I have the following MATLAB table
item_a item_b score
a b 1
a b 1
b c 3
d e 2
d e 1
d e 0
I want to average the redundant rows. The desired result is as follows:
item_a item_b score
a b (1+1)/2
b c 3
d e (2+1+0)/3
This is a classic scenario for the findgroups, split-apply workflow.
Given your table named t:
% Find mean values.
G = findgroups(t.item_a);
meanValues = splitapply(#mean,t.score,G);
% Create new table.
[~,i] = unique(G);
newTable = t(i,:);
newTable.score = meanValues
newTable contains the desired table.
See this documentation page for more examples.
This is what I got. You can tweak with the final results. There is a similar example on MATLAB documentation. Here are two key functions, accumarray and unique. Note that this solution works only for array inputs not cell data types. By manipulating data types, you can also find the solution for table and cell data types. Otherwise, I think for loop will be necessary.
items = ['a' 'b'
'a' 'b'
'b' 'c'
'd' 'e'
'd' 'e'
'd' 'e' ];
scores = [1 1 3 2 1 0]';
[items_unique,ia,ic] = unique(items,'rows');
score_mean = accumarray(ic,scores, [], #mean);
result = {items_unique score_mean};
Related
I have a cell-matrix like the below,
the first column: A, B, C
the second column: A, B, D
the third column: 1, 1, 1
Which means that A and A has 1 unit, B and B has 1 unit and C and D has one unit
How can I conveniently create the following matrix (mat) in matlab?
[Name,A,B,C,D
A,1,NA,NA,NA
mat = B,NA,1,NA,NA
C,NA,NA,NA,NA
D,NA,NA,NA,1]
I think I can use a loop to achieve that, but actually the dimension is much larger than the example able. How can I do that?
A, B, C, D here are characters, if the matrix cannot contain both numeric and character, I can remove the first column and the first row in mat. Also actually the first matrix containing the relationship of A, B, C, D is a 3*3 cell.
Desired output can be produced:
mycell = {...
'A' , 'B' , 'C'
'A' , 'B' , 'D'
1 , 1 , 1};
[U, ~, IDX] = unique(mycell(1:2,:));
IDX = reshape(IDX,2,[]).';
mat = accumarray(IDX,[mycell{3,:}],[],[],NA);
so U is names of rows/columns
IDX shows indices of rows and columns of mat that contain non NA values
accumarray is the function that can convert indices and values to desired matrix
Now you can access elements of mat by numeric indices i.e. mat(1,3) that represent relationship of A and C
If you want to work with character indices instead of numeric ones you can do this:
index=cell2struct(num2cell(1:numel(U)),U,2);
mat(index.A, index.C)
index is a struct that maps characters to numbers. Also you can use index this way:
mat(index.('A'), index.('C'))
In the following toy example, tables t1 and t2 have shapes (3 x 0) and (3 x 1), respectively. Furthermore, both tables have the same row names.
>> t1 = table('RowNames', {'a', 'b', 'c'});
>> t2 = table([3 ; 2 ; 1], ...
'RowNames', {'c', 'a', 'b'}, 'VariableNames', {'x'});
Then a copy of t2's single column is added to t1 as a new column, with the same variable name.
>> t1.('x') = t2.('x');
The resulting table t1, however, differs from t2 in the association between row names and the values in the x-column:
>> t1({'a', 'b', 'c'}, :)
ans =
x
_
a 3
b 2
c 1
>> t2({'a', 'b', 'c'}, :)
ans =
x
_
a 2
b 1
c 3
What's the simplest way to assign t2.('x') to t1.('x') "respecting rownames"? By this last condition I mean that the final t1 should look just like t2; e.g.:
>> t1({'a', 'b', 'c'}, :)
ans =
x
_
a 2
b 1
c 3
You can index the table using row names so if you extract the list of rownames from t1 you can use that as the ordering for t2:
order = t1.Properties.RowNames % cell array
intermediate = t2(order, :);
or just do it all in one go:
t2(t1.Properties.RowNames, :);
Since t1 doesn't have the x column you can concatenate t1 with column x of t2
>> t1=[t1, t2(:,'x')]
t1 =
x
_
a 2
b 1
c 3
It will automatically take care of matching rows.
OK, this is the OP here.
I found a (potential) answer to my question: instead of
t1.('x') = t2.('x');
use
t1.('x') = t2{t1.Properties.RowNames, 'x'};
I say that this is a "potential" answer because with MATLAB I never know when something that works for a particular type, or under particular circumstances, will generalize. E.g., at this point I don't know if the above will work if column x holds non-numeric values.
