Ejemplo n.º 1
0
// modelMean calculates the mean between all points in a model and a centroid.
func modelMean(points, centroid *matrix.DenseMatrix) *matrix.DenseMatrix {
	prows, pcols := points.GetSize()
	pdist := matrix.Zeros(prows, pcols)

	for i := 0; i < prows; i++ {
		diff := matrix.Difference(centroid, points.GetRowVector(i))
		pdist.SetRowVector(diff, i)
	}
	return pdist.MeanCols()
}
Ejemplo n.º 2
0
// CalcDist finds the Euclidean distance between points.
func (ed EuclidDist) CalcDist(p, q *matrix.DenseMatrix) float64 {
	diff := matrix.Difference(q, p)
	diff.Sqrm() // square each value in the matrix in place
	// To avoid allocating a new matrix the sum is placed in the
	// first column of each row.  N.B. this modifies the matrix directly.
	diff.SumRowsM()
	// Each point coordinate consists of 1 row and M cols.  So,
	// the sum will be at [0, 0]
	s := diff.Get(0, 0)

	return math.Sqrt(s)
}
Ejemplo n.º 3
0
// CalcDist finds the Euclidean distance between a centroid
// and a  point in the data set.  Arguments are 1x2 matrices.
// All intermediary l-values except s are matricies. The functions that
// operate on them can all take nXn matricies as arguments.
func (ed EuclidDist) CalcDist(centroid, point *matrix.DenseMatrix) (dist float64, err error) {
	err = nil
	diff := matrix.Difference(centroid, point)
	//square the resulting matrix
	sqr := diff.Pow(2)
	// sum of 1x2 matrix
	sum := sqr.SumRows() // n X 1 matrix
	// square root of sum
	s := sum.Get(0, 0)
	dist = math.Sqrt(s)
	return
}