Could you help to understand how can I load data from PostgreSQL into Deep Learning 4J, please?
I'll appreciate if anyone have an example.
Thanks in advance
If you want to load your data from Postgres before vectorization, you can use the JdbcRecordReader. It is in the datavec-jdbc maven package.
For an example how to use it, check out the unit-test for it: https://github.com/eclipse/deeplearning4j/blob/master/datavec/datavec-jdbc/src/test/java/org/datavec/api/records/reader/impl/JDBCRecordReaderTest.java
This will give you access to your data in record form. If all your data is already numeric: great. If not, you will have to vectorize it. Explaining how to do that is probably too long for Stack Overflow. Take a look at https://www.dubs.tech/guides/quickstart-with-dl4j/ where I explain how to do it with records coming from a CSV file.
If you're confused beyond that, feel also free to ask questions on community.konduit.ai, there you can ask follow up questions better than on StackOverflow.
dl4j, as any other framework works with tensors - INDArray in case of dl4j. So in order to "load data" you'll have to convert it to tensors actually. That applies to any data source - text, images, mp3s - everything is vectorized before sending into neural network.
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I was wondering if UE5 can support 50k+ lines of a db/CSV as they rappresent the parameters of the whole animation. (coordinates[x,y,z], TimeDelta, Speed, Brake)
Any documentation is very much appreciated
There is no existing functionality in the engine itself for this extremely specific use case. Of course, it can "support" it if you write a custom solution using the many available tools within the engine.
You can use IFileHandle to stream in a file (your csv): link
You can then parse the incoming data to create a FVector3 of your coordinates, a float of your TimeDelta, etc. For example, FVector::InitFromString may help: link
However, this depends very much on the format of your data. Parsing string/texts into values is not specific to UE4, you can find a lot of info on converting streams of binary/character data to needed values.
Applying the animation as the data is read is a separate, quite big, task. Since you provide no details on what the animation data represents, or what you need to apply it to, I cannot really help.
In general though, it can help you a lot to break down your question into 3-4 separate, more specific, questions. In any case though, this is a task that will require a lot of research and work.
And even before that, it might be good to research alternative approaches and changing the pipeline, to avoid using such non-standard file structures for animation.
I have been trying to understand VoID in Linked Open Data. It would be great if anyone could help clarify some of my confusions.
Does it need to be stored in a separate file or it can be included in the RDF dataset itself? If so, how do I query it? (A sample query would be really helpful)
How is the information in VoID used in real life?
Does it need to be stored in a separate file or it can be included in the RDF dataset itself? If so, how do I query it? (A sample query would be really helpful)
In theory not, but for practical purposes yes. In the end the information is encoded in triples, so it doesn't really matter in what file you put them and you could argue that it's best to actually put the VoID info into the data files and serve these triples with your data as meta-info. It's query-able as all other forms of RDF, either load it into some SPARQL endpoint or use a library that can directly load RDF files. This however also shows the reason why a separate file makes sense: instead of having to load potentially large data files just to get some dataset meta info, it makes sense to offer the meta-data in its own file.
How is the information in VoID used in real life?
VoID is actually used in several scenarios already, but mostly a recommendation and a good idea. The most prominent use-cases i know of is to get your dataset shown in the LOD Cloud. You currently have to register it with datahub.io and add a VoID file (example from my associations dataset).
Other examples (sadly many defunct nowadays) can be found here: http://semanticweb.org/wiki/VoID.html
I'm currently working on my final year research project, which is an application which analyzes travel reviews found online, and give out a sentiment score for particular tourist attractions as a result, by conducting aspect level sentiment analysis.
I have a newly scraped dataset from a famous travel website which does not allow to use their API for research/academic purposes. (bummer)
My supervisor said that I might need to get this dataset annotated before using it for the aforementioned purpose. I am kind of confused as to what data annotation means in this context. Could someone please explain what exactly is happening when a dataset is annotated and how it helps in getting sentiment analysis done?
I was told that I might have to get two/three human annotators and get the data annotated to make it less biased. I'm on a tight schedule and I was wondering if there are any tools that can get it done for me? If so, what will be the impact of using such tools over human annotators? I would also like suggestions for such tools that you would recommend.
I would really appreciate a detailed explanation to my questions, as I am stuck with my project progressing to the next step because of this.
Thank you in advance.
