Merge pull request #65 from gonum/redomat

matrix, all: combine matrix packages, change matrix to mat

diff/fd/example_test.go

@@ -9,7 +9,7 @@ import (
 	"math"
 
 	"gonum.org/v1/gonum/diff/fd"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 func ExampleDerivative() {
@@ -53,12 +53,12 @@ func ExampleJacobian() {
 		dst[2] = 4*x[1]*x[1] - 2*x[2]
 		dst[3] = x[2] * math.Sin(x[0])
 	}
-	jac := mat64.NewDense(4, 3, nil)
+	jac := mat.NewDense(4, 3, nil)
 	fd.Jacobian(jac, f, []float64{1, 2, 3}, &fd.JacobianSettings{
 		Formula:    fd.Central,
 		Concurrent: true,
 	})
-	fmt.Printf("J ≈ %.6v\n", mat64.Formatted(jac, mat64.Prefix("    ")))
+	fmt.Printf("J ≈ %.6v\n", mat.Formatted(jac, mat.Prefix("    ")))
 
 	// Output:
 	// J ≈ ⎡       1         0         0⎤

diff/fd/jacobian.go

@@ -9,7 +9,7 @@ import (
 	"sync"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 type JacobianSettings struct {
@@ -39,7 +39,7 @@ type JacobianSettings struct {
 //
 // dst must be non-nil, the number of its columns must equal the length of x, and
 // the derivative order of the formula must be 1, otherwise Jacobian will panic.
-func Jacobian(dst *mat64.Dense, f func(y, x []float64), x []float64, settings *JacobianSettings) {
+func Jacobian(dst *mat.Dense, f func(y, x []float64), x []float64, settings *JacobianSettings) {
 	n := len(x)
 	if n == 0 {
 		panic("jacobian: x has zero length")
@@ -93,7 +93,7 @@ func Jacobian(dst *mat64.Dense, f func(y, x []float64), x []float64, settings *J
 	}
 }
 
-func jacobianSerial(dst *mat64.Dense, f func([]float64, []float64), x, origin []float64, formula Formula, step float64) {
+func jacobianSerial(dst *mat.Dense, f func([]float64, []float64), x, origin []float64, formula Formula, step float64) {
 	m, n := dst.Dims()
 	xcopy := make([]float64, n)
 	y := make([]float64, m)
@@ -122,7 +122,7 @@ func jacobianSerial(dst *mat64.Dense, f func([]float64, []float64), x, origin []
 	dst.Scale(1/step, dst)
 }
 
-func jacobianConcurrent(dst *mat64.Dense, f func([]float64, []float64), x, origin []float64, formula Formula, step float64, nWorkers int) {
+func jacobianConcurrent(dst *mat.Dense, f func([]float64, []float64), x, origin []float64, formula Formula, step float64, nWorkers int) {
 	m, n := dst.Dims()
 	for i := 0; i < m; i++ {
 		for j := 0; j < n; j++ {
@@ -138,7 +138,7 @@ func jacobianConcurrent(dst *mat64.Dense, f func([]float64, []float64), x, origi
 		defer wg.Done()
 		xcopy := make([]float64, n)
 		y := make([]float64, m)
-		yVec := mat64.NewVector(m, y)
+		yVec := mat.NewVector(m, y)
 		for job := range jobs {
 			copy(xcopy, x)
 			xcopy[job.j] += job.pt.Loc * step
@@ -182,7 +182,7 @@ func jacobianConcurrent(dst *mat64.Dense, f func([]float64, []float64), x, origi
 		// all columns of dst. Iterate again over all Formula points
 		// because we don't forbid repeated locations.
 
