I have a number of arrays of different sizes, e.g.
A=1:10; B=1:9 etc.
Now I want to save these arrays into one big matrix. In this example I would want it to be 2x10, with NaN for the remaining spot not filled by array B. I know how to preallocate this matrix with NaN(size), but my question here is how to get these arrays in with their different lengths. It must be a super simple command, but I just can't seem to think of it!
You need to specify the column indices:
>> BigMat = NaN(2,10);
>> BigMat(1, 1:numel(A) ) = A;
>> BigMat(2, 2:numel(B) ) = B;
Also take a look at cell structures. They can contain a variety of different data types. For example
BigMat{1}=A;
BigMat{2}=B;
BigMat{3}='Some text string'
Related
I'm really sorry to bother so I hope it is not a silly or repetitive question.
I have been scraping a website, saving the results as a collection in MongoDB, exporting it as a JSON file and importing it in MATLAB.
At the end of the story I obtained a struct object organised
like this one in the picture.
What I'm interested in are the two last cell arrays (which can be easily converted to string arrays with string()). The first cell array is a collection of keys (think unique products) and the second cell array is a collection of values (think prices), like a dictionary. Each field is an instance of possible values for a set of this keys (think daily prices). My goal is to build a matrix made like this:
KEYS VALUES_OF_FIELD_1 VALUES_OF_FIELD2 ... VALUES_OF_FIELDn
A x x x
B x z NaN
C z x y
D NaN y x
E y x z
The main problem is that, as shown in the image and as I tried to explain in the example matrix, I don't always have a value for all the keys in every field (as you can see sometimes they are 321, other times 319 or 320 or 317) and so the key is missing from the first array. In that case I should fill the missing value with a NaN. The keys can be ordered alphabetically and are all unique.
What would you think would be the best and most scalable way to approach this problem in MATLAB?
Thank you very much for your time, I hope I explained myself clearly.
EDIT:
Both arrays are made of strings in my case, so types are not a problem (I've modified the example). The main problem is that, since the keys vary in each field, firstly I have to find all the (unique) keys in the structure, to build the rows, and then for each column (field) I have to fill the values putting NaN where the key is missing.
One thing to remember you can't simply use both strings and number in one matrix. So, if you combine them together they can be either all strings or all numbers. I think all strings will work for you.
Before make a matrix make sure that all the cells have same element.
new_matrix = horzcat(keys,values1,...valuesn);
This will provide a matrix for each row (according to your image). Now you can use a for loop to get matrices for all the rows.
For now, I've solved it by considering the longest array of keys in the structure as the complete set of keys, let's call it keys_set.
Then I've created for each field in the structure a Map object in this way:
for i=1:length(structure)
structure(i).myMap = containers.Map(structure(i).key_field, structure(i).value_field);
end
Then I've built my matrix (M) by checking every map against the keys_set array:
for i=1:length(keys_set)
for j=1:length(structure)
if isKey(structure(j).myMap,char(keys_set(i)))
M(i,j) = string(structure(j).myMap(char(keys_set(i))));
else
M(i,j) = string('MISSING');
end
end
end
This works, but it would be ideal to also be able to check that keys_set is really complete.
EDIT: I've solved my problem by using this function and building the correct set of all the possible keys:
%% Finding the maximum number of keys in all the fields
maxnk = length(structure(1).key_field);
for i=2:length(structure)
if length(structure(i).key_field) > maxnk
maxnk = length(structure(i).key_field);
end
end
%% Initialiting the matrix containing all the possibile set of keys
keys_set=string(zeros(maxnk,length(structure)));
%% Filling the matrix by putting "0" if the dimension is smaller
for i=1:length(structure)
d = length(string(structure(i).key_field));
if d == maxnk
keys_set(:,i) = string(structure(i).key_field);
else
clear tmp
tmp = [string(structure(i).key_field); string(zeros(maxnk-d,1))];
keys_set(:,i) = tmp;
end
end
%% Merging without duplication and removing the "0" element
keys_set = union_several(keys_set);
keys_set = keys_set(keys_set ~= string(0));
I am using a cell array to contain 1x2 vectors of grid locations in the form [row, col].
I would like to check if another grid location is included in this cell array.
Unfortunately, my current code results in an error, and I cannot quite understand why:
in_range = ismember( 1, ismember({[player.row, player.col]}, proximity(:,1)) );
where player.row and player.col are integers, and proximity's first column is the aforementioned cell array of grid locations
the error I am receiving is:
??? Error using ==> cell.ismember at 28
Input must be cell arrays of strings.
Unfortunately, I have not been able to find any information regarding using ismember() in this fashion, only with cell arrays as strings or with single integers in each cell rather than vectors.
I have considered converting using num2str() and str2num(), but since I must perform calculations between the conversions, and due to the number of iterations the code will be looped for (10,000 loops, 4 conversions per loop), this method seems prohibitive.
Any help here would be greatly appreciated, thank you
EDIT: Why does ismember() return this error? Does it treat all vectors in a cell array as string arrays?
