Sunday, 31 March 2013

Fibonacci numbers in Python and Java

Fibonacci numbers,  introduced by Leonardo Pisano Bigollo nearly a thousand years ago, were used to solve a fascinating puzzle about the growth of an idealised rabbit population. Below are the first 11 Fibonacci numbers Fn where n=0, 1, 2,..., 10:

sequenceF0F1F2F3F4F5F6F7F8F9F10
number011235813213455

or we can describe the Fibonacci numbers as
Fn = Fn-1 + Fn-2 (when n>=2)
To write a program to return a Fibonacci number given a sequence, there are many possible implementations. I shall present 2 solutions implemented in both Python and Java, where the first one is using recursion, and the second one is using a while loop, however, the performances of these 2 solutions are drastically different when the sequence is bigger.
Below are the results when running a Python test script. The recursion way is already showing very poor performance comparing to the while loop way.
> python test_fib.py 
.Working out Fibonacci(40)=102334155 recursively took 67.310534 seconds
.Working out Fibonacci(40)=102334155 with while loop took 1.8e-05 seconds
Working out Fibonacci(50)=12586269025 with while loop took 1.6e-05 seconds
Working out Fibonacci(60)=1548008755920 with while loop took 1.8e-05 seconds
..
----------------------------------------------------------------------
Ran 4 tests in 67.312s

OK
If we look at the results when running a similar timed test in Java, the same recursion is not as bad as in Python, though still far poorer than the while loop implementation.
> /somewhere/jdk1.6.0_33/bin/java -classpath ".:" fun.Fib
Working out Fibonacci(40)=102334155 recursively took 408 ms
Working out Fibonacci(40)=102334155 with while loop took 2033 ns
Working out Fibonacci(50)=12586269025 with while loop took 2115 ns
Working out Fibonacci(60)=1548008755920 with while loop took 2368 ns
The reason why recursion is slow in this case is because there are a lot of duplicated operations as the sequence grows. For example, to solve F(5), we need to solve F(4) once, F(3) twice, F(2) three times, and F(1) twice, as shown below

or the number of recursion required given a sequence n is
[1+(n-2)]*(n-2)/2 + (n-3) or n2/2-n/2-2  (when n>=4)
The complexity of using recursion is then actually O(n2), where, on the other hand, the while loop is O(n).
Below are the source code that are used in the test.
fib.py
import datetime


class Fib:
    def recursively(self, n):
        if n == 0:
            return 0
        elif n <= 2:
            return 1
        else:
            return self.recursively(n - 1) + self.recursively(n - 2)

    def whileLoop(self, n):
        if n == 0:
            return 0
        previous, fib, currentIndex = 0, 1, 1
        while currentIndex < n:
            previous, fib = fib, previous + fib
            currentIndex += 1

        return fib

    def timedRecursively(self, n):
        start = datetime.datetime.now()
        result = self.recursively(n)
        end = datetime.datetime.now()
        elapsed = end - start
        print "Working out Fibonacci({})={} recursively took {} seconds".format(str(n), str(result), str(elapsed.total_seconds()))
        return result

    def timedWhileLoop(self, n):
        start = datetime.datetime.now()
        result = self.whileLoop(n)
        end = datetime.datetime.now()
        elapsed = end - start
        print "Working out Fibonacci({})={} with while loop took {} seconds".format(str(n), str(result), str(elapsed.total_seconds()))
        return result

test_fib.py
import unittest
from fib import Fib


class TestFib(unittest.TestCase):
    def setUp(self):
        self.fib = Fib()

    def test_recursively(self):
        self.assertEqual(self.fib.recursively(0), 0)
        self.assertEqual(self.fib.recursively(1), 1)
        self.assertEqual(self.fib.recursively(2), 1)
        self.assertEqual(self.fib.recursively(3), 2)
        self.assertEqual(self.fib.recursively(4), 3)
        self.assertEqual(self.fib.recursively(5), 5)
        self.assertEqual(self.fib.recursively(6), 8)
        self.assertEqual(self.fib.recursively(7), 13)
        self.assertEqual(self.fib.recursively(8), 21)
        self.assertEqual(self.fib.recursively(9), 34)
        self.assertEqual(self.fib.recursively(10), 55)

    def test_whileLoop(self):
        self.assertEqual(self.fib.whileLoop(0), 0)
        self.assertEqual(self.fib.whileLoop(1), 1)
        self.assertEqual(self.fib.whileLoop(2), 1)
        self.assertEqual(self.fib.whileLoop(3), 2)
        self.assertEqual(self.fib.whileLoop(4), 3)
        self.assertEqual(self.fib.whileLoop(5), 5)
        self.assertEqual(self.fib.whileLoop(6), 8)
        self.assertEqual(self.fib.whileLoop(7), 13)
        self.assertEqual(self.fib.whileLoop(8), 21)
        self.assertEqual(self.fib.whileLoop(9), 34)
        self.assertEqual(self.fib.whileLoop(10), 55)

    def test_timedRecursively(self):
        self.assertEqual(self.fib.timedRecursively(40), 102334155)

    def test_timedWhileLoop(self):
        self.assertEqual(self.fib.timedWhileLoop(40), 102334155)
        self.assertEqual(self.fib.timedWhileLoop(50), 12586269025)
        self.assertEqual(self.fib.timedWhileLoop(60), 1548008755920)

unittest.main()

