Lab 0 2015

From 6.034 Wiki

Revision as of 17:50, 2 September 2008

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The purpose of this lab is to familiarize you with this term's lab system and to serve as a diagnostic for programming ability and facility with Python. 6.034 uses Python for all of its labs, and you will be called on to understand the functioning of large systems, as well as to write significant pieces of code yourself.

While coding is not, in itself, a focus of this class, artificial intelligence is a hard subject full of subtleties. As such, it is important that you be able to focus on the problems you are solving, rather than the mechanical code necessary to implement the solution.

If Python doesn't come back to you by the end of this lab, we recommend that you seek extra help through the Course 6/HKN tutoring program, which matches students who want help with students who've taken and done well in a class. The department pays the tutor, and the program comes highly recommended.

Python resources

Some resources to help you knock the rust off of your Python:

• Any of the many good Python handbooks out there, such as:
  • Dive Into Python, for experienced programmers
  • O'Reilly's Learning Python
  • Think Python, for beginning programmers
• The standard Python documentation, at [1] (the Library Reference and the Language Reference are particularly useful, if you know what you're looking for)
• Course 6/HKN tutoring program

Python

There are a number of versions of Python available. This course will use standard Python ("CPython") from http://www.python.org/. If you are running Python on your own computer, you should download and install Python 2.5 from http://www.python.org/download/ .

You can run the Python interpreter on Athena like this:

 add python
 idle &

You can, of course, edit Python files in a plain-text editor, and run them on Athena like this:

 add python
 python "[name-of-file]"

Note that "idle" does not currently run on Solaris Athena machines; the current Athena Solaris version of Python is too old to support it. In general, we recommend that you use Linux Athena when possible. We will support Solaris machines, but you'll probably have a better experience running on your own personal computer or on Linux Athena machines.

Getting the lab code

If you are working on Athena

The code for the labs is in the 6.034 locker. You can get lab 0 like this:

 mkdir -p ~/6.034-labs/lab0/
 cp -R /mit/6.034/www/labs/lab0/* ~/6.034-labs/lab0/

Then, you can edit the code in your ~/6.034-labs/lab0 directory. That way, you won't need to do anything to submit the code - it will already be in the right place.

If you are working on another computer with Python

Create a folder for the lab.

Download this file and extract it: http://web.mit.edu/6.034/www/labs/lab0/lab0.zip

You can also view the code without downloading it: http://web.mit.edu/6.034/www/labs/lab0/

Getting the Submit Key

In order to submit your labs, you must download a "key.py" file and place it in the same directory as your labs. The "key.py" file contains login information used by the tester to identify you personally to the testing server.

You can download a key from https://6.034.scripts.mit.edu:444/fall08/tester/ . Make sure that you have an up-to-date MIT Certificate before going to this page. Note that the page doesn't currently work in Apple's Safari Web browser (because of a bug in Safari regarding certificates); use Firefox instead, or download the file on Athena.

Answering questions

The main file of this lab is called lab0.py. Open that file in IDLE. The file contains a lot of incomplete statements that you need to fill in with your solutions.

The first thing to fill in is a multiple choice question. The answer should be extremely easy. Many problem sets will begin with some simple multiple choice questions to make sure you're on the right track.

Run the tester

Every lab comes with a file called tester.py. This file checks your answers to the problem set. For problems that ask you to provide a function, the tester will test your function with several different inputs and see if the output is correct. For multiple choice questions, the tester will tell you if your answer was right. Yes, that means that you never need to submit wrong answers to multiple choice questions.

The tester has two modes, "offline" and "online". The former runs some basic, self-contained internal tests on your code. It can be run as many times as you would like. The latter uploads your code to the 6.034 grader for grading. You can only run it once; so make sure you are finished before using it!

To run either tester, first open a Terminal window and cd to the directory containing the files for Lab 0. Then, run:

• python tester.py offline

to run the offline tester, and

• python tester.py online

to run the online tester.

You should run the tester early and often, and definitely make sure you pass all the tests before you submit a lab. Think of it as being like the "Check" button from 6.01. It makes sure you're not losing points unnecessarily.

Python programming

Now it's time to write some Python.

Warm-up stretch

Write the following functions:

• cube(n), which takes in a number and returns its cube. For example, cube(3) => 27.
• factorial(n), which takes in a non-negative integer n and returns n!, which is the product of the integers from 1 to n. (0! = 1 by definition.)

