shogun | Shōgun - Unified and efficient Machine Learning | Machine Learning library

by shogun-toolbox C++ Version: shogun_6.1.4 License: BSD-3-Clause

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

kandi X-RAY | shogun Summary

shogun is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. shogun has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

Shōgun

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Quality

Security

License

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Support

shogun has a medium active ecosystem.

It has 2950 star(s) with 1052 fork(s). There are 218 watchers for this library.

It had no major release in the last 12 months.

There are 418 open issues and 1108 have been closed. On average issues are closed in 285 days. There are 11 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of shogun is shogun_6.1.4

Quality

shogun has no bugs reported.

Security

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

License

shogun is licensed under the BSD-3-Clause 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.

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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

You can download it from GitHub.

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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