Fib.java
package fun;

public class Fib {


    public static void main(String[] args) {
        recursively(1);
        whileLoop(1);

        long start = System.nanoTime();

        long result = recursively(40);
        long elapsed = (System.nanoTime() - start) / 1000000;
        System.out.println(String.format("Working out Fibonacci(%s)=%s recursively took %s ms", 40, result, elapsed));

        start = System.nanoTime();
        result = whileLoop(40);
        elapsed = (System.nanoTime() - start);
        System.out.println(String.format("Working out Fibonacci(%s)=%s with while loop took %s ns", 40, result, elapsed));

        start = System.nanoTime();
        result = whileLoop(50);
        elapsed = (System.nanoTime() - start);
        System.out.println(String.format("Working out Fibonacci(%s)=%s with while loop took %s ns", 50, result, elapsed));

        start = System.nanoTime();
        result = whileLoop(60);
        elapsed = (System.nanoTime() - start);
        System.out.println(String.format("Working out Fibonacci(%s)=%s with while loop took %s ns", 60, result, elapsed));
    }

    static long recursively(long i) {
        if (i == 0) return 0;
        if (i <= 2) return 1;
        else return recursively(i - 1) + recursively(i - 2);
    }

    static long whileLoop(long i) {
        long previous = 0, fib = 1, currentIndex = 1;
        while (currentIndex < i) {
            long newFib = previous + fib;
            previous = fib;
            fib = newFib;
            currentIndex++;
        }
        return fib;
    }
}

Saturday, 30 March 2013

First impression of Python

Born some two decades ago, Python has certainly accumulated enough interests to become one of the most popular programming languages used in the financial industry.

Designed to be a write-less-do-more language, Python has certain aspects that differ from Java:
  1. It is an interpreted language, which means no compilation is required. 
  2. It is not a strongly typed language
  3. Its statement grouping is done by indentation instead of open and close braces
  4. It does not require declaration of variable or argument
  5. It supports complex number, for example (using Python interactive interpreter)
  6. >>> 1j + 2J
    (-2+0j)
    >>> (1+2j) * complex(2,3)
    (-4+7j)
    
  7. Strings can be subscripted. For example, the first character of a string has index 0.
  8. >>> word="HelloWorld"
    >>> word[4]
    'o'
    >>> word[1:3]   # using the slice notation, returning characters from index 1 to 2
    'el'
    
  9. It supports multiple assignment, for example, 'a,b=b,a+b', where the expression on the right-hand side 'b,a+b' are evaluated first before any of the assignments take place
  10. Not surprisingly, it supports lamda forms
  11. >>> def incresedBy(n):
    ...     return lambda x: x + n
    ...
    >>> f = incresedBy(42)
    >>> f(1)
    43
    
  12. It supports map and reduce functions as part of its functional programming offerings
  13. It supports Tuples, which are immutable and usually contain an heterogeneous sequence of elements
  14. >>> t=123,321,'hello'
    >>> t
    (123, 321, 'hello')
    
  15. It allows multiple inheritance, for example
  16. class DerivedClassName(Base1, Base2, Base3):
        <statement-1>
        .
        .
        .
        <statement-N>
    

Useful Sed command examples in UNIX

  1. Substitute a regular expression into a new value
  2. A simple example is to change the first occurrence of 'old' on each line from file1 to 'new' and save as file2
    sed 's|old|new|' < file1 > file2
    where 's' is the substitute command, '|' is the delimiter, 'old' is the search pattern in regular expression, and 'new' is the replacement string. We can also test string substitution using 'echo' like this
    echo "old, and some other old stuffs" | sed 's|old|new|'
    which will output 'new, and some other old stuffs'. Note that in this case it only replace the first occurrence of 'old' with 'new'. If we want to replace all 'old' with 'new', we could append a global replacement symbol 'g' after the last delimiter '|'
    echo "old, and some other old stuffs" | sed 's|old|new|g'
    which will output 'new, and some other new stuffs'
  3. Pick any delimiter you like
  4. We were using '|' as the delimiter in the above examples. In fact, we can pick any character as the delimiter, as long as it is not in the string we are looking for. For example,
    echo "old, and some other old stuffs" | sed 's/old/new/g'
    echo "old, and some other old stuffs" | sed 's:old:new:g'
    
    have the same effect.
  5. Use & as the matched string
  6. We could search for a pattern and add some characters on top of the original string like this
    echo "old, and some other old stuffs" | sed 's|[a-z]*|(&)|'
    which will output '(old), and some other old stuffs'. The special character '&' represents the matching pattern. We could have any number of '&' in the replacement string
    echo "old, 123 and 456 some other old stuffs" | sed -r 's|[0-9]+|& &|'
    which will output 'old, 123 123 and 456 some other old stuffs'. Note that we use '-r' here to indicate we are using extended regular expression such that sed will support the use of '+'.

