YARP-Behavior-Trees | Behavior Trees Library in YARP | Artificial Intelligence library

 by   miccol C++ Version: Current License: MIT

kandi X-RAY | YARP-Behavior-Trees Summary

kandi X-RAY | YARP-Behavior-Trees Summary

YARP-Behavior-Trees is a C++ library typically used in Artificial Intelligence applications. YARP-Behavior-Trees has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

This is a work-in-progress library. Stable Version coming soon. A behavior tree library in YARP.

            kandi-support Support

              YARP-Behavior-Trees has a low active ecosystem.
              It has 10 star(s) with 4 fork(s). There are 4 watchers for this library.
              It had no major release in the last 6 months.
              YARP-Behavior-Trees has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of YARP-Behavior-Trees is current.

            kandi-Quality Quality

              YARP-Behavior-Trees has no bugs reported.

            kandi-Security Security

              YARP-Behavior-Trees has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              YARP-Behavior-Trees is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              YARP-Behavior-Trees releases are not available. You will need to build from source code and install.
              Installation instructions are not available. Examples and code snippets are available.

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            YARP-Behavior-Trees Key Features

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            YARP-Behavior-Trees Examples and Code Snippets

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            Community Discussions


            Space Complexity in Breadth First Search (BFS) Algorithm
            Asked 2022-Apr-11 at 08:08

            According to Artificial Intelligence A Modern Approach - Stuart J. Russell , Peter Norvig (Version 4), space complexity of BFS is O(b^d), where 'b' is branching factor and 'd' is depth.

            Complexity of BFS is obtained by this assumption: we store all nodes till we arrive to target node, in other word: 1 + b + b^2 + b^3 + ... + b^d => O(b^d)

            But why should we store all nodes? don't we use queue for implementation?

            If we use queue, don't need to store all nodes, because we enqueue and dequeue some nodes in steps, then when we find target node(s), we can say some nodes are in queue (but not all of them).

            Is my understanding wrong?



            Answered 2022-Apr-10 at 06:16

            At any moment while we apply BFS, the queue would have at most two levels of nodes, for example if we just started searching in depth d, then the queue now contains all nodes at depth d and as we proceed the queue would finish all nodes at depth d and have all nodes at depth d+1, so at any moment we have O(b^d) space.

            Also 1+b+b^2+...+b^d = (b^(d+1)-1)/(b-1).

            Source https://stackoverflow.com/questions/71814173


            Why is there an additional "None" dimension in the tensor shape when uploading a dataset to Activeloop Hub?
            Asked 2022-Mar-24 at 23:15

            I am trying to upload an image datset to Hub (dataset format with an API for creating, storing, & collaborating on AI datasets). I only uploaded part of the dataset, however upon inspecting the uploaded data I noticed that there was an additional None dimension in the tensor shape. Can someone explain why this occurred?

            I am using the following tensor relationship:



            Answered 2022-Mar-24 at 23:15

            The none dimension is present because some of the images might have three channels and the others have four, so dynamic dimensions are shown as None.

            Source https://stackoverflow.com/questions/71610475


            What does stopping the runtime while uploading a dataset to Hub cause?
            Asked 2022-Mar-24 at 01:06

            I am getting the following error while trying to upload a dataset to Hub (dataset format for AI) S3SetError: Connection was closed before we received a valid response from endpoint URL: "<...>".

            So, I tried to delete the dataset and it is throwing this error below.

            CorruptedMetaError: 'boxes/tensor_meta.json' and 'boxes/chunks_index/unsharded' have a record of different numbers of samples. Got 0 and 6103 respectively.

            Using Hub version: v2.3.1



            Answered 2022-Mar-24 at 01:06

            Seems like when you were uploading the dataset the runtime got interrupted which led to the corruption of the data you were trying to upload. Using force=True while deleting should allow you to delete it.

            For more information feel free to check out the Hub API basics docs for details on how to delete datasets in Hub.

            If you stop uploading a Hub dataset midway through your dataset will be only partially uploaded to Hub. So, you will need to restart the upload. If you would like to re-create the dataset, you can use the overwrite = True flag in hub.empty(overwrite = True). If you are making updates to an existing dataset, you should use version control to checkpoint the states that are in good shape.

            Source https://stackoverflow.com/questions/71595867


            What is the loss function used in Trainer from the Transformers library of Hugging Face?
            Asked 2022-Mar-23 at 10:12

            What is the loss function used in Trainer from the Transformers library of Hugging Face?

            I am trying to fine tine a BERT model using the Trainer class from the Transformers library of Hugging Face.

            In their documentation, they mention that one can specify a customized loss function by overriding the compute_loss method in the class. However, if I do not do the method override and use the Trainer to fine tine a BERT model directly for sentiment classification, what is the default loss function being use? Is it the categorical crossentropy? Thanks!



            Answered 2022-Mar-23 at 10:12

            It depends! Especially given your relatively vague setup description, it is not clear what loss will be used. But to start from the beginning, let's first check how the default compute_loss() function in the Trainer class looks like.