We suggest that you should write your functions so that they raise nice clean errors instead of dying messily when the input is invalid. For example, it would be nice if factorial rejected negative inputs right away; otherwise, you might loop forever. You can signal an error like this: raise Exception, "factorial: input must not be negative"

Error handlng doesn't affect your problem set grade, but on later problems it might save you some angst when you're trying to track down a bug.

• count-pattern(pattern lst), which counts the number of times a certain pattern of symbols appears in a list, including overlaps. So count-pattern( ('a', 'b'), ('a', 'b', 'c', 'e', 'b', 'a', 'b', 'f')) should return 2, and count-pattern(('a', 'b', 'a'), ('g', 'a', 'b', 'a', 'b', 'a', 'b', 'a')) should return 3.

Expression depth

One way to measure the complexity of a mathematical expression is the depth of the expression describing it in Python lists. Write a program that finds the depth of an expression.

For example:

• depth('x') => 0
• depth(('expt', 'x', 2)) => 1
• depth(('+', ('expt', 'x', 2), ('expt', 'y', 2))) => 2
• depth(('/', ('expt', 'x', 5), ('expt', ('-', ('expt', 'x', 2), 1), ('/', 5, 2)))) => 4

Note that you can use the built-in Python "isinstance()" function to determine whether a variable points to a list of some sort. "isinstance()" takes two arguments: the variable to test, and the type (or list of types) to compare it to. For example:

>>> x = [1, 2, 3]
>>> y = "hi!"
>>> isinstance(x, (list, tuple))
True
>>> isinstance(y, (list, tuple))
False

Tree reference

Your job is to write a procedure that is analogous to list referencing, but for trees. This "tree-ref" procedure will take a tree and an index, and return the part of the tree (a leaf or a subtree) at that index. For trees, indices will have to be lists of integers. Consider the tree in Figure 1, represented by this Python tuple: (((1, 2), 3), (4, (5, 6)), 7, (8, 9, 10))

To select the element 9 out of it, we’d normally need to do something like tree[3][1]. Instead, we’d prefer to do tree-ref(tree, (3, 1)) (note that we’re using zero-based indexing, as in list-ref, and that the indices come in top-down order; so an index of (3, 1) means you should take the fourth branch of the main tree, and then the second branch of that subtree). As another example, the element 6 could be selected by tree-ref(tree, (1, 1, 1)).

Note that it’s okay for the result to be a subtree, rather than a leaf. So tree-ref(tree (0,)) should return ((1, 2), 3).

Symbolic algebra

Throughout the semester, you will need to understand, manipulate, and extend complex algorithms implemented in Python. You may also want to write more functions than we provide in the skeleton file for a lab.

In this problem, you will write a function def distribute(lst) that applies the distributive law of arithmetic to a symbolic mathematical expression. For example, it would take something like 5*(3+4) and rewrite it as 5*3+5*4.

This procedure would be one small part of a symbolic math system, such as the integrator presented in Monday's lecture.

The function will take a list of the form:

[5, '*', ['x', '+', 4]]

It will return a list of the form:

[5, '*', 3, '+', 5, '*', 4]

Your code should return a list that evaluates to the same value as the input list. It should also deal correctly with nested expressions like the following:

[5, *, [3, '+', 'x', '*', [4, '+', 2]]

should become something like:

[5, '*', 3, '+', 5, '*', 'x', '*', 4, '+', 5, '*', 'x', '*', 2]

You do not have to simplify expressions if you don't want to, although you may if you so choose.

The only operators that will be used are '+' and '*', representing addition and multiplication, respectively. Everything else is a variable or a number. None of the test cases will involve parenthetical expressions being multiplied together (such at [['a', '+', 'b'], '*', ['c', '+', 'd']]).

Survey

We are always working to improve the class. Most labs will have at least one survey question at the end to help us with this. Your answers to these questions are purely informational, and will have no impact on your grade.

Please fill in the answers to the following questions in the definitions at the end of lab0.py:

• When did you take 6.01?
• How many hours did 6.01 projects take you?
• How well do you feel you learned the material in 6.01?
• How many hours did this problem set take you?

When you're done

Remember to run the tester! The tester will automatically upload your code to the 6.034 server for grading and collection.

Questions?

If you have questions about the lab, e-mail Rob Speer (rspeer@mit).