skip-ganomaly | Source code for Skip-GANomaly paper | Predictive Analytics library

by samet-akcay Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | skip-ganomaly Summary

skip-ganomaly is a Python library typically used in Analytics, Predictive Analytics, Deep Learning, Pytorch applications. skip-ganomaly has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. However skip-ganomaly has 1 bugs. You can download it from GitHub.

Source code for Skip-GANomaly paper

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Quality

Security

License

Reuse

Support

skip-ganomaly has a low active ecosystem.

It has 175 star(s) with 65 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 20 open issues and 0 have been closed. On average issues are closed in 204 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of skip-ganomaly is current.

Quality

skip-ganomaly has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 28 code smells.

Security

skip-ganomaly has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

skip-ganomaly code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

skip-ganomaly is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

skip-ganomaly releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

skip-ganomaly saves you 568 person hours of effort in developing the same functionality from scratch.

It has 1328 lines of code, 99 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 skip-ganomaly and discovered the below as its top functions. This is intended to give you an instant insight into skip-ganomaly implemented functionality, and help decide if they suit your requirements.

Run the test
Load the weights of the network
Set the input
Get the current images
Load the test set
Evaluate the ROC curve
Load data set
Get the Cifar anomaly dataset
Get MNISTAN anomaly data
Parse arguments
Train the model
Initialize the test set
Train one epoch
Saves weights for training
Perform optimizer
Gradient - D loss
Gradient of the gradients
Compute the forward and fake features
Create an image dataset
Load a model
Forward computation
Parse command line arguments
Default loader
Optimizes the network
Define the netG
Define the discriminator layer

Get all kandi verified functions for this library.

skip-ganomaly Key Features

No Key Features are available at this moment for skip-ganomaly.

skip-ganomaly Examples and Code Snippets

No Code Snippets are available at this moment for skip-ganomaly.

Community Discussions

Trending Discussions on Predictive Analytics

will TensorFlow utilize GPU for predictive Analysis?

Restructuring Pandas Dataframe for large number of columns

Display data from two json files in react native

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 skip-ganomaly

First clone the repository git clone https://github.com/samet-akcay/skip-ganomaly.git
Create the virtual environment via conda conda create -n skipganomaly python=3.7
Activate the virtual environment. conda activate skipganomaly
Install the dependencies. pip install --user --requirement requirements.txt

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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