stat: clean up lint

stat/cca_example_test.go

@@ -32,7 +32,7 @@ func (s symView) At(i, j int) float64 {
 	return s.sym.At(s.i+i, s.j+j)
 }
 
-func (s symView) T() mat.Matrix { return mat.Transpose{s} }
+func (s symView) T() mat.Matrix { return mat.Transpose{Matrix: s} }
 
 func ExampleCC() {
 	// This example is directly analogous to Example 3.5 on page 87 of

stat/distmv/normal.go

@@ -110,7 +110,10 @@ func NewNormalPrecision(mu []float64, prec *mat.SymDense, src rand.Source) (norm
 		return nil, false
 	}
 	var sigma mat.SymDense
-	chol.InverseTo(&sigma)
+	err := chol.InverseTo(&sigma)
+	if err != nil {
+		return nil, false
+	}
 	return NewNormal(mu, &sigma, src)
 }
 
@@ -341,7 +344,10 @@ func (n *Normal) ScoreInput(score, x []float64) []float64 {
 	copy(tmp, x)
 	floats.Sub(tmp, n.mu)
 
-	n.chol.SolveVecTo(mat.NewVecDense(len(score), score), mat.NewVecDense(len(tmp), tmp))
+	err := n.chol.SolveVecTo(mat.NewVecDense(len(score), score), mat.NewVecDense(len(tmp), tmp))
+	if err != nil {
+		panic(err)
+	}
 	floats.Scale(-1, score)
 	return score
 }

stat/distmv/normal_test.go

@@ -16,14 +16,6 @@ import (
 	"gonum.org/v1/gonum/stat"
 )
 
-type mvTest struct {
-	Mu      []float64
-	Sigma   *mat.SymDense
-	Loc     []float64
-	Logprob float64
-	Prob    float64
-}
-
 func TestNormProbs(t *testing.T) {
 	dist1, ok := NewNormal([]float64{0, 0}, mat.NewSymDense(2, []float64{1, 0, 0, 1}), nil)
 	if !ok {

stat/distmv/statdist.go

@@ -335,7 +335,10 @@ func (Wasserstein) DistNormal(l, r *Normal) float64 {
 
 	// Compute Σ_l^(1/2)
 	var ssl mat.SymDense
-	ssl.PowPSD(&l.sigma, 0.5)
+	err := ssl.PowPSD(&l.sigma, 0.5)
+	if err != nil {
+		panic(err)
+	}
 	// Compute Σ_l^(1/2)*Σ_r*Σ_l^(1/2)
 	var mean mat.Dense
 	mean.Mul(&ssl, &r.sigma)
@@ -343,7 +346,10 @@ func (Wasserstein) DistNormal(l, r *Normal) float64 {
 
 	// Reinterpret as symdense, and take Σ^(1/2)
 	meanSym := mat.NewSymDense(dim, mean.RawMatrix().Data)
-	ssl.PowPSD(meanSym, 0.5)
+	err = ssl.PowPSD(meanSym, 0.5)
+	if err != nil {
+		panic(err)
+	}
 
 	tr := mat.Trace(&r.sigma)
 	tl := mat.Trace(&l.sigma)

