CS 352 PA 7+8 – Autocomplete solution

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1 Overview
A user types a few letters from a word. We look through a big array of words,
and find all of the words that contain the letters that the user typed. We then
print out, for the user, all of the matches that we find. It’s a fairly simple
problem, right? We can just scan through all of the words, call strncmp() at
every possible position inside each of the words, and report any of the matches
that we find.
But what if we wanted to do it more efficiently?
The algorithm that we described above is actually moderately costly. We must
search every single word in our dictionary – worse than that, we have to search
at many different locations inside each word! Can we make this faster? Let’s
consider another algorithm….
SECOND TRY
We start with a tree. Each node in the tree has (up to) 26 children, representing
the letters in the English alphabet. As the user types each letter, we traverse
one of the links, going down through the tree. When the user stops typing, we
are at one of the subtrees; we then iterate through the subtree, and collect a
set of words from each node in the subtree. The words that we collect are all of
the possible matches!
(I didn’t invent this data structure; ask me more about it, after the PA comes
due.)
1.1 The Structure of our Tree
You’ve studied binary trees extensively, in previous classes. And you probably
have heard that some trees allow any number of children for each node. But
the tree that we’re using here is quite odd – it has many children, but a fixed
number of children: each node must store 26 pointers.
Storing 26 pointers takes up a lot of space – 208 bytes, in most computers.
And so, in the real world, I might consider many strategies to make our tree
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more efficient: I might use a flexible-length array of child pointers (and do binary
search to find the one I want); I might use a hash table instead of a fixed array; I
might consider strategies for combining levels together to save space. However,
for this program, we’re going to be simple: exactly 26 pointers in every node.
1.2 What the Tree Represents
Each node in the tree represents a string: namely, the string of characters as
you follow links from the root to the node. Or viewed another way, each node
represents a whole bunch of strings which begin with that substring. That is,
the node CAT represents the words CAT, CATCH, CATEGORY, and many more.
But each node also represents more than that. We want to search for any
instance of the substring, in a whole bunch of words – and not just the words
that *begin* with the word. And so the node CAT also represents BOBCAT,
CONCATENATE, SYNDICATE, and more.
So how, and where, do we list the words that each node represents?
Our solution is to store, with each node, each of the words that end with the
string. From the example above, the node CAT would store the words CAT,
BOBCAT, and more. But it would not store the word CATCH; instead, if you
followed two more links (C, then H), you would find the node CATCH, which
would store CATCH.
Thus, if the user types CAT, then we will go to the node CAT; we will print
out CAT, but also all of the words that we find in all descendant nodes, including
CATCH and many more.
1.3 Multiple Entries For Each Word
So does this mean that each word is stored only once in the tree? No! Instead,
each word is stored many times – once for each tail-aligned substring. So the
word CATCH is stored in 5 different nodes: CATCH, ATCH, TCH, CH, H.
So let’s work an example. If the user types ATC, how do we find the word CATCH?
We follow these steps:
• Always start every search at the root
• Follow the links A, T, C – which takes us to the node ATC
• Print out all of the words (if any) stored there
• Recurse through the entire subtree; one of the descendant nodes is ATCH
• CATCH is one of the strings stored inside ATCH; print that out, too.
(spec continues on the next page)
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1.4 Examples
The following tree is for CATCH. Notice that the result CATCH shows up in
five different nodes, representing the search strings CATCH, ATCH, TCH, CH,
and H. Also notice that the node C is part of the search path for both CATCH
and CH:
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This tree adds the word MATCH to the tree. Note that we add one new
major chain (starting with the node M), but for the most part, this shares the
same nodes that already existed. Also note that many nodes now have multiple
result words.
(spec continues on the next page)
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2 What You Must Turn In
You will turn in two files, named autocomplete.c and autocomplete.h . Your
C file should include your header; my testcases will also include the header.
Unfortunately, when I set up the autograder, I forgot that Ben likes
to put all of the files in sub-directories, inside of a zip file. Instead,
for this PA, you will need to upload the files individually.
