Merge pull request #168 from gonum/keepsigma

Store sigma in Normal and StudentsT

distmv/normal.go

@@ -7,7 +7,6 @@ package distmv
 import (
 	"math"
 	"math/rand"
-	"sync"
 
 	"github.com/gonum/floats"
 	"github.com/gonum/matrix/mat64"
@@ -27,8 +26,7 @@ var (
 type Normal struct {
 	mu []float64
 
-	once  sync.Once
+	sigma mat64.SymDense
-	sigma *mat64.SymDense // only stored if needed
 
 	chol       mat64.Cholesky
 	lower      mat64.TriDense
@@ -59,6 +57,8 @@ func NewNormal(mu []float64, sigma mat64.Symmetric, src *rand.Rand) (*Normal, bo
 	if !ok {
 		return nil, false
 	}
+	n.sigma = *mat64.NewSymDense(dim, nil)
+	n.sigma.CopySym(sigma)
 	n.lower.LFromCholesky(&n.chol)
 	n.logSqrtDet = 0.5 * n.chol.LogDet()
 	return n, true
@@ -141,9 +141,7 @@ func (n *Normal) ConditionNormal(observed []int, values []float64, src *rand.Ran
 		}
 	}
 
-	n.setSigma()
+	_, mu1, sigma11 := studentsTConditional(observed, values, math.Inf(1), n.mu, &n.sigma)
-
-	_, mu1, sigma11 := studentsTConditional(observed, values, math.Inf(1), n.mu, n.sigma)
 	if mu1 == nil {
 		return nil, false
 	}
@@ -164,8 +162,7 @@ func (n *Normal) CovarianceMatrix(s *mat64.SymDense) *mat64.SymDense {
 	if sn != n.Dim() {
 		panic("normal: input matrix size mismatch")
 	}
-	n.setSigma()
+	s.CopySym(&n.sigma)
-	s.CopySym(n.sigma)
 	return s
 }
 
@@ -202,9 +199,8 @@ func (n *Normal) MarginalNormal(vars []int, src *rand.Rand) (*Normal, bool) {
 	for i, v := range vars {
 		newMean[i] = n.mu[v]
 	}
-	n.setSigma()
 	var s mat64.SymDense
-	s.SubsetSym(n.sigma, vars)
+	s.SubsetSym(&n.sigma, vars)
 	return NewNormal(newMean, &s, src)
 }
 
@@ -216,19 +212,9 @@ func (n *Normal) MarginalNormal(vars []int, src *rand.Rand) (*Normal, bool) {
 //
 // The input src is passed to the constructed distuv.Normal.
 func (n *Normal) MarginalNormalSingle(i int, src *rand.Rand) distuv.Normal {
-	var std float64
-	if n.sigma != nil {
-		std = n.sigma.At(i, i)
-	} else {
-		// Reconstruct the {i,i} diagonal element of the covariance directly.
-		for j := 0; j <= i; j++ {
-			v := n.lower.At(i, j)
-			std += v * v
-		}
-	}
 	return distuv.Normal{
 		Mu:     n.mu[i],
-		Sigma:  math.Sqrt(std),
+		Sigma:  math.Sqrt(n.sigma.At(i, i)),
 		Source: src,
 	}
 }
@@ -300,14 +286,6 @@ func (n *Normal) SetMean(mu []float64) {
 	copy(n.mu, mu)
 }
 
-// setSigma computes and stores the covariance matrix of the distribution.
-func (n *Normal) setSigma() {
-	n.once.Do(func() {
-		n.sigma = mat64.NewSymDense(n.Dim(), nil)
-		n.sigma.FromCholesky(&n.chol)
-	})
-}
-
 // TransformNormal transforms the vector, normal, generated from a standard
 // multidimensional normal into a vector that has been generated under the
 // distribution of the receiver.

