I would like to add multiple matrices to multiple ranges within one spreadsheet. Since there are thousands of matrices I do not want to call writematrix/xlswrite multiple times for reasons of computing time. Is there another possibility out there?
Matlabs Documentation has nothing to offer, I wish I could just do:
xlswrite(filename,cell_array_with_matrices,'Sheetname',cell_array_with_ranges);
Related
I have a need to pass a vector of arguments to Rserve from tableau. Specifically, I am using IRR calculations in R (on Rserve), and i want to pass vector of cash-flows that are as columns in my table (instead of rows/measure). So, i want to collect all those CF in a vector and pass it on to Rserve. Passing them one at a time slows down IO.
SCRIPT_REAL("r_func(c(.arg1, .arg2, .arg3))",sum(cf1), sum(cf2), sum(cf3))
cf1..cfn are cashflows corresponding to various periods. Above code works well when cf are few but takes a long time when i have few hundereds. Further, time spent is not in calculation but IO when communicating with remote Rserve. If i have a local Rserve, this calculation happens under few seconds while on remote, it takes well over a minute.
Also, want to point out that tableau / Rserve, set one argument after another and that takes time. My expectation is that once i have a vector, it would be just 1 transfer and setting of arguments, and therefore this should speed up
The first step in understanding how Tableau interacts with R or Python, is understanding how Tableau's table calcs work.
Tableau Script_XXX() functions are table calculations which means that you invoke them on a vector of aggregate query results and the corresponding R or Python code needs to return a vector usually of the same size. (I think you may be able to return a scalar or smaller vector which gets replicated to appear like a vector of the same size as the argument -- but not certain)
You can control how your data is partitioned into vectors, and also the ordering of data in the vectors, by editing the table calc to specify the partitioning and addressing for that calc.
Partitioning determines how your aggregate query results are broken up into vectors for calculation purposes. Addressing determines how the elements of each vector are ordered. You can either do that based on the physical layout of the table structure, or (better) based on the specific dimensions.
See the Tableau on-line help for table calcs for more info, and look online training videos from Tableau or blog entries (especially from anyone named Bora)
One way to test your understanding of these concepts is create a Tableau table (i.e., a viz with a mark type of text) with several dimensions on row and column shelves. Then create calculated fields for INDEX() and SIZE() and display them on text. Finally, change the partitioning and addressing in different ways by editing those table calcs. Try several different permutations. When you can confidently predict what those functions will produce for different settings, then you're ready to do more complex tasks - such as talking to R.
It is also instructive to experiment with FIRST(), LAST(), LOOKUP(), WINDOW_SUM() etc -- and finally dig into PREVIOUS_VALUE(). Warning, PREVIOUS_VALUE() is a bit odd, and does not behave the way you probably assume it does. Still, it is a useful technique that can implement a recursive calculation, and is about as close to a for loop as Tableau gets.
I have a dataset of n observations (nx1 vector) and would like to create a subset of this data, whose mean is known in advance, by selecting at random only n/3 observations (or within some constraint, ie where the mean of the data subset is within a range about the known mean).
Can someone please help me with the code do this in matlab?
Note, I don't want to use the rand function to create random data as I already have my data collected.
For example on a smaller scale: If I had the following dataset of 12 observations:
data = [8;7;4;6;9;6;4;7;3;2;1;1];
but then wanted to randomly select a subset of this data containing only 4 observations with a mean of 4 (or with a mean between 3.5-4.5 for example):
Then the answer might be datasubset=[7;3;2;4] but the answer could also be datasubset=[6;4;2;4] or datasubset=[6;4;3;4].
It doesn't matter if there are several possible solutions, I just need one of them, though I'd like to know the alternative solutions also.
I am trying to create a graph with two lines, with two filters from the same dimension.
I have a dimension which has 20+ values. I'd like one line to show data based on just one of the selected values and the other line to show a line excluding that same value.
I've tried the following:
-Creating a duplicate/copy dimension and filtering the original one with the first, and the copy with the 2nd. When I do this, the graphic disappears.
-Creating a calculated field that tries to split the measures up. This isn't letting me track the count.
I want this on the same axis; the best I've been able to do is create two sheets, one with the first filter and one with the 2nd, and stack them in a dashboard.
My end user wants the lines in the same visual, otherwise I'd be happy with the dashboard approach. Right now, though, I'd also like to know how to do this.
