(1) Matlab强制退出正在运行的程序
A: Ctrl + C
(2)如何让Matlab跑完程序后自动关机？
A: 在程序的末尾加上一条代码：
system('shutdown -s')
当然，记得在这条语句前加上保存结果的save，不然跑了很久的程序就白跑了。
(3) Matlab创建文件夹实例
A: help exist查看下exist函数的用法
示例： if exist('results')~=7
mkdir('result')
end
(4) Matlab中使用动态变量名
A: 编写程序时经常会遇到处理大量数据文件的情况，数据文件的命名比较相似，以一系列编号区分，如“a1.mat，a2.mat，... ，a100.mat " (假设其中的数据名称也为a1...a100) 。为了可以批量处理这些文件，可以采用如下的方法：
for i=1:1:100
s=strcat('a', int2Str(i));
load(strcat(s,'.mat'));
x=eval(s); %将数据a1赋值给x，便于后继统一处理
% ... %统一处理程序
end
为动态变量名赋值示例
for i=1:10
eval(['A',num2str(i),'=rand(1,i)']);
end
动态调用A1~A10
可以使用cell数组，因为A1-A10是长度不同的向量。
for i=1:10
B{i}=eval(['A',num2str(i)]);
end
(5) 通过命令行运行matlab带参函数
在命令行输入
matlab -r "functionanme firstpara secondpara"
注意事项：引号不能少；functionname不包含「.m」后缀
(6) 怎么判定一个文件或者文件夹是否存在
if isequal(exist(filename,'file'),2) %2 means it's a file
if isequal(exist(pathname,'dir'),7) %7 means it's a directory
(7)Matlab中用图片制作小电影
方法：使用VideoWriter()类
%% Movie Test.
%% Set up some function.
% Sine between -2*pi and 2*pi.
x = (10*-pi:0.1:10*pi)'; % Note the transpose.
y = sin(x);
fid = figure;
hold on
% The final plot.
plot(x,y, '*');
%% Set up the movie.
writerObj = VideoWriter('out.avi'); % Name it.
writerObj.FrameRate = 60; % How many frames per second.
open(writerObj);
for i=1:size(y)
% We just use pause but pretend you have some really complicated thing here...
pause(0.1);
figure(fid); % Makes sure you use your desired frame.
plot(x(i),y(i),'or');
%if mod(i,4)==0, % Uncomment to take 1 out of every 4 frames.
frame = getframe(gcf); % 'gcf' can handle if you zoom in to take a movie.
writeVideo(writerObj, frame);
%end
end
hold off
close(writerObj); % Saves the movie.
(8) Matlab计算L2欧氏距离
Matlab内置函数pdist2(p1,p2,'euclidean')
可以计算一个点和一组点的距离。示例：
% Define our points.
aPoint = [1,4]; % A single point with 2 components.
bunchOfPoints = [2,3; 1,4; 0,1]; % A bunch of other points.
d = pdist2(aPoint,bunchOfPoints,'euclidean')
(9) Matlab如何计算均方误差root-mean-square-error
函数rms
% The actual values that we want to predict.
Actual = [1 2 3 4];
% The values we actually predicted.
Predicted = [1 3 1 4];
% One way is to use the Root Mean Square function and pass in the "error" part.
rmse = rms(Predicted-Actual)
(10) Matlab计算平均绝对误差mean-absolute-error
函数mae
% The actual values.
Actual = [1 2 3 4];
% The values we predicted.
Predicted = [1 3 1 4];
% You can just use the built in Mean Absolute Error function and pass in the "error" part.
builtInMAE = mae(Actual-Predicted)
(11) Matlab内置Random Forest算法
% Train the TreeBagger (Decision Forest).
nTrees = 20;
B = TreeBagger(nTrees,traindata,trainlabels, 'Method', 'classification');
predChar = B.predict(testdata);
predClass = str2double(predChar)
(12) 为图像中的部分pixel上色
源图像origImg； binary 图像maskImg其中0代表背景，1代表要上色的点。
%% Example on how to color select pixels in an image.
% The original COLOR image.
origImg = imread('ngc6543a.jpg');
% Make sure the values are within 0-255.
origImg = uint8(origImg);
% View the original image.
figure; fId = imagesc(origImg); axis image;
title('click and hold mouse to draw on the original image');
% The user draws on the image to select the pixels to highlight.
M = imfreehand();
% 0 = background pixels (do not change).
% 1 = foreground pixels (change these colors).
maskImg = M.createMask;
% View the black and white mask.
figure; imagesc(maskImg); colormap gray; axis image;
% Now let's color the mask green to make it more interesting.
% To do this, we have to make three matrices, one for each color channel.