-		originVec := mat64.NewVector(m, origin)
+		originVec := mat.NewVector(m, origin)
 		for _, pt := range formula.Stencil {
 			if pt.Loc != 0 {
 				continue

diff/fd/jacobian_test.go

@@ -10,13 +10,13 @@ import (
 	"testing"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 func vecFunc13(y, x []float64) {
 	y[0] = 5*x[0] + x[2]*math.Sin(x[1]) + 1
 }
-func vecFunc13Jac(jac *mat64.Dense, x []float64) {
+func vecFunc13Jac(jac *mat.Dense, x []float64) {
 	jac.Set(0, 0, 5)
 	jac.Set(0, 1, x[2]*math.Cos(x[1]))
 	jac.Set(0, 2, math.Sin(x[1]))
@@ -26,7 +26,7 @@ func vecFunc22(y, x []float64) {
 	y[0] = x[0]*x[0]*x[1] + 1
 	y[1] = 5*x[0] + math.Sin(x[1]) + 1
 }
-func vecFunc22Jac(jac *mat64.Dense, x []float64) {
+func vecFunc22Jac(jac *mat.Dense, x []float64) {
 	jac.Set(0, 0, 2*x[0]*x[1])
 	jac.Set(0, 1, x[0]*x[0])
 	jac.Set(1, 0, 5)
@@ -39,7 +39,7 @@ func vecFunc43(y, x []float64) {
 	y[2] = 4*x[1]*x[1] - 2*x[2] + 1
 	y[3] = x[2]*math.Sin(x[0]) + 1
 }
-func vecFunc43Jac(jac *mat64.Dense, x []float64) {
+func vecFunc43Jac(jac *mat.Dense, x []float64) {
 	jac.Set(0, 0, 1)
 	jac.Set(0, 1, 0)
 	jac.Set(0, 2, 0)
@@ -61,7 +61,7 @@ func TestJacobian(t *testing.T) {
 	for tc, test := range []struct {
 		m, n int
 		f    func([]float64, []float64)
-		jac  func(*mat64.Dense, []float64)
+		jac  func(*mat.Dense, []float64)
 	}{
 		{
 			m:   1,
@@ -88,15 +88,15 @@ func TestJacobian(t *testing.T) {
 		xcopy := make([]float64, test.n)
 		copy(xcopy, x)
 
-		want := mat64.NewDense(test.m, test.n, nil)
+		want := mat.NewDense(test.m, test.n, nil)
 		test.jac(want, x)
 
-		got := mat64.NewDense(test.m, test.n, nil)
+		got := mat.NewDense(test.m, test.n, nil)
 		fillNaNDense(got)
 		Jacobian(got, test.f, x, nil)
-		if !mat64.EqualApprox(want, got, tol) {
+		if !mat.EqualApprox(want, got, tol) {
 			t.Errorf("Case %d (default settings): unexpected Jacobian.\nwant: %v\ngot:  %v",
-				tc, mat64.Formatted(want, mat64.Prefix("      ")), mat64.Formatted(got, mat64.Prefix("      ")))
+				tc, mat.Formatted(want, mat.Prefix("      ")), mat.Formatted(got, mat.Prefix("      ")))
 		}
 		if !floats.Equal(x, xcopy) {
 			t.Errorf("Case %d (default settings): x modified", tc)
@@ -107,7 +107,7 @@ func TestJacobian(t *testing.T) {
 	for tc, test := range []struct {
 		m, n    int
 		f       func([]float64, []float64)
-		jac     func(*mat64.Dense, []float64)
+		jac     func(*mat.Dense, []float64)
 		tol     float64
 		formula Formula
 	}{
@@ -188,17 +188,17 @@ func TestJacobian(t *testing.T) {
 		xcopy := make([]float64, test.n)
 		copy(xcopy, x)
 
-		want := mat64.NewDense(test.m, test.n, nil)
+		want := mat.NewDense(test.m, test.n, nil)
 		test.jac(want, x)
 
