解释这段代码hist(r1,breaks=-1000+(0:25)*80,col=4,xlab="rt序列",probability=T) curve(dnorm(x,mean(r1),sd(r1)),col="red",add=T)

这段代码绘制了一个直方图和一条密度曲线。其中：

- hist()函数绘制直方图，r1为要绘制直方图的数据，breaks参数指定直方图的分组方式，这里是将数据分为25组，每组的长度为80，-1000是为了确保所有数据都落入某个组内，col参数指定直方图的颜色，xlab参数指定x轴标签为"rt序列"，probability参数为TRUE表示绘制概率密度直方图。
- curve()函数绘制密度曲线，dnorm()函数是正态分布函数，x为自变量，mean(r1)和sd(r1)分别为数据r1的均值和标准差，col参数指定曲线的颜色为红色，add参数为TRUE表示将曲线添加到已有的图形上。

相关问题

hist(r1,breaks=-1000+(0:25)*80,col=4,xlab="rt序列",probability=T) curve(dnorm(x,mean(r1),sd(r1)),col="red",add=T)

Sorry, as an AI language model, I cannot execute codes. However, I can explain what this code does.

This code is used to create a histogram of a set of data called "r1" using the "hist" function in R. The "breaks" argument specifies the number and width of the bins in the histogram. In this case, the "breaks" argument is set to "-1000 (0:25)*80", which means that the histogram will have 26 bins with a width of 80 units, ranging from -1000 to 1920.

The "col" argument sets the color of the bars in the histogram, and in this case, it is set to 4, which corresponds to a shade of blue. The "xlab" argument sets the label of the x-axis in the histogram.

The "probability" argument is set to "T", which means that the histogram will be normalized to represent the probability density function of the data.

The "curve" function is used to overlay a normal distribution curve on top of the histogram. The "dnorm" function is used to create a normal distribution with the mean and standard deviation of the data in "r1". The "col" argument sets the color of the curve, and in this case, it is set to red. The "add" argument is set to "T", which means that the curve will be added to the existing plot rather than creating a new plot.

I=imread('0294.jpg'); I1=RGB2Lab(I); L1 = double(I1(:,:,1)); L2 = double(I1(:,:,2)); L3 = double(I1(:,:,3)); PL1=mean(L1(:)); PL2=mean(L2(:)); PL3=mean(L3(:)); [m,n]=size(L1); for i=1:m for j=1:n LL1(i,j)=abs(L1(i,j)-PL1); end end for i=1:m for j=1:n LL2(i,j)=abs(L2(i,j)-PL2); end end for i=1:m for j=1:n LL3(i,j)=abs(L3(i,j)-PL3); end end LLL1=reshape(LL1,1,[]); SL1=std(LLL1); LLL2=reshape(LL2,1,[]); SL2=std(LLL2); LLL3=reshape(LL3,1,[]); SL3=std(LLL3); %比较标准差最大 SL=max(max(SL1,SL2),SL3); G = fspecial('gaussian', [5 5], 1); GL1=imfilter(LL1, G, 'circular'); hist=imhist(GL1)/prod(size(GL1)); G1=find(hist); H1=-hist(G1)'*log2(hist(G1)); GL2=imfilter(LL2, G, 'circular'); hist=imhist(GL2)/prod(size(GL2)); G2=find(hist); H2=-hist(G2)'*log2(hist(G2)); GL3=imfilter(LL3, G, 'circular'); hist=imhist(GL3)/prod(size(GL3)); G3=find(hist); H3=-hist(G3)'*log2(hist(G3)); %比较二维熵最小的 H=min(min(H1,H2),H3); if(SL==SL1) ZL=LL1; end if(SL==SL2) ZL=LL2; end if(SL==SL3) ZL=LL3; end if(H==H1) HL=LL1; end if(H==H2) HL=LL2; end if(H==H3) HL=LL3; end %计算初始显著图 C=0.35*ZL+0.65*HL; [Z E]=lrr(C,0.01);%全局低秩Z为低秩部分E为稀疏部分 ZZ=(C-Z).*C; figure,imshow(ZZ,[]);运行代码时报错矩阵维度必须一致。 出错 Untitled3 (line 69) ZZ=(C-Z).*C;如何修正

根据错误提示，矩阵维度必须一致，说明在执行`ZZ=(C-Z).*C`这一行代码时，矩阵C和矩阵Z的维度不同。

在这段代码中，矩阵C的维度是由LL1、LL2、LL3中的一个确定的，而矩阵Z是通过`[Z E]=lrr(C,0.01)`计算得到的，因此可能存在矩阵维度不同的情况。

解决方法是检查矩阵C和矩阵Z的维度，确保它们的维度一致。可以通过使用`size()`函数检查矩阵的维度，或者在调试时查看变量的值，找到维度不同的原因并进行调整。