Friday, 29 March 2013

Useful Find command examples in UNIX

Find command is one of the most useful commands in UNIX, as there are many options out there to accomplish various search tasks.

  • Find files which have been modified for more than or less than a certain time
  • We can use 'find * -mtime n' to search files under current directory based on modification time. The numeric argument n can be specified as +n, meaning greater than n days; -n, meaning less than n days; or n, meaning exactly n days. For example,
    to find files which were last modified for more than 30 days ago
    find . -mtime +30
    to find files which were last modified for less than 15 days ago
    find . -mtime -15
    to find files which were last modified for exactly 10 days ago
    find . -mtime 10
    There are other options like
    -mmin n, which refers to file's data was last modified n minutes ago
    -amin n, which refers to file's data was last accessed n minutes ago
    -atime n, which refers to file's data was last accessed n days ago
    -type f, which searches for regular file
    -type d, which searches for directory,
    -exec command, which executes command by passing in the find results,
    -perm /mode, which searches files with given permission 
    
  • Remove files which are older than n days
  • Since we understand how to search for files which were last modified for more than n days, we can then use the rm command to remove them
    find /some-dir/* -mtimes +30 -type f -exec rm {} \;
    
    '-exec' is used to pass in commands like 'rm'; '{}' is replaced by the file name that 'find' returns; and the '-exec' command has to end with '\;'.
    Another way will be
    find /some-dir/* -mtimes +30 -type f -print0 | xargs -0 rm
    where 'xargs' reads 'find' results and execute 'rm' on each of the 'find' results
  • Find files which contain particular strings
  • Below is an example to search all txt files starting from current directory for string "some-string"
    find . -name "*.txt" -print | xargs grep "some-string"
    

Using Python SimpleHTTPServer to share files

There are cases when I want to share some files within the intranet to other users.

Python comes with a simple built-in module called SimpleHTTPServer that can turn any directory in the system into the web server directory. I literally just need a single command line to accomplish this task, given Python is shipped with openSUSE!

Assuming I am running linux, and I want to share my home directory ~
cd ~
Start up the http server on port 10001
youyang@monkey-dev-01:~> python -m SimpleHTTPServer 10001
Now the http server is running on port 10001. Open a browser and type the following address, where monkey-dev-01 happens to be my hostname
http://monkey-dev-01:10001
Since there is no index.html under my home directory, the files in the home directory will be listed. Now my purpose is served, but there is one slight problem -- the program is running on the foreground, meaning I can't do anything on that open terminal as it continues to display the http requests when I browse the directories. To change the running process from foreground to background, I need to press CTRL+Z to pause the process, as shown below
^Z
[1]+  Stopped                 python -m SimpleHTTPServer 10001
and type 'bg' to move the process to run in the background
youyang@monkey-dev-01:~> bg
[1]+ python -m SimpleHTTPServer 10001 &
We can also use 'jobs -l' to display the jobs in current session
youyang@monkey-dev-01:~> jobs -l
[1]+  4127 Running                 python -m SimpleHTTPServer 10001 &
We can also start up the http server running in the background directly, by appending a '&' at the end of the command
youyang@monkey-dev-01:~> python -m SimpleHTTPServer 10001&

Unix Cheat Sheet


List a directoryAlternatively, type 'ls --help' or 'man ls' for more information
ls [path]list given path without any options. Without the path, it is listing current directory
ls -l [path]list given path in long listing format
ls -h [path]list given path in human readable format
ls -a [path]list given path, including hidden files (which has prefix dot)
ls -lrt [path]list given path, combining different options. long listing format, sort by modification time in reverse order
ls [path] list given path, redirect listing results into a file
ls [path] | morelist given path, showing listing results on one screen at a time

Change to a directory
cd <directory>change to the given directory
cd ~change to user's home directory
cd /change to root directory
cd ..go to parent directory
cd ../<directory>go to a sibling directory

Print current working directory
pwddisplay the current working directory

Create directory(ies)
mkdir <directory>create the directory under current working directory
mkdir -p <dir1>/<dir2>create directory2 under directory1; create parent directory2 as needed

