Liblinear-Ruby is Ruby interface to LIBLINEAR using SWIG. Now, this interface is supporting LIBLINEAR 1.95.
Add this line to your application's Gemfile:
gem 'liblinear-ruby'
And then execute:
$ bundle
Or install it yourself as:
$ gem install liblinear-ruby
This sample code execute classification with L2-regularized logistic regression.
require 'liblinear'
# Setting parameters
param = Liblinear::Parameter.new
param.solver_type = Liblinear::L2R_LR
# Training phase
labels = [1, -1]
examples = [
{1=>0, 2=>0, 3=>0, 4=>0, 5=>0},
{1=>1, 2=>1, 3=>1, 4=>1, 5=>1}
]
bias = 0.5
prob = Liblinear::Problem.new(labels, examples, bias)
model = Liblinear::Model.new(prob, param)
# Predicting phase
puts model.predict({1=>1, 2=>1, 3=>1, 4=>1, 5=>1}) # => -1.0
# Analyzing phase
puts model.coefficient
puts model.bias
# Cross Validation
fold = 2
cv = Liblinear::CrossValidator.new(prob, param, fold)
cv.execute
puts cv.accuracy # for classification
puts cv.mean_squared_error # for regression
puts cv.squared_correlation_coefficient # for regressionFirst, you have to make an instance of Liblinear::Parameter:
param = Liblinear::Parameter.newAnd then set the parameters as:
param.[parameter_you_set] = valueOr you can set by Hash as:
parameter = {
parameter_you_set: value,
...
}
param = Liblinear::Parameter.new(parameter)This parameter is comparable to -s option on command line.
You can set as:
param.solver_type = solver_type # default 1 (Liblinear::L2R_L2LOSS_SVC_DUAL)Solver types you can set are shown below.
# for multi-class classification
Liblinear::L2R_LR # L2-regularized logistic regression (primal)
Liblinear::L2R_L2LOSS_SVC_DUAL # L2-regularized L2-loss support vector classification (dual)
Liblinear::L2R_L2LOSS_SVC # L2-regularized L2-loss support vector classification (primal)
Liblinear::L2R_L1LOSS_SVC_DUAL # L2-regularized L1-loss support vector classification (dual)
Liblinear::MCSVM_CS # support vector classification by Crammer and Singer
Liblinear::L1R_L2LOSS_SVC # L1-regularized L2-loss support vector classification
Liblinear::L1R_LR # L1-regularized logistic regression
Liblinear::L2R_LR_DUAL # L2-regularized logistic regression (dual)
# for regression
Liblinear::L2R_L2LOSS_SVR # L2-regularized L2-loss support vector regression (primal)
Liblinear::L2R_L2LOSS_SVR_DUAL # L2-regularized L2-loss support vector regression (dual)
Liblinear::L2R_L1LOSS_SVR_DUAL # L2-regularized L1-loss support vector regression (dual)This parameter is comparable to -c option on command line.
You can set as:
param.C = value # default 1This parameter is comparable to -p option on command line.
You can set as:
param.p = value # default 0.1This parameter is comparable to -e option on command line.
You can set as:
param.eps = value # default 0.1This parameter adjust the parameter C of different classes(see LIBLINEAR's README for details).
nr_weight is the number of elements in the array weight_label and weight.
You can set as:
param.nr_weight = value # default 0
param.weight_label = [Array <Integer>] # default []
param.weight = [Array <Double>] # default []You have to prepare training data.
The format of training data is shown below:
# Labels mean class
label = [1, -1, ...]
# Training data have to be array of hash or array of array
# If you chose array of hash
examples = [
{1=>0, 2=>0, 3=>0, 4=>0, 5=>0},
{1=>1, 2=>1, 3=>1, 4=>1, 5=>1},
...
]
# If you chose array of array
examples = [
[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
]Next, set the bias (this is comparable to -B option on command line):
bias = 0.5 # default -1And then make an instance of Liblinear::Problem and Liblinear::Model:
prob = Liblinear::Problem.new(labels, examples, bias)
model = Liblinear::Model.new(prob, param)If you have already had a model file, you can load it as:
model = Liblinear::Model.new(model_file)In this phase, you can save model as:
model.save(file_name)Input a data whose format is same as training data:
# Hash
model.predict({1=>1, 2=>1, 3=>1, 4=>1, 5=>1})
# Array
model.predict([1, 1, 1, 1, 1])- Fork it
- Create your feature branch (
git checkout -b my-new-feature) - Commit your changes (
git commit -am 'Add some feature') - Push to the branch (
git push origin my-new-feature) - Create new Pull Request
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