Description
To gain a better understanding of search algorithms and symbol tables by
implementing an autocompletion engine.
## Background:
In order to make typing on a mobile device quick and easy, an autocomplete
feature is commonly implemented to try to guess what word a user wishes to type
before they are finished. Such a feature requires extensive use of search
algorithms.
## Specifications:
* First, download a copy of the English dictionary that you will use for this
project from [here](http://people.cs.pitt.edu/~nlf4/cs1501/handouts/dictionary.txt)
and place it in your repository folder
* **Do not** add this file to your git repository, however! (i.e., **do not** run
`git add dictionary.txt`).
* You must implement a De La Briandais (DLB) trie data structure (as described
in lecture) to use in your project. The DLB trie must be all your own code
(e.g., you cannot use the Java LinkedList, you must implement your own linked-list).
* When your program is run, it should first create a new DLB trie and add all
of the words in `dictionary.txt` to that trie (this will be the dictionary
trie).
* You can assume that the file `dictionary.txt` will exist in the current
directory, and hardcode that _relative_ path into your program (do not
use a full path as the TA will be grading your program on a different
machine!).
* Once the dictionary trie is established, you should prompt the user
to start typing a word. For this project, you will be writing only a simple
program to test autocomplete functionality. Due to complexities with
gathering single character input in Java, you should accept a single
character at a time, each followed by `Enter`. After each character,
present the user with the list of predictions of the word that they are
trying to type. If the user types a number (1-5), you should consider the
user to have selected the corresponding prediction, and restart the process
for a new word. Consider `$` to be a terminator character; if the user
enters a `$`, consider the characters input so far to be the full word as
intended (regardless of suggestions). Finally, if the user enters a `!` at
any point, your program should exit.
* To generate the list of predictions, your program should not only consult
the dictionary trie, but also keep track of what words the user has entered
in the past. If the user has previously entered the same sequence of
characters as a prefix to a word, you should prioritize the words that most
frequently resulted from this sequence previously. If the user has never
entered the current sequence before, or has entered fewer than 5 words with
the current seequence as a prefix (i.e., not enough words to complete the
list of 5 predictions), your program should suggest words from
dictionary.txt that have the current sequence as a prefix.
* You program should propose at most 5 suggestions at a time. If there are
fewer than 5 suggestions available from the user’s history and the dictionary
trie, then only print out the available suggestions.
* If the current sequence of characters has not been entered by the user
before and does not appear in `dictionary.txt`, you should display a message
to the user stating that no predicions were found, and allow the user to
continue entering characters one at a time. Once the user enters a “$”, you
should consider the word to be finished and add it to the user’s history so
that you can predict it in the future.
* Your program should run in a case sensitive manner in order to allow for
easier prediction of proper nouns and acronyms.
* The design of the data structure that keeps track of a user’s previously
entered words is entirely up to you. You must create a file named
`approach.txt` that both describes your approach to implementing this symbol
table and justifies your decision to take this approach. Note that this file
does not need to be extensive, just a few lines so the TA is aware of what to
look for in your code and why you chose this approach.
* The history of the user’s entered words should persist across runs of your
program. To enable this, your program should save a representation of this
data structure to the file `user_history.txt` before exiting.
* Do not add this file to your git repository! (i.e., do not run `git
add user_history.txt`) It should be generated by your program if it does
not exist.
* Each time the user enters a character, you should use Java’s
[`System.nanoTime()`]
(https://docs.oracle.com/javase/8/docs/api/java/lang/System.html#nanoTime–)
to calculate how long your program takes to find the predictions. You should
display this time (in seconds) along with the list of predictions.
* After the user enters `!`, your program should output the average time
that was required to produce a list of predictions.
An example run of the program would proceed as follows:
“`
Enter your first character: t
(0.000251 s)
Predictions:
(1) t (2) ta (3) tab (4) tab’s (5) tabbed
Enter the next character: h
(0.000159 s)
Predictions:
(1) thalami (2) thalamus (3) thalamus’s (4) thalidomide (5) thalidomide’s
Enter the next character: e
(0.000052 s)
Predictions:
(1) the (2) theater (3) theater’s (4) theatergoer (5) theatergoer’s
Enter the next character: r
(0.000225 s)
Predictions:
(1) therapeutic (2) therapeutically (3) therapeutics (4) therapeutics’s (5) therapies
Enter the next character: e
(0.000182 s)
Predictions:
(1) there (2) there’s (3) thereabout (4) thereabouts (5) thereafter
Enter the next character: 3
WORD COMPLETED: thereabout
Enter first character of the next word: t
(0.000128 s)
Predictions:
(1) thereabout (2) t (3) ta (4) tab (5) tab’s
Enter the next character: h
(0.000094 s)
Predictions:
(1) thereabout (2) thalami (3) thalamus (4) thalamus’s (5) thalidomide
Enter the next character: e
(0.000085 s)
Predictions:
(1) thereabout (2) the (3) theater (4) theater’s (5) theatergoer
Enter the next character: r
(0.000145 s)
Predictions:
(1) thereabout (2) therapeutic (3) therapeutically (4) therapeutics (5) therapeutics’s
Enter the next character: e
(0.000130 s)
Predictions:
(1) thereabout (2) there (3) there’s (4) thereabouts (5) thereafter
Enter the next character: !
Average time: 0.000145 s
Bye!
“`
## Submission Guidelines:
* **DO NOT** add `dictionary.txt` to your git repository.
* **DO NOT** add `user_history.txt` to your git repository, it must be
generated by your program.
* **DO NOT SUBMIT** any IDE package files.
* You must name your main program file `ac_test.java`.
* You must be able to compile your program by running
`javac ac_test.java`.
* You must be able to run your program by running `java ac_test`.
* You must fill out `info_sheet.txt`.
* Be sure to remember to push the latest copy of your code back to your GitHub
repository before the the assignment is due. At the deadline, the
repositories will automatically be copied for grading. Whatever is present
in your GitHub repository at that time will be considered your submission for
this assignment.
## Additional Notes:
* You are free to use any data structures written by you, the textbook authors,
or in the Java standard library to implement the user history symbol table.
However, if you use code that you do not write yourself, you must research
the runtime of that particular implementation and discuss that in your
`approach.txt`.
* Note that if your user history predictions contain a word that is also
contained in the dictionary predictions, this word should not be presented
as a suggestion to the user twice in the same prompt (e.g., the final list
of predictions in the example above).
* You do not need to implement any sort of autocorrect. You can assume
that each character entered by the user is intentional.
* You can assume that the user will not try to enter any numerical characters
aside from selecting a prediction.
## Grading Rubric:
| Feature | Points
| ——- | ——:
| DLB trie implemented as described in class | 20
| Dictionary predictions are correctly populated | 25
| User history predictions are correctly populated | 30
| Sufficient justification of user history approach | 10
| User interaction works as specified | 5
| Timing data presented | 5
| Assignment info sheet/submission | 5