Remove files or directory(ies)
rm <file>remove the file
rm -rf <directory>remove the contents of given directory recursively without prompt

Move a file or a directory
mv <source> <dest>rename source as dest, and move source to dest

Copy a file or a directory
cp <source> <dest>make a copy from source to dest
cp -r <source> <dest>copy recursively from source directory to dest directory

Download a file from http website
wget <url>download the resource to current directory
curl <url>download the resource to current directory

View a text file
less <file>view file page by page, with a lot of features
more <file>view file with screen length
cat <file>view file, but it will scroll to the end of file
cat <file> | moreview file, and piped the file content to more command

Create a text file
touch <file>create an empty file
vi <file>use vi text editor to create a file (need to save the file though)
echo "foo" >> <file>create a file with content 'foo'
cat > <file>enter multi-line texts and press control-d to save the content to a file

Change a file's modification or access time
stat <file1>display the stat of given file
touch -m -d '1 Mar 2013 02:53' file.txtchange the modification time of file.txt to be '1 Mar 2013 02:53', and create the file if it doesn't exist
touch -a -d '1 Mar 2013 02:53' file.txtchange the access time of file.txt to be '1 Mar 2013 02:53', and create the file if it doesn't exist

Compare two files
diff <file1> <file2>show the differences between file1 and file2
sdiff <file1> <file2>show file1 and file2 side by side

Pipes and redirections
<command> > <file>redirect command output to a file, e.g. ll > file.txt writes current directory listings to file.txt
<command> >> <file>appends command output to the end of a file
<command> < <file>redirect the standard input from the file
<command> < <file1> > <file2>redirect the standard input from the file1, and then redirect the standard output to file2
<command1> | <command2>pipe the output of command1 as the input of command2

File permissionsread=4, write=2, execute=1
chmod 700 <file(s)>file(s) owner can read, write and execute
chmod 600 <file(s)>file(s) owner can read and write, but not execute
chmod 400 <file(s)>file(s) owner can read only
chmod 755 <file(s)>file(s) owner can read, write and execute; both group owner and others can read and write
chmod 644 <file(s)>file(s) owner can read and write; both group owner and others can read only
chmod u+x <file(s)>grant file(s) owner execute permission
chmod u-x <file(s)>remove execute permission from file(s) owner
chmod a+rw <file(s)>grant everyone read and write permission to the file(s)

Other useful comands
df -hreport file system disk space usage in human readable format
du -hestimate file space usage in human readable format
datedisplay or set the current system date and time
topdisplay linux tasks, as well as CPU and memory stats
ps -aefdisplay a snapshot of current processes
ln -s <target> <link_name>create a symbolic link to the target with link_name
aliasdisplay current defined aliases or create an alias
!<command>invoke previous run of the given command

Wednesday, 20 March 2013

Most import aspects of software development

After a few years of software development, it is a good time to recap what are the most important aspects that I should be aware of when working any project.
  1. The code base should be well tested that it has unit-tests, integration/acceptance-tests, external/smoke-tests. Unit test is about testing a single class by mocking its dependencies if necessary. Integration or acceptance tests should be testing the APIs provided by your application, particular front-to-back workflows, story-based features (as someone, I should be able to do something, etc.). External or smoke tests are verifying that the contracts between your application and your external dependencies are intact. There are a few reasons why automated tests are important:
    1. developers have the confidence to refactor or make functional changes as they can run the automated tests to check whether there are any breaking changes
    2. no more painful and error prone manual tests conducted by the QA team -- cost savings, although UAT (User-Acceptance Test) is still required
    3. greatly shortens the release cycle because you spend a lot less time doing the manual tests
  2. Modeling of the business objects is important because it not only affects how easy it is to query your business objects from the persistent layer, but also the performance of querying. One of the project that I worked on was storing a map of data into an oracle database as a CLOB data type (imagine a map is marshalled as XML string and stored as a DB column). It turned out to be rather inefficient to query any field inside that CLOB column, as you have to load the whole CLOB to your application!
  3. Availability and scalability are also crucial to any production system. Even Facebook does not guarantee that their query engine is available 100% of time (as they still have to do the manual failover of their name nodes, part of their Hadoop DFS cluster, from time to time). I won't treat Facebook as a mission critical application that has to be available all the time, but you can sense that to achieve a 100% availability, you will have to really think about your failover or DR (Disaster-Recovery) plan. Scalability is a tricky topic, as you can either scale horizontally by adding more nodes, or scale vertically by adding more cores/memories to the existing nodes (further reading on Amdahl's law). Depends on where the bottleneck of growth is, you might have database scalability, application scalability, caching scalability, etc.

Tuesday, 19 March 2013

An efficient Stack implementation with getMin() and getMax() methods

When we use stack to keep a collection of numbers, we might want to know what are the minimum and maximum numbers in the stack. 

