I have a A matrix of size MxN where M is large and N is around 30.
[A,B,C,...,AD] = A(:,1:30)
The reason I am asking that is that I would like to give the columns a specific name (here A,B a,c,...,AD) and not being force to write:
[A,B,C,...,AD] = deal(A(:,1),A(:,2),A(:,3),...,A(:,30))
It's usually better to keep all columns together in the matrix and just access them through their column index.
Anyway, if you really need to separate them into variables, you can convert the matrix to a cell array of its columns with num2cell, and then generate a comma-separated list to be used in the right-hand side of the assignment. Note also that in recent Matlab versions you can remove deal:
A = magic(3); % example matrix
Ac = num2cell(A, 1);
[c1 c2 c3] = Ac{:}; % or [c1 c2 c3] = deal(Ac{:});
For generating that lexicographical sequence I recently, out of ignorance, wrote this
Data = rand(2,671);
r = rem(size(Data,2),26);
m = floor(size(Data,2)/26);
Alf = char('A'+(0:25)'); %TeX-like char seq
if m == 0
zzz = Alf(1:r);
else
zzz = Alf;
for x = 1:m-1
zzz = char(zzz,[char(Alf(x)*ones(26,1)),Alf]);
end
if r > 0
zzz = char(zzz, [char(Alf(m+1)*ones(r,1)),Alf(1:r)] );
end
end
Depending on the number of columns it generates column names until ZZ. Please let me know if there is a readily made command for this in matlab.
You would never ever use eval for such things!!! eval use is dangerous and wrong (but you can't resist):
% ==========
% Assign Data to indices
% ==========
for ind = 1:size(Data,2)
eval([zzz(ind,:) '= Data(:,' num2str(ind) ');']);
end
and your workspace looks like an alphabet soup.
Related
I am dealing with quite a big sparse matrix, its size is about 150,000*150,000. I need to access into its rows, extract the non-zero elements and replace these values following the rule as the code below:
tic
H = [];
for i = 1: size(A,2)
[a,b,c] = find(A(i,:)); % extract the rows
add = diff([0 c(2:end) 0]); % the replacing rule
aa = i*ones(1,size(a,2)); % return back the old position of rows
G0 = [aa' b' add']; % put it back the old position with replaced values
H = [H; G0];
end
H1 = H(:,1);
H2 = H(:,2);
H3 = H(:,3);
ADD = sparse(H1,H2,H3,nm,nm,nzmax);
toc
I found that the find function is really time consuming (0.1s/rows) in this code and with this current size of my sparse matrix, it takes me up to about 33 hours for this job. I do believe there is some ways out but I am such a newborn to coding and dealing with sparse matrix is really scary.
Would you drop me some ideas?
You can use the find function once applying it on the whole array then use accumarray to apply the function on each row:
[a b c]=find(A.');
add=accumarray(b,c,[],#(x){diff([0 ;x(2:end) ;0])});
H = [b a vertcat(add{:})];
So I want to concatenate an m x n matrix to obtain a 1 x mn matrix. The matrix I want to concatenate are generated from a while loop. Although the number of columns will always be 3, I however cannot tell how many rows there will be for each iteration. Also, the row sizes for each iteration may not always be the same.
The code runs in cases where the row sizes were all equal to 6, but in cases where they aren't equal I get an error:
Error using vertcat Dimensions of matrices being concatenated are not consistent.
parts of the code are as follows:
A = [];
B = [];
searchArea = 2;
for ii = 1: numel(velocity)
Do ....
while area(ii,:) < searchArea
Do ....
% COLLATE vectors for A
A = [A; [Ax(ii), Ay(ii), Az(ii)]];
Do ...
end
%# Copy the A into new variable (B) and Reshape into row vector so as to associate each row to its corresponding velocity
B = [B; reshape(A.',1,[])];
A = [];
end
Could someone please advice me on what I am doing wrong here. I would clarify further if there be need. Thanks guys!
If it's your intent that B ends up being a row vector, then you need to change this:
B = [B; reshape(A.',1,[])]; % Does vertical concatenation
to this:
B = [B reshape(A.',1,[])]; % Does horizontal concatenation (note there's no semicolon)
so that each row vector gotten from reshaping A gets added to the end of the row instead of as a new row (as the semicolon indicates).
I have matrix X (100000 X 10) and vector Y (100000 X 1). X rows are categorical and assume values 1 to 5, and labels are categorical too (11 to 20);
The rows of X are repetitive and there are only ~25% of unique rows, I want Y to have statistical mode of all the labels for a particular unique row.
And then there comes another dataset P (90000 X 10), I want to predict labels Q based on the previous exercise.
What I tried is finding unique rows of X using unique in MATLAB, and then assign statistical mode of each of these labels for the unique rows. For P, I can use ismember and carry out the same.
The issue is in the size of the dataset and it takes an 1.5-2 hours to complete the process. Is there a vectorize version possible in MATLAB?