stat/distmv/statdist_test.go

@@ -62,14 +62,14 @@ func TestBhattacharyyaUniform(t *testing.T) {
 		tol     float64
 	}{
 		{
-			a:       NewUniform([]r1.Interval{{-3, 2}, {-5, 8}}, rnd),
+			a:       NewUniform([]r1.Interval{{Min: -3, Max: 2}, {Min: -5, Max: 8}}, rnd),
-			b:       NewUniform([]r1.Interval{{-4, 1}, {-7, 10}}, rnd),
+			b:       NewUniform([]r1.Interval{{Min: -4, Max: 1}, {Min: -7, Max: 10}}, rnd),
 			samples: 100000,
 			tol:     1e-2,
 		},
 		{
-			a:       NewUniform([]r1.Interval{{-3, 2}, {-5, 8}}, rnd),
+			a:       NewUniform([]r1.Interval{{Min: -3, Max: 2}, {Min: -5, Max: 8}}, rnd),
-			b:       NewUniform([]r1.Interval{{-5, -4}, {-7, 10}}, rnd),
+			b:       NewUniform([]r1.Interval{{Min: -5, Max: -4}, {Min: -7, Max: 10}}, rnd),
 			samples: 100000,
 			tol:     1e-2,
 		},
@@ -257,14 +257,14 @@ func TestKullbackLeiblerUniform(t *testing.T) {
 		tol     float64
 	}{
 		{
-			a:       NewUniform([]r1.Interval{{-5, 2}, {-7, 12}}, rnd),
+			a:       NewUniform([]r1.Interval{{Min: -5, Max: 2}, {Min: -7, Max: 12}}, rnd),
-			b:       NewUniform([]r1.Interval{{-4, 1}, {-7, 10}}, rnd),
+			b:       NewUniform([]r1.Interval{{Min: -4, Max: 1}, {Min: -7, Max: 10}}, rnd),
 			samples: 100000,
 			tol:     1e-2,
 		},
 		{
-			a:       NewUniform([]r1.Interval{{-5, 2}, {-7, 12}}, rnd),
+			a:       NewUniform([]r1.Interval{{Min: -5, Max: 2}, {Min: -7, Max: 12}}, rnd),
-			b:       NewUniform([]r1.Interval{{-9, -6}, {-7, 10}}, rnd),
+			b:       NewUniform([]r1.Interval{{Min: -9, Max: -6}, {Min: -7, Max: 10}}, rnd),
 			samples: 100000,
 			tol:     1e-2,
 		},

stat/distmv/studentst_test.go

@@ -206,7 +206,10 @@ func TestStudentsTConditional(t *testing.T) {
 		newMuVec := mat.NewVecDense(len(muUnob), newMu)
 		shiftVec := mat.NewVecDense(len(shift), shift)
 		var tmp mat.VecDense
-		tmp.SolveVec(&sig22, shiftVec)
+		err := tmp.SolveVec(&sig22, shiftVec)
+		if err != nil {
+			t.Errorf("unexpected error from vector solve: %v", err)
+		}
 		newMuVec.MulVec(sig12, &tmp)
 		floats.Add(newMu, muUnob)
 
@@ -215,7 +218,10 @@ func TestStudentsTConditional(t *testing.T) {
 		}
 
 		var tmp2 mat.Dense
-		tmp2.Solve(&sig22, sig12.T())
+		err = tmp2.Solve(&sig22, sig12.T())
+		if err != nil {
+			t.Errorf("unexpected error from dense solve: %v", err)
+		}
 
 		var tmp3 mat.Dense
 		tmp3.Mul(sig12, &tmp2)

stat/distmv/uniform_test.go

@@ -17,11 +17,11 @@ func TestUniformEntropy(t *testing.T) {
 		Entropy float64
 	}{
 		{
-			NewUniform([]r1.Interval{{0, 1}, {0, 1}}, nil),
+			NewUniform([]r1.Interval{{Min: 0, Max: 1}, {Min: 0, Max: 1}}, nil),
 			0,
 		},
 		{
-			NewUniform([]r1.Interval{{-1, 3}, {2, 8}, {-5, -3}}, nil),
+			NewUniform([]r1.Interval{{Min: -1, Max: 3}, {Min: 2, Max: 8}, {Min: -5, Max: -3}}, nil),
 			math.Log(48),
 		},
 	} {

stat/sampleuv/example_rate_test.go

@@ -6,13 +6,6 @@ package sampleuv
 
 import "gonum.org/v1/gonum/stat/distuv"
 
-func max(a, b int) int {
-	if a < b {
-		return b
-	}
-	return a
-}
-
 func ExampleMetropolisHastings_samplingRate() {
 	// See Burnin example for a description of these quantities.
 	n := 1000

stat/spatial/spatial_areal_example_test.go

@@ -31,7 +31,7 @@ func (e Euclid) At(i, j int) float64 {
 	y := float64(j/e.x - i/e.x)
 	return 1 / math.Hypot(x, y)
 }
-func (e Euclid) T() mat.Matrix { return mat.Transpose{e} }
+func (e Euclid) T() mat.Matrix { return mat.Transpose{Matrix: e} }
 
 func ExampleGlobalMoransI_areal() {
 	locality := Euclid{10, 10}