Suppose we are pushing {72,12,55,78,22,34} into a stack, this is what happens

Action Stack Elements (Top ... Bottom) Min Max
push(72)727272
push(12)12,721272
push(55)55,12,721272
push(78)78,55,12,721278
push(22)22,78,55,12,721278
push(34)34,22,78,55,12,721278

If we pop the stack above 3 times, the max should change to 72; if we pop 2 more times, the min should change to 72. It sounds like we need a data structure that keeps the current min and max when we push or pop the stack. Indeed, a stack is a perfect choice here. When pushing a number to the stack, we also push the current min and max values into the min and max stacks respectively. Similarly, when poping out a number from the stack, we also pop out from the min and max stacks, such that the top of the min and max stacks will always have the min and max values of the current stack respectively.

The complexity of the getMin() and getMax() methods are O(1), regardless of how many items are in the stack, same as the push(item) and pop() methods.

Here is an implementation of the Stack with getMin() and getMax() methods
import java.util.Stack;
import java.util.concurrent.locks.ReentrantReadWriteLock;

public class SmartStack<E extends Comparable<E>> {

    private ReentrantReadWriteLock readWriteLock = new ReentrantReadWriteLock();
    private ReentrantReadWriteLock.ReadLock readLock = readWriteLock.readLock();
    private ReentrantReadWriteLock.WriteLock writeLock = readWriteLock.writeLock();

    private Stack<E> mins = new Stack<E>();
    private Stack<E> maxs = new Stack<E>();
    private Stack<E> delegate = new Stack<E>();

    public E push(E item) {
        writeLock.lock();
        try {
            // stores the current min to the mins stack
            if (mins.isEmpty() || (!mins.isEmpty() && item.compareTo(mins.peek()) < 0)) {
                mins.push(item);
            } else {
                mins.push(mins.peek());
            }

            // stores the current max to the maxs stack
            if (maxs.isEmpty() || (!maxs.isEmpty() && item.compareTo(maxs.peek()) > 0)) {
                maxs.push(item);
            } else {
                maxs.push(maxs.peek());
            }

            return delegate.push(item);
        } finally {
            writeLock.unlock();
        }
    }

    public E pop() {
        writeLock.lock();
        try {
            E item = delegate.pop();
            // pop both mins and maxs stacks, such that peeking on them will tell us the current min or max
            mins.pop();
            maxs.pop();
            return item;
        } finally {
            writeLock.unlock();
        }
    }

    public E peek() {
        readLock.lock();
        try {
            return delegate.peek();
        } finally {
            readLock.unlock();
        }
    }

    public E getMin() {
        readLock.lock();
        try {
            return mins.peek();
        } finally {
            readLock.unlock();
        }
    }

    public E getMax() {
        readLock.lock();
        try {
            return maxs.peek();
        } finally {
            readLock.unlock();
        }
    }

    public boolean empty() {
        readLock.lock();
        try {
            return delegate.empty();
        } finally {
            readLock.unlock();
        }
    }

    public int search(E item) {
        readLock.lock();
        try {
            return delegate.search(item);
        } finally {
            readLock.unlock();
        }
    }
    
}

Here is the unit test of the above class written in Groovy
import org.junit.Before
import org.junit.Test

class SmartStackTest {

    SmartStack stack

    @Before
    public void before() {
        stack = new SmartStack()
        stack.push(72)
        stack.push(12)
        stack.push(55)
        stack.push(78)
        stack.push(22)
        stack.push(34)
    }

    @Test
    public void canGetCurrentMinValue() {
        assert stack.min == 12

        5.times { stack.pop() }

        assert stack.min == 72
    }

    @Test
    public void canGetCurrentMaxValue() {
        assert stack.max == 78

        4.times { stack.pop() }

        assert stack.max == 72
    }

    @Test
    public void canPeek() {
        assert stack.peek() == 34
    }

    @Test
    public void isEmpty() {
        assert !stack.empty()
    }

    @Test
    public void canSearch() {
        // the 1-based position from the top of the stack where the object is located
        assert stack.search(34) == 1
        assert stack.search(22) == 2
        assert stack.search(78) == 3
        assert stack.search(55) == 4
        assert stack.search(12) == 5
        assert stack.search(72) == 6
        // not in the stack should return -1
        assert stack.search(11112) == -1
    }
    
}

Sorting objects with one-way dependency using Topological Sort with Directed Graphs

Suppose there are a bunch of objects to be evaluated, each of which might depend on a number of other objects being evaluated first before it can be evaluated. The objects being depended on might depend on other objects. There is no cyclic dependency that object A depends on object B, and object B depends on object C, and object C depends on object A.