% Increase the color by half the max value so we can see some transparancy
% in the original image.
amountIncrease = 255/2;
alphaImg(:,:,1) = zeros(size(maskImg)); % All zeros.
alphaImg(:,:,2) = round(maskImg*(amountIncrease)); % Round since we're dealing with integers.
alphaImg(:,:,3) = zeros(size(maskImg)); % All zeros.
% Convert alphaImg to have the same range of values (0-255) as the origImg.
alphaImg = uint8(alphaImg);
% View alphaImg.
figure; imagesc(alphaImg); axis image;
% Combine the original images and the alpha values to highlight the select
% pixels.
blendImg = origImg + alphaImg;
% Show the blended images.
figure; imagesc(blendImg); axis image;
(13) print图片打印
print -fhandle -rresolution -dfileformat filename
例如print -f2 -r300 -djpeg myfigure命令将句柄为2的图像生成myfigure.jpg文件，分辨率为dpi300
如果要求矢量图，可以用-depsc
如果要批量制图，参考如下循环流程
for kk = 1:10
%%%生成图像
% 如果需要调整图像大小
% 在存储前使用
% set(handle,'Position',[left, bottom, width, height])
% 例如 set(gca, 'Position',[80,100,800,600])
...
print('-r150','-depsc',['d:imagemyfigure',sprintf('%02d',kk)])
end
print 调整图像大小的命令
默认状态下打印大小为[0.25 2.5 8.0 6.0] in inches
调整命令三行连用
set(gcf, 'PaperPositionMode', 'manual');
set(gcf, 'PaperUnits', 'inches');
set(gcf, 'PaperPosition', [2 1 4 2]);
units: inches,centimeters, normalized, points
(14) print命令保存eps文件与figure文件显示不同之解决方法
在print('-depsc','a.eps');一句前加上 "set(gcf,'paperpositionmode','auto');" 即可
(15) 使用matlab计算两幅图片的Jaccard距离
以下示例比较画线的相似度和距离
其中白色的pixel代表线；黑色的pixel代表背景
% JaccardTest.m
% Compute the Jaccard similarity coefficient (index) of two images.
% A value of "1" = the line object (foreground).
% A value of "0" = the background.
% Alice draws a vertical line.
Alice = [0 1 0;
0 1 0;
0 1 0];
% RobotBob tries to draw a line.
RobotBob = [0 0 0;
0 1 1;
0 0 1];
% Carol tries to draw a line.
Carol = [0 1 0;
0 1 0;
1 1 0;];
% Let's see their two drawings.
figure;
subplot(1,3,1); imagesc(Alice); axis image; colormap gray;
title('Alice''s nice line drawing');
subplot(1,3,2); imagesc(RobotBob); axis image; colormap gray;
title('RobotBob tries to draw Alice''s line');
subplot(1,3,3); imagesc(Carol); axis image; colormap gray;
title('Carol''s tries to draw Alice''s line');
% How similar are Alice's and Bob's drawing of a line?
% An intuitive way to measure this is to compare each of the white "line"
% pixels (a value of "1") to each other and see how many white pixels
% overlap compared to the total number of white line pixels.
% We compute the intersection of the two lines using the "AND" operator "&".
intersectImg = Alice & RobotBob;
figure; imagesc(intersectImg); axis image; colormap gray; title('intersection');
% We compute the union of the two lines using the "OR" operator "|".
unionImg = Alice | RobotBob;
figure; imagesc(unionImg); axis image; colormap gray; title('union');
% There is only one pixel that overlaps (intersects)
numerator = sum(intersectImg(:));
% There are 5 pixels that are unioned.
denomenator = sum(unionImg(:));
% So intuitively we might expect that a similarity of 1/5 would
% be a good indication. This is exactly what Jaccard's does.
jaccardIndex = numerator/denomenator
% jaccardIndex =
% 0.2000
% Jaccard distance shows how dis-similar the two line drawings are.
jaccardDistance = 1 - jaccardIndex
% jaccardDistance =
% 0.8000
%% How simililar are Alice and Carol's two line drawings?
% We can compute Jaccard's index in a single line,
jaccardIndex_ac = sum(Alice(:) & Carol(:)) / sum(Alice(:) | Carol(:))
%jaccardIndex_ac =
% 0.7500
%
% As expected, we can see that Alice's and Carol's drawing of a line is
% much MORE "similar" than Alice's and Bob's drawing (0.2).
% Let's check the Jaccard distance.
jaccardDistance_ac = 1 - jaccardIndex_ac
% jaccardDistance_ac =
% 0.2500
%
% As expected, we can see there is LESS "distance" between Alice's and
% Carol's drawing of a line than Alice's and Bob's drawing of a line (0.8).
通常我们不关心两幅图片的 Jaccard距离，我们关心的是图片中的形状之间的相似度。
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作者：xmjdh
来源：CSDN
原文：https://blog.csdn.net/lqhbupt/article/details/20292113
版权声明：本文为博主原创文章，转载请附上博文链接！