For e.g., if [1,2,3,4,5,6] is a vector, then all possible combinations of 3 items at a time is
4 5 6
3 5 6
3 4 6
3 4 5
2 5 6
2 4 6
2 4 5
2 3 6
2 3 5
2 3 4
1 5 6
1 4 6
1 4 5
1 3 6
1 3 5
1 3 4
1 2 6
1 2 5
1 2 4
1 2 3
How do I find this in MATLAB?
Try this link. Basically you just need to type c = combnk(1:6,3). Hope it helps.
Edit: The difference between what I proposed and #nash 's combntns is the toolbox that the commands are in. combnk is in the Statistics Toolbox, while combntns is in the Mapping Toolbox.
>> combos = combntns([1 2 3 4 5 6], 3)
Output:
1 2 3
1 2 4
1 2 5
1 2 6
1 3 4
1 3 5
1 3 6
1 4 5
1 4 6
1 5 6
2 3 4
2 3 5
2 3 6
2 4 5
2 4 6
2 5 6
3 4 5
3 4 6
3 5 6
4 5 6
I use:
allCombos = nchoosek([1:n],k);
I prefer this, as nchoosek comes with Matlab, no toolbox needed.
Related
I have this example matrix:
1 2 4 5 1 3
2 3 5 6 3 4
1 2 3 4 5 6
3 2 4 6 1 5
...
I need to delete each row that contains duplicate elements. In this example I have to delete the first and second rows.
I know how to do this in a for-loop, but I don't want to use a for-loop.
Assuming A as the input matrix, you could do -
A(all(diff(sort(A,2),[],2),2),:)
Sample run -
>> A
A =
1 2 4 5 1 3
2 3 5 6 3 4
1 2 3 4 5 6
3 2 4 6 1 5
>> A(all(diff(sort(A,2),[],2),2),:)
ans =
1 2 3 4 5 6
3 2 4 6 1 5
Alternatively, if you don't mind some bsxFUN -
A(~any(sum(bsxfun(#eq,A,permute(A,[1 3 2])),2)>1,3),:)
I'm using knnclassify to do a kNN classification in Matlab and it's working well. Now, I need to know the distances to the neighbors and it seems that knnsearch funcion gives me that.
The problem is the results are not the same. I am quite sure knnsearch is not working properly, but I don't know the reason.
This is my code:
k=1;
distance='euclidean';
rule='nearest';
%kNN classification
result = knnclassify(sample_matrix, training_matrix, label_matrix,k,distance,rule);
%showing which element is recognized and the distance to it
[recognition,distances] = knnsearch(training_matrix, sample_matrix,'k',k);
So, result and recognition should be the same, and then I can see the distances.
This is result:
4 1 1 2 4 1 1 4 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 2 3 3 3 3 3 3 4 4 4 4 4
4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7
8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9
And this is recognition:
3 1 1 2 3 1 1 3 1 1 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 3 2 2 4 3 3 4 3
4 4 3 3 3 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 6 6 6 5 5 6 6 6
7 6 6 6 6 7 6 6 6 7 8 7 7 7 7 7 7 7 7 7
(They're supposed to be two vectors).
The desired result is
1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4
4 4 4 4 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 8
8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9
So, result is almost well (good enough) and recognition is a disaster.
As you can see the beginning is much better than the end.
Anybody can help me??
Thank you very much.
Can someone check did I guess correct number of neurons in input/hidden/output layer and overall params please.
My idea of this ANN:
Input neurons : 784 (28x28)
Hidden Layers : 1
Size of hidden layer(s) : 25
Activation function : Log-sigmoid
Training method : gradient descent
Data size : 400 + 200
There are 400 bmp images used for training of it, and 200 for checking (however only 1-50 get guessed with 100% rate and others 0% rate...)
clear all;
clc
for kk=1:400
pl=ones(28,28); %³õʼ»¯28*28¶þֵͼÏñΪȫ°×
m=strcat('b',int2str(kk),'.bmp'); %Á¬½Ó×Ö·ûµÃµ½Ñù±¾ÎļþÃû
x=imread(m,'bmp'); %¶ÁÈëÑб¾ÎļþͼÏñ
pl=im2bw(x,0.5); %°ÑÑù±¾Í¼Ïñת»¯Îª¶þֵͼ
for m=0:27 %ÐγÉÉñ¾ÍøÂçÊäÈëÏòÁ¿
p(m*28+1:(m+1)*28,kk)=pl(1:28,m+1);
end
end
%ÊÖдÌåÑù±¾¶ÔÓ¦µÄÊý×Ö£¨´Ób1.bmpµ½b400.bmp ¹²400¸ö£©£º
t=[5 0 4 1 9 2 1 3 1 4 3 6 3 6 1 7 2 8 6 9 4 0 9 1 1 2 4 3 2 7 8 8 6 9 0 5 6 0 7......
