Lecture Code #
Code from this lecture available at https://github.com/Berkeley-CS61B/lectureCode-fa22/tree/main/lec7_lists4.
Lists vs. Arrays Our
DLList has a drawback. Getting the i’th item is slow;
we have to scan through each item in the list, starting from the beginning or
the end, until we reach the i’th item. For an array named
A, however, we can
quickly access the i’th item using bracket notation,
A[i]. Thus, our goal is
to implement a list with an array.
AList will have the same API as our
DLList, meaning it will
have the same methods as
get(int i)). The
AList will also have a
size variable that tracks its
AList Invariants There are a few invariants for our
addLast: The next item we want to add, will go into position
getLast: The item we want to return is in position
size - 1.
size: The number of items in the list should be
Implementing AList Each
AList has an
addLast, we place our item in
getLast, we simply return
items[size - 1].
removeLast, we simply decrement
size(we don’t need to change
items). Thus, if
addLastis called next, it simply overwrites the old value, because size was decremented. However, it is good practice to null out objects when they are removed, as this will save memory. Notice how closely these methods were related to the invariants.
Abstraction One key idea of this course is that the implementation details can be hidden away from the users. For example, a user may want to use a list, but we, as implementers, can give them any implementation of a list, as long as it meets their specifications. A user should have no knowledge of the inner workings of our list.
Array Resizing When the array gets too full, we can resize the array. However, we have learned that array size cannot change. The solution is, instead, to create a new array of a larger size, then copy our old array values to the new array. Now, we have all of our old values, but we have more space to add items.
Resizing Speed In the lecture video, we started off resizing the array by one more each time we hit our array size limit. This turns out to be extremely slow, because copying the array over to the new array means we have to perform the copy operation for each item. The worst part is, since we only resized by one extra box, if we choose to add another item, we have to do this again each time we add to the array.
Improving Resize Performance Instead of adding by an extra box, we can instead
create a new array with
size * FACTOR items, where
FACTOR could be any number,
like 2 for example. We will discuss why this is fast later in the course.
Downsizing Array Size What happens if we have a 1 million length array, but we remove 990,000 elements of the array? Well, similarly, we can downsize our array by creating an array of half the size, if we reach 250,000 elements, for example. Again, we will discuss this more rigorously later in the course.
Aside: Breaking Code Up Sometimes, we write large methods that do multiple things. A better way is to break our large methods up into many smaller methods. One advantage of this is that we can test our code in parts.
Generic AList Last time, we discussed how to make a generic
DLList. We can
do something similar for
AList. But we find that we error out on array creation.
Our problem is that generic arrays are not allowed in Java. Instead, we will
change the line:
items = new Item;
items = (Item) new Object;
This is called a cast, and we will learn about it in the future.
We did not touch upon the method
AList. Think of some of the problems you would experience in writing an
addFirstmethod, and think of some potential solutions.
Once you think of three potential solutions, evaluate their strengths and pick an approach to attempt that you feel is the strongest. Why did you pick that approach? Would you be able to convince your peer that the implementation you picked was the optimal choice?
Implement the approach you selected in the conceptual questions.
Do the bonus question from this slide