How to find out all array element indices equal to several values (>2)
For example, I have an array a=[1 2 3 4 5 5 4 3 2 2 2 1], I want to know the indices of all elements equal to b=[2 5]
Remember, I cannot use style like a==b(1) | a==b(2) because number of elements in b is arbitrary.
Do I have to use a for loop to do this?
You can use ismember (as Daniel said just seconds before I hit enter...) ;-)
a=[1 2 3 4 5 5 4 3 2 2 2 1];
b=[2 5];
c=find(ismember(a,b))
Output:
c =
2 5 6 9 10 11
If you want to do it more manually, you can use bsxfun:
c = find(any(bsxfun(#eq, a(:).', b(:)), 1));
Related
I am trying to generate random numbers between 1 and 5 using Matlab's randperm and calling randperm = 5.
Each time this gives me a different array let's say for example:
x = randperm(5)
x = [3 2 4 1 5]
I need the vector to be arranged such that 4 and 5 are always next to each other and 2 is always between 1 and 3... so for e.g. [3 2 1 4 5] or [4 5 1 2 3].
So essentially I have two "blocks" of unequal length - 1 2 3 and 4 5. The order of the blocks is not so important, just that 4 & 5 end up together and 2 in between 1 and 3.
I can basically only have 4 possible combinations:
[1 2 3 4 5]
[3 2 1 4 5]
[4 5 1 2 3]
[4 5 3 2 1]
Does anyone know how I can do this?
Thanks
I'm not sure if you want a solution that would somehow generalize to a larger problem, but based on how you've described your problem above it looks like you are only going to have 8 possible combinations that satisfy your constraints:
possible = [1 2 3 4 5; ...
1 2 3 5 4; ...
3 2 1 4 5; ...
3 2 1 5 4; ...
4 5 1 2 3; ...
5 4 1 2 3; ...
4 5 3 2 1; ...
5 4 3 2 1];
You can now randomly select one or more of these rows using randi, and can even create an anonymous function to do it for you:
randPattern = #(n) possible(randi(size(possible, 1), [1 n]), :)
This allows you to select, for example, 5 patterns at random (one per row):
>> patternMat = randPattern(5)
patternMat =
4 5 3 2 1
3 2 1 4 5
4 5 3 2 1
1 2 3 5 4
5 4 3 2 1
You can generate each block and shuffle each one then and set them as members of a cell array and shuffle the cell array and finally convert the cell array to a vector.
b45=[4 5]; % block 1
b13=[1 3]; % block 2
r45 = randperm(2); % indices for shuffling block 1
r13 = randperm(2); % indices for shuffling block 2
r15 = randperm(2); % indices for shuffling the cell
blocks = {b45(r45) [b13(r13(1)) 2 b13(r13(2))]}; % shuffle each block and set them a members of a cell array
result = [blocks{r15}] % shuffle the cell and convert to a vector
I have for example a=[1 2 3 4 5 6 7 8 9 10]; and I have to delete each 2 following numbers from 3.
like at the end it should be a=[1 4 7 10];
How to do this without a for loop.
And also if there is a way to guarantee that at the end the resulting array will have exact number of entries, like here it should be a with 4 entries at the end.
But for example we have b=[1 2 3 4 5 6 7 8 9 ]; and if I want make sure that at the end I still have 4 entries in the rest array, so that b can't be equal to [1 4 7] because I need 4 entries for sure.
You can use indexing for this:
A = 1:10;
B = A(1:3:end)
B =
[1 4 7 10]
Or, if you really want to remove elements:
A = 1:10;
A(2:3:end) = [];
A(3:3:end) = [];
For your second question regarding length checking, it's unclear what you're asking. Would an if comparison be enough ?
if numel(A) ~= 4
% ... Handle unexpected values here
end
Best,
As you mentioned in the question and in the comments that you need 4 elements at the end and if elements are less than 4 then you want to include the last element/s of b, the following should work:-
b=[1 2 3 4 5 6 7 8 9]
b_req=b(1:3:end);
temp=length(b_req);
if temp<4 b_req(end+1:4)=b(end-3+temp:end); % for including the elements of b so that total elements are 4 at the end
elseif temp>4 b_req=b_req(1:4); % for removing the extra elements
end
b_req
Output:-
b =
1 2 3 4 5 6 7 8 9
b_req =
1 4 7 9
and
if instead b=[1 2 3 4 5 6 7 8 9 10]; then the same code gives what you require, i.e. b_req = [1 4 7 10]
This code speaks for itself:
a = 1:15; % some vector
% returns every third element after the first one:
third_elemets = a(1:3:end);
% returns the missing elements for the vector to be in size 4 from the end of a
last_elements = a(end-3+length(third_elemets):end);
% take maximum 4 elements from a
logic_ind = true(min(4,length(third_elemets)),1);
% and concatanate them with last_elements (if there are any)
a = [third_elemets(logic_ind) last_elements]
and under the assumption that whenever there are less than 4 elements you simply take the last one(s) - it should always work.
This question pops up quite often in one form or another (see for example here or here). So I thought I'd present it in a general form, and provide an answer which might serve for future reference.
