change mat64.Vec to []float64

covariancematrix.go

@@ -18,7 +18,7 @@ import (
 // data matrix x.  cov should be a square matrix with the same number of
 // columns as the input data matrix x, or if it is nil then a new Dense
 // matrix will be constructed.
-func CovarianceMatrix(cov *mat64.Dense, x mat64.Matrix, wts mat64.Vec) *mat64.Dense {
+func CovarianceMatrix(cov *mat64.Dense, x mat64.Matrix, wts []float64) *mat64.Dense {
 
 	// matrix version of the two pass algorithm.  This doesn't use
 	// the correction found in the Covariance and Variance functions.
@@ -50,7 +50,7 @@ func CovarianceMatrix(cov *mat64.Dense, x mat64.Matrix, wts mat64.Vec) *mat64.De
 	// normalization factor, typical n-1
 	var N float64
 	if wts != nil {
-		if wr, _ := wts.Dims(); wr != r {
+		if wr := len(wts); wr != r {
 			panic("weight vector length mismatch")
 		}
 

covariancematrix_test.go

@@ -47,13 +47,13 @@ func TestCovarianceMatrix(t *testing.T) {
 				1, -2,
 				2, 4,
 			}),
-			weights: mat64.Vec([]float64{
+			weights: []float64{
 				1.5,
 				.5,
 				1.5,
 				.5,
 				1,
-			}),
+			},
 			r: 2,
 			c: 2,
 			x: []float64{