CS62 - Spring 2010 - Lecture 24

  • destructor exercise

  • a few things from last time
       - C++ does NOT have a root, Object class
       - memory leaks in Java
          - Most commonly, when an object who's lifespan is longer than the lifespan of another object still keeps a reference
          - Circular references:
             Node n1 = new Node();
             Node n2 = new Node();

  • a few notes about the assignment
       - make sure to pass file streams by reference:
          readHelper(ifstream& in)
          writeHlper(ofstream& out)
       - I've given you code for clear and the big 3, you just need to move it over and use it (modulo a change or two to accommodate the empty tree)
       - if you're confused about height and depth, look in the notes for binary trees
       - "empty" in the code I gave you refers to the empty tree. Ideally, you only have one empty tree for all required empty trees. You can just make this a public variable and use it whenever you need an empty tree (e.g. in the constructors).
       - talk about BinaryTreeIO:: (and why NOT to use it :)

  • other announcements
       - Sr. presentations today and tomorrow
       - Pre-registration pizza
       - TA reminder

  • random thing in C++ for the day :)
       - arrays in C++
          - arrays in C++ inherit most of their functionality (and weirdness) from C
          - my advice... just don't use them... use the vector class and call it a day
          - If for some reason you come across them...
             - int myArray[50];
                - allocations an array of ints with 50 elements in it
                - the [] have to come AFTER the variable name (unlike in Java where either before or after the variable is fine)
                - string myArray[50], etc.
             - What is an array in Java?
                - reference
                - how could you check this?
                   - I've told you that Java always uses call-by value
                   - make an array
                   - pass it to a method
                   - change an array entry in the method
                   - see if the change is seen outside the method
             - Creating a new array does the following:
                - allocates enough memory for the, say 50, objects/items
                   - the "sizeof" function returns the size in bytes of an object, if you're curious
                - an array is then shorthand for a pointer to the beginning of that chunk of memory
                   - when you access the array, say,

                   this is just shorthand for:

                      - add i "int"s worth to the pointer myArray (remember adding to an int pointer adds 4 bytes!)
                         - if it were a different object, for example:
                            IntCell myIntCellArray[50]
                         then it would increment a different amount of memory, since an IntCell would be larger than an int
                      - give me the value referenced by this new pointer
                   - For example, the following are equivalent:

                   for( int i = 0; i < 50; i++ ){
                      myArray[i] = 0;

                   int* ptr;

                   for( ptr = myArray; ptr < myArray+50; ptr++ ){
                      *ptr = 0;

             - other differences
                - there is no .length member variable for arrays, and no way of telling how long an array is, so you need to pass along the length
                - there is no bounds checking, so be careful with your array indices   

       - iterators
          - what methods does an Iterator have in Java?
             - next()
             - hasNext()
             - remove() // optional
          - how are they used?
             - to traverse through a data set
          - In C++, iterators are implemented in a fashion similar to pointers
          - unlike Java, each class has it's own iterator type
             - vector<int>::iterator
             - map<int, int>::iterator
             - map<int, list<pair<int,int> > >::iterator
          - Look at vector_iterator() method in iterator.cpp code
             - we start out at the beginning of our elements with the begin() method
                - returns an iterator at the beginning of the data to iterate through
             - we can access the elements we're iterating through via the iterator variable
                - notice that we're given a pointer to that object, so we can actually modify the object
                - "it" is like a pointer, though, so you need to dereference it or use -> if you want to call methods
             - incrementing the iterator, is just like incrementing a pointer
                - it++
             - the end() method also returns an iterator that is past the end of the data
                - we use this to see if we've iterated through all of the data
             - just to make it clear, iterators are NOT pointers, but by using operator overloading, they're made to function like operators   
          - What does map_iterator() method do in iterator.cpp code ?
             - first, we create a map object
                - the key is an int
                - the value is a pair of ints
             - next, we add things to that object
                - the key is i
                - the value is (i, i)
             - finally, we make an iterator and traverse the data
                - again, each different type has a different iterator type
                - the iterator for a map returns a pointer to a key/value pair
                - it->first is the key
                - it->second is the value (which is itself a pair)
          - look at map_iterator(const ...) method in iterator.cpp code
             - often we want to pass objects by constant references
             - in this case, we can't use a normal iterator!
                - a normal iterator would allow us to modify the values
             - instead, we use a const_iterator
             - almost all classes that have iterators also implement const_iterator

  • graphs, a quick recap
       - A graph is a set of vertices (or nodes) V and a set of edges (u,v) in E where u, v are in V
       - a path is a list of vertices p_1, p_2, ..., p_k where there exists an edge (p_i, p_{i+1}) in E
          - a "simple" path is a path where all edges are unique

  • representing graphs
       - so far, we've drawn them on the board fine, but how are we going to store them for processing?
       - adjacency list
          - each vertex u in V contains a linked list of all the vertices v such that there exists an edge (u, v) in E, that is that there is an edge from u to v
          A: B->D
          B: A->D
          C: D
          D: A->B->C->E
          E: D

       - adjacency matrix
          - a |V| by |V| matrix A, such that A_ij is 1 if edge (i, j) is in E, 0 otherwise