EDIT: Would there be a better / more efficient method of determining if a 1 is in the returned vector than
ismember( 1, ismember(...))?
I'm short of time at the moment (being Chrissy eve and all), so this is going to have to be a very quick answer.
As I understand it, the problem is to find if an x y coordinate lies in a sequence of many x y coordinates, and if so, the index of where it lies. If this is the case, and if you're interested in efficiency, then it is wasteful to mess around with strings or cell arrays. You should be using numeric matrices/vectors for this.
So, my suggestion: Convert the first row of your cell array to a numeric matrix. Then, compare your x y coordinates to the rows of this numerical matrix. Because you only want to know when both coordinates match a row of the numerical matrix, use the 'rows' option of ismember - it will return a true only on matching an entire row rather than matching a single element.
Some example code that will hopefully help follows:
%# Build an example cell array with coordinates in the first column, and random strings in the second column
CellOfLoc = {[1 2], 'hello'; [3 4], 'world'; [5 6], '!'};
%# Convert the first column of the cell array to a numerical matrix
MatOfLoc = cell2mat(CellOfLoc(:, 1));
%# Build an example x y coordinate location to test
LocToTest = [5 6];
%# Call ismember, being sure to use the rows option
Index = ismember(MatOfLoc, LocToTest, 'rows');
Note, if the indices in your cell array are in string form, then obviously you'll also need a call to str2num in there somewhere before you call ismember.
One other thing, I notice you're a new member, so welcome to the site. If you think this response satisfactorily answered your question, then please mark the question answered by clicking the tick mark next to this response.
I'm working on a model to use matlab as graphical representation for other model. Therefore I'd like to have a matrix that can be updated with both letters and numbers. Numbers will represent a speed while for example '-' may represent a empty section. In the matlab documentation and on internet I found a lot of interesting tips, but not what I need.
Thanks in advance!
You cannot represent data of numeric type (integers/floating points) and data of char type in a matrix. However, you can, use cells, which are similar to matrices, and can hold different data types in each cell. Here's an example.
A={[1 2 3],'hello';'world',[4,5,6]'}
A =
[1x3 double] 'hello'
'world' [3x1 double]
Here the first cell contains a row vector, the second and third cells contain strings and the fourth cell contains a column vector. Indexing into a cell is similar to that of arrays, with one minor difference: use {} to group the indices. e.g., to access the element in the second row, first column, do
A{2,1}
ans =
world
You can also access an element of an array inside a cell like
A{2,2}(2)
ans =
5
If you're wanting to store mixtures of numeric and character type data, yoda has the correct suggestion: use cell arrays.
However, based on the example you described you may have another option. If the character entries in your matrix are there for the purpose of identifying "missing data", it may make more sense to use a purely numeric matrix containing unique values like NaN or Inf to identify data points that are empty or where data is not available.
When performing operations on your matrix, you would then have to index only elements that are finite (using, for example, ISFINITE) and perform your calculations on them. There are even some functions in the Statistics Toolbox that will perform operations ignoring NaN values. This may be a cleaner way to go since you can keep your matrix as a numeric type ('single' or 'double' precision) instead of having to mess with cell arrays.
I'm currently working in an area that is related to simulation and trying to design a data structure that can include random variables within matrices. To motivate this let me say I have the following matrix:
[a b; c d]
I want to find a data structure that will allow for a, b, c, d to either be real numbers or random variables. As an example, let's say that a = 1, b = -1, c = 2 but let d be a normally distributed random variable with mean 0 and standard deviation 1.
The data structure that I have in mind will give no value to d. However, I also want to be able to design a function that can take in the structure, simulate a uniform(0,1), obtain a value for d using an inverse CDF and then spit out an actual matrix.
I have several ideas to do this (all related to the MATLAB icdf function) but would like to know how more experienced programmers would do this. In this application, it's important that the structure is as "lean" as possible since I will be working with very very large matrices and memory will be an issue.
EDIT #1:
Thank you all for the feedback. I have decided to use a cell structure and store random variables as function handles. To save some processing time for large scale applications, I have decided to reference the location of the random variables to save time during the "evaluation" part.
One solution is to create your matrix initially as a cell array containing both numeric values and function handles to functions designed to generate a value for that entry. For your example, you could do the following:
generatorMatrix = {1 -1; 2 #randn};
Then you could create a function that takes a matrix of the above form, evaluates the cells containing function handles, then combines the results with the numeric cell entries to create a numeric matrix to use for further calculations:
function numMatrix = create_matrix(generatorMatrix)
index = cellfun(#(c) isa(c,'function_handle'),... %# Find function handles
generatorMatrix);
generatorMatrix(index) = cellfun(#feval,... %# Evaluate functions
generatorMatrix(index),...