To solve this problem, we want to sort these objects such that when traversing through these objects, they should be ready for evaluation and their dependencies, if applicable, should have been evaluated. The sorting algorithm and data structure that are applicable for this problem is Topological Sort with Directed Graphs.




If the edges in a graph have a direction, the graph is called a directed graph, as shown above. The arrows in the figure show the direction of the edges. We also include two vertices here to represent isolated objects that don't depend on other objects.

The arrows in the figure represent evaluation direction. For example, to evaluate E, we must first evaluate A and B. To evaluate H, we might want a sorted collection like this:

ADBECFDGH or ADGBECFH

There are many other possible ordering, depending on the approach you take to implement the topological sort.

DirectedGraph.java
import java.util.*;

public class DirectedGraph<T> implements Iterable<T> {

    // key is a Node, value is a set of Nodes connected by outgoing edges from the key
    private final Map<T, Set<T>> graph = new HashMap<T, Set<T>>();

    public boolean addNode(T node) {
        if (graph.containsKey(node)) {
            return false;
        }

        graph.put(node, new HashSet<T>());
        return true;
    }

    public void addNodes(Collection<T> nodes) {
        for (T node : nodes) {
            addNode(node);
        }
    }

    public void addEdge(T src, T dest) {
        validateSourceAndDestinationNodes(src, dest);

        // Add the edge by adding the dest node into the outgoing edges
        graph.get(src).add(dest);
    }

    public void removeEdge(T src, T dest) {
        validateSourceAndDestinationNodes(src, dest);

        graph.get(src).remove(dest);
    }

    public boolean edgeExists(T src, T dest) {
        validateSourceAndDestinationNodes(src, dest);

        return graph.get(src).contains(dest);
    }

    public Set<T> edgesFrom(T node) {
        // Check that the node exists.
        Set<T> edges = graph.get(node);
        if (edges == null)
            throw new NoSuchElementException("Source node does not exist.");

        return Collections.unmodifiableSet(edges);
    }

    public Iterator<T> iterator() {
        return graph.keySet().iterator();
    }

    public int size() {
        return graph.size();
    }

    public boolean isEmpty() {
        return graph.isEmpty();
    }

    private void validateSourceAndDestinationNodes(T src, T dest) {
        // Confirm both endpoints exist
        if (!graph.containsKey(src) || !graph.containsKey(dest))
            throw new NoSuchElementException("Both nodes must be in the graph.");
    }

}

TolopogicalSort.java
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import java.util.Set;

public class TopologicalSort {

    public static <T> List<T> sort(DirectedGraph<T> graph) {
        DirectedGraph<T> reversedGraph = reverseGraph(graph);

        List<T> result = new ArrayList<T>();
        Set<T> visited = new HashSet<T>();

        /* We'll also maintain a third set consisting of all nodes that have
         * been fully expanded.  If the graph contains a cycle, then we can
         * detect this by noting that a node has been explored but not fully
         * expanded.
         */
        Set<T> expanded = new HashSet<T>();

        // Fire off a Depth-First Search from each node in the graph
        for (T node : reversedGraph)
            explore(node, reversedGraph, result, visited, expanded);

        return result;
    }


    /**
     * Recursively performs a Depth-First Search from the specified node, marking all nodes
     * encountered by the search.
     *
     * @param node     The node to begin the search from.
     * @param graph    The graph in which to perform the search.
     * @param result   A list holding the topological sort of the graph.
     * @param visited  A set of nodes that have already been visited.
     * @param expanded A set of nodes that have been fully expanded.
     */
    private static <T> void explore(T node, DirectedGraph<T> graph, List<T> result, Set<T> visited, Set<T> expanded) {
        if (visited.contains(node)) {
            // if this node has already been expanded, then it's already been assigned a
            // position in the final topological sort and we don't need to explore it again.
            if (expanded.contains(node)) return;

            // if it hasn't been expanded, it means that we've just found a node that is currently being explored,
            // and therefore is part of a cycle.  In that case, we should report an error.
            throw new IllegalArgumentException("A cycle was detected within the Graph when exploring node " + node.toString());
        }

        visited.add(node);

        // recursively explore all predecessors of this node
        for (T predecessor : graph.edgesFrom(node))
            explore(predecessor, graph, result, visited, expanded);

        result.add(node);
        expanded.add(node);
    }

    private static <T> DirectedGraph<T> reverseGraph(DirectedGraph<T> graph) {
        DirectedGraph<T> result = new DirectedGraph<T>();

        // Add all the nodes from the original graph
        for (T node : graph) {
            result.addNode(node);
        }

