This assignment is designed to give you more experience programming in C and using the Unix
environment. Your task will be to write one program that implements a simple machine-learning
algorithm. This will require file I/O, dynamic memory allocation, and correctly implementing a
moderately complex algorithm.
Machine learning (ML) techniques are increasingly used to provide services, such as face recognition in photographs, spelling correction, automated translation, and predicting what YouTube
videos you might want to watch next. Implementing a full ML algorithm is beyond the scope of this
course, so you will implement a “one shot” learning algorithm that uses historical data to predict
house prices based on particular attributes.
For example, a house might have x1 bedrooms, x2 bathrooms, x3 square footage, and be built in
year x4. If we had appropriate weights, we could estimate the price of the house y with the formula
y = w0 + w1x1 + w2x2 + w3x3 + w4x4. (1)
The goal of one-shot learning is to find values for the weights wi using a large provided set
of training data. Once those weights have been found, they can be used to estimate prices for
For example, if the training data includes n houses and has k attributes, this data can be
represented as an n × (k + 1) matrix X, of the form
1 x0,1 x0,2 · · · x0,k
1 x1,1 x1,2 · · · x1,k
1 xn−1,1 xn−1,2 · · · xn−1,k
where each row corresponds to a house and each column corresponds to an attribute. Note that the
first column contains 1 for all rows: this corresponds to the weight w0.
Similarly, house prices can be represented as an n × 1 matrix Y , of the form
where each row gives the price of a house.
Finally, the weights will be a (k + 1) × 1 matrix W, of the form
where each row gives the weight of an attribute.
We can relate the matrices X, Y , and W with this equation:
XW = Y. (2)
Our goal will be to estimate the prices Y
for some houses with attributes X′
. This is easily done
if we know the weights W, but we do not. Instead, we can observe the attributes X for houses with
known prices Y . We will use a strategy known as one-shot learning to deduce W, given X and Y .
If X were a square matrix, we could find W by rewriting the equation as W = X−1Y , but in
general X will not be a square matrix. Thus, we will find its pseudo-inverse by calculating
W = (XT X)
−1XT Y, (3)
is the transpose of X. XT X is a square matrix, and can be inverted.1
Once W has been found, it can be used with a new set of house attributes X′
to estimate prices
for those houses by computing X′W = Y
Given matrices X and Y , your program will compute (XT X)
−1XT Y in order to learn W. This will
require (1) multiplying, (2) transposing, and (3) inverting matrices. Programming Assignment I
already involved matrix multiplication; you may adapt your implementation for this assignment.
Transposing an m × n matrix produces an n × m matrix. Each row of the X becomes a column
To find the inverse of XT X, you will use a simplified form of Gauss-Jordan elimination.
1.1 Gauss-Jordan elimination for finding inverses
Gauss-Jordan is a method for solving systems of equations by manipulating matrices with row
operations. This method can also be used to find the inverse of a matrix.
You will implement two of the three row operations in your program. The first multiplies all
elements of a particular row by some number. The second adds the contents of one row to another,
element-wise. More generally, the second operation adds a multiple of the elements of one row to
another so that element xi,k will become xi,k + axj,k.
The third row operation, which swaps two rows, will not be needed for this assignment. Again,
the training data used to grade this assignment will not require swapping rows.
Algorithm 1 is the implementation of Gauss-Jordan your program must implement. Note that
the only row operations used are multiplying (or dividing) a row by a number and adding (or
1This is not true in general, but for this assignment you may assume that XT X is invertable.
subtracting) a multiple of one row to another. Given a matrix M, we will use Mi to refer to row i of
M and Mi,j to refer to the number in row i, column j. For this assignment, we will start counting
rows and columns from 0.
Algorithm 1 Simplified Gauss-Jordan elimination
procedure invert(M : n × n matrix)
N ← n × n identity matrix
for p ← 0, 1, · · · , n − 1 do
f ← Mp,p
divide Mp by f
divide Np by f
for i ← p + 1, · · · , n − 1 do
f ← Mi,p
subtract Mp × f from Mi
subtract Np × f from Ni
for p ← n − 1, · · · , 0 do
for i ← p − 1, · · · , 0 do
f ← Mi,p
subtract Mp × f from Mi
subtract Np × f from Ni
To illustrate Gauss-Jordan, we will walk through the process of inverting the matrix
1 2 4
1 6 8
1 1 6
As a notational convenience, we will create an augmented matrix A = M|I by adjoining the identity
matrix I to M.