If someone knows of a better way, or can point to documentation in support of my lucky guess here, please post it. I'll be glad to accept it as the answer.
I have the following table
name = ['A' 'A' 'A' 'B' 'B' 'C' 'C' 'C' 'C' 'D' 'D' 'E' 'E' 'E']';
value = randn(14, 1);
T = table(name, value);
i,e.
T =
name value
____ _________
A 0.0015678
A -0.76226
A 0.98404
B -1.0942
B 0.71249
C 1.688
C 1.4001
C -0.9278
C -1.3725
D 0.11563
D 0.076776
E 1.0568
E 1.1972
E 0.29037
I want to transform it in the following way: take the first two cells in value corresponding to different values in name and put it in the 5x2 matrix. This matrix would have rows corresponding to different names A,B,C,D,E and columns corresponding to values, e.g. the first two rows are
0.0015678 -0.76226
-1.0942 0.71249
This can be done with accumarray using a custom function. The first step is to convert the name column of T into a numeric vector; and then accumarray can be applied.
This approach requires T being sorted according to column 1, because only in this case is accumarray guaranteed to preserve order (as indicated in its documentation). So if T may not be sorted (although it is in your example), sort it first using sortrows.
T = sortrows(T, 1); %// you can remove this line if T is guaranteed to be sorted
[~, ~, names] = unique(T(:,1)); %// names as a numeric vector
result = cell2mat(accumarray(names, T.value, [], #(x) {x([1 2]).'}));
First figure out where each name has values located in the table, then cycle through each name and place the first two values encountered for each name into individual cell arrays. Once you're done, reshape the matrix to 5 x 2 as you have said. As such, do something like this:
names = unique(T.name); %// 1
ind = arrayfun(#(x) find(T.name == x), names, 'uni', 0); %// 2
vals = cellfun(#(x) T.value(x(1:2)), ind, 'uni', 0); %// 3
m = [vals{:}].'; %// 4
Let's go through each line of code slowly.
Line #1
The first line finds all unique names through unique and we store them into names.
Line #2
The next line goes through all of the unique names and finds those locations / rows in the table that share that particular name. I use arrayfun and go through each name in names, find those rows that share the same name as one we are looking for, and place those row locations into individual cells; these are stored into ind. To find the locations of each valid name in our table, I use find and the locations are placed into a column vector. As such, we will have five column vectors where each column vector is placed into an individual cell. These column vectors will tell us which rows match a particular name located in your table.
Line #3
The next line uses cellfun to go through each of the cells in ind and extracts the first two row locations that share a particular name, indexes into the value field for your table to pull those two values, and these are placed as two-element vectors into individual cells for each name.
Line #4
The last line of code simply unrolls each two-element vector. The first two elements of each name get stored into columns. To get them into rows, I simply transpose the unrolling. The output matrix is stored into m.
If you want to see what the output looks like, this is what I get when I run the above code with your example table:
m =
0.0016 -0.7623
-1.0942 0.7125
1.6880 1.4001
0.1156 0.0768
1.0568 1.1972
Be advised that I only showed the first 5 digits of precision so there is some round-off at the end. However, this is only for display purposes and so what I got is equivalent to what your expect for the output.
Hope this helps!
If you want use the tables, you could try something like this:
count = 1;
U = unique(table2array(T(:,1)));
for ii = 1:size(U,1)
A = find(table2array(T(:,1)) == U(ii));
A = A(1:2);
B(count,1:2) = table2array(T(A,2));
count = count + 1;
end
Personally, I would find this simpler to do with your name and value arrays and forget about the table. If it is a requirement then I understand, however I will provide my solution still. It may provide some insight either way.
count = 1;
U = unique(name);
for ii = 1:size(U,1)
A = find(name == U(ii));
A = A(1:2);
B(count,1:2) = value(A);
count = count + 1;
end
Quick and dirty, but hopefully it's good enough. Good luck.
Another solution that is more manageable and easily scalable exists. Since MATLAB R2013b you can use a specialized function for pivoting a table (which is what you want to do): unstack.