            You can find the corresponding function here, if you want to have a look for yourself (current version at time of writing is 4.17). The actual loss that will be returned with default parameters is taken from the model's output values:

            loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]

            which means that the model itself is (by default) responsible for computing some sort of loss and returning it in outputs.

            Following this, we can then look into the actual model definitions for BERT (source: here, and in particular check out the model that will be used in your Sentiment Analysis task (I assume a BertForSequenceClassification model.

            The code relevant for defining a loss function looks like this:

            Source https://stackoverflow.com/questions/71581197


            I do not split well in pytorch
            Asked 2022-Mar-21 at 09:57

            I would like to do a tensor split in pytorch. However, I get an error message because I can't get the splitting to work.
            The behavior I want is to split the input data into two Fully Connected layers. I then want to create a model that combines the two Fully Connected layers into one. I believe the error is due to a wrong code in x1, x2 = torch.tensor_split(x,2)



            Answered 2022-Mar-21 at 09:57

            Specify dim=1 in torch.tensor_split(x,2) .


            The x comes from two tensors with the shape [100,1] stacked at dim 1, so its shape is [100, 2]. After applying tensor_split, you get two tensors both with shape [50, 2].

            Source https://stackoverflow.com/questions/71554131


            Alan AI Error Uncaught Error: The Alan Button instance has already been created. There cannot be two Alan Button instances created at the same time
            Asked 2022-Mar-21 at 09:48

            I am developing an E-commerce website AI powered Voice Command Using Alan AI. But Whenever I come back from another route, there's a blank page appears.and this error message shows in the console: "Uncaught Error: The Alan Button instance has already been created. There cannot be two Alan Button instances created at the same time". What can I do? my code is given below:



            Answered 2022-Mar-21 at 09:48

            It's critical but easy...!

            Use requestAnimationFrame for your webpage visual changes.

            If run as a requestAnimationFrame callback, this will be run at the start of the frame.

            const Alan = () => {

            Source https://stackoverflow.com/questions/71548257


            KeyedVectors\' object has no attribute \'wv for gensim 4.1.2
            Asked 2022-Mar-20 at 19:43

            i have migrated from gensim 3.8.3 to 4.1.2 and i am using this

            claim = [token for token in claim_text if token in w2v_model.wv.vocab]

            reference = [token for token in ref_text if token in w2v_model.wv.vocab]

            i am not sure how to replace w2v_model.wv.vocab to newer attribute and i am getting this error

            KeyedVectors' object has no attribute 'wv' can anyone please help.



            Answered 2022-Mar-20 at 19:43

            You only use the .wv property to fetch the KeyedVectors object from another more complete algorithmic model, like a full Word2Vec model (which contains a KeyedVectors in its .wv attribute).

            If you're already working with just-the-vectors, there's no need to request the word-vectors subcomponent. Whatever you were going to do, you just do to the KeyedVectors directly.

            However, you're also using the .vocab attribute, which has been replaced. See the migration FAQ for more details:


            (Mainly: instead of doing an in w2v_model.wv.vocab, you may only need to do in kv_model or in kv_model.key_to_index.)

            Source https://stackoverflow.com/questions/71544767


            Can't initialize object of Detector class from py-feat
            Asked 2022-Mar-19 at 20:41

            I try to detecting FEX from videos according to this instruction: https://py-feat.org/content/detector.html#detecting-fex-from-videos

            But I can't initialize object of Detector class. Code that I use:



            Answered 2022-Mar-19 at 20:41

            It looks like one of your files was corrupted.

            You can try to solve the problem by opening the directory C:\Users\User\AppData\Roaming\Python\Python39\site-packages\feat\resources\ and deleting the file ResMaskNet_Z_resmasking_dropout1_rot30.pth.

            Then run again the code and it should redownload the deleted file.

            The warning in the first two lines is just a warning, it's saying that some of the code in the library nilearn is deprecated. Most of the times you would just ignore this line, this will be probably fixed by the coders of nilearn in a future patch.

            Source https://stackoverflow.com/questions/71541634


            How to load an onnx model using ONNX.js
            Asked 2022-Mar-08 at 09:10

            I am trying to import an ONNX model using onnxjs, but I get the below error:



            Answered 2022-Mar-01 at 20:37


            Spacy NER not recognising NAME
            Asked 2022-Mar-03 at 21:37

            Can anyone please help me understand why Spacy NER refuses to recognize the last NAME 'Hagrid' in the sentence, no matter the model used (sm, md, lg)?:

            "Hermione bought a car, then both Hermione and Hagrid raced it on the track. Tom Brady was very happy with Hagrid this year."



            Answered 2022-Mar-03 at 21:37

            Well, Neural Network Models are basically a black box, so there is no way to know this for sure.

            I could imagine that the grammar in last sentence is a bit too "fancy"/literature-like if the model was trained on news or web data and might be throwing the model off. This difficulty of seeing the sentence context as something that would be followed up by a name as well as the fact that "Hagrid" is a kind of unusual name could be the reason.

            You can try some other models such as the one integrated in Flair:


            or this fine-tuned BERT model:


            They are more powerful and get it right, from my experience SpaCy is a nice tool and quite fast, but not the most precise for NER.

            Source https://stackoverflow.com/questions/71340177

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


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