1 2 4 1 0 0
1 6 8 0 1 0
1 1 6 0 0 1
It is not necessary for your program to create A; instead, you can represent the two sides of A as
two separate matrices.
The goal of Gauss-Jordan is to turn the left half of A into an identity matrix by applying row
operations. At each step, we will identify a particular row as the pivot row. The element that lies
on the diagonal (that is, element Ap,p) is the pivot element.
The first step is to turn the M into an upper triangular matrix, where all elements on the
diagonal are 1 and elements below the diagonal are 0. The pivot row will start at A0 and advance
to A2. At each step, we will first multiply the pivot row by a constant so that the pivot element will
become 1. Next, we will subtract the pivot row from the rows below it, so that the elements below
the pivot element become 0.
Starting with row A0, we see that A0,0 is already 1. To make the elements below A0,0 become 0,
we subtract A0 from A1 and A2, yielding
1 2 4 1 0 0
0 4 4 −1 1 0
0 −1 2 −1 0 1
Next, for pivot A1 we see that A1,1 = 4. We divide A1 by 4 (that is, multiply A4 by 1
1 2 4 1 0 0
0 1 1 −
0 −1 2 −1 0 1
To zero out A2,1, we add A1 to A2.
1 2 4 1 0 0
0 1 1 −
0 0 3 −
Now the pivot row is A2. To make A2,2 = 1, we divide row A2 by 3.
1 2 4 1 0 0
0 1 1 −
0 0 1 −
A is now an upper triangular matrix. To turn the left side of A into an identity matrix, we will
reverse the process and turn the elements above the diagonal into 0. The pivot row will start at A2
and advance in reverse to A0. For each step, we will subtract the pivot row from the rows above it
so that the elements above the pivot element become 0.
We begin with A2. First, we subtract A2 from A1, yielding
1 2 4 1 0 0
0 1 0 1
0 0 1 −
Then we subtract 4 × A2 from A0, yielding
1 2 0 8
0 1 0 1
0 0 1 −
Now the elements above A2,2 are 0.
Next, we subtract 2 × A1 from A0, yielding
1 0 0 7
0 1 0 1
0 0 1 −
Now the element above A1,1 is 0.
After that step, the left half of A is the identity matrix and the right half of A contains M−1
That is, A = I|M−1
. The algorithm is complete and the inverse of M has been found.
You are strongly encouraged to write this algorithm in more detailed pseudocode before you begin
implementing it in C. Try using it to invert a small square matrix and make sure you understand
the operations you must perform at each step.
In particular, ask yourself (1) given a matrix A, how can I multiply (or divide) Ai by a constant
c, and (2) given a matrix A, how can I add (or subtract) c × Ai to (or from) Aj ?
You MUST use the algorithm as described. Performing different row operations, or the same
row operations in a different order, may change the result of your program due to rounding. This
may cause your program to produce results different from the reference result.
You will write a program estimate that uses a training data set to learn weights for a set of house
attributes, and then applies those weights to a set of input data to calculate prices for those houses.
estimate takes two arguments, which are the paths to files containing the training data and input
Training data format The first line will be the word “train”. The second line will contain an
integer k, giving the number of attributes. The third line will contain an integer n, giving the
number of houses. The next n lines will contain k + 1 floating-point numbers, separated by spaces.
Each line gives data for a house. The first k numbers give the values x1 · · · xk for that house, and
the last number gives its price y.
For example, a file train.txt might contain:
3.000000 1.000000 1180.000000 1955.000000 221900.000000
3.000000 2.250000 2570.000000 1951.000000 538000.000000
2.000000 1.000000 770.000000 1933.000000 180000.000000
4.000000 3.000000 1960.000000 1965.000000 604000.000000
3.000000 2.000000 1680.000000 1987.000000 510000.000000
4.000000 4.500000 5420.000000 2001.000000 1230000.000000
3.000000 2.250000 1715.000000 1995.000000 257500.000000
This file contains data for 7 houses, with 4 attributes and a price for each house. The corresponding
matrix X will be 7 × 5 and Y will be 7 × 1. (Recall that column 0 of X is all ones.)
Input data format The first line will be the word “data”. The second line will be an integer k,
giving the number of attributes. The third line will be an ineteger m, giving the number of houses.
