stat/distmv: rename NewNormalChol and add Sigma as an input argument

stat/distmv/normal.go

@@ -64,19 +64,25 @@ func NewNormal(mu []float64, sigma mat.Symmetric, src *rand.Rand) (*Normal, bool
 	return n, true
 }
 
-// NewNormalChol creates a new Normal distribution with the given mean and
-// covariance matrix represented by its Cholesky decomposition. NewNormalChol
-// panics if len(mu) is not equal to chol.Size().
-func NewNormalChol(mu []float64, chol *mat.Cholesky, src *rand.Rand) *Normal {
+// NewNormalData creates a new Normal distribution with the given mean and
+// covariance matrix when the Cholesky decomposition of the covariance matrix
+// is already known. NewNormalChol panics if len(mu) is not equal to chol.Size()
+// or sigma.Symmetric().
+func NewNormalData(mu []float64, sigma mat.Symmetric, chol *mat.Cholesky, src *rand.Rand) *Normal {
 	dim := len(mu)
 	if dim != chol.Size() {
 		panic(badSizeMismatch)
 	}
+	if dim != sigma.Symmetric() {
+		panic(badSizeMismatch)
+	}
 	n := &Normal{
 		src: src,
 		dim: dim,
 		mu:  make([]float64, dim),
 	}
+	n.sigma = *mat.NewSymDense(dim, nil)
+	n.sigma.CopySym(sigma)
 	n.chol.Clone(chol)
 	copy(n.mu, mu)
 	chol.LTo(&n.lower)

stat/distmv/normal_test.go

@@ -66,11 +66,12 @@ func TestNewNormalChol(t *testing.T) {
 		if !ok {
 			panic("bad test")
 		}
-		n := NewNormalChol(test.mean, &chol, nil)
+		n := NewNormalData(test.mean, test.cov, &chol, nil)
 		// Generate a random number and calculate probability to ensure things
 		// have been set properly. See issue #426.
 		x := n.Rand(nil)
 		_ = n.Prob(x)
+		_, _ = n.ConditionNormal([]int{0}, []float64{8.7}, nil)
 	}
 }