do not support old style classes
  • 48. Class Definition Syntax __metaclass__ = type class ClassName: <statement-1> … <statement-N>
  • 49. Class Defimition
  • 50. Class Definition Evaluation ● ● ● ● When a class definition is evaluated, a new namespace is created and used as the local scope All assignments of local variables occur in that new namespace Function definitions bind function names in that new namespace When a class definition is exited, a class object is created
  • 51. class Statement ● class statement defines a named class ● class statements can be placed inside functions ● Multiple classes can be defined in one .py file ● Class definition must have at least one statement in its body (pass can be used as a placeholder)
  • 52. Class Documentation ● To document a class, place a docstring immediately after the class statement class <ClassName>: """ Does nothing for the moment """ pass
  • 53. Creating Objects ● There is no new in Python ● Class objects (instances) are created by the class name followed by () ● This object creation process is called class instantiation: class SimplePrinter: """ This is class Printer. """ pass >>> x = Printer()
  • 54. Operations Supported by Class Objects ● Class objects support two types of operations: attribute reference and instantiation __metaclass__ = type class A: ''' this is class A. ''' x = 12 def g(self): return 'Hello from A!'
  • 55. Class Objects >>> A.x ## attribute reference >>> A.g ## attribute reference >>> A.__doc__ ## attribute reference >>> a = A() ## a is an instance of ## class A (instantiation)
  • 56. Defining Class Methods ● ● ● In C++ terminology, all class members are public and all class methods are virtual All class methods are defined with def and must have the parameter self as their first argument One can think of self as this in Java and C++ class SimplePrinter: def println(self): print def print_obj(self, obj): print obj,
  • 57. Calling Methods on Instances ● To call a method on an instance, use the dot operator ● Do not put self as the first argument >>> sp = SimplePrinter() >>> sp.print_obj([1, 2]); sp.println()
  • 58. Calling Methods on Instances ● What happens to self in sp.println()? ● The definition inside the SimplePrinter class is def println(self): print ● How come self is not the first argument?
  • 59. Calling Methods on Instances ● The statement sp.println() is converted to SimplePrinter.println(sp) so self is bound to sp ● ● In general, suppose there is a class C with a method f(self, x1, ..., xn) Suppose we do: >>> x = C() >>> x.f(v1, ..., vn) ● Then x.f(v1, ..., vn) is converted to C.f(x, v1, ..., vn)
  • 60. Example class C: def f(self, x1, x2, x3): return [x1, x2, x3] >>> x = C() >>> x.f(1, 2, 3) [1, 2, 3] >>> C.f(x, 1, 2, 3) 3) [1, 2, 3] ## equivalent to x.f(1, 2,
  • 61. Attributes and Attribute References ● The term attribute is used for any name that follows a dot ● For example, in the expression “a.x”, x is an attribute class A: """ This is class A. """ def printX(self): print self._x, ● A.__doc__, A._x, A.printX, A._list are valid attribute references
  • 62. Types of Attribute Names ● There are two types of attribute names: data attributes and method attributes class A: """ This is class A. """ _x =0 ## data attribute _x _list = [] ## data attribute _list def printX(self): ## method attribute print self._x, ● A.__doc__, A._x, A._list are data attributes ● A.printX is a method attribute
  • 63. Data Attributes ● ● ● ● Data attributes loosely correspond to data members in C++ A data attribute does not have to be explicitly declared in the class definition A data attribute begins to exist when it is first assigned to Of course, integrating data attributes into the class definition makes the code easier to read and debug
  • 64. Data Attributes ● This code illustrates that attributes do not have to be declared and begin their existence when they are first assigned to class B: """ This is class B. """ def __init__(self): self._number = 10 self._list = [1, 2, 3] >>> b = B() >>> b._number 10 >>> b._list [1, 2, 3]
  • 65. Method Attributes ● ● ● ● Method attributes loosely correspond to data member functions in C++ A method is a function that belongs to a class If a is an object of class A, then a.printX is a method object Like function objects, method objects can be used outside of their classes, e.g. assigned to variables and called at some later point
  • 66. Method Attributes ● Method attributes loosely correspond to data member functions in C++ class A: _x = 0 def printX(self): print self._x, >>> a = A() >>> m = a.printX >>> m() 0 >>> a._x = 20 >>> m() 20
  • 67. Method Attributes ● Data attributes override method attributes class C: def f(self): print "I am a C object." >>> c = C() >>> c.f() I am a C object. >>> c.f = 10 >>> c.f 10 >>> c.f() ### error
  • 68. Method Attributes ● ● ● Consistent naming conventions help avoid clashes between data attributes and method attributes Choosing a naming convention and using it consistently makes reading and debugging code much easier Some naming conventions:  First letter in data attributes is lower case; first letter in method attributes is upper case  First letter in data attributes is underscore; first letter in method attributes is not underscore  Nouns are used for data attributes; verbs are used for methods
  • 69. Reading & References ● ● ● ● ● www.python.org Ch 02, H. Abelson and G. Sussman. Structure and Interpretation of Computer Programs, MIT Press S. Roman, Coding and Information Theory, Springer-Verlag Ch 03, M. L. Hetland. Beginning Python From Novice to Professional, 2nd Ed., APRESS Ch 04, M. L. Hetland. Beginning Python From Novice to Professional, 2nd Ed., APRESS

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