support-vector-machines | An investigation of Support Vector Machines in Python

by pascalc Python Version: Current License: No License

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

kandi X-RAY | support-vector-machines Summary

support-vector-machines is a Python library. support-vector-machines has no bugs, it has no vulnerabilities and it has low support. However support-vector-machines build file is not available. You can download it from GitHub.

An implementation of support vector machines in python. ** Radial basis kernel - K = e^( -1*[(x-y)² / 2sigma²] ) The performance relies on the value chosen for sigma. To chose the optimum sigma that gives the best division of the data set one can implement a cross validation check, a method derived from statistics. see section Properties my = 1/2s², my \in (2⁻¹⁵, ... , 2³).

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support-vector-machines has a low active ecosystem.

It has 5 star(s) with 3 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

support-vector-machines has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of support-vector-machines is current.

Quality

support-vector-machines has no bugs reported.

Security

support-vector-machines has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

support-vector-machines does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

support-vector-machines releases are not available. You will need to build from source code and install.

support-vector-machines has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

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

Runs the test .
Solve QP .
Calculate the P matrix P .
r Generates the radial basis function .
Calculate an indicator function based on alpha_list .
Sigmoid kernel function .
Print a debug message
Linear linear kernel .

Get all kandi verified functions for this library.

support-vector-machines Key Features

No Key Features are available at this moment for support-vector-machines.

support-vector-machines Examples and Code Snippets

No Code Snippets are available at this moment for support-vector-machines.

Community Discussions

Trending Discussions on support-vector-machines

Can we plot image data in Altair?

Interpretation of method plt.fill_between()? Discussion

Which algorithm does R use for computing one-class SVM ? (package e1071)

QUESTION

Can we plot image data in Altair?

Asked 2020-Feb-02 at 01:08

I am trying to plot image data in altair, specifically trying to replicate face recognition example in this link from Jake VDP's book - https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html.

Any one had luck plotting image data in altair?

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ANSWER

Answered 2020-Feb-02 at 01:08

Altair features an image mark that can be used if you want to plot images that are available at a URL; for example:

Source https://stackoverflow.com/questions/60019006

QUESTION

Interpretation of method plt.fill_between()? Discussion

Asked 2020-Jan-20 at 23:26

A question for discussion:

The matplotlib documentation says that the method plt.fill_between is used to "fill the area between two horizontal curves".

What exactly is meant by "horizontal"? Intuitively, I would say "two parallel curves". Like in this example

The curves are not horizontal, but parallel.

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ANSWER

Answered 2020-Jan-19 at 17:07

"Two horizontal curves" is a set of data where you have two arrays y1 and y2 defined on a single support x.
Equally, "two vertical curves" would be a set of data where you have a single y support for two x arrays.

Source https://stackoverflow.com/questions/59812171

QUESTION

Which algorithm does R use for computing one-class SVM ? (package e1071)

Asked 2019-Oct-12 at 08:31

Which algorithm does R use for computing one-class SVM ? This is the function

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ANSWER

Answered 2019-Oct-12 at 07:57

You can see the following link: https://cran.r-project.org/web/packages/e1071/vignettes/svmdoc.pdf

The link shows the dual problem formulation of the SVM algorithm this package uses (when one use one-class SVM, page 7 index (3)), easy transformation from the dual to the primal problem shows that this default implementation is the one Schölkopf suggested, see paper: https://www.stat.purdue.edu/~yuzhu/stat598m3/Papers/NewSVM.pdf

Source https://stackoverflow.com/questions/58351777

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

Vulnerabilities

No vulnerabilities reported

Install support-vector-machines

You can download it from GitHub.
You can use support-vector-machines 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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