Can anyone please explain the logic behind the functions like "tsclean" & "nnetar" of the package "forecast" written by Professor Rob J Hyndman.
I can able to use the tsclean & nnetar.
I hope these are used for the outlier treatment in time series.
tsclean is used for outlier treatment, i.e. it removes outliers & it fills the missing values.
Request:
What is the method or logic behind it, please let me know (any reference material)
Thanks in Advance!!
Related
I'm using Z3 with the ml interface. I had created a formula
f(x_i)
that is satisfiable, according to the solver
Solver.mk_simple_solver ctxr.
The problem is: I can get a model, but he find me values only for some variables of the formula, and not all (some of my Model.get_const_interp_er end with a type None)
How can it be possible that the model can give me only a part of the x_ir? In my understanding, if the model work for one of the values, it means that the formula was satisfiable (in my case, it is) and so all the values can be given...
I don't understand something..
Thanks for reading me!
You should always post full examples so people can help with actual coding issues; without seeing your actual code, it's impossible to know what might be the actual reason.
Having said that, this sounds very much like the following question: Why Z3Py does not provide all possible solutions So, perhaps the answer given there will help you.
Long story short: Z3 models will only contain values for variables that matter for the model. For anything that is not explicitly assigned, any value will do. There are ways to get "full" models as explained in that answer of course; which I'm sure is also possible from the ML interface.
I saw how to do this from a DataSet object, and I saw a setLabel method, and I saw a getLabelMaskArrays, but none of these are what I'm looking for.
Am I just blind or is there not a way?
Thanks
Masking is for variable length time series in RNNs. Most of the time you don't need it. Our built in sequence dataset iterators also tend to handle these cases. For more details see our rnn page: https://deeplearning4j.org/usingrnns
I was understanding the CGAN model given here.
The generative model has symmetric skip connections as it is explained in the paper here. Hence, I understand the lines such as:
d2 = {d2_,e4} - nn.CAddTable(true)
However, instead of doing the same thing after the last deconv layer d6, the following thing is done:
d6 = d61 - nn.Identity()
Can someone please help me understand why nn.Identity() is used here?
nn.Identity() is a module that forwards the input as such. It could be skipped in their code. Nevertheless it seems that they aren't implementing the model they described in figure 3 of their paper. Maybe it performs better without the third skip connection.
nn.Identity() is a placeholder identity operator.
Recently i am working on machine learning and build some Models for classification problem with the help of some tutorials. Though i solved my problem successfully but cant get the use and inference of using "NumerictoNominal" method please explain me.
I have tried to learn from the available text but it is very hard core i am seeking for simple explanation.
thanks and regards
I search a lot and finally got a simple example "A set of data is said to be nominal if the values / observations belonging to it can be assigned a code in the form of a number where the numbers are simply labels" for example PIN CODE of a City. Although we use Numeric value to build codes and also u can apply simple Algebra on PIN Codes but it won't make any sense. Also attribute SEX could be male or female it is also a kind of nominal attribute.
thanks
Consider the following problem of locating a 2d pattern inside an image (0-255).
A match is said to be found at (x, y) if most of the elements of the bigger matrix (say > 50%) are in some range of the respective elements of the smaller matrix i.e
0.8*small[i][j] <= bigger[x+i][y+j] <= 1.2*smaller[i][j]
I remember this problem to be a standard problem in image searching, but couldn't recollect, neither find the exact name.
I would be very grateful if someone could figure out the name of an equivalent standard problem.
Thank you in advance.
I thought it might have been something like "moving windows" or something, so that's what I looked for. Thinking of the right name can be tricky, and with so many similar methods, finding the actual one you want can get hard. Glad I could help you out.
Anyway, it's template matching.
In the context of video compression (as opposed to image recognition) this is called block matching: http://en.wikipedia.org/wiki/Block-matching_algorithm