Can someone please share a link or source where I can find the derivation of FFT(base-2) from the DFT. I need to put this in latex for my thesis and am finding so many different explanations that I don't know which one to use.
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I am trying to extract relation between two entities (entity1- relation- entity2) from news articles for stock prediction. I have used NER for entity extraction. It would be great if anyone could help me with relationship extraction.
Relation Extraction is a difficult task in NLP and most of the time there's not a one-size-fits-all solution to that. Depending on the task that you're trying to solve, I would suggest reading some literature about it on Google Scholar and see if there's something similar to what you're trying to do.
Sometimes, authors are kind enough to publish the code of their solution, which are mainly PyTorch/Tensorflow models (hopefully) trained on a specific dataset. One example is this paper.
If you want to stick with Spacy, there are some guides that might help you, but I'm not sure how well it could scale with the task that you need to solve.
Another more basic approach could be to just extract the shortest path between two entities in the semantic graph of a sentence. This might be quite limited, but can be fairly easy to implement.
One final idea that comes to mind is to use encoders and compute the similarity between sentences. If you're doing multi-class classification, this could help solving your problem.
Hope you find something useful among these.
I need to find a deep learning based prediction model, where can I find it?
You can use Pytorch and Tensorflow pretrained models.
https://pytorch.org/docs/stable/torchvision/models.html
They can be automatically downloaded. There are some sample codes, that you can try:
https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py
If you are interested in deep learning, I suggest you review the basics of it in cs231n stanford. Your question is a bit odd, because you first need to define your task specifically. Prediction is not a good description. You could look for models for classification, segmentation, object detection, sequence2sequence(like translation), and so on...
Then you need to know how to search through projects on github, and then you need to know python (in most cases), and then use a pretrained model or use your own dataset to train or fine-tune the model for that task. Then you could pray that you have found a good model for your task, after that you need to validate the results on a test set. However, implementation of a model for real-life scenarios is another thing that you need to consider many other things, and you usually need some online-learning strategy, like Federated Learning. I hope that I could help you.
I am starting to work with LLVM, clang, and passes. This is a developed field, and I quickly become overwhelmed with the wealth of information out there. I was hoping someone could shed some light on these topics, with just enough detail to understand the bigger picture and just enough information to get started. Some of the details which I think would be useful are:
Q1) What is the relationship between LLVM and clang?
Q2) I know LLVM has a large set of existing static analysis techniques are these called passes?
Q3) What code representation format do I need to use to run a pass?
Q4) How do I run passes?
I know this is a very broad question. I am just looking for someone to give me enough information so that I have a footing and can get started fiddling with these tools.
I'm working on converting a mathematical formula into a program. This formula is called as optimal pricing policy for perishable products. I've seen this in an article and it is called Karush-Kuhn-Tucker condition. Somehow I lost all my maths skills and unable to understand the formula explained in that. I'm able to understand how to come up with a solution for getting optimal price but I'm worried that I may not address the condition given in this article. For your reference I'm giving the link here. If somebody can explain me this Karush-Kuhn-Tucker condition in plain english so that I can think in terms programming language. I'm not interested in the language, i'm ready to implement in any language.
Also giving link of question I posted in mathematics stack exchange.
Did anyone come across this kind of situation? How to come up with a programmatic solution for this kind of mathematical formula?
Wiki article for the same is here
If there are any already developed libraries for this kind of formula please let me know.
If you have a program described by KKT conditions, than you just need a nonlinear solver.
http://en.wikipedia.org/wiki/Nonlinear_programming
http://extensions.services.openoffice.org/project/NLPSolver
The data can be found here:
http://rredc.nrel.gov/solar/old_data/nsrdb/bluebook/data/24283.SBF
How do I figure the solar diffuse proportion and the transmissivity value from what's there?
Anyone know?
Thanks!
You might start by reading the documentation for the data format...and this may lead to some textbooks or papers that you need to know...
If these types of resources are not available, try to learn the name of the author and send them an email (or even give them call). Be polite, and be prepared to explain who you are, and why you want to do this...
Just about the only alternative, is to find an existing code (perhaps in language you can't run, but can read) and copy the approach taken therein.
Welcome to the wonderful world of scientific data archeology.