-		got := mat64.NewDense(test.m, test.n, nil)
+		got := mat.NewDense(test.m, test.n, nil)
 		fillNaNDense(got)
 		Jacobian(got, test.f, x, &JacobianSettings{
 			Formula: test.formula,
 		})
-		if !mat64.EqualApprox(want, got, test.tol) {
+		if !mat.EqualApprox(want, got, test.tol) {
 			t.Errorf("Case %d: unexpected Jacobian.\nwant: %v\ngot:  %v",
-				tc, mat64.Formatted(want, mat64.Prefix("      ")), mat64.Formatted(got, mat64.Prefix("      ")))
+				tc, mat.Formatted(want, mat.Prefix("      ")), mat.Formatted(got, mat.Prefix("      ")))
 		}
 		if !floats.Equal(x, xcopy) {
 			t.Errorf("Case %d: x modified", tc)
@@ -209,9 +209,9 @@ func TestJacobian(t *testing.T) {
 			Formula:    test.formula,
 			Concurrent: true,
 		})
-		if !mat64.EqualApprox(want, got, test.tol) {
+		if !mat.EqualApprox(want, got, test.tol) {
 			t.Errorf("Case %d (concurrent): unexpected Jacobian.\nwant: %v\ngot:  %v",
-				tc, mat64.Formatted(want, mat64.Prefix("      ")), mat64.Formatted(got, mat64.Prefix("      ")))
+				tc, mat.Formatted(want, mat.Prefix("      ")), mat.Formatted(got, mat.Prefix("      ")))
 		}
 		if !floats.Equal(x, xcopy) {
 			t.Errorf("Case %d (concurrent): x modified", tc)
@@ -224,9 +224,9 @@ func TestJacobian(t *testing.T) {
 			Formula:     test.formula,
 			OriginValue: origin,
 		})
-		if !mat64.EqualApprox(want, got, test.tol) {
+		if !mat.EqualApprox(want, got, test.tol) {
 			t.Errorf("Case %d (origin): unexpected Jacobian.\nwant: %v\ngot:  %v",
-				tc, mat64.Formatted(want, mat64.Prefix("      ")), mat64.Formatted(got, mat64.Prefix("      ")))
+				tc, mat.Formatted(want, mat.Prefix("      ")), mat.Formatted(got, mat.Prefix("      ")))
 		}
 		if !floats.Equal(x, xcopy) {
 			t.Errorf("Case %d (origin): x modified", tc)
@@ -238,9 +238,9 @@ func TestJacobian(t *testing.T) {
 			OriginValue: origin,
 			Concurrent:  true,
 		})
-		if !mat64.EqualApprox(want, got, test.tol) {
+		if !mat.EqualApprox(want, got, test.tol) {
 			t.Errorf("Case %d (concurrent, origin): unexpected Jacobian.\nwant: %v\ngot:  %v",
-				tc, mat64.Formatted(want, mat64.Prefix("      ")), mat64.Formatted(got, mat64.Prefix("      ")))
+				tc, mat.Formatted(want, mat.Prefix("      ")), mat.Formatted(got, mat.Prefix("      ")))
 		}
 		if !floats.Equal(x, xcopy) {
 			t.Errorf("Case %d (concurrent, origin): x modified", tc)
@@ -258,7 +258,7 @@ func randomSlice(n int, bound float64) []float64 {
 }
 
 // fillNaNDense fills the matrix m with NaN values.
-func fillNaNDense(m *mat64.Dense) {
+func fillNaNDense(m *mat.Dense) {
 	r, c := m.Dims()
 	for i := 0; i < r; i++ {
 		for j := 0; j < c; j++ {

graph/network/page.go

@@ -10,7 +10,7 @@ import (
 
 	"gonum.org/v1/gonum/floats"
 	"gonum.org/v1/gonum/graph"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 // PageRank returns the PageRank weights for nodes of the directed graph g
@@ -31,7 +31,7 @@ func PageRank(g graph.Directed, damp, tol float64) map[int]float64 {
 		indexOf[n.ID()] = i
 	}
 