To a first approximation, machine learning algorithms (e.g., a sentiment analysis algorithm) is learning to perform a task that humans currently perform by collecting many examples of the human performing the task, and then imitating them. When your supervisor talks about "annotation," they're talking about collecting these examples of a human doing the sentiment annotation task: annotating a sentence for sentiment. That is, collecting pairs of sentences and their sentiment as judged by humans. Without this, there's nothing for the program to learn from, and you're stuck hoping the program can give you something from nothing -- which it never will.
That said, there are tools for collecting this sort of data, or at least helping. Amazon Mechanical Turk and other crowdsourcing platforms are good resources for this sort of data collection. You can also take a look at something like: http://www.crowdflower.com/type-sentiment-analysis.
I like knockoutjs, the sooner we get rid of coding directly toward the DOM the better. I'm having trouble understanding how I would do something which I'm going to explain in terms of a question/answer site. (This is probably a general MVC/MVVM question)
In my data model I have a question[id, description] and answer[id, question_id, text]. The browser requests a list of questions which is bound to a tbody, one column will display the question description, while the other should be bound to an answer textbox.
One obvious way of doing this is to have a QuestionAnswer[question_id, answer_id, question_descrition, answer_text] model. Ideally I'd like to keep them separate to minimize transformation when sending/receiving/storing, if there isn't some way of keeping them separate then I have the following question:
Where is the ideal place to create the QuestionAnswer model ? My bet is that by convention its created on the server.
If there is such an example somewhere please point me to it, otherwise I think it would make a good example.
Please help me wrap my head around this, Thanks!
What you could do is to create the combined model on the server, serialize it to json and then use the mapping plugin to add the serialized list to the view model.
I'm doing that here only it isn't a combined model, but shouldn't make any difference. Especially since it seems like your relation is one-to-one.
If you need to create an "object" in your view model, you can use the mapping definition to do so, like I do here.
I use C# to build my model on the server, but I guess you can use whatever you are comfortable with.
The cool thing with the mapping plugin is that it adds the data to the view model so that you can focus on behaviour.
Ok,
I'v gathered my thoughts on what my question is actually asking.
To do data binding on the client side you obviously need your data model there as well. I was conflicted on what I needed to send over and at what time.
To continue with the Question/Answer site idea: Sending down a list of answers each of which have a question in them is what should be done. That way you can bind to the answer list and simply bind the question description of each answer to the first table column.
If later I want to make a question editor I would potentially send a complete different data structure down and not reuse the Answer has a Question structure previously used.
I thought there might be a way of sending down a more complex data structure that references itself. Which apparently is possible in JSon with some extra libraries.
I have some static data that is going to be shipped in my iPhone app's bundle. It is updated very rarely (about twice a year) and the app will not be doing any networking. I'm going to update the data manually when these changes occur.
I want to know what the best way to load this data is. I've already started using an XML file and parsing it as needed, but it's a huge amount of data to do this with. I'm finding it tedious. There are about 120 pages worth of stuff, with images etc. Just not fun.
I've heard about core data, but I don't really know if it's going to do what I want. I want to find a way to just create a UITableView controller and a detail view, then somehow bind the data to these controllers. (a teensy amount of example code would be appreciated for this part)
If anyone has any suggestions, please feel free to leave an answer or comment.
Here's a sample of my XML:
<?xml version="1.0"?>
<jftut>
<heading name="Introduction">
<subheading>Jungleflasher Overview</subheading>
<subheading>Before Using Jungleflasher</subheading>
</heading>
<heading name="Which Drive do I have?">
<image>DriveIdentification.png</image>
</heading>
<heading name="Drives">
<manufacturer name="Samsung">
<version>MS25</version>
<version>MS28</version>
</manufacturer>
<manufacturer name="Hitachi">
<version>32 through 59</version>
<version>78</version>
<version>79</version>
</manufacturer>
<manufacturer name="BenQ">
<version>VAD6038</version>
</manufacturer>
<manufacturer name="LiteOn">
<version>74850c</version>
<version>83850c v1</version>
<version>83850c v2</version>
<version>93450c</version>
</manufacturer>
</heading>
</jftut>
Below each of those bottom nodes will be an article detailing how to perform a task related to them.
If the question needs more detail, just ask :)
Thanks,
Aurum Aquila
I think core data will serve your purpose in a flexible way.You said updation is rare, even if it is not so, it would'nt be tedious while using core data,where entities are mapped in to objects and assigning values will automatically update db without writing a single line of sql statement.
I want to know in which format is your data.
First of all store all your local data in a sqlite file.Then you can use use core data as a wrapper for all your operation.
For reference you can follow tutorial 1 & Tutorial 2