shogun | A GUI for SDKMAN - Shogun is a GUI front end for SDKMAN | User Interface library

by yusuke Java Version: 1.0.11-PREVIEW License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | shogun Summary

shogun is a Java library typically used in User Interface, Selenium applications. shogun has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can download it from GitHub.

Shogun is a GUI front end for SDKMAN!. With Shogun, you can install JDKs, switch between JDKs at ease.

Support

Quality

Security

License

Reuse

Support

shogun has a highly active ecosystem.

It has 86 star(s) with 7 fork(s). There are 5 watchers for this library.

It had no major release in the last 12 months.

There are 15 open issues and 27 have been closed. On average issues are closed in 1 days. There are 3 open pull requests and 0 closed requests.

It has a negative sentiment in the developer community.

The latest version of shogun is 1.0.11-PREVIEW

Quality

shogun has 0 bugs and 0 code smells.

Security

shogun has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

shogun code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

shogun is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

shogun releases are available to install and integrate.

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

Installation instructions are available. Examples and code snippets are not available.

shogun saves you 1165 person hours of effort in developing the same functionality from scratch.

It has 2629 lines of code, 172 functions and 23 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed shogun and discovered the below as its top functions. This is intended to give you an instant insight into shogun implemented functionality, and help decide if they suit your requirements.

Scans all JVM versions
Searches for the JDK files
Lists the paths of all libraries in the directory
Convert a string to a notregistered version
Convert a version to a human readable label
Checks if current directory is in use
Gets the current directory
Uninstall a single instance
Uninstalls a version
Installs native - image command
Returns a human - installed version string
Makes a default
Returns the list of versions
Returns the absolute path
Checks if the given version is gralal
Returns a string with a candidate
Returns the size of the archive in bytes
Checks if the archive exists
Invoked when the browser is clicked
Deletes the archive
Opens the platform in - terminal
Compares this version with the specified version
Start the shiro script
Waits for the action to finish
Create a label for the current version
Creates a hash code

Get all kandi verified functions for this library.

shogun Key Features

No Key Features are available at this moment for shogun.

shogun Examples and Code Snippets

No Code Snippets are available at this moment for shogun.

Community Discussions

Trending Discussions on shogun

how render specific array of string in unorderlist

Rebuild vcpkg without installing again

How to save/load trained model in Shogun?

Can the random seed be controlled from shogun's python interface?

Forming JSON with Key and Value if Key Exists

Set up Vicon stream device via Python

Get weight parameters from Shogun Shareboost model

Shogun / quadratic MMD error caused by varying train_test_ratio

When using Stochastic Gradient Descent with Shogun NeuralNetwork, everything classified the same

Error when trying to build with meta examples in shogun

QUESTION

how render specific array of string in unorderlist

Asked 2021-Mar-13 at 08:23

I'm trying to render a specific array of strings on this object however the result is showing in a single line. how can I fix this?

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ANSWER

Answered 2021-Mar-13 at 08:12

change span to li you will have sth like this

Source https://stackoverflow.com/questions/66611314

QUESTION

Rebuild vcpkg without installing again

Asked 2020-Oct-13 at 11:51

I have installed a vcpkg package with vcpkg install shogun:x64-windows. The build fails and I need to change a file to fix it.
How can I rebuild the package without the install option, that overwrites my changes?
I cannot find any VC project in the vcpkg\buildtrees\shogun\x64-windows-\src\shogun folder.

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ANSWER

Answered 2020-Oct-13 at 11:51

You should:

delete buildtrees directory
Execute command "vcpkg remove shogun:x64-windows"
If you need to change shogun solution before install you should write a .patch file and copy to this path("(install_path)\vcpkg\ports\shogun")
Add your patch file to "(install_path)\vcpkg\ports\shogun\portfile.cmake"
Find this line "set(ABSEIL_PATCHES" and add your patch file name in next line
Then "vcpkg install shogun:x64-windows"

Source https://stackoverflow.com/questions/64162414

QUESTION

How to save/load trained model in Shogun?

Asked 2019-Nov-13 at 21:15

How to save/load trained model in Shogun? Didn't find in docs.

https://www.shogun-toolbox.org/examples/latest/quickstart/interfaces.html

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ANSWER

Answered 2019-Nov-13 at 21:15

depends on what version you are using. in case release here's an example: https://github.com/shogun-toolbox/shogun/blob/master/examples/undocumented/libshogun/library_serialization.cpp

if you are using develop then simply use

Source https://stackoverflow.com/questions/58379780

QUESTION

Can the random seed be controlled from shogun's python interface?

Asked 2019-Aug-13 at 15:23

Using shogun 6.1.3 and python 3.6.

I am trying to get replicable results in shogun for testing purposes, but I do not see a way to control the random seed.

