1. 下列說法正確的是 （ ）
A. Java語言不區(qū)分大小寫
B. Java程序以類為基本單位
C. JVM為Java虛擬機JVM的英文縮寫
D. 運行Java程序需要先安裝JDK
2. 下列說法中錯誤的是 ( )
A. Java語言是編譯執(zhí)行的
B. Java中使用了多進程技術(shù)
C. Java的單行注視以//開頭
D. Java語言具有很高的安全性
3. 下面不屬于Java語言特點的一項是( )
A. 安全性
B. 分布式
C. 移植性
D. 編譯執(zhí)行
4. 下列語句中，正確的項是 ( )
A . int $e,a,b=10
B. char c,d=’a’
C. float e=0.0d
D. double c=0.0f
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數(shù)字運算，判斷一個數(shù)是否接近素數(shù)
A Niven number is a number such that the sum of its digits divides itself. For example, 111 is a Niven number because the sum of its digits is 3, which divides 111. We can also specify a number in another base b, and a number in base b is a Niven number if the sum of its digits divides its value.
Given b (2 <= b <= 10) and a number in base b, determine whether it is a Niven number or not.
Input
Each line of input contains the base b, followed by a string of digits representing a positive integer in that base. There are no leading zeroes. The input is terminated by a line consisting of 0 alone.
Output
For each case, print "yes" on a line if the given number is a Niven number, and "no" otherwise.
Sample Input
10 111
2 110
10 123
6 1000
8 2314
0
Sample Output
yes
yes
no
yes
no
The government of a small but important country has decided that the alphabet needs to be streamlined and reordered. Uppercase letters will be eliminated. They will issue a royal decree in the form of a String of B and A characters. The first character in the decree specifies whether a must come ( B )Before b in the new alphabet or ( A )After b . The second character determines the relative placement of b and c , etc. So, for example, "BAA" means that a must come Before b , b must come After c , and c must come After d .
Any letters beyond these requirements are to be excluded, so if the decree specifies k comparisons then the new alphabet will contain the first k+1 lowercase letters of the current alphabet.
Create a class Alphabet that contains the method choices that takes the decree as input and returns the number of possible new alphabets that conform to the decree. If more than 1,000,000,000 are possible, return -1.
Definition
There is an example of how to use the LDPC encode/decode with AWGN
channel model in files .\ldpc_decode.m and .\GFq\ldpc_decode.m.
There are a few parity check matrices available in the code but
you can use other matrices provided you have enough memory to load
them. I suggest checking out matrices in Alist format available on
David MacKay s web site.You will need to have access to a MEX compiler
to be able to use a few functions written in C.
LDPC的仿真代碼
OpenSVM was developped under Visual C++ 6.0 SP6,
You can open the workspace file(*.dsw) in the opensvm-src folder.
The folder include the svm.h and svm.cpp which in the libsvm (Copyright (c) 2000-2007 Chih-Chung Chang and Chih-Jen Lin All rights reserved) in the opensvm-src\libsvm.
However, the files svm.h and svm.cpp codes were copied/merged into stdafx.h and stdafx.cpp in order to support the development, and OpenSVM still use other codes of libsvm.
So you can see the libsvm package in the source package.
You can open and build it with Visual C++ 6.0.
Note: the problems must be in LIBSVM format.
OpenSVM project page:
http://sourceforge.net/projects/opensvm
If you had any question, please mail to:
cloudbyron@gmail.com
The XML Toolbox converts MATLAB data types (such as double, char, struct, complex, sparse, logical) of any level of nesting to XML format and vice versa.
For example,
>> project.name = MyProject
>> project.id = 1234
>> project.param.a = 3.1415
>> project.param.b = 42
becomes with str=xml_format(project, off )
"<project>
<name>MyProject</name>
<id>1234</id>
<param>
<a>3.1415</a>
<b>42</b>
</param>
</project>"
On the other hand, if an XML string XStr is given, this can be converted easily to a MATLAB data type or structure V with the command V=xml_parse(XStr).
WMTSA toolbox is an implemenation for MATLAB of the wavelet methods for
time series analysis techniques presented in:
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for
Time Series Analysis. Cambridge: Cambridge University Press.
The task of clustering Web sessions is to group Web sessions based on similarity and consists of maximizing the intra-
group similarity while minimizing the inter-group similarity．
The first and foremost question needed to be considered in clustering
W b sessions is how to measure the similarity between Web
sessions．However．there are many shortcomings in traditiona1
measurements．This paper introduces a new method for measuring
similarities between Web pages that takes into account not only the
URL but also the viewing time of the visited web page．Yhen we
give a new method to measure the similarity of Web sessions using
sequence alignment and the similarity of W eb page access in detail
Experiments have proved that our method is valid and e幣cient．
