Method ProjectCol

tensor/stats/pca/pca.go:151–189

ProjectCol projects values from the given column of given table (via IndexView) onto the idx'th eigenvector (0 = largest eigenvalue, 1 = next, etc). Must have already called PCA() method.

(vals *[]float64, ix *table.IndexView, column string, idx int)

// onto the idx'th eigenvector (0 = largest eigenvalue, 1 = next, etc).
// Must have already called PCA() method.
func (pa *PCA) ProjectCol(vals *[]float64, ix *table.IndexView, column string, idx int) error {
	col, err := ix.Table.ColumnByNameTry(column)
	if err != nil {
		return err
	}
	if pa.Vectors == nil {
		return fmt.Errorf("PCA.ProjectCol Vectors are nil -- must call PCA first")
	}
	nr := pa.Vectors.DimSize(0)
	if idx >= nr {
		return fmt.Errorf("PCA.ProjectCol eigenvector index > rank of matrix")
	}
	cvec := make([]float64, nr)
	eidx := nr - 1 - idx // eigens in reverse order
	vec := pa.Vectors.(*tensor.Float64)
	for ri := 0; ri < nr; ri++ {
		cvec[ri] = vec.Value([]int{ri, eidx}) // vecs are in columns, reverse magnitude order
	}
	rows := ix.Len()
	if len(*vals) != rows {
		*vals = make([]float64, rows)
	}
	ln := col.Len()
	sz := ln / col.DimSize(0) // size of cell
	if sz != nr {
		return fmt.Errorf("PCA.ProjectCol column cell size != pca eigenvectors")
	}
	rdim := []int{0}
	for row := 0; row < rows; row++ {
		sum := 0.0
		rdim[0] = ix.Indexes[row]
		rt := col.SubSpace(rdim)
		for ci := 0; ci < sz; ci++ {
			sum += cvec[ci] * rt.Float1D(ci)
		}
		(*vals)[row] = sum
	}
	return nil
}

// ProjectColToTable projects values from the given column of given table (via IndexView)
// onto the given set of eigenvectors (idxs, 0 = largest eigenvalue, 1 = next, etc),