-	m := mat64.NewDense(len(nodes), len(nodes), nil)
+	m := mat.NewDense(len(nodes), len(nodes), nil)
 	dangling := damp / float64(len(nodes))
 	for j, u := range nodes {
 		to := g.From(u)
@@ -45,17 +45,17 @@ func PageRank(g graph.Directed, damp, tol float64) map[int]float64 {
 			}
 		}
 	}
-	mat := m.RawMatrix().Data
+	matrix := m.RawMatrix().Data
 	dt := (1 - damp) / float64(len(nodes))
-	for i := range mat {
+	for i := range matrix {
-		mat[i] += dt
+		matrix[i] += dt
 	}
 
 	last := make([]float64, len(nodes))
 	for i := range last {
 		last[i] = 1
 	}
-	lastV := mat64.NewVector(len(nodes), last)
+	lastV := mat.NewVector(len(nodes), last)
 
 	vec := make([]float64, len(nodes))
 	var sum float64
@@ -68,7 +68,7 @@ func PageRank(g graph.Directed, damp, tol float64) map[int]float64 {
 	for i := range vec {
 		vec[i] *= f
 	}
-	v := mat64.NewVector(len(nodes), vec)
+	v := mat.NewVector(len(nodes), vec)
 
 	for {
 		lastV, v = v, lastV
@@ -122,7 +122,7 @@ func PageRankSparse(g graph.Directed, damp, tol float64) map[int]float64 {
 	for i := range last {
 		last[i] = 1
 	}
-	lastV := mat64.NewVector(len(nodes), last)
+	lastV := mat.NewVector(len(nodes), last)
 
 	vec := make([]float64, len(nodes))
 	var sum float64
@@ -135,7 +135,7 @@ func PageRankSparse(g graph.Directed, damp, tol float64) map[int]float64 {
 	for i := range vec {
 		vec[i] *= f
 	}
-	v := mat64.NewVector(len(nodes), vec)
+	v := mat.NewVector(len(nodes), vec)
 
 	dt := (1 - damp) / float64(len(nodes))
 	for {
@@ -171,7 +171,7 @@ func (m rowCompressedMatrix) addTo(i, j int, v float64) { m[i].addTo(j, v) }
 // mulVecUnitary multiplies the receiver by the src vector, storing
 // the result in dst. It assumes src and dst are the same length as m
 // and that both have unitary vector increments.
-func (m rowCompressedMatrix) mulVecUnitary(dst, src *mat64.Vector) {
+func (m rowCompressedMatrix) mulVecUnitary(dst, src *mat.Vector) {
 	dMat := dst.RawVector().Data
 	for i, r := range m {
 		dMat[i] = r.dotUnitary(src)
@@ -190,7 +190,7 @@ func (r *compressedRow) addTo(j int, v float64) {
 
 // dotUnitary performs a simplified scatter-based Ddot operations on
 // v and the receiver. v must have have a unitary vector increment.
-func (r compressedRow) dotUnitary(v *mat64.Vector) float64 {
+func (r compressedRow) dotUnitary(v *mat.Vector) float64 {
 	var sum float64
 	vec := v.RawVector().Data
 	for _, e := range r {
@@ -208,7 +208,7 @@ type sparseElement struct {
 // onesDotUnitary performs the equivalent of a Ddot of v with
 // a ones vector of equal length. v must have have a unitary
 // vector increment.
-func onesDotUnitary(alpha float64, v *mat64.Vector) float64 {
+func onesDotUnitary(alpha float64, v *mat.Vector) float64 {
 	var sum float64
 	for _, f := range v.RawVector().Data {
 		sum += alpha * f

graph/path/shortest.go

@@ -9,7 +9,7 @@ import (
 	"math/rand"
 
