Install HTTrack On CentOS

Since I could not find the rpm in the repo, here is the quick How To to install HTTrack website copier on CentOS.

$ yum install zlib-devel
$ wget http://download.httrack.com/cserv.php3?File=httrack.tar.gz -O httrack.tar.gz
$ tar xvfz httrack.tar.gz
$ cd httrack-3.47.27
$ ./configure
$ make && sudo make install

This should do all. If you wish not to install zlib compression support, you can skip the first step and run the configure as ./configure --without-zlib. I hope this helps :)


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JPEG To PDF With Imagemagick

ImageMagick is an awesome toolkit with several powerful features for image creation and manipulation. You can use ImageMagick to translate, flip, mirror, rotate, scale, shear and transform images, adjust image colors, apply various special effects, or draw text, lines, polygons, ellipses and Bezier curves. Here, I will show how you can use ImageMagick suite to convert JPEG to PDF quickly.

First make sure imagemagick suite is installed in your system.

Ubuntu/Debian

$ sudo apt-get install imagemagick

CentOS/Fedora

$ sudo yum install imagemagick

Below are some of the examples of using convert which is a part of ImageMagick to convert Jpeg to PDF.

Single Image

$ convert image.jpg image.pdf

Multiple Images

$ convert 1.jpg 2.jpg 3.jpg output.pdf

Resize and Convert

$ convert -resize 80% image.jpg image.pdf

Negate and Convert

$ convert -negate image.jpg image.pdf

You can actually use different available switches to get your output as expected. I usually use PdfTk in conjunction with this technique to work in different scenarios and it really works great. I hope this helps :)


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Fix Your Ubuntu

Recently Ubuntu has been known for turning into an advertising company and has been accused of not protecting user's privacy so just came across this site that fixes your ubuntu by applying some patches to turn off some of the invasive features of Ubuntu.

FixUbuntu.com


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Recursion and Memoization - A Fibonacci Example

In this post, I will try to describe why memoization can be a great optmization technique in the recursive function implementations with an example of fibonacci sequence.

Straight from Wikipedia, memoization is an optimization technique used primarily to speed up computer programs by having function calls avoid repeating the calculation of results for previously processed inputs.

Basically, we maintain a lookup table and store the computer values for particular cases which lets us query and use the corresponding value for particular case present in the lookup tables. This reduces function call overheads. Now in order to understand why this is a great optimization technique in recursion, lets first draw a recursion tree for finding n^th term in fibonacci sequence.

                           fib(5)                          
                             /\                              
                            /  \
                           /    \
                          /      \                      
                     fib(4)       fib(3)                                 
                      /\               /\                          
                     /  \             /  \
                    /    \           /    \
                   /      \         /      \                         
               fib(3)    fib(2)     fib(2) fib(1) -> 1                                    
                  /\         /\          /\                          
                 /  \       /  \        /  \                         
                /    \     /    \      /    \
               /      \   /      \    /      \                        
          fib(2) fib(1) fib(1) fib(0) fib(1) fib(0) -> 0                                        
          /\        |     |      |        |    |               
         /  \       1     1      0        1    0               
    fib(1) fib(0)                                                       
       |      |                                               
       1      0 

We can clearly see the calls to fib() with same arguments several times. For example, fib(1) is called 5 times and fib(2) 3 times. Thus, we are repeating same calculations multiple times and imagine how this would look like for large value of n. If we would have maintained the value of fib(n) in the lookup table when computed the value for the first time.

The python code without memoization looks like below and notice the runtime:

#!/usr/bin/python

def fib(n):
        if n == 0:
                return 0
        if n == 1:
                return 1
        val = fib(n-1) + fib(n-2)
        return val

print fib(50)

And, now with the memoization, you will notice significant improvement in runtime.

#!/usr/bin/python

known = {0:0, 1:1}

def fib(n):
        if n in known:
                return known[n]
        known[n] = fib(n-1) + fib(n-2)
        return known[n]

print fib(50)

If you run and compare above two codes, you will find that the addition of memoization significantly improves the performance of recursive functions. Recursion are generally known to be terribly slow however memoization can make the difference insignificant. Some languages now provide memoization as the language feature natively or via third party APIs such as groovy memoize.


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