'UniformOutput',false);
numMatrix = cell2mat(generatorMatrix); %# Change from cell to numeric matrix
end
Some additional things you can do would be to use anonymous functions to do more complicated things with built-in functions or create cell entries of varying size. This is illustrated by the following sample matrix, which can be used to create a matrix with the first row containing a 5 followed by 9 ones and the other 9 rows containing a 1 followed by 9 numbers drawn from a uniform distribution between 5 and 10:
generatorMatrix = {5 ones(1,9); ones(9,1) #() 5*rand(9)+5};
And each time this matrix is passed to create_matrix it will create a new 10-by-10 matrix where the 9-by-9 submatrix will contain a different set of random values.
An alternative solution...
If your matrix can be easily broken into blocks of submatrices (as in the second example above) then using a cell array to store numeric values and function handles may be your best option.
However, if the random values are single elements scattered sparsely throughout the entire matrix, then a variation similar to what user57368 suggested may work better. You could store your matrix data in three parts: a numeric matrix with placeholders (such as NaN) where the randomly-generated values will go, an index vector containing linear indices of the positions of the randomly-generated values, and a cell array of the same length as the index vector containing function handles for the functions to be used to generate the random values. To make things easier, you can even store these three pieces of data in a structure.
As an example, the following defines a 3-by-3 matrix with 3 random values stored in indices 2, 4, and 9 and drawn respectively from a normal distribution, a uniform distribution from 5 to 10, and an exponential distribution:
matData = struct('numMatrix',[1 nan 3; nan 2 4; 0 5 nan],...
'randIndex',[2 4 9],...
'randFcns',{{#randn , #() 5*rand+5 , #() -log(rand)/2}});
And you can define a new create_matrix function to easily create a matrix from this data:
function numMatrix = create_matrix(matData)
numMatrix = matData.numMatrix;
numMatrix(matData.randIndex) = cellfun(#feval,matData.randFcns);
end
If you were using NumPy, then masked arrays would be the obvious place to start, but I don't know of any equivalent in MATLAB. Cell arrays might not be compact enough, and if you did use a cell array, then you would have to come up with an efficient way to find the non-real entries and replace them with a sample from the right distribution.
Try using a regular or sparse matrix to hold the real values, and leave it at zero wherever you want a random variable. Then alongside that store a sparse matrix of the same shape whose non-zero entries correspond to the random variables in your matrix. If you want, the value of the entry in the second matrix can be used to indicate which distribution (ie. 1 for uniform, 2 for normal, etc.).
Whenever you want to get a purely real matrix to work with, you iterate over the non-zero values in the second matrix to convert them to samples, and then add that matrix to your first.
I've got an n-by-k sized matrix, containing k numbers per row. I want to use these k numbers as indexes into a k-dimensional matrix. Is there any compact way of doing so in MATLAB or must I use a for loop?
This is what I want to do (in MATLAB pseudo code), but in a more MATLAB-ish way:
for row=1:1:n
finalTable(row) = kDimensionalMatrix(indexmatrix(row, 1),...
indexmatrix(row, 2),...,indexmatrix(row, k))
end
If you want to avoid having to use a for loop, this is probably the cleanest way to do it:
indexCell = num2cell(indexmatrix, 1);
linearIndexMatrix = sub2ind(size(kDimensionalMatrix), indexCell{:});
finalTable = kDimensionalMatrix(linearIndexMatrix);
The first line puts each column of indexmatrix into separate cells of a cell array using num2cell. This allows us to pass all k columns as a comma-separated list into sub2ind, a function that converts subscripted indices (row, column, etc.) into linear indices (each matrix element is numbered from 1 to N, N being the total number of elements in the matrix). The last line uses these linear indices to replace your for loop. A good discussion about matrix indexing (subscript, linear, and logical) can be found here.
Some more food for thought...
The tendency to shy away from for loops in favor of vectorized solutions is something many MATLAB users (myself included) have become accustomed to. However, newer versions of MATLAB handle looping much more efficiently. As discussed in this answer to another SO question, using for loops can sometimes result in faster-running code than you would get with a vectorized solution.
I'm certainly NOT saying you shouldn't try to vectorize your code anymore, only that every problem is unique. Vectorizing will often be more efficient, but not always. For your problem, the execution speed of for loops versus vectorized code will probably depend on how big the values n and k are.
To treat the elements of the vector indexmatrix(row, :) as separate subscripts, you need the elements as a cell array. So, you could do something like this
subsCell = num2cell( indexmatrix( row, : ) );
finalTable( row ) = kDimensionalMatrix( subsCell{:} );
To expand subsCell as a comma-separated-list, unfortunately you do need the two separate lines. However, this code is independent of k.
Convert your sub-indices into linear indices in a hacky way
ksz = size(kDimensionalMatrix);
cksz = cumprod([ 1 ksz(1:end-1)] );
lidx = ( indexmatrix - 1 ) * cksz' + 1; #'
% lindx is now (n)x1 linear indices into kDimensionalMatrix, one index per row of indexmatrix
% access all n values:
selectedValues = kDimensionalMatrix( lindx );
Cheers!