 	"gonum.org/v1/gonum/graph"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 // Shortest is a shortest-path tree created by the BellmanFordFrom or DijkstraFrom
@@ -125,7 +125,7 @@ type AllShortest struct {
 	//
 	// dist contains the pairwise
 	// distances between nodes.
-	dist *mat64.Dense
+	dist *mat.Dense
 	// next contains the shortest-path
 	// tree of the graph. The first index
 	// is a linear mapping of from-dense-id
@@ -159,7 +159,7 @@ func newAllShortest(nodes []graph.Node, forward bool) AllShortest {
 		nodes:   nodes,
 		indexOf: indexOf,
 
-		dist:    mat64.NewDense(len(nodes), len(nodes), dist),
+		dist:    mat.NewDense(len(nodes), len(nodes), dist),
 		next:    make([][]int, len(nodes)*len(nodes)),
 		forward: forward,
 	}

graph/simple/dense_directed_matrix.go

@@ -9,7 +9,7 @@ import (
 
 	"gonum.org/v1/gonum/graph"
 	"gonum.org/v1/gonum/graph/internal/ordered"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 // DirectedMatrix represents a directed graph using an adjacency
@@ -17,7 +17,7 @@ import (
 // Edges are stored implicitly as an edge weight, so edges stored in
 // the graph are not recoverable.
 type DirectedMatrix struct {
-	mat   *mat64.Dense
+	mat   *mat.Dense
 	nodes []graph.Node
 
 	self   float64
@@ -29,17 +29,17 @@ type DirectedMatrix struct {
 // specifies the cost of self connection, and absent specifies the weight
 // returned for absent edges.
 func NewDirectedMatrix(n int, init, self, absent float64) *DirectedMatrix {
-	mat := make([]float64, n*n)
+	matrix := make([]float64, n*n)
 	if init != 0 {
-		for i := range mat {
+		for i := range matrix {
-			mat[i] = init
+			matrix[i] = init
 		}
 	}
-	for i := 0; i < len(mat); i += n + 1 {
+	for i := 0; i < len(matrix); i += n + 1 {
-		mat[i] = self
+		matrix[i] = self
 	}
 	return &DirectedMatrix{
-		mat:    mat64.NewDense(n, n, mat),
+		mat:    mat.NewDense(n, n, matrix),
 		self:   self,
 		absent: absent,
 	}
@@ -255,10 +255,10 @@ func (g *DirectedMatrix) Degree(n graph.Node) int {
 	return deg
 }
 
-// Matrix returns the mat64.Matrix representation of the graph. The orientation
+// Matrix returns the mat.Matrix representation of the graph. The orientation
 // of the matrix is such that the matrix entry at G_{ij} is the weight of the edge
 // from node i to node j.
-func (g *DirectedMatrix) Matrix() mat64.Matrix {
+func (g *DirectedMatrix) Matrix() mat.Matrix {
 	// Prevent alteration of dimensions of the returned matrix.
 	m := *g.mat
 	return &m

graph/simple/dense_undirected_matrix.go

@@ -9,7 +9,7 @@ import (
 
 	"gonum.org/v1/gonum/graph"
 	"gonum.org/v1/gonum/graph/internal/ordered"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 // UndirectedMatrix represents an undirected graph using an adjacency
@@ -17,7 +17,7 @@ import (
 // Edges are stored implicitly as an edge weight, so edges stored in
 // the graph are not recoverable.
 type UndirectedMatrix struct {
-	mat   *mat64.SymDense
+	mat   *mat.SymDense
 	nodes []graph.Node
 
 	self   float64
@@ -29,17 +29,17 @@ type UndirectedMatrix struct {
 // specifies the cost of self connection, and absent specifies the weight
 // returned for absent edges.
 func NewUndirectedMatrix(n int, init, self, absent float64) *UndirectedMatrix {
-	mat := make([]float64, n*n)
+	matrix := make([]float64, n*n)
 	if init != 0 {
-		for i := range mat {
+		for i := range matrix {
-			mat[i] = init
+			matrix[i] = init
 		}
 	}
-	for i := 0; i < len(mat); i += n + 1 {
+	for i := 0; i < len(matrix); i += n + 1 {
-		mat[i] = self
+		matrix[i] = self
 	}
 	return &UndirectedMatrix{
-		mat:    mat64.NewSymDense(n, mat),
+		mat:    mat.NewSymDense(n, matrix),
 		self:   self,
 		absent: absent,
 	}
@@ -216,8 +216,8 @@ func (g *UndirectedMatrix) Degree(n graph.Node) int {
 	return deg
 }
 