By mistake, I posted the already-completed version of this header to
D2L. So you do not need to edit it, after all. I have provided an complete
version of autocomplete.h for you. You should make only one change to it:
change the children field of struct LookupTreeNode to be the proper type; see
the comment in the header for details. Don’t change autocomplete.h otherwise.
Your C file must not contain a main() function, because each testcase will have
its own main() function. How, then, will you test your code? Write some
testcases of your own!
3 valgrind
This is required for both PA 7 and PA 8.
We are requiring valgrind for this project! We will run each testcase twice:
once without valgrind (just checking to see if your code works) and once with
it. To pass both testcases, you must get the correct output and also not have
any memory leaks or errors.
4 Word Lists
There are two parts of our program where “lists of words” will be useful. We
don’t want to duplicate our code for handling this, so one of your tasks will be
to implement a WordList struct.
A WordList is simply a struct that models a growing array of string pointers;
I have provided the following definition in autocomplete.h:
typedef struct WordList WordList;
struct WordList
{
int count;
int alloc;
char **words;
};
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The words field stores a pointer to malloc()ed buffer of pointers; you must
allocate this buffer, but you should not allocate any buffer to hold the strings
themselves (the caller will do that for you). You should treat this field as public;
my testcase code will read it directly.
The count field indicates how many strings there are in the buffer; it must
be 0 when you initialize a new WordList, and will be incremented every time
that we add a new string to the list. You should treat this field as public; my
testcase code will read it directly.
The alloc field is optional, and you can treat it as private; you have complete freedom about how to use it, and can even ignore it if you want. See the
Appendix of this spec to see how to use it, if you want.
4.1 Required Functions: WordList Constructor, Destructor
This is required for both PA 7 and PA 8.
Write a constructor and destructor for the WordList type:
WordList *wl_create (void)
void wl_destroy(WordList *list)
A WordList is made up of two malloc()ed buffers: one for the struct itself,
and one for the array of pointers. These must be separate buffers, because
you will need to re-allocate the array from time to time, without moving the
WordList struct itself. The destructor must free both of these buffers – but do
not free the strings themselves; your caller will be responsible for that.
LIMITATION:
In wl_create(), you will almost certainly want to pre-allocate an array for the
pointers. And many of you will be tempted to pre-allocate a huge array, so that
you don’t have to write the array-expansion code. To prevent this, your initial
array, allocated by wl_create(), must not be any larger than 4 pointers.
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4.2 Required Function: wl add()
This is required for both PA 7 and PA 8.
You must write the following function:
int wl_add(WordList *list, char *word)
This function adds a new word to the list. If the buffer is not large enough to
hold another pointer, then you must reallocate the buffer to make more space.
Do not duplicate the string itself; store the pointer that you have been given
into the array.
This function must return 0 if it works properly, and nonzero if there is any
type of failure.
IMPORTANT PERFORMANCE NOTE:
Most of you will probably, at first, expand the array one step at a time; each time
that a new string is added, you reallocate the memory to make it a little larger.
This is permissible, but slow – meaning that some of the testcases will
time out. To pass the largest testcases, you will need to use a more efficient
algorithm. It is described in the Appendix at the end of this spec.
(spec continues on the next page)
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5 The LookupTreeNode Struct
A LookupTreeNode represents one of the nodes in the search tree that we described at the beginning of this spec. I have provided part of a declaration for
it in autocomplete.h . It has only two fields: children and words; words is
simply a pointer to a WordList.
The children field must be an array of exactly 26 LookupTreeNode pointers.
However, I’m not showing you the syntax for how to declare this; instead, I
want you to experiment with it, using the sizeof() operator. Confirm that
your declaration has the proper size (208 bytes), and that each field inside the
array is 8 bytes in size.
Why do I do this? Because even though I’ve been programming in C for 30
years, I still forget! In fact, I forgot the proper syntax when I was writing my
solution for this program! Every time that I need to declare a variable like this,
I write a tiny test program that uses sizeof() to remind myself how to do this
correctly. Get used to it. 🙂
5.1 Required Functions: LookupTreeNode Constructor, Destructor
This is only required for PA 8.
