List of lectures. Lecture content. Imperative Programming. TDDA69 Data and Program Structure Imperative Programming and Data Structures

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List of lectures TDDA69 Data and Program Structure Imperative Programming and Data Structures Cyrille Berger 1Introduction and Functional Programming

Collection and Native Code 9Distributed Computing 10Declarative Programming 11Logic 12Summary 2 / 59 Lecture content Imperative Programming Statements

1 List of lectures TDDA69 Data and Program Structure Imperative Programming and Data Structures Cyrille Berger 1Introduction and Functional Programming 2Imperative Programming and Data Structures 3Parsing 4Evaluation 5Object Oriented Programming 6Macros and 7Virtual Machines and Bytecode 8Garbage Collection and Native Code 9Distributed Computing 10Declarative Programming 11Logic 12Summary 2 / 59 Lecture content Imperative Programming Statements Conditional Statement Iterative Statements Advantages / Data structures Array and Array vs list vs Tree Tree Walking Tree Recursion Dictionnary Imperative Programming 3 / 59

2 What is imperative programming? Foundations of imperative programming Express how computation are executed Describes computation in term of statements that change the internal state Very similar to recipes Examples: C/C++, Pascal, Java, Python, JavaScript... Most hardware follow the Von Neumann architecture, which is imperative Input device Central Processing Unit Control Unit Arithmetic/Logic Unit Memory Unit Output device Many of the early imperative programming languages (Fortran, C...) are abstractions of assembly language Example of non Von Neumann: FPGA (reconfigurable computer architecture) 5 6 Statements Statements A statement is executed by the interpreter to perform an action <header>: <statement> <statement>... <statement> <optional separating header>: <statement> <statement>... <statement>... Statement Suite Clause 8

3 Expression statements Same expressions as in functional programming 2 plus 'hello' max ( 2, 3 ) Compound expressions max(min(pow(3, 5), -4), min(1, -2) * 2) + 6 Assignment expression Assignment expression a = 2; a := 2; a <- 2; (set a 2)... Global 9 10 Conditional Statement Conditional Statement A statement that perform different computations depending on a boolean expression 12

4 if/else Imperative condition vs Functional conditio In Python: def abs_val(x): if(x < 0): return -x elif(x == 0): return 0 else return x How many statements in the abs_val function, in Python? In ECMAScript? In ECMAScript/JavaScript: function abs_val(x) if(x < 0) return -x; else if(x === 0) return 0; else return x; start Imperative: function abs_val(x) var result; if(x < 0) result = -x; else if(x === 0) result = 0; else result = x; return result; Functional: function abs_val(x) return cond(x < 0, -x, cond(x === 0, 0, x); What does this program output? Boolean expressions x = 2 y = 4 if x == y - 2: print("a") else: print("b") if x: print("c") else: print("d") if x - 2: print("e") else: print("f") start In Python: False values: False, 0, '', None, 0.0, [],, () True values: anything else (True, 1, 2, 'hello'...) 15 16

5 switch Conditional switch based on the value of an switch(a) case 0: do something; break; case 'hello' do something else; break; case True: do some other thing; break; This is not just syntaxic it gives performance advantage Iterative Statements 17 While loop Allow statements to be executed repeatedly as long as a condition hold i, total =0 while i < 3: i = i + 1 total = total + i For loop Convenient statement for looping on ranges In JavaScript/ECMAScript: var total = 0; for(var i = 0; i < 3; ++i) total = total + i; In Python: total = 0 for i in range(0, 3): total = total + i 19 20

6 Break statements Exit the current loop i, total =0 while True: i = i + 1 total = total + i if i < 3: break; Advantages / Incovenients 21 Imperative Programming's strength Natural way of expressing programs: Close to the Programs are written as a list of More efficient than functional Popular States are useful Simulations How do you implement a functional random numbers? Inconvenients States induce side- Unexpected behavior Data race 23 24

7 Data structures Array and List Array Collection of elements, identified by an index Index usually starts at 0 until length-1 Arrays allow random var array = [1, 4, 8] console.log(array[0]. array[1], array[2]); Why should array index starts at 0? Historically, in C, arrays are implemented as pointers Dijkstra suggests that the natural notation to denote a subsequence of natural numbers should have the following properties: 1The subsequence includes the smallest natural number, 0 2The subsequence is empty For the numbers a to b, we can represent it 1 a < i < b 2 a i < b 3 a < i b 4 a i b 27 28

8 (Linked-)List A (linked-)list is a sequence of values where each entry contains a pointer to the next element ø List Operations Operations on list Constructor for creating an empty list new_list Test if a list is empty is_empty Prepend an element to the list prepend Append an element to the list append Return the first element head Return all the elements except the first tail Example of List Implementation (1/3) Example of List Implementation (2/3) Each element is a two elements The first element is the The second is the next element or a null- Example: var list = [ 1, [4, [8, null ] ] Constructor: def list_new(): return [ ] Empty test: def list_is_empty(arr): return len(arr) == 0 Each element is a two elements array var list = [ 1, [4, [8, null ] ] ] Prepend an element: def list_prepend(arr, val): if(list_is_empty(arr)): arr.append(val) arr.append(none) else: arr[1] = [ arr[0], arr[1] ] arr[0] = val return arr Append an element: def list_append(arr, val): if(list_is_empty(arr)): arr.append(val) arr.append(none) else: ptr = arr while(ptr[1]!= None): ptr = ptr[1] ptr[1] = [val, None] return arr Suggest a solution to improve the append function