-// Matrix returns the mat64.Matrix representation of the graph.
+// Matrix returns the mat.Matrix representation of the graph.
-func (g *UndirectedMatrix) Matrix() mat64.Matrix {
+func (g *UndirectedMatrix) Matrix() mat.Matrix {
 	// Prevent alteration of dimensions of the returned matrix.
 	m := *g.mat
 	return &m

graph/undirect_test.go

@@ -10,7 +10,7 @@ import (
 
 	"gonum.org/v1/gonum/graph"
 	"gonum.org/v1/gonum/graph/simple"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 var directedGraphs = []struct {
@@ -19,7 +19,7 @@ var directedGraphs = []struct {
 	absent float64
 	merge  func(x, y float64, xe, ye graph.Edge) float64
 
-	want mat64.Matrix
+	want mat.Matrix
 }{
 	{
 		g: func() graph.DirectedBuilder { return simple.NewDirectedGraph(0, 0) },
@@ -28,7 +28,7 @@ var directedGraphs = []struct {
 			{F: simple.Node(1), T: simple.Node(0), W: 1},
 			{F: simple.Node(1), T: simple.Node(2), W: 1},
 		},
-		want: mat64.NewSymDense(3, []float64{
+		want: mat.NewSymDense(3, []float64{
 			0, (1. + 2.) / 2., 0,
 			(1. + 2.) / 2., 0, 1. / 2.,
 			0, 1. / 2., 0,
@@ -43,7 +43,7 @@ var directedGraphs = []struct {
 		},
 		absent: 1,
 		merge:  func(x, y float64, _, _ graph.Edge) float64 { return math.Sqrt(x * y) },
-		want: mat64.NewSymDense(3, []float64{
+		want: mat.NewSymDense(3, []float64{
 			0, math.Sqrt(1 * 2), 0,
 			math.Sqrt(1 * 2), 0, math.Sqrt(1 * 1),
 			0, math.Sqrt(1 * 1), 0,
@@ -57,7 +57,7 @@ var directedGraphs = []struct {
 			{F: simple.Node(1), T: simple.Node(2), W: 1},
 		},
 		merge: func(x, y float64, _, _ graph.Edge) float64 { return math.Min(x, y) },
-		want: mat64.NewSymDense(3, []float64{
+		want: mat.NewSymDense(3, []float64{
 			0, math.Min(1, 2), 0,
 			math.Min(1, 2), 0, math.Min(1, 0),
 			0, math.Min(1, 0), 0,
@@ -79,7 +79,7 @@ var directedGraphs = []struct {
 			}
 			return math.Min(x, y)
 		},
-		want: mat64.NewSymDense(3, []float64{
+		want: mat.NewSymDense(3, []float64{
 			0, math.Min(1, 2), 0,
 			math.Min(1, 2), 0, 1,
 			0, 1, 0,
@@ -93,7 +93,7 @@ var directedGraphs = []struct {
 			{F: simple.Node(1), T: simple.Node(2), W: 1},
 		},
 		merge: func(x, y float64, _, _ graph.Edge) float64 { return math.Max(x, y) },
-		want: mat64.NewSymDense(3, []float64{
+		want: mat.NewSymDense(3, []float64{
 			0, math.Max(1, 2), 0,
 			math.Max(1, 2), 0, math.Max(1, 0),
 			0, math.Max(1, 0), 0,
@@ -116,10 +116,10 @@ func TestUndirect(t *testing.T) {
 			}
 		}
 
