func (nb *NaiveBayes) Predict(X *mat64.Dense) []Prediction {
nSamples, _ := X.Dims()
prediction := []Prediction{}
for i := 0; i < nSamples; i++ {
scores := map[int]float64{}
for langIdx, _ := range nb.params.LangsCount {
scores[langIdx] = nb.tokensProba(X.Row(nil, i), langIdx) + nb.langProba(langIdx)
}
bestScore := scores[0]
bestLangIdx := 0
for langIdx, score := range scores {
if score > bestScore {
bestScore = score
bestLangIdx = langIdx
}
}
prediction = append(prediction, Prediction{
Label: bestLangIdx,
Language: "TODO: PENDING",
Score: bestScore,
})
}
return prediction
}
func removeRow(df *mat64.Dense, row int) *mat64.Dense {
r, c := df.Dims()
if row > r || row < 0 {
panic("Row Index not supported")
}
cop := mat64.NewDense(r-1, c, nil)
m := 0
for i := 0; i < r; i++ {
if i != row {
cop.SetRow(m, df.Row(nil, i))
m++
}
}
return cop
}