9 Example of List Implementation (3/3) Each element is a two elements var list = [ 1, [4, [8, null ] ] Return the first element: def list_head(arr): return arr[0] Return all the elements except the first: def list_tail(arr): return arr[1] Double-ended queue A double-ended queue (deque): array for which elements can be added at the front or at the back Implemented: circular buffer centered in an array stored in multiple arrays Performance (1/3) Insertion at the back Array vs list vs deque Insertion at the front Source: 36

10 Performance (2/3) Performance (3/3) Random insertion Linear search Random Sort Source: Source: Array vs list Arrays: random-access, efficient unless you need to resize Lists: access through iteration, o(1) for insertion Tuple ordered list of elements In Python: Not mutable list Convenient for function return: def make_tuple(a, b, c): return (a, b, c) (a, b, c) = make_tuple(1, 2, 3) 39 40

11 Iterating Range Using the array index: function print_arr(arr) for(var i = 0; i < arr.length; + +i) console.log(arr[i]) Using for each: function print_arr(arr) for(var val of arr) console.log(val) Using the array index: def print_arr(arr): i = 0 while(i < size(arr)): print(arr[i]) Using for each: def print_arr(arr): for(val in arr): print(val) A range is a sequence of range(starting value, ending value) range(-2, 2) -> [-2, -1, 0, 1 ] Useful in for loop for idx in range(0, arr.length): print(arr[idx]) Or in functional programming: (take 25 (squares-of (integers))) Tree Tree A tree is either a single value called a leaf or a sequence of trees ø 8 ø 44

12 Example of Tree Implementation Using an array The first element is the value The second is a list of children Example: [1, [[2, []], [4, [[8, []]]]]] def create_tree(val, *children): return [val, list(children)] tree = create_tree(1, create_tree(2), create_tree(4, create_tree(8))) Tree Walking 45 Listener A tree listener is a type of tree walking where a function is called when entering a node and when exiting it def tree_listener(tree, entering, exiting): entering(tree) for c in tree[1]: tree_listener(c, entering, exiting) exiting(tree) def print_node(tree): print(tree[0]) tree_listener(tree, print_node, print_node) Visitor A tree visitor is a type of tree walking the nodes in tree are visited recursively def tree_visitor(tree): if(len(tree[1]) == 0): return 1 val = 1 for c in tree[1]: val = max(val, tree_visitor(c)) return val + 1 tree_visitor([1, [[2, []], [4, [[8, []]]]]]) >> start How well would that work in Python for infinitely deep trees? Why? start 47 48

13 Tree Recursion Tree Recursion Tree-shaped processes arise whenever executing the body of a recursive function makes more n: 0, 1, 2, 3, 4, 5, 6, 7, 8,..., 35 fib(n): 0, 1, 1, 2, 3, 5, 8, 13, 21,..., def fib(n): if n == 0: return 0 elif n == 1: return 1 else: return fib(n-2) + 50 A Tree-Recursive Process The computational process of fib evolves into a tree structure fib(5) Repetition n Tree-Recursive Computation This process is highly repetitive; fib is called on the same argument multiple times. fib(4) fib(5) fib(3) fib(4) fib(3) fib(3) fib(2) fib(2) fib(3) fib(2) fib(2) fib(2) fib(0) fib(0) fib(2) fib(0) fib(0) fib(0) fib(0) How would you improve? start 51 52

14 Memoization Memoization: store results of expensive function calls fib_cache = 0: 0, 1: 1 def fib_cached(n): if n in fib_cache: return fib_cache[n] else: fib_cache[n] = fib_cached(n-2) + fib_cached(n-1) return fib_cache[n] Timing: fib(9000) -> >a week fib_cached(9000) -> 8.77ms (array version fib_fast_doubling(9000) -> 6.1ms Dictionnary 53 Dictionnary Dictionnary Operations A dictionnary is an associative array, where each element is associated to an index Type of index, anything that can be ordered (with <) or compared and and even better if a hash can be computed Integer, not necesseraly continuous Strings Example: var map = 1:0, 'hello':'world' console.log(map[1], map['hello']) In many dynamic object oriented programming languages, Objects are Dictionnaries: Python, JavaScript/ ECMAScript Creation of a dictionnary: def dict_create(): return [] Set a value: def dict_set(h, nk, nv): for i in range(0, len(h)): (k,v) = h[i] if(k == nk): h[i] = (nk, nv) return h.append((nk, nv)) Get a value: def dict_get(h, nk): for i in range(0, len(h)): (k,v) = h[i] if(k == nk): return v return None endpythoncode 56

15 Hashmap In a hashmap a hash of the key is computed, using that hash the values are stored in different bucket A hash function is a function that map arbitrary data to fix size Examples: def hash_integer(k, m): return k % m def hash_string(k, m): h = 0 for c in k: h = h + ord(c) return h % m 57 Hashmap Operations Create a hashmap: def hashmap_create(hash_function, buckets): hash = [hash_function, buckets, []] for i in range(0, buckets): hash[2].append(dict_create()) return hash Set a value: def hashmap_set(hashmap, key, value): key_hash = hashmap[0](key, hashmap[1]) dict_set(hashmap[2][key_hash], key, value) Get a value: def hashmap_get(hashmap, key): key_hash = hashmap[0](key, hashmap[1]) return dict_get(hashmap[2][key_hash], key) 58 Summary Imperative programming Data 59 / 59

Lecture content. Course goals. Course Introduction. TDDA69 Data and Program Structure Introduction

Lecture content. Course goals. Course Introduction. TDDA69 Data and Program Structure Introduction Lecture content TDDA69 Data and Program Structure Introduction Cyrille Berger Course Introduction to the different Programming Paradigm The different programming paradigm Why different paradigms? Introduction