The next m lines will contain k floating-point numbers, separated by spaces. Each line gives data
for a house, not including its price.
For example, a file data.txt might contain:
3.000000 2.500000 3560.000000 1965.000000
2.000000 1.000000 1160.000000 1942.000000
This contains data for 2 houses, with 4 attributes for each house. The corresponding matrix X
will be 2 × 5.
Output format Your program should output the prices computed for each house in the input
data using the weights derived from the training data. Each house price will be printed on a line,
rounded to the nearest integer.
To print a floating-point number rounded to the nearest integer, use the formatting code %.0f,
Usage Assuming the files train.txt and data.txt exist in the same directory as estimate:
$ ./estimate train.txt data.txt
Implementation notes The description of Gauss-Jordan elimination given in section 1.1 uses an
augmented matrix with twice as many columns as the input matrix X. This is an illustrative tool,
and not meant as an implementation requirement. It is simpler to use two matrices that begin as X
and the identity matrix I and apply identical row operations to both, until the first matrix becomes
I and the second is X−1
It is recommended to write separate functions to compute the inverse of a matrix, the transpose
of a matrix, and the product of two matrices. You may find it simpler to avoid allocating memory
within these functions; instead, pass them the input matrix or matrices and a pre-allocated matrix
that will be used for the output.
Having separate functions will simplify your development, as you can test your implementations
of each separately.
You MUST use double to represent the attributes, weights, and prices. Using float may result
in incorrect results due to rounding. To read double values from the training and input data files,
you can use fscanf with the format code %lf.
If estimate successfully completes, it MUST return exit code 0.
You MAY assume that the training and input data files are correctly formatted. You MAY
assume that the first argument is a training data file and that the second argument is an input data
file. However, checking that the training data file begins with “train” and that the input data file
begins with “data” may be helpful if you accidentally give the wrong arguments to estimate while
you are testing it. To read a string containing up to 5 non-space characters, you can use the fscanf
format code %5s.
estimate SHOULD check that the training and input data files specify the same value for k.
If the training or input files do not exist, are not readable, are incorrectly formatted, or specify
different values of k, estimate MAY print “error” and return exit code 1. Your code will not be
tested with these scenarios.
Your submission will be awarded up to 100 points, based on how many test cases your program
The auto-grader provided for students includes half of the test cases that will be used during
grading. Thus, it will award up to 50 points.
Make sure that your programs meet the specifications given, even if no test case explicitly checks
it. It is advisable to perform additional tests of your own devising.
3.1 Academic integrity
You must submit your own work. You should not copy or even see code for this project written by
anyone else, nor should you look at code written for other classes. We will be using state of the art
plagiarism detectors. Projects which the detectors deem similar will be reported to the Office of
Do not post your code on-line or anywhere publically readable. If another student copies your
code and submits it, both of you will be reported.
Your solution to the assignment will be submitted through Canvas. You will submit a Tar archive
file containing the source code and makefiles for your project. Your archive should not include any
compiled code or object files.
The remainder of this section describes the directory structure, the requirements for your
makefiles, how to create the archive, how to use the provided auto-grader, and how to create your
own test files to supplement the auto-grader.
4.1 Directory structure
Your project should be stored in a directory named src, which will contain (1) a makefile, and (2)
any source files needed to compile your program. Typically, you will provide a single C file named
for the program (estimate.c).
This diagram shows the layout of a typical project:
Note that your code and makefile go directly in src, without any subdirectories.
We will use make to manage compilation. Your src directory will contain a file named Makefile
that describes at least two targets. The first target must compile the program. An additional
target, clean, must delete any files created when compiling the program (typically just the compiled
The auto-grader script is distributed with an example makefile, which looks like this (note that
an actual makefile must use tabs rather than spaces for indentation):
TARGET = estimate
CC = gcc
CFLAGS = -g -std=c99 -Wall -Wvla -Werror -fsanitize=address,undefined
$(CC) $(CFLAGS) $^ -o $@
rm -rf $(TARGET) *.o *.a *.dylib *.dSYM
It is simplest to copy this file into your src directory, renaming it Makefile.
It is further recommended that you use make to compile your programs, rather than invoking the
compiler directly. This will ensure that your personal testing is performed with the same compiler
settings as the auto-grader. The makefiles created in the build directory by the auto-grader refer to
the makefiles you create in the source directory and therefore pick up any changes made.