Given an arbitrary number n of vectors of possibly different sizes, generate an n-column matrix whose rows describe all combinations of elements taken from those vectors (Cartesian product) .
For example,
vectors = { [1 2], [3 6 9], [10 20] }
should give
combs = [ 1 3 10
1 3 20
1 6 10
1 6 20
1 9 10
1 9 20
2 3 10
2 3 20
2 6 10
2 6 20
2 9 10
2 9 20 ]
The ndgrid function almost gives the answer, but has one caveat: n output variables must be explicitly defined to call it. Since n is arbitrary, the best way is to use a comma-separated list (generated from a cell array with ncells) to serve as output. The resulting n matrices are then concatenated into the desired n-column matrix:
vectors = { [1 2], [3 6 9], [10 20] }; %// input data: cell array of vectors
n = numel(vectors); %// number of vectors
combs = cell(1,n); %// pre-define to generate comma-separated list
[combs{end:-1:1}] = ndgrid(vectors{end:-1:1}); %// the reverse order in these two
%// comma-separated lists is needed to produce the rows of the result matrix in
%// lexicographical order
combs = cat(n+1, combs{:}); %// concat the n n-dim arrays along dimension n+1
combs = reshape(combs,[],n); %// reshape to obtain desired matrix
A little bit simpler ... if you have the Neural Network toolbox you can simply use combvec:
vectors = {[1 2], [3 6 9], [10 20]};
combs = combvec(vectors{:}).' % Use cells as arguments
which returns a matrix in a slightly different order:
combs =
1 3 10
2 3 10
1 6 10
2 6 10
1 9 10
2 9 10
1 3 20
2 3 20
1 6 20
2 6 20
1 9 20
2 9 20
If you want the matrix that is in the question, you can use sortrows:
combs = sortrows(combvec(vectors{:}).')
% Or equivalently as per #LuisMendo in the comments:
% combs = fliplr(combvec(vectors{end:-1:1}).')
which gives
combs =
1 3 10
1 3 20
1 6 10
1 6 20
1 9 10
1 9 20
2 3 10
2 3 20
2 6 10
2 6 20
2 9 10
2 9 20
If you look at the internals of combvec (type edit combvec in the command window), you'll see that it uses different code than #LuisMendo's answer. I can't say which is more efficient overall.
If you happen to have a matrix whose rows are akin to the earlier cell array you can use:
vectors = [1 2;3 6;10 20];
vectors = num2cell(vectors,2);
combs = sortrows(combvec(vectors{:}).')
I've done some benchmarking on the two proposed solutions. The benchmarking code is based on the timeit function, and is included at the end of this post.
I consider two cases: three vectors of size n, and three vectors of sizes n/10, n and n*10 respectively (both cases give the same number of combinations). n is varied up to a maximum of 240 (I choose this value to avoid the use of virtual memory in my laptop computer).
The results are given in the following figure. The ndgrid-based solution is seen to consistently take less time than combvec. It's also interesting to note that the time taken by combvec varies a little less regularly in the different-size case.
Benchmarking code
Function for ndgrid-based solution:
function combs = f1(vectors)
n = numel(vectors); %// number of vectors
combs = cell(1,n); %// pre-define to generate comma-separated list
[combs{end:-1:1}] = ndgrid(vectors{end:-1:1}); %// the reverse order in these two
%// comma-separated lists is needed to produce the rows of the result matrix in
%// lexicographical order
combs = cat(n+1, combs{:}); %// concat the n n-dim arrays along dimension n+1
combs = reshape(combs,[],n);
Function for combvec solution:
function combs = f2(vectors)
combs = combvec(vectors{:}).';
Script to measure time by calling timeit on these functions:
nn = 20:20:240;
t1 = [];
t2 = [];
for n = nn;
%//vectors = {1:n, 1:n, 1:n};
vectors = {1:n/10, 1:n, 1:n*10};
t = timeit(#() f1(vectors));
t1 = [t1; t];
t = timeit(#() f2(vectors));
t2 = [t2; t];
end
Here's a do-it-yourself method that made me giggle with delight, using nchoosek, although it's not better than #Luis Mendo's accepted solution.
For the example given, after 1,000 runs this solution took my machine on average 0.00065935 s, versus the accepted solution 0.00012877 s. For larger vectors, following #Luis Mendo's benchmarking post, this solution is consistently slower than the accepted answer. Nevertheless, I decided to post it in hopes that maybe you'll find something useful about it:
Code:
tic;
v = {[1 2], [3 6 9], [10 20]};
L = [0 cumsum(cellfun(#length,v))];
V = cell2mat(v);
J = nchoosek(1:L(end),length(v));
J(any(J>repmat(L(2:end),[size(J,1) 1]),2) | ...
any(J<=repmat(L(1:end-1),[size(J,1) 1]),2),:) = [];
V(J)
toc
gives
ans =
1 3 10
1 3 20
1 6 10
1 6 20
1 9 10
1 9 20
2 3 10
2 3 20
2 6 10
2 6 20
2 9 10
2 9 20
Elapsed time is 0.018434 seconds.