        // Scan over all the edges in the graph, adding their reverse to the reverse graph.
        for (T node : graph) {
            for (T endpoint : graph.edgesFrom(node)) {
                result.addEdge(endpoint, node);
            }
        }

        return result;
    }

}

Below demonstrate the setup of the directed graph and will print out the sorted result.
    public static void main(String[] args) {
        DirectedGraph graph = new DirectedGraph()
        graph.addNode("A");
        graph.addNode("B");
        graph.addNode("C");
        graph.addNode("D");
        graph.addNode("E");
        graph.addNode("F");
        graph.addNode("G");
        graph.addNode("H");
        graph.addNode("I");
        graph.addNode("J");

        graph.addEdge("A", "D");
        graph.addEdge("A", "E");
        graph.addEdge("B", "E");
        graph.addEdge("C", "F");
        graph.addEdge("D", "G");
        graph.addEdge("E", "H");
        graph.addEdge("F", "H");
        graph.addEdge("G", "H");

        List sorted = TopologicalSort.sort(graph)

        System.out.println(sorted)
    }

Merge Sort in Java

The merge sort is a much more efficient sorting algorithm than those simple ones, for example, bubble sort, selection sort, and insertion sort. While the simple sorting algorithms have O(N2) complexity, the merge sort is O(N*logN). 

The merge sort routine tries to break down an array into 2 sub-arrays, sort these sub-arrays, and merge them back, in a recursive way, as you shall see in the implementation below, as well as the graph below.




 
import java.util.Arrays;
import java.util.Random;

public class MergeSortTest {

    static int numberOfElements = 10;
    static int maxValue = 100;

    public static void main(String[] args) {
        int[] original = createArrayWithRandomValues();
        // workspace array is used to keep the partial sorted results
        int[] workspace = new int[original.length];
        mergeSort(original, workspace, 0, numberOfElements - 1);

        System.out.println("The sorted array is " + Arrays.toString(original));
    }

    private static int[] createArrayWithRandomValues() {
        Random random = new Random();
        int[] original = new int[numberOfElements];
        for (int i = 0; i < numberOfElements; i++) {
            original[i] = random.nextInt(maxValue);
        }
        System.out.println("The original array is " + Arrays.toString(original));
        return original;
    }

    private static void mergeSort(int[] original, int[] workSpace, int lowerBound, int upperBound) {
        if (lowerBound != upperBound) {
            // find midpoint
            int mid = (lowerBound + upperBound) / 2;
            // sort lower half
            mergeSort(original, workSpace, lowerBound, mid);
            // sort upper half
            mergeSort(original, workSpace, mid + 1, upperBound);
            // merge them
            merge(original, workSpace, lowerBound, mid, upperBound);
        }
    }

    private static void merge(int[] original, int[] workSpace, int lowerBound, int middle, int upperBound) {
        // Copy both parts into the workspace array
        System.arraycopy(original, lowerBound, workSpace, lowerBound, upperBound + 1 - lowerBound);

        int leftPointer = lowerBound;
        int rightPointer = middle + 1;
        int originalPointer = lowerBound;

        // Copy the smallest values from either the left or the right side back to the original array
        while (leftPointer <= middle && rightPointer <= upperBound) {
            if (workSpace[leftPointer] <= workSpace[rightPointer]) {
                // copy and move the pointer on workspace to the next smallest value on left (if there is any)
                original[originalPointer++] = workSpace[leftPointer++];
            } else {
                // copy and move the pointer on workspace to the next smallest value on right (if there is any)
                original[originalPointer++] = workSpace[rightPointer++];
            }
        }
        // Copy the rest of the left side of the array into the target array
        while (leftPointer <= middle) {
            original[originalPointer++] = workSpace[leftPointer++];
        }

        // if there are elements left in the right side of the workspace, those
        // are the largest values of the merged array, so no action is required
    }
    
}

Monday, 18 March 2013

Insertion Sort in Java

Insertion sort is about twice as fast as the bubble sort, and faster than selection sort.

In an insertion sort routine, we will pick a marker (starting from index=1), store the marked number, and compare the marked number with its neighbours on the left. Shift all the neighbours, which are larger than the marked number, one position to the right. After the shift, insert the marked number to the empty slot. Repeat this procedure until the marker index points to the last number on the right.

Below demonstrates one of the implementations of the insertion sort algorithm.

import java.util.Arrays;
import java.util.Random;

public class InsertionSortTest {

    static int numberOfElements = 10;
    static int maxValue = 100;

    public static void main(String[] args) {
        int[] original = createArrayWithRandomValues();

        insertionSort(original);

        System.out.println("The sorted array is " + Arrays.toString(original));
    }

    private static int[] createArrayWithRandomValues() {
        Random random = new Random();
        int[] original = new int[numberOfElements];
        for (int i = 0; i < numberOfElements; i++) {
            original[i] = random.nextInt(maxValue);
        }
        System.out.println("The original array is " + Arrays.toString(original));
        return original;
    }

    private static void insertionSort(int[] original) {
        int markerIndex, emptySlotIndex;
        for (markerIndex = 1; markerIndex < original.length; markerIndex++) {
            int marked = original[markerIndex]; // use a temp variable to store the marked number
            emptySlotIndex = markerIndex;
            while (emptySlotIndex > 0 && original[emptySlotIndex - 1] >= marked) {
                original[emptySlotIndex] = original[emptySlotIndex - 1]; // shift element right
                emptySlotIndex--; // moves the empty slot index one position to the left
            }
            original[emptySlotIndex] = marked; // insert marked element
        }
    }
    
}

Selection Sort in Java

Selection sort is slightly faster than bubble sort because the number of required swaps is reduced from O(N2) to O(N), while the number of comparisons remains O(N2).