Write a constructor and destructor for the LookupTreeNode type:
LookupTreeNode *ltn_create (void)
void ltn_destroy(LookupTreeNode *list)
When you build your LookupTreeNode object, you must create a WordList
object for it as well. Do not try to build the WordList by hand; make sure
to call wl_create(). Likewise, make sure to use wl_destroy() to destroy the
WordList, when the time comes.
ltn_destroy() must be recursive: you must also destroy all of your child
nodes – and they must destroy their nodes, all the way down through the subtree.
(spec continues on the next page)
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5.2 Required Function: ltn add result word()
This function is the heart of your tree-building algorithm.
This is only required for PA 8.
Implement the following function:
void ltn_add_result_word(LookupTreeNode *ltn, char *search_word, char *result_word)
The first parameter is the root of a tree (or subtree); the second is the path
that you will follow through the tree, and the third is the word that you should
store into the WordList, in the destination.
This function may assume that first parameter is certainly not NULL, but
must build any missing nodes along the path, as necessary. Thus, when this
function returns, the tree must contain all of the necessary nodes to get to the
destination, and the destination must contain the result word.
(If there is a malloc() failure along the way, detect it and return immediately. But if you notice, there is no return value – so there is no way to report
this error to your user.)
The search and result words might be the same, but do not have to be; the
caller will ensure that the search word is a subset of the result (aligned at the
end). Your function does not need to confirm this.
CASE:
You may assume that both the search string, and the result string, are made
up only of uppercase letters.
HINT:
While it’s permissible to write this function using a loop, I found recursion
helpful.
(EXAMPLE on the next page)
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EXAMPLE:
Let’s assume that the function is first called on our root node. The search_word
is ATCH, and our result_word is CATCH. We will follow these steps:
• Starting at the root node, we follow the A link. Let’s assume that it exists.
• Looking at node A, we follow the T link. Let’s assume that it exists as
well.
• Looking at node AT, we attempt to follow the C link. However, it doesn’t
exist, so we call ltn_create() to build a new node, and store its pointer
into the children array of node AT. We then follow the link.
• Since node ATC is brand-new, obviously it has no children; we thus build
an H child for it. We then head into that node.
• In node ATCH, since we have consumed the entire search string, we add
the result word CATCH to the words field.
5.3 Required Functions: Counting
This is only required for PA 8.
You must implement two functions which traverse a tree, and count either
the number of nodes in the tree, or else the total number of result words in the
entire tree:
int node_count (LookupTreeNode *root)
int result_count(LookupTreeNode *root)
Both of these functions should return 0 if passed an empty tree.
(spec continues on the next page)
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6 The Core Algorithms
The functions in this section represent the major steps of the search program.
We will have testcases on GradeScope that test most of these individually. Additionally, we will have some testcases that test the entire program at once –
which will ony work properly if you have completed all of these functions.
6.1 Required Function: lookup()
This is only required for PA 8.
The following function traverses a tree from a root node (or subtree) based
on a search string, and returns the node that it finds.
LookupTreeNode *lookup(LookupTreeNode *root, char *search)
For example, if we start at the true root of the tree and our search string is ATCH,
then this should return the node ATCH. However, if we start from the node CAT
and our search string is EGOR, then this should return the node CATEGOR.
If the search starts with an empty tree, or if it at any point traverses into a
non-existent child, then the function must return NULL.
CASE:
The search string will only contain alphabetic characters, but you must handle
both upper and lower-case characters. The tree ignores case, so treat everything
as upper-case.
(spec continues on the next page)
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6.2 Required Functions: Converting Input to a Tree
The following functions are used to convert the input to a tree:
WordList *build_wordlist_from_file(FILE *fp)
LookupTreeNode *build_tree_from_words (WordList *words)
build_wordlist is required for both PA 7 and PA 8.
build_tree is only required for PA 8.
