memae-anomaly-detection | -- Gong | Predictive Analytics library

 by   donggong1 Python Version: Current License: MIT

kandi X-RAY | memae-anomaly-detection Summary

kandi X-RAY | memae-anomaly-detection Summary

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

MemAE for anomaly detection. -- Gong, Dong, et al. "Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection". ICCV 2019.
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            kandi-support Support

              memae-anomaly-detection has a low active ecosystem.
              It has 387 star(s) with 95 fork(s). There are 19 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 22 open issues and 6 have been closed. On average issues are closed in 78 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of memae-anomaly-detection is current.

            kandi-Quality Quality

              memae-anomaly-detection has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              memae-anomaly-detection 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

              memae-anomaly-detection releases are not available. You will need to build from source code and install.
              memae-anomaly-detection 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.
              memae-anomaly-detection saves you 224 person hours of effort in developing the same functionality from scratch.
              It has 547 lines of code, 32 functions and 13 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed memae-anomaly-detection and discovered the below as its top functions. This is intended to give you an instant insight into memae-anomaly-detection implemented functionality, and help decide if they suit your requirements.
            • Evaluate a video
            • Get a list of files in path
            • Perform a forward pass on the input
            • Permute feature channel
            • Parse command line options
            • Print options
            • Forward computation
            • Hard shrinkage of input ReLU
            • Convert vframes to images
            • Convert a tensor to a numpy array
            • Crops an image
            • Get model setting
            • Convert frames to BTF
            • Sets random seed
            • Returns a list of all subdirectories in path
            • Add scalar summary
            • Writes image summary
            • Create a directory
            Get all kandi verified functions for this library.

            memae-anomaly-detection Key Features

            No Key Features are available at this moment for memae-anomaly-detection.

            memae-anomaly-detection Examples and Code Snippets

            No Code Snippets are available at this moment for memae-anomaly-detection.

            Community Discussions

            QUESTION

            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.

            ...

            ANSWER

            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.

            Source https://stackoverflow.com/questions/64948197

            QUESTION

            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.

            ...

            ANSWER

            Answered 2020-Nov-01 at 19:39

            You might want try pivoting your table:

            Source https://stackoverflow.com/questions/64630691

            QUESTION

            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

            ...

            ANSWER

            Answered 2020-May-17 at 23:55

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

            Source https://stackoverflow.com/questions/61859411

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

            Vulnerabilities

            No vulnerabilities reported

            Install memae-anomaly-detection

            You can download it from GitHub.
            You can use memae-anomaly-detection 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.

            Support

            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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