Algo-1 | The first edition of the algo course in Hack Bulgaria | Functional Programming library

 by   HackBulgaria Java Version: Current License: No License

kandi X-RAY | Algo-1 Summary

kandi X-RAY | Algo-1 Summary

Algo-1 is a Java library typically used in Programming Style, Functional Programming applications. Algo-1 has no bugs, it has no vulnerabilities and it has low support. However Algo-1 build file is not available. You can download it from GitHub.

The first edition of the algo course in Hack Bulgaria

            kandi-support Support

              Algo-1 has a low active ecosystem.
              It has 31 star(s) with 56 fork(s). There are 25 watchers for this library.
              It had no major release in the last 6 months.
              There are 0 open issues and 4 have been closed. On average issues are closed in 1 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of Algo-1 is current.

            kandi-Quality Quality

              Algo-1 has 0 bugs and 0 code smells.

            kandi-Security Security

              Algo-1 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              Algo-1 code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              Algo-1 does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              Algo-1 releases are not available. You will need to build from source code and install.
              Algo-1 has no build file. You will be need to create the build yourself to build the component from source.
              Algo-1 saves you 98 person hours of effort in developing the same functionality from scratch.
              It has 250 lines of code, 80 functions and 40 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Algo-1 and discovered the below as its top functions. This is intended to give you an instant insight into Algo-1 implemented functionality, and help decide if they suit your requirements.
            • Return the number of birthdays in the given ranges .
            • Insert an integer .
            • Merge two lists .
            • Set an integer value .
            • Sort the sequence array in descending order
            • Returns the capacity of this buffer
            • Push a value value onto the stack .
            • Lists the pages .
            • Test if a node is min max max max .
            • Send Rcv request .
            Get all kandi verified functions for this library.

            Algo-1 Key Features

            No Key Features are available at this moment for Algo-1.

            Algo-1 Examples and Code Snippets

            No Code Snippets are available at this moment for Algo-1.

            Community Discussions


            Missing -symbol.json error when trying to compile a SageMaker semantic segmentation model (built-in algorithm) with SageMaker Neo
            Asked 2022-Mar-23 at 12:23

            I have trained a SageMaker semantic segmentation model, using the built-in sagemaker semantic segmentation algorithm. This deploys ok to a SageMaker endpoint and I can run inference in the cloud successfully from it. I would like to use the model on a edge device (AWS Panorama Appliance) which should just mean compiling the model with SageMaker Neo to the specifications of the target device.

            However, regardless of what my target device is (the Neo settings), I cant seem to compile the model with Neo as I get the following error:



            Answered 2022-Mar-23 at 12:23

            For some reason, AWS has decided to not make its built-in algorithms directly compatible with Neo... However, you can re-engineer the network parameters using the model.tar.gz output file and then compile.

            Step 1: Extract model from tar file



            Sagemaker Random Cut Forest Training with Validation
            Asked 2021-Jun-30 at 13:42

            troubling for some days with the sagemaker built-in rcf algorithm.

            I would like to validate the model during training, but there might be things I didn't understand correctly.

            First fitting only with training channel works fine:



            Answered 2021-Jun-30 at 13:42

            I found the error: instead of 'validation' you need to name the channel 'test', then it works:{'train': train_data, 'test': test_data}, wait=True)



            Aws Sagemaker - ModuleNotFoundError: No module named 'cv2'
            Asked 2021-Apr-14 at 14:21

            I am trying to run a object detection code in Aws. Although opencv is listed in the requirement file, i have the error "no module named cv2". I am not sure how to fix this error. could someone help me please.

            My requirement.txt file has

            • opencv-python
            • numpy>=1.18.2
            • scipy>=1.4.1
            • wget>=3.2
            • tensorflow==2.3.1
            • tensorflow-gpu==2.3.1
            • tqdm==4.43.0
            • pandas
            • boto3
            • awscli
            • urllib3
            • mss

            I tried installing "imgaug" and "opencv-python headless" as well.. but still not able to get rid of this error.



            Answered 2021-Apr-14 at 14:21

            Make sure your estimator has

            • framework_version = '2.3',
            • py_version = 'py37',



            How to annotate a broken_barh chart while hovering?
            Asked 2021-Mar-06 at 01:53

            I am trying to plot the Gantt chart using matplotlib in python, wherein there are two solutions suggested by different algorithms. Solution by each algorithm contains a group of batches (shown in different colors) starting and finishing at different points of time.

            I am able to plot the same, but I want to annotate the graph in such a way that whenever I hover the mouse over the solution, it shows batch detail or length of the bar (processing time). I tried several ways, but not happening. [I would like to see (x,y)= (Batch Processing Time, Algorithm Name) value when I move the mouse over the batch solution.



            Answered 2021-Mar-06 at 01:47

            broken_barh doesn't create individual bars, but one big BrokenBarHCollection object. When contains(event) is called, either False or True is returned, together with the index telling which of the small bars has been clicked on.

