Esempio n. 1
0
func NewNaiveBayes(c *LabeledCorpus) *NaiveBayes {
	labelCounts := make([]float64, c.LabelIndex.Len())
	wordCounts := make([][]float64, c.LabelIndex.Len())

	for i := range wordCounts {
		wordCounts[i] = make([]float64, c.V)
		for v := 0; v < c.V; v++ {
			wordCounts[i][v]++ // smoothing
		}
	}

	for d := 0; d < c.M; d++ {
		label := c.LabelIndex.Id(c.Labels[d])
		labelCounts[label]++
		for n := 0; n < c.N[d]; n++ {
			wordCounts[label][c.W[d][n]]++
		}
	}

	util.Normalize(labelCounts)
	util.Lcounts(labelCounts)
	for _, counts := range wordCounts {
		util.Normalize(counts)
		util.Lcounts(counts)
	}

	return &NaiveBayes{c.LabelIndex, labelCounts, wordCounts}
}
Esempio n. 2
0
File: itm.go Progetto: jlund3/modelt
func (i *ITM) Threshold(cutoff float64) {
	for _, d := range rand.Perm(i.M) {
		for n := 0; n < i.N[d]; n++ {
			i.unsetZ(d, n)
			counts := i.conditional(d, n)
			util.Normalize(counts)

			bestZ := -1
			bestP := math.Inf(-1)
			useBest := true
			for z, count := range counts {
				if count > bestP {
					bestZ = z
					bestP = count
				}

				if count < cutoff {
					counts[z] = 0
				} else {
					useBest = false
				}
			}

			if useBest {
				i.setZ(d, n, bestZ)
			} else {
				i.setZ(d, n, util.SampleCounts(counts))
			}
		}
	}
}
Esempio n. 3
0
func FindAnchors(Q *matrix.DenseMatrix, k, r int) []int {
	QRed := Q.Copy()
	for _, row := range QRed.Arrays() {
		util.Normalize(row)
	}
	QRed = QRed.Transpose()
	QRed = RandomProjection(QRed, r)
	QRed = QRed.Transpose()
	return GramSchmidt(QRed, k)
}