mirror of
https://github.com/sjwhitworth/golearn.git
synced 2025-05-01 22:18:10 +08:00
981d43f1dd
This patch
* Adds a one-vs-all meta classifier into meta/
* Adds a LinearSVC (essentially the same as LogisticRegression
but with different libsvm parameters) to linear_models/
* Adds a MultiLinearSVC into ensemble/ for predicting
CategoricalAttribute classes with the LinearSVC
* Adds a new example dataset based on classifying article headlines.
The example dataset is drawn from WikiNews, and consists of an average,
min and max Word2Vec representation of article headlines from three
categories. The Word2Vec model was computed offline using gensim.
49 lines
1.8 KiB
Go
49 lines
1.8 KiB
Go
package ensemble
|
|
|
|
import (
|
|
"github.com/sjwhitworth/golearn/base"
|
|
"github.com/sjwhitworth/golearn/linear_models"
|
|
"github.com/sjwhitworth/golearn/meta"
|
|
)
|
|
|
|
// MultiLinearSVC implements a multi-class Support Vector Classifier using a one-vs-all
|
|
// voting scheme. Only one CategoricalAttribute class is supported.
|
|
type MultiLinearSVC struct {
|
|
m *meta.OneVsAllModel
|
|
}
|
|
|
|
// NewMultiLinearSVC creates a new MultiLinearSVC using the OneVsAllModel.
|
|
// The loss and penalty arguments can be "l1" or "l2". Typical values are
|
|
// "l1" for the loss and "l2" for the penalty. The dual parameter controls
|
|
// whether the system solves the dual or primal SVM form, true should be used
|
|
// in most cases. C is the penalty term, normally 1.0. eps is the convergence
|
|
// term, typically 1e-4.
|
|
func NewMultiLinearSVC(loss, penalty string, dual bool, C float64, eps float64) *MultiLinearSVC {
|
|
classifierFunc := func() base.Classifier {
|
|
ret, err := linear_models.NewLinearSVC(loss, penalty, dual, C, eps)
|
|
if err != nil {
|
|
panic(err)
|
|
}
|
|
return ret
|
|
}
|
|
return &MultiLinearSVC{
|
|
meta.NewOneVsAllModel(classifierFunc),
|
|
}
|
|
}
|
|
|
|
// Fit builds the MultiLinearSVC by building n (where n is the number of values
|
|
// the singular CategoricalAttribute can take) seperate one-vs-rest models.
|
|
func (m *MultiLinearSVC) Fit(instances base.FixedDataGrid) error {
|
|
m.m.Fit(instances)
|
|
return nil
|
|
}
|
|
|
|
// Predict issues predictions from the MultiLinearSVC. Each underlying LinearSVC is
|
|
// used to predict whether an instance takes on a class or some other class, and the
|
|
// model which definitively reports a given class is the one chosen. The result is
|
|
// undefined if all underlying models predict that the instance originates from some
|
|
// other class.
|
|
func (m *MultiLinearSVC) Predict(from base.FixedDataGrid) (base.FixedDataGrid, error) {
|
|
return m.m.Predict(from)
|
|
}
|