I have tried:

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ANSWER

Answered 2019-Aug-13 at 15:23

In Shogun 6.1.3 (and earlier versions), you can use a (global) static call Math.init_random(seed).

Since having a global seed leads to reproducibility issues in multi-threaded settings, in the develop branch of Shogun, we have recently removed this. Instead you can set the seed (recursively) of particular objects using obj.put("seed", my_seed). Or, even simpler, using kwargs style initializers in Python: km = sg.machine("KMeans", k=2, distance=d, seed=1).

Both of those are documented in the generated meta examples, using the 6.1.3 and develop branch respectively. The website examples will be updated with the next release.

Source https://stackoverflow.com/questions/57384802

QUESTION

Forming JSON with Key and Value if Key Exists

Asked 2018-Sep-22 at 03:10

I have a JSON Structure something like:

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ANSWER

Answered 2018-Sep-21 at 13:24

Here is what you can do! First iterate through the data and use the method "hasOwnProperty" to check if the cuisine exists and if it does then check if your cuisines object has that cuisine and if does then add it to it.

Source https://stackoverflow.com/questions/52444448

QUESTION

Set up Vicon stream device via Python

Asked 2018-Jun-14 at 21:34

I'd like to use python to setup a Vicon streaming device in Motionbuilder 2017.

Vicon Stream Device | Download Page

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ANSWER

Answered 2018-May-30 at 09:18

I had similar issues with adding devices in a c++ tool. My work around was to create a template file containing my device and open that from the c++ code. You can try same trick using python.

Source https://stackoverflow.com/questions/50497136

QUESTION

Get weight parameters from Shogun Shareboost model

Asked 2018-Jun-09 at 09:22

I have a trained ShareBoost model. How do I obtain the model's weight parameters/vectors?

I tried to get the individual linear machines and extract the individual weight vectors but unlike the linear svm it does not seem to have a get_w() method.

Also, even though the C++ ShareBoost class is a subclass of CMachine, the Parameters object obtained from m_parameters (see docs) does not appear to have the parameters available.

The following code is what I have tried.

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ANSWER

Answered 2018-Jun-09 at 09:22

in case you are using the C++ API you could get the weight vector the following way:

Source https://stackoverflow.com/questions/50691698

QUESTION

Shogun / quadratic MMD error caused by varying train_test_ratio

Asked 2018-May-14 at 14:38

I'm using Shogun to run MMD (quadratic) and compare two nonparametric distributions based on their samples (code below is for 1D, but I've also looked at 2D samples). In the toy problem shown below, I try to change the ratio between training and testing samples in the process of selecting an optimized kernel (KSM_MAXIMIZE_MMD is the selection strategy; I've also used KSM_MEDIAN_HEURISTIC). It appears that any ratio other than 1 yields an error.

Am I allowed to change this ratio in this setting? (I see that it is used at: http://www.shogun-toolbox.org/examples/latest/examples/statistical_testing/quadratic_time_mmd.html, but it is set to 1 there)

Concise version of the my code (inspired by the notebook available at: http://www.shogun-toolbox.org/notebook/latest/mmd_two_sample_testing.html):

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ANSWER

Answered 2018-Jan-27 at 06:31

The train_test_ratio attribute is the ratio between the number of samples used in training and the number of samples used in testing. When you have train_test_mode turned on, the way it decides how many samples to fetch in each mode goes something like this.

Source https://stackoverflow.com/questions/48467554

QUESTION

When using Stochastic Gradient Descent with Shogun NeuralNetwork, everything classified the same

Asked 2018-Mar-06 at 14:32

I am attempting to classify a number of samples as 1 or 0 but when using Stochastic Gradient Descent as the optimization algorithm everything is classified as either a 1 or a 0.

When using the default (L-BFGS), it works as expected and classifies samples as both 1 and 0. I have tried adjusting the momentum, learning rate, batch size, decay and error coefficient but the error is the same every time. Any help would be greatly appreciated!

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ANSWER

Answered 2018-Mar-06 at 14:32

you should lower (significantly) your mini-batch size - try with 20 or so.

Source https://stackoverflow.com/questions/49119857

QUESTION

Error when trying to build with meta examples in shogun

Asked 2018-Feb-17 at 09:16

I successfully built shogun without the examples (this question) but now when I am trying to do the same with meta examples, I am getting an error as follows:

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ANSWER

Answered 2018-Feb-17 at 09:16

https://github.com/shogun-toolbox/shogun/issues/4179

Sorry I made a silly mistake as can be seen by the link.

Source https://stackoverflow.com/questions/48838748

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

Vulnerabilities

No vulnerabilities reported

Install shogun

Java 11 is required to build Shogun. To build Shogun, have Java 11 installed on your mac, and run $ mvn clean package Shogun.app will be located at target/.