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));
Related
I have a row cell vector M, containing matrices in each cell. Every matrix m (matrix inside the big matrix M) is made of 2 channels (columns), of which I only want to use the first.
The approach I thought about was going through each m, check if it has 2 channels, and if that is the case delete the second channel.
Is there a way to just slice it in matlab? or loop it and obtain the matrix M as the matrix m would disappear.
First code is:
load('ECGdata.mat')
I have the below.
when I double-click in one of the variable , here is what I can see:
As you can see the length of each matrix in each cell is different. Now let's see one cell:
The loop I'm trying to get must check the shape of the matrix (I'm talking python here/ I mean if the matrix has 2 columns then delete the second) because some of the variables of the dataframe have matrix containing one column (or just a normal column).
In here I'm only showing the SR variable that has 2 columns for each matrix. Its not the case for the rest of the variables
You do not need to delete the extra "channel", what you can do is quite simple:
newVar = cellfun(#(x)x(:,1), varName, 'UniformOutput', false);
where varName is SR, VF etc. (just run this command once for each of the variables you load).
What the code above does is go over each element of the input cell (an Nx2 matrix in your example), and select the first column only. Then it stores all outputs in a new cell array. In case of matrices with a single column, there is no effect - we just get the input back.
(I apologize in advance if there is some typo / error in the code, as I am writing this answer from my phone and cannot test it. Please leave a comment if something is wrong, and I'll do my best to fix it tomorrow.)
Lets say i have a matrix A of 300x65. the last column(65th) contains ordered values (1,2,3). the first 102 elements are '1', the second 50 elements are '2' and the remainder will be '3'.
I have another matrix B, which is 3x65 and i want to copy the first row of B by the number of '1's in matrix A. The second row of B should be copied by the number of '2's in in matrix A and the 3th row should be copied by the remaining value of matrix A. By doing this, matrix B should result in a 300x65 matrix.
I've tried to use the repmat function of matlab with no succes, does anyone know how to do this?
There are many inconsistencies in your problem
first if you copy 1 row of B for every element of A(which will end up happening by your description) that will result in a matrix 19500x65
secondly copy its self is a vague term, do you mean duplicate? do you want to store the copied value into a new var?
what I gathered from your problem is you want to preform some operation between A and B to create a matrix and store it in B which in itself will cause the process to warp as it goes if you do not have another variable to store the result in
so i suggest using a third variable c to store the result in and then if you need it to be in b set b = C
also for whatever process you badly described I recommend learning to use a 'for' loop effectively because it seems like that is what you would need to use
syntax for 'for' loop
for i = [start:increment:end]
//loops for the length of [start:increment:end]
//sets i to the nth element of [start:increment:end] where n is the number of times the loop has run
end
If I understand your question, this should do it
index = A(:,end); % will be a column of numbers with values of 1, 2, or 3
newB = B(index,:); % B has 3 rows, which are copied as required by "index"
This should result in newB having the same number of rows as A and the same number of columns as the original B
First, a little background to my problem:
I am building an object recognition system using a geometric hashing technique. My hash table is indexed by the affine co-ordinates of points in a model determined by a basis triplet (allowing an affine invariant representation of any learned object). Each hash table entry is a structure :
entry = struct('ModelName', modelName, 'BasisTriplet', [a; b; c])];
Now, an arbitrary basis triplet is extracted from image points then the affine co-ordinates of all other points are calculated relative to this basis and used as indices to the hash table. For each entry that exists in this hash bin, a vote is cast for the modelName and basis triplet.
After checking all points, the models and their corresponding basis triplets with a sufficiently high number of votes are taken as candidates for an object and a further verification step is performed.
I am unsure however what is the most efficient method of casting these votes. Currently I am using a dynamic cell array, each time a new model and basis triplet pair is voted for, an additional row is added to the array. Otherwise the vote count of an existing candidate is incremented.
for keylist = 1:length(keylist)
% Where keylist is an array of indicies to the relevant keys to look up
% xkeys is the n by 2 array of all of the keys in the hash table
% Obtain this hash bin
bin = hashTable(xkeys(keylist(i), 1), xkeys(keylist(i), 2));
% Vote for every entry in the bin
for entry = 1:length(bin)
% Find the index of this model/basis in the voting accumulator
indAcc = find( strcmp(bin.ModelName, v_models(:, 1)) & myIsEqual(v_basisTriplets, bin.BasisTriplet) );
if isempty(indAcc)
% If entries do not exist yet, Add new entries
v_models = [v_models; {bin.ModelName, 1}];
v_basisTriplets = cat(3, v_basisTriplets, bin.BasisTriplet);
else
% Otherwise increment the count
v_models(indAcc, 2) = v_models(indAcc, 2)+1;
end
end
end
There is a separate 3D array (v_basisTriplets) in which the 2D basis array is concatenated and indexed along the 3rd dimension. I did have these basis triplets in the cell array also, however I had difficulty searching this cell array for a 2D array. The myIsEqual function just searches through the third dimension and checks if the 2D array at each index is equal, returning a 1D vector of which arrays are equal for use in the find.
function ind = myIsEqual(vec3D, A)
ind = zeros(size(vec3D, 3), 1);
for i = 1:size(vec3D, 3)
ind(i) = isequal(vec3D(:, :, i), A);
end
This is most certainly not the most efficient way. Immediately I can see that it would be more efficient to initialize the arrays to store the votes beforehand. However however is there a better way in general of going about this? I need to try and find the most efficient and elegant way of voting as there are usually hundreds of points to check and time is valuable.
Thanks
If you are only considering time efficiency, consider using a 4d matrix.
The dimensions would be:
Model
coordinateA
coordinateB
coordinateC
Depending on the ratio between this matrix size and the amount of points that you check, consider using a sparse matrix.
Note that especially if you can't use a sparse array, this method can be rather memory inefficient and may therefore be infeasible.
I want to sum up several vectors of different size in an array. Each time one of the vectors drops out of my program, I want to append it to my array. Like this:
array = [array, vector];
In the end I want to let this array be the output of a function. But it gives me wrong results. Is this possible with MATLAB?
Thanks and kind regards,
Damian
Okay, given that we're dealing with column vectors of different size, you can't put them all in a numerical array, since a numerical array has to be rectangular. If you really wanted to put them in the numerical array, then the column length of the array will need to be the length of the longest vector, and you'll have to pad out the shorter vectors with NaNs.
Given this, a better solution would be, as chaohuang hinted at in the comments, to use a cell array, and store one vector in each cell. The problem is that you don't know beforehand how many vectors there will be. The usual approach that I'm aware of for this problem is as follows (but if someone has a better idea, I'm keen to learn!):
UpperBound = SomeLargeNumber;
Array = cell(1, UpperBound);
Counter = 0;
while SomeCondition
Counter = Counter + 1;
if Counter > UpperBound
error('You did not choose a large enough upper bound!');
end
%#Create your vector here
Array{1, Counter} = YourVectorHere;
end
Array = Array(1, 1:Counter);
In other words, choose some upper bound beforehand that you are sure you won't go above in the loop, and then cut your cell array down to size once the loop is finished. Also, I've put in an error trap in case you're choice of upper bound turns out to be too small!
Oh, by the way, I just noted in your question the words "sum up several vectors". Was this a figure of speech or did you actually want to perform a sum operation somewhere?
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.