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Description: 六 程序实现：
#include <stdio.h>
typedef struct {
int row
int col
}Position
int FindPath (Position start, Position finish, int &PathLen, Position *&path)
{ //计算从起始位置start到目标位置finish的最短布线路径，找到返回1，否则，返回0
int i
if ((start.row = = finish.row) && (start.col = = finish.col)) {
PathLen = 0 return 0 } //start = finish
//设置方格阵列”围墙”
for (i = 0 i <= m+1 i++)
grid[0][i] = grid[n+1
-6 program :# include
Platform: |
Size: 11264 |
Author: 敖斌 |
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Description: 用2051单片机制作的LED数字闹钟，个人小制作，非产品，用了4位数码管，4个指示灯，其中2个做成冒号，4个按键，1个蜂鸣器。采用T0和T1两个定时器，T0用作数码管的动态扫描，T1用于计时分秒，对初学者学习定时器的使用及数码管的动态扫描有参考价值。-A LED digital alarm clock Produced by 2051 MCU, individual small-production, non-product, spent four digital tube, four lights, which caused two colon, four buttons, a buzzer. T0 and T1 using the two timers, T0 for digital control of dynamic scanning, T1 for a time every minute, for beginners to learn the use of timers and digital control of dynamic scanning a valuable reference.
Platform: |
Size: 2048 |
Author: 顾振宇 |
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Description: (estructura de datos) programa en C++ para ejemplificar pila y colas
Platform: |
Size: 1024 |
Author: jim |
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Description: 该类包括矩阵行列数变量int rows和int cols，矩阵数据数组double data[][]，构造c++语言实现任意维数matrix类，方法Matrix()、获取某元素值的方法getData(int row,int col)，设置某元素值的方法setData，计算两个矩阵的乘积的方法multiply(Matrix m)等-Class including the the matrix ranks number variable int rows int cols, matrix data array double data [] [], constructor Matrix () to get the value of an element methods getData (int row, int col) to set the value of an element setData, calculation method multiply (Matrix m) of the product of two matrices
Platform: |
Size: 7168 |
Author: suncoke |
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Description: 绘制彩色抛物线 c语言源代码
/*画抛物线的子函数spara()*/
/*row,col代表抛物线顶点的坐标，x1,y1是抛物线起点相对顶点的坐标*/
/*t为抛物线绕顶点旋转的角度*/-To draw color parabola c language source code
Platform: |
Size: 1024 |
Author: fei |
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Description: Description
All the filters have needed neighbor data the current pixel in the image,here this function(res_window=cover_window(how many neighbor value for row ,how many neighbor value for column ,input image) produce the result( res_window(row,col,total size))
input
inp_img=2d data(row * col)
w=cover size
w=[3 3] = 3x3 cover pixel generation
output
o_img=result 3d
o_img(w(1),w(2),r*col)
if
inp_img=[1 2 3 4 5 6 ]
w=[3 3]
Soloution
it will construct new image
new_img=[0 0 0 0 0 0 1 2 3 0 0 4 5 6 0 0 0 0 0 0]
result
o_img(:,:,1)=[0 0 0 0 1 2 0 4 5]
o_img(:,:,2)=[0 0 0 1 2 3 4 5 6]
finally
o_img(1:3,1:3,6)
example
a=imread( pears.png )
b=rgb2gray(a)
c=cover_window( b,[3 3]) 3x3 window construction
-Description
All the filters have needed neighbor data the current pixel in the image,here this function(res_window=cover_window(how many neighbor value for row ,how many neighbor value for column ,input image) produce the result( res_window(row,col,total size))
input
inp_img=2d data(row * col)
w=cover size
w=[3 3] = 3x3 cover pixel generation
output
o_img=result 3d
o_img(w(1),w(2),r*col)
if
inp_img=[1 2 3 4 5 6 ]
w=[3 3]
Soloution
it will construct new image
new_img=[0 0 0 0 0 0 1 2 3 0 0 4 5 6 0 0 0 0 0 0]
result
o_img(:,:,1)=[0 0 0 0 1 2 0 4 5]
o_img(:,:,2)=[0 0 0 1 2 3 4 5 6]
finally
o_img(1:3,1:3,6)
example
a=imread( pears.png )
b=rgb2gray(a)
c=cover_window( b,[3 3]) 3x3 window construction
Platform: |
Size: 179200 |
Author: anandg |
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Description: 度优先爬虫就是这样一个爬虫，爬走在这个有向图上，从根节点开始一层一层往外爬取新的节点的数据。
宽度遍历算法如下所示：
(1) 顶点 V 入队列。
(2) 当队列非空时继续执行，否则算法为空。
(3) 出队列，获得队头节点 V，访问顶点 V 并标记 V 已经被访问。
(4) 查找顶点 V 的第一个邻接顶点 col。
(5) 若 V 的邻接顶点 col 未被访问过，则 col 进队列。
(6) 继续查找 V 的其他邻接顶点 col，转到步骤(5)，若 V 的所有邻接顶点都已经被访问过，则转到步骤(2)。
按照宽度遍历算法，上图的遍历顺序为：A->B->C->D->E->F->H->G->I，这样一层一层的遍历下去。-java spider
Platform: |
Size: 28760064 |
Author: tuxedo |
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Description: lc;
clear;
A=[1 1.2 1.5 1.5;
0.833 1 1.2 1.2;
0.667 0.833 1 1.2;
0.667 0.833 0.833 1];
%因素对比矩阵A，只需要改变矩阵A
[m,n]=size(A); %获取指标个数
RI=[0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51];
R=rank(A); %求判断矩阵的秩
[V,D]=eig(A); %求判断矩阵的特征值和特征向量，V特征值，D特征向量；
tz=max(D);
B=max(tz); %最大特征值
[row, col]=find(D==B); %最大特征值所在位置
C=V(:,col); %对应特征向量
CI=(B-n)/(n-1); %计算一致性检验指标CI
CR=CI/RI(1,n);
if CR<0.10
disp('CI=');disp(CI);
disp('CR=');disp(CR);
disp('对比矩阵A通过一致性检验，各向量权重向量Q为：');
Q=zeros(n,1);
for i=1:n
Q(i,1)=C(i,1)/sum(C(:,1)); %特征向量标准化
end(lc;
clear;
A=[1 1.2 1.5 1.5;
0.833 1 1.2 1.2;
0.667 0.833 1 1.2;
0.667 0.833 0.833 1];)
Platform: |
Size: 69632 |
Author: 嘻嘻13
|
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