I am fairly new to matlab and I am trying to figure out when it is best to use cells, tables, or matrixes to store sets of data and then work with the data.
What I want is to store data that has multiple lines that include strings and numbers and then want to work with the numbers.
For example a line would look like
'string 1' , time, number1, number 2
. I know a matrix works best if al elements are numbers, but when I use a cell I keep having to convert the numbers or strings to a matrix in order to work with them. I am running matlab 2012 so maybe that is a part of the problem. Any help is appreciated. Thanks!
Use a matrix when :
the tabular data has a uniform type (all are floating points like double, or integers like int32);
& either the amount of data is small, or is big and has static (predefined) size;
& you care about the speed of accessing data, or you need matrix operations performed on data, or some function requires the data organized as such.
Use a cell array when:
the tabular data has heterogeneous type (mixed element types, "jagged" arrays etc.);
| there's a lot of data and has dynamic size;
| you need only indexing the data numerically (no algebraic operations);
| a function requires the data as such.
Same argument for structs, only the indexing is by name, not by number.
Not sure about tables, I don't think is offered by the language itself; might be an UDT that I don't know of...
Later edit
These three types may be combined, in the sense that cell arrays and structs may have matrices and cell arrays and structs as elements (because thy're heterogeneous containers). In your case, you might have 2 approaches, depending on how you need to access the data:
if you access the data mostly by row, then an array of N structs (one struct per row) with 4 fields (one field per column) would be the most effective in terms of performance;
if you access the data mostly by column, then a single struct with 4 fields (one field per column) would do; first field would be a cell array of strings for the first column, second field would be a cell array of strings or a 1D matrix of doubles depending on how you want to store you dates, the rest of the fields are 1D matrices of doubles.
Concerning tables: I always used matrices or cell arrays until I
had to do database related things such as joining datasets by a unique key; the only way I found to do this in was by using tables. It takes a while to get used to them and it's a bit annoying that some functions that work on cell arrays don't work on tables vice versa. MATLAB could have done a better job explaining when to use one or the other because it's not super clear from the documentation.
The situation that you describe, seems to be as follows:
You have several columns. Entire columns consist of 1 datatype each, and all columns have an equal number of rows.
This seems to match exactly with the recommended situation for using a [table][1]
T = table(var1,...,varN) creates a table from the input variables,
var1,...,varN . Variables can be of different sizes and data types,
but all variables must have the same number of rows.
Actually I don't have much experience with tables, but if you can't figure it out you can always switch to using 1 cell array for the first column, and a matrix for all others (in your example).
Related
I have a large table in MATLAB which contains over 1000 rows of data, in two columns. Column 1 is the ID of the sensor which gathered the data, and column two is the data itself (in this case a voltage).
I have been able to sort my table to gather all the data for sensors together. So, all the data from Sensor 1 is in rows 1 to 100, the data for Sensor 2 is in rows 101 to 179, the data for Sensor 3 is in rows 180 to 310, and so on. In other words, the number of rows which contain data for a given sensor is never the same.
Now, I want to split this main table into separate tables for each sensor ID, and I am having trouble figuring out a way to do it. I imagine I could do it with a loop, where my I cycle through the various IDs, but that doesn't seem like a very, MATLAB way of doing it.
What would be an efficient way to complete this task? Or would a loop really be the only way?
I have attached a small screenshot of some of my data.
The screenshot you shared shows a 1244x1 structure array with 2 fields but the question describes a table. You could convert the structure array to a table using,
T = struct2table(S); % Assuming S is the name of your structure
Whether the variable is a structure or table, it's better to not separate the variable and to use indexing instead. For example, assuming the variable is a table, you can compute the mean voltage for sensor1 using,
mean(T.reported_voltage(strcmp(T.sensor_id,'Sensor1')))
and you could report the mean of all groups using,
groupsummary(T,'sensor_id', 'mean')
or
splitapply(#mean,T.reported_voltage,findgroups(T.sensor_id))
But if you absolutely must break apart and tidy, well-organized table, you can do so by splitting the table into sub-tables stored within a cell array using,
unqSensorID = unique(T.sensor_id);
C = arrayfun(#(id){T(strcmp(T.sensor_id, id),:)},unqSensorID)
In this case the for loop is fine because (I guess) there aren't that many different sensors and your code will likely spend most of its time processing the data anyway - the loop won't give you a significant overhead.
