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Algorithm 的代码
1379.cpp
#include
#include
#include
using namespace std;
struct dnasort{
char s[55];
int num;
};
int cmp(dnasort a,dnasort b){
return a.num
1029.cpp
#include
#include
using namespace std;
short a[999999];
void main()
{
int n,i;
while(cin>>n){
for(i=0;i>a[i];
for(i=0;i
1009.cpp
#include
#include
#include
#include
using namespace std;
struct mouse{
int j;int f;double d;
}mo[1001];
int compare(mouse a,mouse b){
return a.d >
src_gen.m
function src_rly=src_gen(K,L)
src_rly=zeros(1,K);
%src_rly(2:K)=rand(1,K-1)
pid.h
//*****************************************************************************
//
// pid.h - Prototypes for the PID feedback control algorithm.
//
// Copyright (c) 2005,2006 Luminary Micro, Inc. All
pid.c
//*****************************************************************************
//
// pid.c - PID feedback control algorithm.
//
// Copyright (c) 2005,2006 Luminary Micro, Inc. All rights reserved.
/
readme.txt
----------------------------------------------------------------------
Genetic Algorithm Toolbox for MATLAB, v1.2
==========================================
Thank you for requesting a copy of t
djikstra_init.m
function data = dijkstra_init(W, start_verts, heuristic)
% dijkstra_init - initialisation of dijkstra algorithm
%
% data = dijkstra_init(W, start_verts [,heuristic]);
%
% 'heuristic' is a structu
djikstra.m
function data = dijkstra(W, start_verts, options, heuristic)
% dijkstra - launch the Dijkstra algorithm.
%
% You can provide special conditions for stop in options :
% 'options.stop_at' : sto
rankboost_train.m
function [model,time_taken]=RankBoost_train(data,T,verbose,plot_enable)
% RankBoost Training
%
%Y. Freund, R. Iyer, and R. Schapire, 揂n efficient boosting algorithm for combining preferences,