I have a vector (or a matrix) which I want to print out certain values of it.
I will explain:
Let say I have a complex array.
I want to find all the values inside the array that are between 2 to 5.
How do I do that? I dont want to find the indices of these values! I want to print out (create a new array) my desire values.
Let's denote A your input matrix.
You say A is complex, so there are two cases:
A(real(A)>=2 & real(A)<=5) %% real values between 2 and 5
A(abs(A)>=2 & abs(A)<=5) %% modulus between 2 and 5
Related
I am essentially trying to generate all possible nxm matrices. I have seen some codes in R and Python that kind of does this with a single function, but I cant find anything similar for matlab :(
here's a way to do that in matlab:
n=4;
m=3;
for c=0:2^(n*m)-1
A(:,c+1)=str2double(split(dec2bin(c,n*m),'',1));
end
A(1,:)=[]; A(end,:)=[];
A=reshape(A,n,m,[]);
the logic, it doesnt matter if it is an nxm array or a 1D vector of length nxm, we can later reshape the vector to an nxm array (last line). Then, the # of possible permutation for a binary string of Length L is just all the decimal # from 0 to 2^L-1 transformed to binary string of length nxm. This is what the dec2bin(...) function does. Then we get a long string (for example '010001010111') that needs to be split to individual elements('0','1',...) , so we can later convert this strings to numbers using str2double. The split(...) function does that but generates NaN at the edges, so we get rid of them in the row before the last one...
so for this example I chose n=4, m=3, which generates just 2^(nxm)=4096 possible array...
you can see them if if you try A(:,:,j) with j being a # from 1 to 4096
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'm struggling with one of my matlab assignments. I want to create 10 different models. Each of them is based on the same original array of dimensions 1x100 m_est. Then with for loop I am choosing 5 random values from the original model and want to add the same random value to each of them. The cycle repeats 10 times chosing different values each time and adding different random number. Here is a part of my code:
steps=10;
for s=1:steps
for i=1:1:5
rl(s,i)=m_est(randi(numel(m_est)));
rl_nr(s,i)=find(rl(s,i)==m_est);
a=-1;
b=1;
r(s)=(b-a)*rand(1,1)+a;
end
pert_layers(s,:)=rl(s,:)+r(s);
M=repmat(m_est',s,1);
end
for k=steps
for m=1:1:5
M_pert=M;
M_pert(1:k,rl_nr(k,1:m))=pert_layers(1:k,1:m);
end
end
In matrix M I am storing 10 initial models and want to replace the random numbers with indices from rl_nr matrix into those stored in pert_layers matrix. However, the last loop responsible for assigning values from pert_layers to rl_nr indices does not work properly.
Does anyone know how to solve this?
Best regards
Your code uses a lot of loops and in this particular circumstance, it's quite inefficient. It's better if you actually vectorize your code. As such, let me go through your problem description one point at a time and let's code up each part (if applicable):
I want to create 10 different models. Each of them is based on the same original array of dimensions 1x100 m_est.
I'm interpreting this as you having an array m_est of 100 elements, and with this array, you wish to create 10 different "models", where each model is 5 elements sampled from m_est. rl will store these values from m_est while rl_nr will store the indices / locations of where these values originated from. Also, for each model, you wish to add a random value to every element that is part of this model.
Then with for loop I am choosing 5 random values from the original model and want to add the same random value to each of them.
Instead of doing this with a for loop, generate all of your random indices in one go. Since you have 10 steps, and we wish to sample 5 points per step, you have 10*5 = 50 points in total. As such, why don't you use randperm instead? randperm is exactly what you're looking for, and we can use this to generate unique random indices so that we can ultimately use this to sample from m_est. randperm generates a vector from 1 to N but returns a random permutation of these elements. This way, you only get numbers enumerated from 1 to N exactly once and we will ensure no repeats. As such, simply use randperm to generate 50 elements, then reshape this array into a matrix of size 10 x 5, where the number of rows tells you the number of steps you want, while the number of columns is the total number of points per model. Therefore, do something like this:
num_steps = 10;
num_points_model = 5;
ind = randperm(numel(m_est));
ind = ind(1:num_steps*num_points_model);
rl_nr = reshape(ind, num_steps, num_points_model);
rl = m_est(rl_nr);
The first two lines are pretty straight forward. We are just declaring the total number of steps you want to take, as well as the total number of points per model. Next, what we will do is generate a random permutation of length 100, where elements are enumerated from 1 to 100, but they are in random order. You'll notice that this random vector uses only a value within the range of 1 to 100 exactly once. Because you only want to get 50 points in total, simply subset this vector so that we only get the first 50 random indices generated from randperm. These random indices get stored in ind.
Next, we simply reshape ind into a 10 x 5 matrix to get rl_nr. rl_nr will contain those indices that will be used to select those entries from m_est which is of size 10 x 5. Finally, rl will be a matrix of the same size as rl_nr, but it will contain the actual random values sampled from m_est. These random values correspond to those indices generated from rl_nr.
Now, the final step would be to add the same random number to each model. You can certainly use repmat to replicate a random column vector of 10 elements long, and duplicate them 5 times so that we have 5 columns then add this matrix together with rl.... so something like:
a = -1;
b = 1;
r = (b-a)*rand(num_steps, 1) + a;
r = repmat(r, 1, num_points_model);
M_pert = rl + r;
Now M_pert is the final result you want, where we take each model that is stored in rl and add the same random value to each corresponding model in the matrix. However, if I can suggest something more efficient, I would suggest you use bsxfun instead, which does this replication under the hood. Essentially, the above code would be replaced with:
a = -1;
b = 1;
r = (b-a)*rand(num_steps, 1) + a;
M_pert = bsxfun(#plus, rl, r);
Much easier to read, and less code. M_pert will contain your models in each row, with the same random value added to each particular model.
The cycle repeats 10 times chosing different values each time and adding different random number.
Already done in the above steps.
I hope you didn't find it an imposition to completely rewrite your code so that it's more vectorized, but I think this was a great opportunity to show you some of the more advanced functions that MATLAB has to offer, as well as more efficient ways to generate your random values, rather than looping and generating the values one at a time.
Hopefully this will get you started. Good luck!
Starting wish a 7x4 binary matrix I need to change a random bit in each column to simulate error. Have been trying to no avail.
A very straightforward way to do this is to use a for loop. It might not be the most efficient approach in MATLAB, but it's probably good enough considering your data set is so small.
Iterate through each of the four columns. On each iteration, randomly chose a number from 1 to 7 to represent the row in that column that you have selected to change. Finally, flip the bit at that row/column. The following code does just this. Assume that "A" is a binary matrix with 7 rows and 4 columns
for col=1:4; %// Iterate through each column
row = ceil(7*rand()); %// Randomly chose a number from 1 to 7 to represent row
A(row,col) = ~A(row,col); %// Flip the bit at the specified row/col
end
Another possibility is to create 4 random numbers in one call, and assign in a vectorized fashion:
rowNumbers = randi(4,[1 4])
A(rowNumbers,:) = ~A(rowNumbers,:);
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.