-		if !mat64.Equal(dst.Matrix(), test.want) {
+		if !mat.Equal(dst.Matrix(), test.want) {
 			t.Errorf("unexpected result:\ngot:\n%.4v\nwant:\n%.4v",
-				mat64.Formatted(dst.Matrix()),
+				mat.Formatted(dst.Matrix()),
-				mat64.Formatted(test.want),
+				mat.Formatted(test.want),
 			)
 		}
 	}

mat/cblas_test.go

@@ -4,7 +4,7 @@
 
 //+build cblas
 
-package mat64
+package mat
 
 import (
 	"gonum.org/v1/gonum/blas/blas64"

mat/cholesky.go

@@ -3,7 +3,7 @@
 // license that can be found in the LICENSE file.
 // Based on the CholeskyDecomposition class from Jama 1.0.3.
 
-package mat64
+package mat
 
 import (
 	"math"
@@ -11,7 +11,6 @@ import (
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/blas/blas64"
 	"gonum.org/v1/gonum/lapack/lapack64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 const (
@@ -48,8 +47,8 @@ func (c *Cholesky) updateCond(norm float64) {
 		// the condition number somewhat.
 		// The norm of the original factorized matrix cannot be stored because of
 		// update possibilities.
-		unorm := lapack64.Lantr(matrix.CondNorm, c.chol.mat, work)
+		unorm := lapack64.Lantr(CondNorm, c.chol.mat, work)
-		lnorm := lapack64.Lantr(matrix.CondNormTrans, c.chol.mat, work)
+		lnorm := lapack64.Lantr(CondNormTrans, c.chol.mat, work)
 		norm = unorm * lnorm
 	}
 	sym := c.chol.asSymBlas()
@@ -65,15 +64,15 @@ func (c *Cholesky) updateCond(norm float64) {
 func (c *Cholesky) Factorize(a Symmetric) (ok bool) {
 	n := a.Symmetric()
 	if c.isZero() {
-		c.chol = NewTriDense(n, matrix.Upper, nil)
+		c.chol = NewTriDense(n, Upper, nil)
 	} else {
-		c.chol = NewTriDense(n, matrix.Upper, use(c.chol.mat.Data, n*n))
+		c.chol = NewTriDense(n, Upper, use(c.chol.mat.Data, n*n))
 	}
 	copySymIntoTriangle(c.chol, a)
 