            With .get_paths()[ind].get_extents() one can get the bounding box of that small bar. The coordinates of the bounding box lead to the start time and the duration.



            mp4 created by ffmpeg will not play with IPython.display.Video
            Asked 2020-Nov-18 at 23:49

            I'm creating a mp4 video from jpegs with ffmpeg, using the following command:

            ffmpeg -y -threads 0 -f image2 -i jpegs/%05d.jpg -framerate 10 video.mp4

            The resulting video will play fine with VLC, but will not play in a Jupyter notebook via:



            Answered 2020-Nov-18 at 23:49
            algo-1-poqk5_1  | Stream mapping:
            algo-1-poqk5_1  |   Stream #0:0 -> #0:0 (mjpeg (native) -> mpeg4 (native))



            SageMaker in local Jupyter notebook: cannot use AWS hosted XGBoost container ("KeyError: 'S3DistributionType'" and "Failed to run: ['docker-compose'")
            Asked 2020-Aug-14 at 01:04

            Running SageMaker within a local Jupyter notebook (using VS Code) works without issue, except that attempting to train an XGBoost model using the AWS hosted container results in errors (container name:

            Jupyter Notebook ...


            Answered 2020-Aug-14 at 01:04

            When running SageMaker in a local Jupyter notebook, it expects the Docker container to be running on the local machine as well.

            The key to ensuring that SageMaker (running in a local notebook) uses the AWS hosted docker container, is to omit the LocalSession object when initializing the Estimator.




            How to plot history of training metrics in Sagemaker .py training
            Asked 2020-Jul-13 at 16:28

            I am running a notebook in Sagemaker and I use a .py file for training:



            Answered 2020-Jul-13 at 16:28

            A SageMaker training job in "local" is actually executing inside of a Docker container that is isolated from the Python kernel that is executing your notebook. Therefore, the in the script doesn't actually get routed to the notebook UI in the same way that executing that command directly from a notebook would.

            Instead of using, consider using plt.savefig() to output the plot to an image:



            Different solution for Activity Selection problem with greedy approach
            Asked 2020-Jul-02 at 03:20

            There is a classic solution for famous Activity Selection problem with greedy approach that you can see here.

            But now, I think about another solution. Let's see this sudo code:



            Answered 2020-Jul-02 at 03:20

            Your solution doesn't always work. Here is a counter example



            sagemaker notebook instance Elastic Inference tensorflow model local deployment
            Asked 2020-Jun-23 at 01:37

            I am trying to replicate

            My elastic inference accelerator is attached to notebook instance. I am using conda_amazonei_tensorflow_p36 kernel. According to documentation I made the changes for local EI:



            Answered 2020-Jun-23 at 01:37

            Solved it. The error I was getting is due to roles/permission of elastic inference attached to notebook. Once fixed these permissions by our devops team. It worked as expected. See



            How to make inference on local PC with the model trained on AWS SageMaker by using the built-in algorithm Semantic Segmentation?
            Asked 2020-Mar-02 at 05:15

            Similar to the issue of The trained model can be deployed on the other platform without dependency of sagemaker or aws service?.

            I have trained a model on AWS SageMaker by using the built-in algorithm Semantic Segmentation. This trained model named as model.tar.gz is stored on S3. So I want to download this file from S3 and then use it to make inference on my local PC without using AWS SageMaker anymore. Since the built-in algorithm Semantic Segmentation is built using the MXNet Gluon framework and the Gluon CV toolkit, so I try to refer the documentation of mxnet and gluon-cv to make inference on local PC.

            It's easy to download this file from S3, and then I unzip this file to get three files:

            1. hyperparams.json: includes the parameters for network architecture, data inputs, and training. Refer to Semantic Segmentation Hyperparameters.
            2. model_algo-1
            3. model_best.params

            Both model_algo-1 and model_best.params are the trained models, and I think it's the output from net.save_parameters (Refer to Train the neural network). I can also load them with the function mxnet.ndarray.load.

            Refer to Predict with a pre-trained model. I found there are two necessary things:

            1. Reconstruct the network for making inference.
            2. Load the trained parameters.

            As for reconstructing the network for making inference, since I have used PSPNet from training, so I can use the class gluoncv.model_zoo.PSPNet to reconstruct the network. And I know how to use some services of AWS SageMaker, for example batch transform jobs, to make inference. I want to reproduce it on my local PC. If I use the class gluoncv.model_zoo.PSPNet to reconstruct the network, I can't make sure whether the parameters for this network are same those used on AWS SageMaker while making inference. Because I can't see the image in detail.

            As for loading the trained parameters, I can use the load_parameters. But as for model_algo-1 and model_best.params, I don't know which one I should use.



            Answered 2020-Mar-02 at 05:15

            The following code works well for me.


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


            No vulnerabilities reported

            Install Algo-1

            You can download it from GitHub.
            You can use Algo-1 like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the Algo-1 component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer For Gradle installation, please refer .


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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