           A B C D E
          A 0 1 0 1 0
          B 1 0 0 1 0
          C 0 0 0 1 0
          D 1 1 1 0 1
          E 0 0 0 1 0

          - what will this matrix look like if the graph is undirected?
             - it will be symmetric
       - examples:
          - draw the following graphs
             A: B C
             B: A C
             C: A B
             A: D
             B: D E
             C: D B
             D: A B C D E
             E: B C
              A B C
             A 1 0 0
             B 0 0 1
             C 0 1 0
       - how would we incorporate weights into both of these approaches?
          - adjacency list: just keep that additional piece of information in the linked list
          - adjacency matrix: store that value in the matrix (instead of just a 0 or a 1)
       - What are the benefits/drawbacks of each approach and when might each be useful?
          - adjacency list
             - good for sparse graphs
             - more space efficient (for sparse graphs)
             - must traverse the adjacency list to discover if an edge exists
          - adjacency matrix
             - good for dense graphs
             - constant time lookup to discover if an edge exists
             - for non-weighted graphs, only requires a boolean matrix
       - Can we get the best of both worlds (constant lookup, good sparse representation)?
       - sparse adjacency matrix
          - rather than storing adjacent vertices as a linked list, store as a hashtable
          - benefits/drawbacks?
             - constant time lookup
             - fairly space efficient (though some overhead with keeping the table)
             - not good for dense graphs

  • finding cycles
       - given a connected graph, how can we determine if it has a cycle in it?
          - or, given a connected graph, determine that it is not a tree
       - what is the definition of a cycle?
          - a simple path, where the endpoints are the same
       - idea:
          - start at a node, go down a path
             - stop when either we find a vertex on the path that we've already seen
             - or when we hit a dead-end
          - if we hit a dead-end, backtrack and find another path
          - if we visit all of the nodes, without finding a repeat vertex, it's acyclic
       - does this sound like anything we've seen before?
          - depth first search!

       void dfs(vertex u, visited) {
             for (v: neighbors of u){
                if (!visited(v)){
                   dfs(v, visited);

       - what modifications need to be made?
          - if we visit a node that we've already visited, then we've found a cycle
          - what about where we just came from?
             - need to know where we came from so we can avoid calling that a cycle
          - want to return true if we find a cycle, false otherwise

       bool dfsCycle(vertex u, vertex parent, visited) {
          bool result = false;

          for(v: neighbors of u){
                result = result || dfsCycle(v, u);
             } else if (v != parent){
                result = true;

          return result;

       - observations:
          - what does it do?
             - runs depth first search
             - if it finds a visited node that was not it's parent (i.e. a cycle) returns true
             - otherwise, false
          - how is this different from DFS that we saw before?
             - we have the additional else if to see if we've found a cycle
             - why do we need the parent as a parameter?
                - so we can distinguish finding a visited node in a cycle vs. a visited node where we just came from
          - what does "result ||= dfsCycle(v, u)" do?
             - result = result || dfsCycle(v, u)
             - which is true if we find a cycle anywhere
       - walk through an example
       - let's try and actually implement our boolean cycle detector
          - how can we represent a vertex?
             - simplest is just use a number, i.e. an int
          - we'll use an adjacency list represenation
             - in C++ there is a "list" class in the STL library
          - we have a few options for declaring the graph type:
             - what if we wanted to use a vector to store the vertices?
                - vector<list<int> > graph
                - what is one downside to this approach?
                   - assumes the vertices are sequential, that is 0, 1, 2, ...
             - what is another option?
                - map<int, list<int> >
             - what if we wanted to add weights?
                - map<int, list<pair<int, int> > >
          - look at dfs_hasCycles in graph_algorithms.cpp code
             - what does "list<int> nbrList = adjMap.find(v)->second" do?
                - get's the adjacency list associated with vertex v
                - why the "->second"?
                   - recall, the map iterator returns a pair
                   - "->first" would give us the key (in this case, just v)
                - why can't we write "list<int> nbrList = adjMap[v]"?
                   - the operator[] is not a const method
                   - it can be used to change the map
                      adjMap[v] = ...
             - use an iterator to iterate thorough the neighbor list
             - what does "visited.find(v) != visited.end()" do?
                - checks to see if v is in the list
                - another option:
                   visited.count(v) > 0
             - note the recursive call to dfs_hasCycles
          - look at grop_hasCycles in graph_algorithms.cpp code
             - takes a graph
                - why passed by reference?
                   - to avoid copying
             - the "set" class is useful for keeping track of which nodes we've visited
             - why do we have the for loop?
                - graphs may not be connected!
                - still could have a cycle though
                - need to make sure we've visited all possible sections of the graph when looking for cycles
             - notice the use of a const_iterator (and the type of that iterator)
          - will be implementing a version of this where you actually return the cycle
       - running time
          - how many times do we call dfsCycle on each vertex?
             - exactly once for a connected graph
             - the first thing we do is set visited to true for that vertex
             - and will never revisit a visited vertex
          - what is the cost of each call to dfsCycle?
             - depends on the representation
          - adjacency matrix:
             - we need to traverse all V entries to get the neighbors
             - O(|V|^2) overall
          - adjacency list:
             - a little trickier
             - how many times do we process each edge?
                - once
             - O(|V| + |E|), which for a connected graph is O(|E|)