Below demonstrates one of the implementations of the selection sort algorithm.
 
import java.util.Arrays;
import java.util.Random;

public class SelectionSortTest {

    static int numberOfElements = 10;
    static int maxValue = 100;

    public static void main(String[] args) {
        int[] original = createArrayWithRandomValues();

        selectionSort(original);

        System.out.println("The sorted array is " + Arrays.toString(original));
    }

    private static int[] createArrayWithRandomValues() {
        Random random = new Random();
        int[] original = new int[numberOfElements];
        for (int start = 1; start <= numberOfElements; start++) {
            original[start - 1] = random.nextInt(maxValue);
        }
        System.out.println("The original array is " + Arrays.toString(original));
        return original;
    }


    private static void selectionSort(int[] original) {
        int outer, inner, minIndex;
        for (outer = 0; outer < original.length - 1; outer++) { // from 1st element to next-to-last element
            minIndex = outer; // each round the min index starts from the outer index
            for (inner = outer + 1; inner < original.length; inner++) { // from outer + 1 element to the last element
                if (original[inner] < original[minIndex]) { // remember the index for the new minimum
                    minIndex = inner;
                }
            }

            // swap the elements between outer index and min index
            int temp = original[outer];
            original[outer] = original[minIndex];
            original[minIndex] = temp;

        }
    }
    
}

Bubble Sort in Java

Given an array of n (n>1) numbers, we want to sort them such that the smallest number is on the left, and the largest number is on the right. This is where all the sorting fun comes from. 

Bubble sort is one of the slowest of all sorting algorithms, but it is conceptually the simplest. 

In bubble sort routine, we start by comparing two numbers in the array, say the 1st (array[0]) and 2nd (array[1]) numbers. If the 1st number is larger than the 2nd number, we swap them, otherwise don't do anything. Then we move over one position and compare the 2nd and 3rd numbers, and so on, until we've completed the comparison of the second last and the last number. At this point, the largest number has been bubbled up all the way to the right (array[n-1]). On the 2nd round, we only need to do the comparison and swap from array[0] to array[n-2], which will ensure array[n-2] has the 2nd largest value in the array. Continue like this until the (n-1)th round.

The algorithm complexity, when expressed in Big O notation, is O(N2). This is because, given an array of 10 numbers, you will need 9 comparisons on the first round to bubble the largest number to the right, and 8 comparisons on the second round, and so on, while the last round requires 1 comparison. That is

9 + 8 + 7 + 6 + 5 + 4 + 3 + 2 + 1 = 45, or n*(n-1)/2, 

where n is the number of elements in the array. On the other hand, a swap is only required when the number on the left is larger than the one on the right, although in the worst case scenario, when the initial array is sorted such that the largest number is on the left and the smallest number is on the right, a swap is necessary for every comparison. Therefore, the number of swaps and comparisons are proportional to n2.This is how we work out bubble sort's complexity O(N2).

Below demonstrates one of the implementations of the bubble sort algorithm.

import java.util.Arrays;
import java.util.Random;

public class BubbleSortTest {

    static int numberOfElements = 10;
    static int maxValue = 100;

    public static void main(String[] args) {
        int[] original = createArrayWithRandomValues();

        bubbleSort(original);

        System.out.println("The sorted array is " + Arrays.toString(original));
    }

    private static int[] createArrayWithRandomValues() {
        Random random = new Random();
        int[] original = new int[numberOfElements];
        for (int start = 1; start <= numberOfElements; start++) {
            original[start - 1] = random.nextInt(maxValue);
        }
        System.out.println("The original array is " + Arrays.toString(original));
        return original;
    }

    private static void bubbleSort(int[] original) {
        int outer, inner;
        for (outer = original.length - 1; outer > 1; outer--) { // from last element to the 2nd element
            for (inner = 0; inner < outer; inner++) { // from the 1st element to outer index - 1
                if (original[inner] > original[inner + 1]) { // bubble up the bigger value to the right
                    int temp = original[inner];
                    original[inner] = original[inner + 1];
                    original[inner + 1] = temp;
                }
            }
        }
    }

}