shogun | Shogun : Functions As Commands for the true samurai | Command Line Interface library
kandi X-RAY | shogun Summary
kandi X-RAY | shogun Summary
Shogun: Functions As Commands for the true samurai developer.
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Top functions reviewed by kandi - BETA
- Build builds a list of sub packages .
- pullFunction extracts a function definition from a function declaration .
- main is the main entry point for testing
- buildAction handles the build action .
- getArgumentsState returns the arguments state of argType .
- Load returns a map of flag values .
- TestNoArgumentFunction verifies that the passed function returns an error .
- addAction handles the add action
- Test map function
- ListFunctionsForDir returns a PackageFunctionList for the given directory .
shogun Key Features
shogun Examples and Code Snippets
Lines of Code : 85License : Permissive (MIT)
Copyfunc()
func() error
func(Context)
func(Context) error
func(string)
func(string) error
func(Context, string) error
func(Context, string, io.WriteCloser) error
func([]string)
func([]string) error
func(Context, []string) error
func(Context, []string, > shogun build
shogun build
shogun build -rm
shogun build -skip
shogun build -d=./examples
shogun build -nomain
shogun build -bin=./
shogun build -single -d=./examples -cmd=./cmd
shogun build -d=./examples -cmd=./cmd
shogun build -skip -d // +build shogun
package something
// +build shogun
// Package do does something.
//
//@binaryName(name => shogunate_bin)
package do
// +build shogun
/* Package do does something.
@binaryName(asJSON, name => shogunate_bin, {
{
"desc Community Discussions
Trending Discussions on shogun
QUESTION
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?
...ANSWER
Answered 2021-Mar-13 at 08:12change span to li
you will have sth like this
QUESTION
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.
ANSWER
Answered 2020-Oct-13 at 11:51You 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"
QUESTION
How to save/load trained model in Shogun? Didn't find in docs.
https://www.shogun-toolbox.org/examples/latest/quickstart/interfaces.html
...ANSWER
Answered 2019-Nov-13 at 21:15depends 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
QUESTION
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:
...ANSWER
Answered 2019-Aug-13 at 15:23In 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.
QUESTION
I have a JSON Structure something like:
...ANSWER
Answered 2018-Sep-21 at 13:24Here 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.
QUESTION
I'd like to use python to setup a Vicon streaming device in Motionbuilder 2017.
...ANSWER
Answered 2018-May-30 at 09:18I 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.
QUESTION
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.
...ANSWER
Answered 2018-Jun-09 at 09:22in case you are using the C++ API you could get the weight vector the following way:
QUESTION
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):
...ANSWER
Answered 2018-Jan-27 at 06:31The
train_test_ratioattribute is the ratio between the number of samples used in training and the number of samples used in testing. When you havetrain_test_modeturned on, the way it decides how many samples to fetch in each mode goes something like this.
QUESTION
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!
...ANSWER
Answered 2018-Mar-06 at 14:32you should lower (significantly) your mini-batch size - try with 20 or so.
QUESTION
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:
...ANSWER
Answered 2018-Feb-17 at 09:16https://github.com/shogun-toolbox/shogun/issues/4179
Sorry I made a silly mistake as can be seen by the link.
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