distmv/normal_test.go

@@ -489,8 +489,6 @@ func TestMarginalSingle(t *testing.T) {
 		if !ok {
 			t.Fatalf("Bad test, covariance matrix not positive definite")
 		}
-		// Verify with nil Sigma.
-		normal.sigma = nil
 		for i, mean := range test.mu {
 			norm := normal.MarginalNormalSingle(i, nil)
 			if norm.Mean() != mean {
@@ -501,19 +499,6 @@ func TestMarginalSingle(t *testing.T) {
 				t.Errorf("StdDev mismatch nil Sigma, idx %v: want %v, got %v.", i, std, norm.StdDev())
 			}
 		}
-
-		// Verify with non-nil Sigma.
-		normal.setSigma()
-		for i, mean := range test.mu {
-			norm := normal.MarginalNormalSingle(i, nil)
-			if norm.Mean() != mean {
-				t.Errorf("Mean mismatch non-nil Sigma, idx %v: want %v, got %v.", i, mean, norm.Mean())
-			}
-			std := math.Sqrt(test.sigma.At(i, i))
-			if math.Abs(norm.StdDev()-std) > 1e-14 {
-				t.Errorf("StdDev mismatch non-nil Sigma, idx %v: want %v, got %v.", i, std, norm.StdDev())
-			}
-		}
 	}
 
 	// Test matching with TestMarginal.

distmv/normalbench_test.go

@@ -7,7 +7,6 @@ package distmv
 import (
 	"log"
 	"math/rand"
-	"sync"
 	"testing"
 
 	"github.com/gonum/matrix/mat64"
@@ -38,8 +37,6 @@ func BenchmarkMarginalNormalReset10(b *testing.B) {
 		if !ok {
 			b.Error("bad test")
 		}
-		normal.sigma = nil
-		normal.once = sync.Once{}
 		_ = marg
 	}
 }

distmv/studentst.go

@@ -8,7 +8,6 @@ import (
 	"math"
 	"math/rand"
 	"sort"
-	"sync"
 
 	"golang.org/x/tools/container/intsets"
 
@@ -36,8 +35,7 @@ type StudentsT struct {
 	mu  []float64
 	src *rand.Rand
 
-	once  sync.Once
+	sigma mat64.SymDense // only stored if needed
-	sigma *mat64.SymDense // only stored if needed
 
 	chol       mat64.Cholesky
 	lower      mat64.TriDense
@@ -71,6 +69,8 @@ func NewStudentsT(mu []float64, sigma mat64.Symmetric, nu float64, src *rand.Ran
 	if !ok {
 		return nil, false
 	}
+	s.sigma = *mat64.NewSymDense(dim, nil)
+	s.sigma.CopySym(sigma)
 	s.lower.LFromCholesky(&s.chol)
 	s.logSqrtDet = 0.5 * s.chol.LogDet()
 	return s, true
@@ -101,9 +101,7 @@ func (s *StudentsT) ConditionStudentsT(observed []int, values []float64, src *ra
 		}
 	}
 
-	s.setSigma()
+	newNu, newMean, newSigma := studentsTConditional(observed, values, s.nu, s.mu, &s.sigma)
-
-	newNu, newMean, newSigma := studentsTConditional(observed, values, s.nu, s.mu, s.sigma)
 	if newMean == nil {
 		return nil, false
 	}
@@ -231,8 +229,7 @@ func (st *StudentsT) CovarianceMatrix(s *mat64.SymDense) *mat64.SymDense {
 	if sn != st.dim {
 		panic("normal: input matrix size mismatch")
 	}
-	st.setSigma()
+	s.CopySym(&st.sigma)
-	s.CopySym(st.sigma)
 	s.ScaleSym(st.nu/(st.nu-2), s)
 	return s
 }
@@ -286,9 +283,8 @@ func (s *StudentsT) MarginalStudentsT(vars []int, src *rand.Rand) (dist *Student
 	for i, v := range vars {
 		newMean[i] = s.mu[v]
 	}
-	s.setSigma()
 	var newSigma mat64.SymDense
-	newSigma.SubsetSym(s.sigma, vars)
+	newSigma.SubsetSym(&s.sigma, vars)
 	return NewStudentsT(newMean, &newSigma, s.nu, src)
 }
 