Explanation:
L gets the lengths of each vector using cellfun. Although cellfun is basically a loop, it's efficient here considering your number of vectors will have to be relatively low for this problem to even be practical.
V concatenates all the vectors for easy access later (this assumes you entered all your vectors as rows. v' would work for column vectors.)
nchoosek gets all the ways to pick n=length(v) elements from the total number of elements L(end). There will be more combinations here than what we need.
J =
1 2 3
1 2 4
1 2 5
1 2 6
1 2 7
1 3 4
1 3 5
1 3 6
1 3 7
1 4 5
1 4 6
1 4 7
1 5 6
1 5 7
1 6 7
2 3 4
2 3 5
2 3 6
2 3 7
2 4 5
2 4 6
2 4 7
2 5 6
2 5 7
2 6 7
3 4 5
3 4 6
3 4 7
3 5 6
3 5 7
3 6 7
4 5 6
4 5 7
4 6 7
5 6 7
Since there are only two elements in v(1), we need to throw out any rows where J(:,1)>2. Similarly, where J(:,2)<3, J(:,2)>5, etc... Using L and repmat we can determine whether each element of J is in its appropriate range, and then use any to discard rows that have any bad element.
Finally, these aren't the actual values from v, just indices. V(J) will return the desired matrix.
how to Compare Values in Array on Specific Indexes in Matlab?
Suppose:
A= [2 2 3 3 3 4 4 4 5 5 6 6 7 8 8]
so i want to Find that
on index 2,3,4,5,6 values or same or not ?
Note: Index can be Dynamically Entered.
Number(length) of Values in Array also can be changed..
To check if they are all equal: use diff to subtract pairs of values, and then check if all those differences are 0.
A = [2 2 3 3 3 4 4 4 5 5 6 6 7 8 8];
ind = [2 3 4 5 6];
result = ~any(diff(A(ind)));
This is faster than using unique. With A and ind as in your example,
>> tic
for cont = 1:1e5
result = ~any(diff(A(ind)));
end
toc
tic
for cont = 1:1e5
result=numel(unique(A(ind)))==1;
end
toc
Elapsed time is 0.371142 seconds.
Elapsed time is 4.754007 seconds.
Hey this should do the trick:
A= [2 2 3 3 3 4 4 4 5 5 6 6 7 8 8];
B= [1,3,5];
C=A(B);
result=numel(unique(C))==1;
Here A is your data.
B is the index vector.
C contains the elements corresponding to the index vector.
result is 1 if all values were the same and 0 otherwise.
You can even "shorten" the code further by joining the two line:
result=numel(unique(A(B)))==1;
There are some ways, it depends on your taste.
For example, if the variable indexing contain the corresponding indexes:
unique(A(indexing));
will give you a vector with the unique elements in the sub-vector A(indexing). Then you just need to check the length:
length(unique(A(indexing))) == 1
I would avoid the use of numel when the function length is available (it is much more clearer what you are trying to achieve).
Other option is to compare the first element to the rest of the element in the sub-vector:
sub_vector = A(indexing);
all(sub_vector == sub_vector(1));
The second option assumes that the sub-vector will never be empty!
This question already has answers here:
Element-wise array replication in Matlab
(7 answers)
A similar function to R's rep in Matlab [duplicate]
(4 answers)
Closed 8 years ago.
Let's say, I have:
A=[1 2; 3 4];
I want to use repmat that return:
B = [1 1 2 2; 1 1 2 2; 3 3 4 4; 3 3 4 4]
Kindly need your help. Thank you
I do not know a method using repmat but here is a method using kron
kron([1 2 ; 3 4],[1 1;1 1])
ans =
1 1 2 2
1 1 2 2
3 3 4 4
3 3 4 4
An alternative which uses repmat is
A=[1 2; 3 4];
cell2mat(arrayfun(#(x)repmat(x,2,2),A,'UniformOutput',false))
ans =
1 1 2 2
1 1 2 2
3 3 4 4
3 3 4 4
arrayfun is used to evaluate each element in A using the anonymous function #(x)repmat(x,2,2) which replicates that single element into a 2x2 matrix.
The result of arrayfun is a 2x2 cell array where each element is a 2x2 matrix. We then convert this cell array into a matrix via cell2mat.
Let the data be defined as
A = [1 2; 3 4];
R = 2; %// number of repetitions of each row
C = 2; %// number of repetitions of each column. May be different from R
Two possible approaches are as follows:
The simplest method is to use indexing:
B = A(ceil(1/R:1/R:size(A,1)), ceil(1/C:1/C:size(A,2)));
If you really want to do it with repmat, you need to play with dimensions using permute and reshape: move original dimensions 1, 2 to dimensions 2, 4 (permute); do the repetition along new dimensions 1, 3 (repmat); collapse dimensions 1, 2 into one dimension and 3, 4 into another dimension (reshape):
[r c] = size(A);
B = reshape(repmat(permute(A, [3 1 4 2]), [R 1 C 1]), [r*R c*C]);
Example result for R=2, C=3 (obtained with any of the two approaches):
B =
1 1 1 2 2 2
1 1 1 2 2 2
3 3 3 4 4 4
3 3 3 4 4 4