Here is my code:
[X_unique,~,ic] = unique(X,'rows','stable');
labels=zeros(length(X_unique),1);
for i=1:length(X_unique)
labels(i)=mode(Y(ic==i));
end
Q=zeros(length(P),1);
for j=1:length(X_unique)
Q(all(repmat(X_unique(j,:),length(P),1)==P,2))=label(j);
end
You will be able to accelerate your first loop a great deal if you replace it entirely with:
labels = accumarray(ic, Y, [], #(y) mode(y));
The second loop can be accelerated by using all(bsxfun(#eq, X_unique(i,:), P), 2) inside Q(...). This is a good vectorized approach assuming your arrays are not extremely large w.r.t. the available memory on your machine. In addition, to save more time, you could use the unique trick you did with X on P, run all the comparisons on a much smaller array:
[P_unique, ~, IC_P] = unique(P, 'rows', 'stable');
EDIT:
to compute Q_unique in the following way: and then convert it back to the full array using:
Q_unique = zeros(length(P_unique),1);
for i = 1:length(X_unique)
Q_unique(all(bsxfun(#eq, X_unique(i,:), P_unique), 2)) = labels(i)
end
and convert back to Q_full to match the original P input:
Q_full = Q_unique(IC_P);
END EDIT
Finally, if memory is an issue, in addition to everything above, you might want you use a semi-vectorized approach inside your second loop:
for i = 1:length(X_unique)
idx = true(length(P), 1);
for j = 1:size(X_unique,2)
idx = idx & (X_unique(i,j) == P(:,j));
end
Q(idx) = labels(i);
% Q(all(bsxfun(#eq, X_unique(i,:), P), 2)) = labels(i);
end
This would take about x3 longer compared with bsxfun but if memory is limited then you gotta pay with speed.
ANOTHER EDIT
Depending on your version of Matlab, you could also use containers.Map to your advantage by mapping textual representations of the numeric sequences to the calculated labels. See example below.
% find unique members of X to work with a smaller array
[X_unique, ~, IC_X] = unique(X, 'rows', 'stable');
% compute labels
labels = accumarray(IC_X, Y, [], #(y) mode(y));
% convert X to cellstr -- textual representation of the number sequence
X_cellstr = cellstr(char(X_unique+48)); % 48 is ASCII for 0
% map each X to its label
X_map = containers.Map(X_cellstr, labels);
% find unique members of P to work with a smaller array
[P_unique, ~, IC_P] = unique(P, 'rows', 'stable');
% convert P to cellstr -- textual representation of the number sequence
P_cellstr = cellstr(char(P_unique+48)); % 48 is ASCII for 0
% --- EDIT --- avoiding error on missing keys in X_map --------------------
% find which P's exist in map
isInMapP = X_map.isKey(P_cellstr);
% pre-allocate Q_unique to the size of P_unique (can be any value you want)
Q_unique = nan(size(P_cellstr)); % NaN is safe to use since not a label
% find the labels for each P_unique that exists in X_map
Q_unique(isInMapP) = cell2mat(X_map.values(P_cellstr(isInMapP)));
% --- END EDIT ------------------------------------------------------------
% convert back to full Q array to match original P
Q_full = Q_unique(IC_P);
This takes about 15 seconds to run on my laptop. Most of which is consumed by computation of mode.
I need some help to vectorize the following operation since I'm a little confused.
So, I have a m-by-2 matrix A and n-by-1 vector b. I want to create a n-by-1 vector c whose entries should be the values of the second column of A whose line is given by the line where the correspondent value of b would fall...
Not sure if I was clear enough. Anyway, the code below does compute c correctly so you can understand what is my desired output. However, I want to vectorize this function since my real n and m are in the order of many thousands.
Note that values of bare non-integer and not necessarily equal to any of those in the first column of A (these ones could be non-integers too!).
m = 5; n = 10;
A = [(0:m-1)*1.1;rand(1,m)]'
b = (m-1)*rand(n,1)
[bincounts, ind] = histc(b,A(:,1))
for i = 1:n
c(i) = A(ind(i),2);
end
All you need is:
c = A(ind,2);
i just started with matlab and stuck somewhere...consider example
X=(3:7)
Z=(2:6)
for (i=1:5)
y=abs(X(i)-Z);
dm=min(y);
D=find(y==min(y))
D1=Z(D);
end
i want D and D1 to be a column/row vector.Please help.
Currently you are storing scalar values into D and D1.
Maybe you wanted to save the values into i-th column of D and D1?
X=(3:7)
Z=(2:6)
for (i=1:5)
y=abs(X(i)-Z);
dm=min(y);
D(i)=find(y==min(y));
D1(i)=Z(D(i));
end
If you're looking to simply convert D and D1 from row vectors to column vectors, you can simply add the following lines at the end of your code:
D = D';
D1 = D1';
The ' operation simply gives you the transpose of the matrix in question.
use:
if isrow(D)
D = D.'; % .' is the transpose operator
end
BTW: you don't need to use parentheses that often.
I think the following will help too:
% convert ANY array into a column vector
D = D(:);
% convert ANY array into a row vector
D1 = D1(:).';
doing it like this will guarantee that one is column and the other row, without any performance loss.