Assuming your table is called t, the following should do what you want.
unique_sensors = unique(t.sensor_id)
for i = 1:length(unique_sensors)
sensor_data = t(t.sensor_id == unique_sensors(i), :);
% save or do some processing on this data
end
I have recorded WiFi CSI sensor data 5000 packets in 5 seconds(5000 packets x 57 subcarriers). But due to dynamic hardware configuration sometimes I only receive 4998 x 57. I want to add and estimate 2 rows so that my original design has consistent 5000 rows x 57 columns.
As you can see some data are 5000x57, and some are 4998x57.
You can achieve your desired output using mean()-function combined with the concatenation operator [] and the repmat() like this:
A=randi(100,4998,57);
A=[A;repmat(mean(A),2,1)];
Most of the functions in Matlab that take arrays as an input will calculate for each column except if the input array hast just 1 row. So does the mean function and you can just append means output to your arrays.
If you show me the code that you used to import the data, I might be able to help you create a cleaner data structure and thus be able to automatically process all of your arrays. The way the data is currently designed it's only possible to do this with dynamic variable names which is considered bad programming practice.
I'm trying to create a dataset from a double matrix and cell array of labels.
I don't have access to the mat2dataset function so I'm trying to write something similar.
>> whos data feature_labels
Name Size Bytes Class Attributes
data 2x208 3328 double
feature_labels 1x208 50776 cell
In actual use the data will have ~2million rows and always be double format. The number of columns will range from 20 up to 2000, so doing something like;
>> D = dataset([],[],[],[],[],...[], 'VarNames', feature_labels);
isn't really feasible.
Any suggestions?
edit:
Currently using a for loop and horzcat to concatenate new dataset columns on each loop. I don't see a way to pre-allocate the dataset size is this way so I imagine performance will chug with the larger datasets though..
Have you considered using a struct? I use these all the time in MATLAB for database things. I know it works absolutely fantastic for up to 20,000 elements with about 15 fields each, so I think it would still work just as well as anything else for 2 million items with 2 fields.
Alternatively, can't you just put it in a cell array?
DataBase{rowNum,1}=dataVector(rowNum,:);
DataBase{rowNum,2}=label{rowNum};
To preallocate a struct or cell, its relatively easy, with a struct, once you make your first one to initialize the fields, just say Struct(2000000).fieldName =[]
TO preallocate your cell array, just do
DataBase={[]}
DataBase{2000000,2}=[]
This preallocates all of it and fills it with empty values.
I am using MATLAB to load a text file that I want to make a sparse matrix out of. The columns in the text file refer to the row indices and are double type. I need them to be integers to be able to use them as indices for rows and columns. I tried using uint8, int32 and int64 to convert them to integers to use them to build a sparse matrix as so:
??? Undefined function or method 'sparse' for input
arguments of type 'int64'.
Error in ==> make_network at 5
graph =sparse(int64(listedges(:,1)),int64(listedges(:,2)),ones(size(listedges,1),1));
How can I convert the text file entries loaded as double so as to be used by the sparse function?
There is no need for any conversion, keep the indices double:
r = round(listedges);
graph = sparse(r(:, 1), r(:, 2), ones(size(listedges, 1), 1));
There are two reasons why one might want to convert to int:
The first, because you have data type restrictions.
The second, your inputs may contain fractions and are un-fit to be used as integers.
If you want to convert because of the first reason - then there's no need to: Matlab works with double type by default and often treats doubles as ints (for example, when used as indices).
However, if you want to convert to integers becuase of the second reason (numbers may be fractionals), then you should use round(), ceil() or floor() - whatever suits your purpose best.
There is another very good reason ( and really the primary one..) why one may want to convert indices of any structure (array, matrix, etc.) to int.
If you ever program in any language other than Matlab, you would be familiar with wanting to save memory space, especially with large structures. Being able to address elements in such structures with indices other than double is key.
One major issue with Matlab is the inability to more finely control the size of multidimensional structures in this way. There are sparse matrix solutions, but those are not adequate for many cases. Cell arrays will preserve the data types upon access, however the storage for every element in the cell array is extremely wasteful in terms of storage (113 bytes for a single uint8 encapsulated in a cell).
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.