ESRNN-GPU | PyTorch GPU implementation of the ES-RNN model | Predictive Analytics library

 by   damitkwr Python Version: Current License: MIT

kandi X-RAY | ESRNN-GPU Summary

kandi X-RAY | ESRNN-GPU Summary

ESRNN-GPU is a Python library typically used in Analytics, Predictive Analytics, Deep Learning, Pytorch, Neural Network applications. ESRNN-GPU has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However ESRNN-GPU build file is not available. You can download it from GitHub.

PyTorch GPU implementation of the ES-RNN model for time series forecasting

            kandi-support Support

              ESRNN-GPU has a low active ecosystem.
              It has 272 star(s) with 69 fork(s). There are 21 watchers for this library.
              It had no major release in the last 6 months.
              There are 10 open issues and 12 have been closed. On average issues are closed in 2 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of ESRNN-GPU is current.

            kandi-Quality Quality

              ESRNN-GPU has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              ESRNN-GPU 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

              ESRNN-GPU releases are not available. You will need to build from source code and install.
              ESRNN-GPU has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              ESRNN-GPU saves you 258 person hours of effort in developing the same functionality from scratch.
              It has 626 lines of code, 45 functions and 12 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed ESRNN-GPU and discovered the below as its top functions. This is intended to give you an instant insight into ESRNN-GPU implemented functionality, and help decide if they suit your requirements.
            • Performs the forward regression
            • Forward a series
            • Log a scalar value
            • Train the model
            • Perform the DNN layer
            • Apply a cell
            • Pads inputs to n_steps
            • DNN layer
            • Train the model
            • Evaluate the model
            • Compute the similarity between predictions
            • Save the trained model
            • Create train and test sets
            • Read data from file
            • Create a numpy array from train data
            • Funcated error function
            • Calculates the WQuantLoss loss
            • Return the configuration for LSTM training
            • Compute non - SMAPE
            Get all kandi verified functions for this library.

            ESRNN-GPU Key Features

            No Key Features are available at this moment for ESRNN-GPU.

            ESRNN-GPU Examples and Code Snippets

            No Code Snippets are available at this moment for ESRNN-GPU.

            Community Discussions


            will TensorFlow utilize GPU for predictive Analysis?
            Asked 2020-Nov-21 at 21:35

            GPU is good for parallel computing but the problem is some machine learning libraries don't utilize the GPU, unless that machine learning based on image processing or some sort of graphics processing, what if I am using machine learning for predictive Analytics? do libraries like TensorFlow utilize the GPU? or they use only CPU? or can I choose which processing unit to use? whats the deal here?

            note: predictive Analysis requires no graphics processing.



            Answered 2020-Nov-21 at 21:35
            The short answer: yes, it will! The slightly longer answer:

            The computation that happens in the GPU in any of the machine learning frameworks that support GPUs is not limited to graphical processing. For instance, if your model is a simple logistic regression, a framework such as TensorFlow will run it on the GPU if properly configured.

            The advantage of GPUs for machine learning is that training big neural networks benefits greatly from the high level of parallelism that the GPUs offer.

            If you want to know more about this, I'd recommend you start here or here.

            some things to consider:
            • how much a model will benefit from running in the GPU will depend on how much it will benefit from parallel computation in general.
            • Deep Learning models can be applied to predictive analytics, as well as more classical machine learning models. Bear in mind that neural nets are possibly the category of models that will benefit inherently from the GPU (see links above).
            • Even though running models using GPUs (or even more specialised hardware) can bring benefits, I would suggest that you don't choose a framework and, especially, don't choose an algorithm based solely on the fact that it will benefit from parallelism, but rather look at how appropriate a given algorithm is for the data you have.



            Restructuring Pandas Dataframe for large number of columns
            Asked 2020-Nov-01 at 19:39

            I have a pandas dataframe which is a large number of answers given by users in response to a survey and I need to re-structure it. There are up to 105 questions asked each year, but I only need maybe 20 of them.

            The current structure is as below.

            What I want to do is re-structure it so that the row values become column names and the answer given by the user is then the value in that column. In a picture (from Excel), what I want is the below (I know I'll need to re-name my columns, but that's fine once I can create the structure in the first place):

            Is it possible to re-structure my dataframe this way? The outcome of this is to use some predictive analytics to predict a target variable, so I need to re-strcture before I can use Random Forest, kNN, and so on.



            Answered 2020-Nov-01 at 19:39

            You might want try pivoting your table:



            Display data from two json files in react native
            Asked 2020-May-17 at 23:55

            I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files



            Answered 2020-May-17 at 23:55

            The new object to get params in React Navigation 5 is:


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


            No vulnerabilities reported

            Install ESRNN-GPU

            You can download it from GitHub.
            You can use ESRNN-GPU like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            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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