It is a little hard to tell exactly what you want to achieve, but the problem with filtering is common.
The principle that is important is that Tableau will filter the whole dataset by row. So duplicating the dimension you want to filter won't help as the filter on the original dimension will also filter the corresponding rows in the second dimension. Any solution has to be clever enough to work around this issue.
One solution is to build two new dimensions that use a calculation rather than a filter to create the new result. Let's say you have a dimension, [size] that has a range of numbers from 1 to 10 and you want to compare the total number of rows including and excluding the number 5. You could create a new field using a formula like if [size] <> 5 then 1 else 0 end
Summing the new field will give a count of the number of rows that don't contain a 5 and this can be compared directly to a rowcount of the original [size] field which will give the number including the value 5.
This basic principle can be extended to much more complex logic. The essential point is to realise that filters act on every row in your data and can't, by themselves, show comparisons with alternative filter choices on a single visualisation.
Depending on the nature of your problem there may be other solutions worth looking at including sets and groups but you would need to provide more specific details for users here to tell you whether they would be useful.
We can make a a set out of the values of the dimension and then place it in the required shelf. So, you will have your dimension which will plot accordingly and set which will have data as per the requirement because with filter you can't have that independence of showing data everytime you want.
How can I store a matrix with 2^100 rows in MatLab! it is my search space and I really need to do it .
In your opinion, is it possible ? if yes, please help me that how can i do it?
2100 is about 1030, which is much too large for you to fit in memory - so you won't be able to store this matrix.
A couple of alternatives that you might want to think about -
Are many of the entries in the matrix zero? If so, you could consider using a sparse matrix which is much more memory efficient.
Do you need to be able to access the rows in an arbitrary order, or sequentially? If sequentially, you can generate the rows on an as-needed basis (perhaps in blocks of ten thousand at a time)
Do you need to look at all the rows at all? If not, perhaps you can define a function which generates the entries on the fly, as they are requested.
I have several datasets i.e. matrices that have a 2 columns, one with a matlab date number and a second one with a double value. Here an example set of one of them
>> S20_EavesN0x2DEAir(1:20,:)
ans =
1.0e+05 *
7.345016409722222 0.000189375000000
7.345016618055555 0.000181875000000
7.345016833333333 0.000177500000000
7.345017041666667 0.000172500000000
7.345017256944445 0.000168750000000
7.345017465277778 0.000166875000000
7.345017680555555 0.000164375000000
7.345017888888889 0.000162500000000
7.345018104166667 0.000161250000000
7.345018312500001 0.000160625000000
7.345018527777778 0.000158750000000
7.345018736111110 0.000160000000000
7.345018951388888 0.000159375000000
7.345019159722222 0.000159375000000
7.345019375000000 0.000160625000000
7.345019583333333 0.000161875000000
7.345019798611111 0.000162500000000
7.345020006944444 0.000161875000000
7.345020222222222 0.000160625000000
7.345020430555556 0.000160000000000
Now that I have those different sensor values, I need to get them together into a matrix, so that I could perform clustering, neural net and so on, the only problem is, that the sensor data was taken with slightly different timings or timestamps and there is nothing I can do about that from a data collection point of view.
My first thought was interpolation to make one sensor data set fit another one, but that seems like a messy approach and I was thinking maybe I am missing something, a toolbox or function that would enable me to do this quicker without me fiddling around. To even complicate things more, the number of sensors grew over time, therefore I am looking at different start dates as well.
Someone a good idea on how to go about this? Thanks
I think your first thought about interpolation was the correct one, at least if you plan to use NNs. Another option would be to use approaches which are designed to deal with missing data, like http://en.wikipedia.org/wiki/Dempster%E2%80%93Shafer_theory for example.
It's hard to give an answer for the clustering part, because I have no idea what you're looking for in the data.
For the neural network, beside interpolating there are at least two other methods that come to mind:
training separate networks for each matrix
feeding them all together to the same network, with a flag specifying which matrix the data is coming from, i.e. something like: input (timestamp, flag_m1, flag_m2, ..., flag_mN) => target (value) where the flag_m* columns are mutually exclusive boolean values - i.e. flag_mK is 1 iff the line comes from matrix K, 0 otherwise.
These are the only things I can safely say with the amount of information you provided.