@@ -300,20 +296,9 @@ func (s *StudentsT) MarginalStudentsT(vars []int, src *rand.Rand) (dist *Student
 //
 // The input src is passed to the call to NewStudentsT.
 func (s *StudentsT) MarginalStudentsTSingle(i int, src *rand.Rand) distuv.StudentsT {
-	var std float64
-	if s.sigma != nil {
-		std = s.sigma.At(i, i)
-	} else {
-		// Reconstruct the {i,i} diagonal element of the covariance directly.
-		for j := 0; j <= i; j++ {
-			v := s.lower.At(i, j)
-			std += v * v
-		}
-	}
-
 	return distuv.StudentsT{
 		Mu:    s.mu[i],
-		Sigma: math.Sqrt(std),
+		Sigma: math.Sqrt(s.sigma.At(i, i)),
 		Nu:    s.nu,
 		Src:   src,
 	}
@@ -367,11 +352,3 @@ func (s *StudentsT) Rand(x []float64) []float64 {
 	floats.Add(x, s.mu)
 	return x
 }
-
-// setSigma computes and stores the covariance matrix of the distribution.
-func (s *StudentsT) setSigma() {
-	s.once.Do(func() {
-		s.sigma = mat64.NewSymDense(s.dim, nil)
-		s.sigma.FromCholesky(&s.chol)
-	})
-}

distmv/studentst_test.go

@@ -182,12 +182,9 @@ func TestStudentsTConditional(t *testing.T) {
 			muOb[i] = test.mean[v]
 		}
 
-		s.setSigma()
-		sUp.setSigma()
-
 		var sig11, sig22 mat64.SymDense
-		sig11.SubsetSym(s.sigma, unob)
+		sig11.SubsetSym(&s.sigma, unob)
-		sig22.SubsetSym(s.sigma, ob)
+		sig22.SubsetSym(&s.sigma, ob)
 
 		sig12 := mat64.NewDense(len(unob), len(ob), nil)
 		for i := range unob {
@@ -221,7 +218,7 @@ func TestStudentsTConditional(t *testing.T) {
 
 		dot := mat64.Dot(shiftVec, &tmp)
 		tmp3.Scale((test.nu+dot)/(test.nu+float64(len(ob))), &tmp3)
-		if !mat64.EqualApprox(&tmp3, sUp.sigma, 1e-10) {
+		if !mat64.EqualApprox(&tmp3, &sUp.sigma, 1e-10) {
 			t.Errorf("Sigma mismatch")
 		}
 	}
@@ -248,8 +245,6 @@ func TestStudentsTMarginalSingle(t *testing.T) {
 		if !ok {
 			t.Fatalf("Bad test, covariance matrix not positive definite")
 		}
-		// Verify with nil Sigma.
-		studentst.sigma = nil
 		for i, mean := range test.mu {
 			st := studentst.MarginalStudentsTSingle(i, nil)
 			if st.Mean() != mean {
@@ -263,18 +258,5 @@ func TestStudentsTMarginalSingle(t *testing.T) {
 				t.Errorf("Nu mismatch nil Sigma, idx %v: want %v, got %v ", i, test.nu, st.Nu)
 			}
 		}
-
-		// Verify with non-nil Sigma.
-		studentst.setSigma()
-		for i, mean := range test.mu {
-			st := studentst.MarginalStudentsTSingle(i, nil)
-			if st.Mean() != mean {
-				t.Errorf("Mean mismatch non-nil Sigma, idx %v: want %v, got %v.", i, mean, st.Mean())
-			}
-			std := math.Sqrt(test.sigma.At(i, i))
-			if math.Abs(st.Sigma-std) > 1e-14 {
-				t.Errorf("StdDev mismatch non-nil Sigma, idx %v: want %v, got %v.", i, std, st.StdDev())
-			}
-		}
 	}
 }