Rubygems | Latest Versions for ruby_linear_regressionhttps://rubygems.org/gems2020-12-14T16:40:37Zruby_linear_regression (0.1.5)https://rubygems.org/gems/ruby_linear_regression/versions/0.1.52017-07-04T14:20:56ZSoren Blond DaugaardLinear regression implemented in Ruby.
An implementation of a linear regression machine learning algorithm implemented in Ruby.
The library supports simple problems with one independent variable used to predict a dependent variable as well as multivariate problems with multiple independent variables to predict a dependent variable.
You can train your algorithms using the normal equation or gradient descent.
The library is implemented in pure ruby using Ruby's Matrix implementation. ruby_linear_regression (0.1.4)https://rubygems.org/gems/ruby_linear_regression/versions/0.1.42017-06-16T01:54:58ZSoren Blond DaugaardLinear regression implemented in Ruby.
An implementation of a linear regression machine learning algorithm implemented in Ruby.
The library supports simple problems with one independent variable used to predict a dependent variable as well as multivariate problems with multiple independent variables to predict a dependent variable.
You can train your algorithms using the normal equation or gradient descent.
The library is implemented in pure ruby using Ruby's Matrix implementation. ruby_linear_regression (0.1.2)https://rubygems.org/gems/ruby_linear_regression/versions/0.1.22017-06-15T13:19:08ZSoren Blond DaugaardLinear regression implemented in Ruby.
An implementation of a linear regression machine learning algorithm implemented in Ruby.
The library supports simple problems with one independent variable used to predict a dependent variable as well as multivariate problems with multiple independent variables to predict a dependent variable.
You can train your algorithms using the normal equation or gradient descent.
The library is implemented in pure ruby using Ruby's Matrix implementation. ruby_linear_regression (0.1.1)https://rubygems.org/gems/ruby_linear_regression/versions/0.1.12017-06-15T13:15:42ZSoren Blond DaugaardLinear regression implemented in Ruby.
An implementation of a linear regression machine learning algorithm implemented in Ruby.
Features:
- Supports simple problems with one independent variable to predict a dependent variable and multivariate problems with multiple independent variables to predict a dependent variable.
- Supports training using the normal equation
- Supports training using gradient descent
- The library is implemented in pure ruby using Ruby's Matrix implementation.
An example of how to use the library can be found in the blog post: [Implementing linear regressing using Ruby](http://www.practicalai.io/implementing-linear-regression-using-ruby/). ruby_linear_regression (0.1.0)https://rubygems.org/gems/ruby_linear_regression/versions/0.1.02017-06-14T01:19:15ZSoren Blond DaugaardLinear regression implemented in Ruby.
An implementation of a linear regression machine learning algorithm in Ruby.
This algorithm uses Ruby's Matrix implementation and the normal equation to train the data to the best fit.
The algorithm works with one independent variable or as a multivariate linear regression with multiple variables to predict a dependent variable. ruby_linear_regression (0.0.2)https://rubygems.org/gems/ruby_linear_regression/versions/0.0.22017-06-10T23:36:25ZSoren Blond DaugaardLinear regression implemented in Ruby.
An implementation of a linear regression machine learning algorithm implemented in Ruby.
This algorithm uses Ruby's Matrix implementation and the normal equation to train the data to the best fit.
The algorithm works with multiple independent variables to predict a dependent variable. ruby_linear_regression (0.0.1)https://rubygems.org/gems/ruby_linear_regression/versions/0.0.12017-06-10T23:25:59ZSoren Blond DaugaardLinear regression implemented in Ruby.
An implementation of a linear regression machine learning algorithm implemented in Ruby.
This algorithm uses Ruby's Matrix implementation and the normal equation to train the data to the best fit.
The algorithm works with multiple independent variables to predict a dependent variable.