6 1 8 7 9 3 9 8 5 9 3 3 0 7 4 9 8 0 9 4 1 4 4 6 0 4 5 6 1 0 0 1 7 1 6 3 0 2 1......
1 7 8 0 2 6 7 8 3 9 0 4 6 7 4 6 8 0 7 8 3 1 5 7 1 7 1 1 6 3 0 2 9 3 1 1 0 4 9......
2 0 0 2 0 2 7 1 8 6 4 1 6 3 4 1 9 1 3 3 9 5 4 7 7 4 2 8 5 8 6 0 3 4 6 1 9 9 6......
0 3 7 2 8 2 9 4 4 6 4 9 7 0 9 2 7 5 1 5 9 1 2 3 1 3 5 9 1 7 6 2 8 2 2 6 0 7 4......
9 7 8 3 2 1 1 8 3 6 1 0 3 1 0 0 1 1 2 7 3 0 4 6 5 2 6 4 7 1 8 9 9 3 0 7 1 0 2......
0 3 5 4 6 5 8 6 3 7 5 8 0 9 1 0 3 1 2 2 3 3 6 4 7 5 0 6 2 7 9 8 5 9 2 1 1 4 4......
5 6 4 1 2 5 3 9 3 9 0 5 9 6 5 7 4 1 3 4 0 4 8 0 4 3 6 8 7 6 0 9 7 5 7 2 1 1 6......
8 9 4 1 5 2 2 9 0 3 9 6 7 2 0 3 5 4 3 6 5 8 9 5 4 7 4 2 7 3 4 8 9 1 9 2 1 7 9......
1 8 7 4 1 3 1 1 0 2 3 9 4 9 2 1 6 8 4 7 7 4 4 9 2 5 7 2 4 4 2 1 9 2 2 8 7 6 9......
8 2 3 8 1 6 5 1 1 0];
%´´½¨BPÍøÂç
pr(1:784,1)=0;
pr(1:784,2)=1;
t1=clock; %¼Æʱ¿ªÊ¼
%ÉèÖÃѵÁ·²ÎÊý
net=newff(pr,[25 1],{'logsig','purelin'},'traingdx','learngdm');
net.trainParam.epochs=5000; %ÉèÖÃѵÁ·´ÎÊý
net.trainParam.goal=0.05; %ÉèÖÃÐÔÄܺ¯Êý
net.trainParam.show=10; %ÿ10ÏÔʾ
net.trainParam.Ir=0.05; %ÉèÖÃѧϰËÙÂÊ
net=train(net,p,t); %ѵÁ·BPÍøÂç
datat=etime(clock,t1) %¼ÆËãÉè¼ÆÍøÂçµÄʱ¼äΪ66.417s
%Éú³É²âÊÔÑù±¾
pt(1:784,1)=1;
pl=ones(28,28); %³õʼ»¯28*28¶þֵͼÏñÏñËØ
for kk=401:600
pl=ones(28,28); %³õʼ»¯28*28¶þֵͼÏñΪȫ°×
m=strcat('b',int2str(kk),'.bmp'); %Á¬½Ó×Ö·ûµÃµ½Ñù±¾ÎļþÃû
x=imread(m,'bmp'); %¶ÁÈëÑб¾ÎļþͼÏñ
pl=im2bw(x,0.5); %°ÑÑù±¾Í¼Ïñת»¯Îª¶þֵͼ
for m=0:27 %ÐγÉÉñ¾ÍøÂçÊäÈëÏòÁ¿
pt(m*28+1:(m+1)*28,kk-400)=pl(1:28,m+1);
end
end
[a,Pf,Af]=sim(net,pt); %ÍøÂç·ÂÕæ
a=round(a) %Êä³öʶ±ð½á¹û
%²âÊÔÑù±¾¶ÔÓ¦µÄÊý×Ö£¨´Ób401.bmpµ½b600.bmp ¹²200¸ö£©£º
tl=[2 6 4 5 8 3 1 5 1 9 2 7 4 4 4 8 1 5 8 9 5 6 7 9 9 3 7 0 9......
0 6 6 2 3 9 0 7 5 4 8 0 9 4 1 1 8 7 1 2 6 1 0 3 0 1 1 8 2 0 3 9 4 0 5 0 6 1 7......
7 8 1 9 2 0 5 1 2 2 7 3 5 4 4 7 1 8 3 9 6 0 3 1 1 2 0 3 5 7 6 8 2 9 5 8 5 7 4......
1 1 3 1 7 5 5 5 2 5 8 2 0 9 7 7 5 0 9 0 0 8 9 2 4 8 1 6 1 6 5 1 8 3 4 0 5 5 8......
3 4 2 3 9 2 1 1 5 2 1 3 2 8 7 3 7 2 4 6 9 7 2 4 2 8 1 1 3 8 4 0 6 5 9 3 0 9 2......
4 7 1 1 9 4 2 6 1 8 9 0 6 6 7];
k=0;
for i=1:200
if a(i)==tl(i)
k=k+1;
end
end
rate=1.00*k/200; %¼ÆËã×îºóÕýÈ·ÂÊΪ0.495
I might be wrong, since you don't specify the number of output neurons and the number of patterns per class in your dataset. However, it seems that you have created only one output neuron for your network. In this case, the network assings ALL patterns to the same class, and the classification accuracy you get is equal to the a priori probability. If, for example, the first 50 patterns of your dataset belong to the same class, and the rest to different classes, a classifier with one output will assign all patterns to the first class, so you will get the first 50 right.
If this is the case, you should create a classifier with N outputs, where N is the number of classes in your dataset. In this case, the classifier will vote for each class, and the pattern will be assigned to the class with the maximum output. If for example you have 3 classes, and the output for a specific pattern is [0.2, 0.83, 0.6], the pattern will be assigned to the second class.
Moreover, converting to image to black-and-white is probably not the best way. It would be better to convert the image to grayscale (to preserve the histogram to some extent), and use some normalization to compensate for differences in lighting.
Finally, keep in mind that neural networks essentially detect similarity between input vectors. So, if you need to classify pictures, you need to find a representation such that similar images produce similar input vectors. Feeding the values of the pixels into the classifier is not a such representation. For example, if you turn the image upside down, the input vector changes completely, even though it still shows the same object. You don't want that. You want features that depend on the object shown, and not on lighting/angle etc. However, extracting such features is a different matter altogether (for example see some examples for image preprocessing and feature extraction from the OpenCV framework, the standard image processing and computer vision tool in C++/python)
If you are interested in neural networks and not image processing, it would be better to start with some standard classification problems from the UCI repository (eg. iris flower, wisconsin breast cancer) and practice with them until you produce good results and feel comfortable with the tools you are using.
I want to use MATLAB code in C++. I use Matcom (A tool used to covert M-files to C++) to add M-files to C++ current project. It compiles successfully but errors when running.
I created a matrix:
bit_in_char = uint16([...
0 1 1 2 1 2 2 3 1 2 2 3 2 3 3 4 1 2 2 3 2 3 ...
3 4 2 3 3 4 3 4 4 5 1 2 2 3 2 3 3 4 2 3 3 4 ...
3 4 4 5 2 3 3 4 3 4 4 5 3 4 4 5 4 5 5 6 1 2 ...
2 3 2 3 3 4 2 3 3 4 3 4 4 5 2 3 3 4 3 4 4 5 ...
3 4 4 5 4 5 5 6 2 3 3 4 3 4 4 5 3 4 4 5 4 5 ...
5 6 3 4 4 5 4 5 5 6 4 5 5 6 5 6 6 7 1 2 2 3 ...
2 3 3 4 2 3 3 4 3 4 4 5 2 3 3 4 3 4 4 5 3 4 ...
4 5 4 5 5 6 2 3 3 4 3 4 4 5 3 4 4 5 4 5 5 6 ...
3 4 4 5 4 5 5 6 4 5 5 6 5 6 6 7 2 3 3 4 3 4 ...
4 5 3 4 4 5 4 5 5 6 3 4 4 5 4 5 5 6 4 5 5 6 ...
5 6 6 7 3 4 4 5 4 5 5 6 4 5 5 6 5 6 6 7 4 5 ...
5 6 5 6 6 7 5 6 6 7 6 7 7 8]);
The code is running successful in MATLAB, but has error "uint6 is not yet implemented" in VC. Are data structures the same in MATLAB and VC?
The problem most likely is caused by VC6. That's a compiler from the previous century. Upgrade to something still supported, and it will probably work.
Use matlab corder in MATLAB 2012b.
I am trying to create a matrix that is 3 x n, with each of the columns being the same. What's the easiest way of achieving it? Concatenation?
After
n=7
x=[1;2;3]
it's either
repmat(x,[1 n])
or
x(:,ones(1,n))
(Octave can be considered as an open source/free version of MATLAB)
octave-3.0.3:2> rowvec = [1:10]
rowvec =
1 2 3 4 5 6 7 8 9 10
octave-3.0.3:3> [rowvec; rowvec; rowvec]
ans =
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
Use repmat if the number of rows is large.
octave-3.0.3:7> repmat(rowvec, 10, 1)
ans =
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
Use multiplication with a 1 x 3 matrix of ones
eg, x * [1 1 1]
Edit:
In Octave:
octave-3.0.3.exe:1> x = [1;2;3;4]
x =
1
2
3
4
octave-3.0.3.exe:5> x * [1 1 1]
ans =
1 1 1
2 2 2
3 3 3
4 4 4