// 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()
}
// 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)
}
// 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
}