Skip to content

Implementation of TSVM and quasi-Newton S3VM algorithm by python and its Application in Mineral Prospectivity Mapping.

Notifications You must be signed in to change notification settings

taojintao/TSVM-and-quasi-Newton-S3VM

Repository files navigation

TSVM and quasi-Newton S3VM

Implementation of TSVM and quasi-Newton S3VM algorithm by python and its Application in Mineral Prospectivity Mapping.

Experimental enviroment

OS: Windows 10

Memory:16G

IDE: Pycharm Edu 2019

Dependencies

The code is written in Python 3.6. The following dependent libraries are required:

Numpy

Pandas

Scipy

Scikit-learn

Matplotlib

Seaborn

These packages could be installed via 'pip install -r requirements.txt'.

Introduction

Example data is extracted from real data. TSVM.py and Quasi_Newton_S3VM.py are an implementation of both algorithms. Application.py is to apply SVM, TSVM and QN-S3VM to example data. Results folder is the output results of these three algorithms.

About

Implementation of TSVM and quasi-Newton S3VM algorithm by python and its Application in Mineral Prospectivity Mapping.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages