Huffman 壓縮/解壓算法的ANSI C實(shí)現(xiàn)
This archive contains a simple and readable ANSI C implementation of Huffman
coding and decoding. This implementation is not intended to be the best,
fastest, smallest, or any other performance related adjective.
More information on Huffman encoding may be found at:
http://michael.dipperstein.com/huffman
http://datacompression.info/Huffman.shtml
Input
The input contains blocks of 2 lines. The first line contains the number of sticks parts after cutting, there are at most 64 sticks. The second line contains the lengths of those parts separated by the space. The last line of the file contains zero.
Output
The output should contains the smallest possible length of original sticks, one per line.
Sample Input
9
5 2 1 5 2 1 5 2 1
4
1 2 3 4
0
Sample Output
6
5
1) Write a function reverse(A) which takes a matrix A of arbitrary dimensions as input and returns a matrix B consisting of the columns of A in reverse order. Thus for example, if
A = 1 2 3 then B = 3 2 1
4 5 6 6 5 4
7 8 9 9 8 7
Write a main program to call reverse(A) for the matrix A = magic(5). Print to the screen both A and reverse(A).
2) Write a program which accepts an input k from the keyboard, and which prints out the smallest fibonacci number that is at least as large as k. The program should also print out its position in the fibonacci sequence. Here is a sample of input and output:
Enter k>0: 100
144 is the smallest fibonacci number greater than or equal to 100.
It is the 12th fibonacci number.
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The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order
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Perl and XML
XML is a text-based markup language that has taken the programming world by storm. More
powerful than HTML yet less demanding than SGML, XML has proven itself to be flexible and
resilient. XML is the perfect tool for formatting documents with even the smallest bit of
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be indispensable for organizing and conveying other sorts of data as well, thus its central role
in web services like SOAP and XML-RPC.
As the Perl programming language was tailor-
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