The main() function in the testcase will open up a file (that the user provides
as a command-line argument). It will then pass this file to the first function.
This function must call wl_create() and then populate this list with all of the
strings that it finds in the file.
This function should also close the file.
Next, main() will pass this WordList to the second function; this function
must build a tree (as described in the top of this spec). Remember to use
ltn_add_result_word() as a helper for this function!
6.2.1 Handling the Word List
Why do we have two functions? Why a WordList in-between them? This is
because we need to remember the list of words until the end of the program (so
that main() can free them).
Remember, we said (at the top of this spec) that each word will show up as
a “result” word in many different nodes – and that all of these nodes will share
the same pointers. Thus, in order to clean up our memory at the end, we need
some way to find (and free) all of these word buffers. This is why the main()
function needs to know the WordList – it will save this until the very end, and
free all of the pointers after all searches have completed.
To make this work, you need to ensure that:
• In build_wordlist_from_file(), you must malloc() a separate buffer
for each word that you read, and copy the word into that buffer before
you add it to the WordList
• build_tree_from_words() must not free any of the words – nor may it
reallocate or copy them. Simply use the pointers that you find in the
WordList
6.2.2 Input Format
The input file will be a simple text file, with exactly one word on each line.
There may be blank lines (skip over them), but there will be no whitespace in
the file, other than newlines.
All of the words will be only made up of alphabetic characters. Since all of
your algorithms in this program are case-insensitive, convert any lowercase
characters to uppercase when you read it from the file.
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6.3 Required Function: print words()
This is only required for PA 8.
You must implement the following function:
void print_words(LookupTreeNode *result, char *search)
This function must print out all of the result words in the entire subtree, rooted
at the given node. The search parameter does not change what words you
print out – instead, it is used only for printing the output.
For each word that you find, print it out using the following format:
printf(“The string ’%s’ was found inside the word ’%s’\n”, search, result_word);
You may print these lines out in any order; I will sort the output from your
program before checking it against the testcase, so order doesn’t matter. (That’s
why we included the search string in the output – so we can figure out which
output lines came from which searches.)
7 Turning in Your Solution
You must turn in your code using GradeScope.
8 Appendix – Efficiently Appending to an Array
Arrays in C are never resizable – at least, not automatically. You have to
resize them by hand. In the case of a malloc() array, we can do this by calling
realloc() to expand the buffer (or move it to a new location in memor, if
necessary).
When we are handling appending into an array, it’s tempting reallocate every
time that a new value is added. After all, it’s easy – we already know the length
of the array, and so we just increment that size, and reallocate the memory. But
remember, realloc() potentially copies the entire array from one location
in memory to another. So the first time that we resize the array, it moves 1
element; then 2; then 3; then 4; etc.
The total work done, resizing the array, is thus O(n
2
):
Xn
i=1
i =
n(n + 1)
2
= O(n
2
)
Instead, let’s expand the array rarely. What if, instead of doing +1
each time, we always expanded the array by doubling its size? If we add up
the total cost of all of these expansions over time, it is far better! One way to
look at it is to realize that we pay a cost of 1, then 2, then 4:
1 + 2 + 4 + 8 + … + n ≈ 2n = O(n)
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Another way is to think backwards; our last copy was (roughly) a cost of n,
and the one before that was half the cost, and so on, back to the beginning:
n +
n
2
+
n
4
+
n
8
+ … = n(1 + 1
2
+
1
4
+
1
8
+ …) = 2n = O(n)
Thus, we see that all of the work done to expand the array totals up
to O(n); thus, the average cost (per single append is O(1)).
8.1 The Algorithm in Practice
In practice, this means that we need two different integers to keep track of
the array size: one that tracks the current length, and another that tracks the
allocated length. Most of the time, when a user wants to append a new value
into the array, we discover that there is some free space, and so we can write
into the array, with no need for realloc().
But once in a while, a user will try to append into an array, but the array is
completely full. In that case, we will double its size – and then there will be
plenty of space to add the new value.
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