Filename: main.c * Description: A simple test program for the CRC implementations. * Notes: To test a different CRC standard, modify crc.h. * * * Copyright (c) 2000 by Michael Barr. This software is placed into * the public domain and may be used for any purpose. However, this * notice must not be changed or removed and no warranty is either * expressed or implied by its publication or distribution.
C++完美演繹 經典算法 如 /* 頭文件：my_Include.h */ #include <stdio.h> /* 展開C語言的內建函數指令 */ #define PI 3.1415926 /* 宏常量，在稍后章節再詳解 */ #define circle(radius) (PI*radius*radius) /* 宏函數，圓的面積 */ /* 將比較數值大小的函數寫在自編include文件內 */ int show_big_or_small (int a,int b,int c) { int tmp if (a>b) { tmp = a a = b b = tmp } if (b>c) { tmp = b b = c c = tmp } if (a>b) { tmp = a a = b b = tmp } printf("由小至大排序之后的結果:%d %d %d\n", a, b, c) } 程序執行結果： 由小至大排序之后的結果:1 2 3 可將內建函數的include文件展開在自編的include文件中 圓圈的面積是=201.0619264
Invoke JavaProg.main() from a win32 program: . compile Main.java . compile VC project . set include path for jni.h, which is under java_Home\include . set linking path for jvm.lib, which is under java_Home\lib\jvm.lib . copy Main.class and Main$1.class to the working dir of the VC project (or the same dir with the output executable App.exe) . run App.exe . set searching path for jvm.dll
Description: FASBIR(Filtered Attribute Subspace based Bagging with Injected Randomness) is a variant of Bagging algorithm, whose purpose is to improve accuracy of local learners, such as kNN, through multi-model perturbing ensemble.
Reference: Z.-H. Zhou and Y. Yu. Ensembling local learners through multimodal perturbation. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.4, pp.725-735.
Description: S-ISOMAP is a manifold learning algorithm, which is a supervised variant of ISOMAP.
Reference: X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.6, pp.1098-1107.
Description: C4.5Rule-PANE is a rule learning method which could generate accurate and comprehensible symbolic rules, through regarding a neural network ensemble as a pre-process of a rule inducer.
Reference: Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, vol.7, no.1, pp.37-42.
使用神經網絡集成方法診斷糖尿病,肝炎,乳腺癌癥的案例研究.
The tar file contains the following files:
ptfsf.c: heart of the perfect TFSF code
ptfsf.h: header file for same
ptfsf-demo.c: FDTD code which demonstrates use of perfect TFSF code. Essentially this program used to generate results shown in the paper
ptfsf-file-maker.c: code to generate an incident-field file using the "perfect" incident fields
ptfsf-demo-file.c: FDTD code which uses the perfect incident fields stored in a file
fdtdgen.h: defines macros used in much of my code
Makefile: simple make-file to compile programs
Also include are some simple script files to run the programs with reasonable values.
The code assumes a two-dimensional computational domain with TMz polarization (i.e., non-zero field Ez, Hx, and Hy). The program is currently written so that the incident field always strikes the lower-left corner of the total-field region first. (If you want a different corner, that should be a fairly simple tweak to the code, but for now you ll have to make that tweak yourself.)
Routine mampres: To obtain amplitude response from h(exp(jw)).
input parameters:
h :n dimensioned complex array. the frequency response is stored
in h(0) to h(n-1).
n :the dimension of h and amp.
fs :sampling frequency (Hz).
iamp:If iamp=0: The Amplitude Res. amp(k)=abs(h(k))
If iamp=1: The Amplitude Res. amp(k)=20.*alog10(abs(h(k))).
output parameters:
amp :n dimensioned real array. the amplitude-frequency response is
stored in amp(0) to amp(n-1).
Note:
this program will generate a data file "filename.dat" .
in chapter 2
