Performant logistic regression in Go.

• func New(learningRate float64) *Model
• func NewL2Regularized(learningRate, l2Parameter float64) *Model
• Model
  • Weights []float64
  • Bias float64
  • func (mdl *Model) TrainBatch(inputs [][]float64, outputs []float64, epochs int)
  • func (mdl *Model) TrainMiniBatch(inputs [][]float64, outputs []float64, epochs, batchSize int)
  • func (mdl *Model) TrainSGD(inputs [][]float64, outputs []float64, epochs int)
  • func (mdl *Model) Train(input []float64, output float64)
  • func (mdl *Model) Predict(input []float64) float64
• func Marshal(mdl *Model) ([]byte, error)
• func Unmarshal(data []byte) (*Model, error)

• All output values used as training parameters should be 0 or 1.
• Predict returns the probability, not 0 or 1.
• Input standardization is always performed.
• l2Parameter should be between 0 (no regularization) and 1 (full regularization).