CS62 - Fall 2020 - Class 6
Example code in this lecture
GradeGenerator
AsymptoticsExamples
Lecture notes
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- Darwin part 1 (World and Species)
- Due Tuesday
- My office hours tomorrow:
- 3:30-4pm
- Where we are
- covered most of the "big" new things in Java
- We'll continue to introduce a few random Java things each class, but we will now transition to the data structures side of things
Exceptions wrap-up
- How to we raise our own exceptions?
- throws
- similar to return except must the value after the throws statement must be an exception (actually, something that inherits from "Throwable")
- Look at:
https://www.manishsanger.com/wp-content/uploads/2018/03/Exception-Hierarchy.png
- Look at the checkRowCol method in the Matrix class from Darwin
- If we find a bad argument to get/set, we throw a new IllegalArgumentException
- IllegalArgumentException is a class, so we have to create a new instance before we throw it
throws
- there is another way to handle checked exceptions besides adding a try/catch block
- let someone else handle it! :)
- You can add "throws NameOfException" at the end of a method declaration and that indicates to any method that calls you that either 1) you explicitly throw that exception or 2) that you call a method that throws that exception
- If you do this, you're off the hook for having the catch the exception
- Look at GradeGeneratorFileThrows class from
GradeGenerator code
this(...)
- We'll often have multiple constructors in a class. Why?
- Allow for different ways of creating the object
- Often, allow for having versions that specify more details
- It can be convenient in these cases to call one of the constructors from another constructor
- there's a special syntax to do this because we can just say new ..., since that would create a whole new instance of the class
- Look at the Matrix class from Darwin
- We have two constructors
- The first one simply calls the second one with the default parameters
- the "this" call (like a super call), must be the first line in the constructor
char
- one of the other built in types for representing a single character
- to create them use single quotes
char c = 'a';
- returned by some of the String methods, e.g.
String s = "...";
char first = s.charAt(0);
look at the sum method in
AsymptoticsExamples code
- How long will it take if we pass it an array with 1000 numbers in it? 10,000 numbers?
- We don't know!
- We could time it and find out
- Even if you time it, can you say conclusively how long it will take?
- No!
- Variation from run to run
- Depends on the computer
- etc.
- If I tell you the time it took on 10,000 numbers was t. Could you tell me approximately how long it would take on 20,000 numbers?
- would take about twice as long, i.e., 2t
- why?
- does a linear pass through the data
- doubling the size of the data, means about twice as much work
Asymptotics
- Precisely calculating the actual cost of a method is tedious and not generally useful
- Different operations take different amounts of time. Even from run to run, things such as caching, etc. will complicate things
- Want to identify categories of algorithmic runtimes
- What we really want to do is compare different algorithms
- Want to know which algorithms will be much worse (or much better) than others
Big O
- We write that an algorithm's run-time is O(g(n)) if the run-time can be bounded as n gets larger by some constant times g(n)
- For example, sum is O(n), i.e. linear
look at the lastElement and sumProduct method in
AsymptoticsExamples code
- What do they do?
- What are their Big O running times?
- Put another way, how does their run-time grow as you increase the size of the input?
- lastElement
- O(1), aka constant
- no matter how large the array is (assuming .length is a constant time operation) it always does the same amount of work
- sumProduct
- O(n^2), aka quadratic
- for each element, it must do a linear amount of work
if we know that sumProduct on an array of size 10,000 takes time t. Could you tell me approximately how long it would take on 20,000 numbers?
- about 4t
- We can figure this out by plugging our time into the O equation:
- t time for size of size x array
g(n) = n^2
g(10000) = 10000^2 = t
- doubling the size means:
g(20000) = 20000^2 = (2 * 10000)^2 = 4 * 10000^2 = 4t
- the time will roughly quadruple if we double the size
How does Big-O notation allow us to ignore irrelevant details?
- look at the doubleSum method in
AsymptoticsExamples code
- What is the Big O runtime of this method?
- O(n)
- How does it's runtime compare to that of sum?
- twice as long (calls sum twice)
- Even though doubleSum is twice as slow as sum, they're still in the same category since they will roughly grow at the same rate
Show running time table
https://cs.pomona.edu/classes/cs62/lectures/big_O.jpg