In order to get exactly what you wanted, you need to add an extra variable to your table that will indicate replications:
name = ['A' 'A' 'A' 'B' 'B' 'C' 'C' 'C' 'C' 'D' 'D' 'E' 'E' 'E']';
value = randn(14, 1);
rep = [1, 2, 3, 1, 2, 1, 2, 3, 4, 1, 2, 1, 2, 3];
T = table(name, value, rep);
T =
name value rep
____ _________ ___
A 0.53767 1
A 1.8339 2
A -2.2588 3
B 0.86217 1
B 0.31877 2
C -1.3077 1
C -0.43359 2
C 0.34262 3
C 3.5784 4
D 2.7694 1
D -1.3499 2
E 3.0349 1
E 0.7254 2
E -0.063055 3
Then you just use unstack like this:
pivotTable = unstack(T, 'value','name')
pivotTable =
rep A B C D E
___ _______ _______ ________ _______ _________
1 0.53767 0.86217 -1.3077 2.7694 3.0349
2 1.8339 0.31877 -0.43359 -1.3499 0.7254
3 -2.2588 NaN 0.34262 NaN -0.063055
4 NaN NaN 3.5784 NaN NaN
Afterwards, it's a matter of re-arranging the table if you still want to.
The easiest way is to first convert the table into a matrix form and then reshape it by using the "reshape" function in Matlab.
matrix = t{:,:};% t-- your table variable
reshape_matrix = reshape(matrix ,[2,3]) % [2,3]--> the size of the matrix you desire
These two steps can be done by one line of code
reshape_matrix = reshape(t{:,:},[2,3]);
I've got 2 string cell arrays, one is the unique version of the other. I would like to count the number of occurrence of each values in the unique cell array given the other cell array. I got a large cell array so I thought I'd try my best to find answers to a more faster approach as oppose to looping...
An example:
x = {'the'
'the'
'aaa'
'b'
'the'
'c'
'c'
'd'
'aaa'}
y=unique(x)
I am looking for an output in any form that contains something like the following:
'aaa' = 2
'b' = 1
'c' = 2
'd' = 1
'the' = 3
Any ideas?
One way is to count the indices unique finds:
[y, ~, idx] = unique(x);
counts = histc(idx, 1:length(y));
which gives
counts =
2
1
2
1
3
in the same order as y.
histc is my default fallback for counting things, but the function I always forget about is probably better in this case:
counts = accumarray(idx, 1);
should give the same result and is probably more efficient.
I have two arrays of the following form:
v1 = [ 1 2 3 4 5 6 7 8 9 ... ]
c2 = { 'a' 'a' 'a' 'b' 'b' 'c' 'c' 'c' 'c' ... }
(all values are examples only, no pattern can be assumed in the real data. v1 and c2 have the same size)
I want to obtain a vector containing the summation of the components of v1 corresponding to equal values in c2. In the example above, the first component of the resulting vector would be 1+2+3, the second 4+5, and so on.
I know I can do it in a loop of the form:
uni_c2 = unique(c2);
result = zeros(size(uni_c2));
for i = 1:numel(uni_c2)
result(i) = sum( v1(strcmp(uni_c2(i),c2)) );
end
Is there a single command or a vectorized way of doing the same operation?
You can do this in two lines:
[b, m, n] = unique(c2)
result = accumarray(n', v1)
The elements of result correspond to the strings in the cell array b.
This is vectorized but a bad idea for very large vectors. For some problems a "vectorized" solution is worse than a for loop.
>> v1 = [ 1 2 3 4 5 6 7 8 9];
>> c2 = 'aaabbcccc'-'a'
c2 =
0 0 0 1 1 2 2 2 2
>> N = repmat(c2',1,max(c2)-min(c2)+1) == repmat([min(c2):max(c2)],size(c2,2),1);
>> v1*N
ans =
6 9 30
I think a very general (and vectorized) solution is something like this:
v1 = [ 1 2 3 4 5 6 7 8 9 ]
c2 = { 'a' 'a' 'a' 'b' 'b' 'c' 'c' 'c' 'c' }
uniqueValuesInC2 = unique(c2);
conditionalSumOfV1 = #(x)(sum(v1(strcmp(c2, x))));
result = cellfun(conditionalSumOfV1, uniqueValuesInC2)
Perhaps my solution needs a bit of an explanation to the untrained eye:
So first you actually need to compute the different possible values in c2, which is done by unique.
The conditionalSumOfV1 function takes an argument x, it compares every element in c2 with x and selects the corresponding elements in v1 and sums them.
Finally cellfun is comparable to a foreach construct in some other languages: the function conditionalSum is evaluated for every value in the cell array you provide (in this case: every unique value in c2) and stores it in the output array. For other types of container variables (arrays, structs), MATLAB has equivalent foreach-like constructs: arrayfun, structfun.
This will work for contents of c2 that are longer than a single character and it does not require a large repmat operation as stardt's solution. I do however have my doubts when it comes to long arrays where c2 has only a few duplicate values., but I guess that will be a hard case for most algorithms. If you are in such a case, you might need to take a look at the extra outputs of unique or write your own alternative to unique (i.e. write for loops, preferably in a compiled language/MEX).