You may add additional compiler options as you see fit, but you are advised to leave the compiler
warnings, sanitizers, and debugger information (-g). The makefile shown here specifies the C99
standard, in order to allow C++-style // comments; you may change that to C89, if you prefer.
Compiler options The sample makefile uses the following compiler options, listed in the CFLAGS
-g Include debugger information, used by GDB and AddressSanitizer.
-std=c99 Require conformance with the 1999 C Standard. (Disable GCC extensions.) You may
change this to -std=c89 or -std=c90 at you discretion.
-Wall Display most common warning messages.
-Wvla Warn when using variable-length arrays.
-Werror Promote all warnings to errors.
-fsanitize=address,undefined Include run-time checks provided by AddressSanitizer and UBSan.
This will add code that detects many memory errors and guards against undefined behavior.
(Note that these checks discover problems with your code. Disabling them will not make your
code correct, even if it seems to execute correctly.)
Target and dependency variables Note the use of $@ (indicating the target name) and $^
(indicating the dependencies). The auto-grader uses some advanced features of make to put the
source files and object files in different directories. If you prefer to write your own Makefile instead
of using the sample, you will need to use these variables in order for the auto-grader to successfully
compile your project. Contact me with any questions about how to do this.
4.3 Creating the archive
We will use tar to create the archive file. To create the archive, first ensure that your src directory
contains only the source code and makefiles needed to compile your project. Any compiled programs,
object files, or other additional files must be moved or removed.
Next, move to the directory containing src and execute this command:
pa2$ tar -czvf pa2.tar src/Makefile src/estimate.c
tar will create a file pa2.tar that contains your makefile and source code. (If you are using multiple
source files, or have re-named your source code, you will need to adjust this command accordingly.)
This file can now be submitted through Canvas.
To verify that the archive contains the necessary files, you can print a list of the files contained
in the archive with this command:
pa2$ tar -tf pa2.tar
You should also use the auto-grader to confirm that your archive is correctly structured.
pa2$ ./grader.py -a pa2.tar
4.4 Using the auto-grader
We have provided a tool for checking the correctness of your project. The auto-grader will compile
your programs and execute them several times with different arguments, comparing the results
against the expected results.
Setup The auto-grader is distributed as an archive file pa2_grader.tar. To unpack the archive,
move the archive to a directory and use this command:
$ tar -xf pa2_grader.tar
This will create a directory pa2 containing the auto-grader itself, grader.py, a library autograde.py,
a makefile template template.make, and a directory of test cases data.
Do not modify any of the files provided by the auto-grader. Doing so may prevent the auto-grader
from correctly assessing your program.
You may create your src directory inside pa1. If you prefer to create src outside the pa1
directory, you will need to provide a path to grader.py when invoking the auto-grader (see below).
Usage While in the same directory as grader.py and src, use this command:
The auto-grader will compile and execute the program in the directory src, assuming src has the
structure described in section 4.1. The compiled program will be in a directory build, which is
created by the auto-grader.
During development, you may prefer to use the –stop or -1 option, which produces more
program output but stops after the first failed test case.
pa2$ ./grader.py -1
To obtain usage information, use the -h option.
Program output By default, the auto-grader will not print the output from your programs,
except for lines that are incorrect. To see all program output for all unsuccessful tests, use the
–verbose or -v option:
pa2$ ./grader.py -v
To see program output for all tests, use -vv. To see no program output, use -q.
Disabling leak detection When run on an iLab machine, AddressSanitizer will automatically
provide leak detection, printing an error message if any memory allocated by your program has not
been freed by the time the program concludes. The auto-grader will report this as a failed test case.
To disable leak detection for a specific test run, use the option –lsan off.
pa2$ ./grader.py –lsan off
Checking your archive We recommend that you use the auto-grader to check an archive
before submitting. To do this, use the –archive or -a option with the archive file name. For
pa2$ ./grader.py -a pa2.tar
This will unpack the archive into a temporary directory, grade the programs, and then delete the
Specifying source directory If your src directory is not located in the same directory as
grader.py, you may specify it using the –src or -s option. For example,
pa2$ ./grader.py -s ../path/to/src
Refreshing the build directory In the unlikely event that your build directory has become
corrupt or otherwise unusable, you can simply delete it using rm -r build. Alternatively, the
–fresh or -f option will delete and recreate the build directory before testing.