 	sym := c.chol.asSymBlas()
 	work := getFloats(c.chol.mat.N, false)
-	norm := lapack64.Lansy(matrix.CondNorm, sym, work)
+	norm := lapack64.Lansy(CondNorm, sym, work)
 	putFloats(work)
 	_, ok = lapack64.Potrf(sym)
 	if ok {
@@ -98,13 +97,13 @@ func (c *Cholesky) Reset() {
 // not stored inside, the receiver.
 func (c *Cholesky) SetFromU(t *TriDense) {
 	n, kind := t.Triangle()
-	if kind != matrix.Upper {
+	if kind != Upper {
 		panic("cholesky: matrix must be upper triangular")
 	}
 	if c.isZero() {
-		c.chol = NewTriDense(n, matrix.Upper, nil)
+		c.chol = NewTriDense(n, Upper, nil)
 	} else {
-		c.chol = NewTriDense(n, matrix.Upper, use(c.chol.mat.Data, n*n))
+		c.chol = NewTriDense(n, Upper, use(c.chol.mat.Data, n*n))
 	}
 	c.chol.Copy(t)
 	c.updateCond(-1)
@@ -119,9 +118,9 @@ func (c *Cholesky) Clone(chol *Cholesky) {
 	}
 	n := chol.Size()
 	if c.isZero() {
-		c.chol = NewTriDense(n, matrix.Upper, nil)
+		c.chol = NewTriDense(n, Upper, nil)
 	} else {
-		c.chol = NewTriDense(n, matrix.Upper, use(c.chol.mat.Data, n*n))
+		c.chol = NewTriDense(n, Upper, use(c.chol.mat.Data, n*n))
 	}
 	c.chol.Copy(chol.chol)
 	c.cond = chol.cond
@@ -164,7 +163,7 @@ func (m *Dense) SolveCholesky(chol *Cholesky, b Matrix) error {
 	n := chol.chol.mat.N
 	bm, bn := b.Dims()
 	if n != bm {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	m.reuseAs(bm, bn)
@@ -173,8 +172,8 @@ func (m *Dense) SolveCholesky(chol *Cholesky, b Matrix) error {
 	}
 	blas64.Trsm(blas.Left, blas.Trans, 1, chol.chol.mat, m.mat)
 	blas64.Trsm(blas.Left, blas.NoTrans, 1, chol.chol.mat, m.mat)
-	if chol.cond > matrix.ConditionTolerance {
+	if chol.cond > ConditionTolerance {
-		return matrix.Condition(chol.cond)
+		return Condition(chol.cond)
 	}
 	return nil
 }
@@ -188,7 +187,7 @@ func (m *Dense) solveTwoChol(a, b *Cholesky) error {
 	}
 	bn := b.chol.mat.N
 	if a.chol.mat.N != bn {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	m.reuseAsZeroed(bn, bn)
@@ -196,8 +195,8 @@ func (m *Dense) solveTwoChol(a, b *Cholesky) error {
 	blas64.Trsm(blas.Left, blas.Trans, 1, a.chol.mat, m.mat)
 	blas64.Trsm(blas.Left, blas.NoTrans, 1, a.chol.mat, m.mat)
 	blas64.Trmm(blas.Right, blas.NoTrans, 1, b.chol.mat, m.mat)
-	if a.cond > matrix.ConditionTolerance {
+	if a.cond > ConditionTolerance {
-		return matrix.Condition(a.cond)
+		return Condition(a.cond)
 	}
 	return nil
 }
@@ -211,7 +210,7 @@ func (v *Vector) SolveCholeskyVec(chol *Cholesky, b *Vector) error {
 	n := chol.chol.mat.N
 	vn := b.Len()
 	if vn != n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	if v != b {
 		v.checkOverlap(b.mat)
@@ -222,8 +221,8 @@ func (v *Vector) SolveCholeskyVec(chol *Cholesky, b *Vector) error {
 	}
 	blas64.Trsv(blas.Trans, chol.chol.mat, v.mat)
 	blas64.Trsv(blas.NoTrans, chol.chol.mat, v.mat)
-	if chol.cond > matrix.ConditionTolerance {
+	if chol.cond > ConditionTolerance {
-		return matrix.Condition(chol.cond)
+		return Condition(chol.cond)
 	}
 	return nil
 
@@ -237,7 +236,7 @@ func (t *TriDense) UFromCholesky(chol *Cholesky) {
 		panic(badCholesky)
 	}
 	n := chol.chol.mat.N
-	t.reuseAs(n, matrix.Upper)
+	t.reuseAs(n, Upper)
 	t.Copy(chol.chol)
 }
 
@@ -249,7 +248,7 @@ func (t *TriDense) LFromCholesky(chol *Cholesky) {
 		panic(badCholesky)
 	}
 	n := chol.chol.mat.N
-	t.reuseAs(n, matrix.Lower)
+	t.reuseAs(n, Lower)
 	t.Copy(chol.chol.TTri())
 }
 
@@ -307,13 +306,13 @@ func (c *Cholesky) SymRankOne(orig *Cholesky, alpha float64, x *Vector) (ok bool
 	}
 	n := orig.Size()
 	if x.Len() != n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	if orig != c {
 		if c.isZero() {
-			c.chol = NewTriDense(n, matrix.Upper, nil)
+			c.chol = NewTriDense(n, Upper, nil)
 		} else if c.chol.mat.N != n {
-			panic(matrix.ErrShape)
+			panic(ErrShape)
 		}
 		c.chol.Copy(orig.chol)
 	}

mat/cholesky_example_test.go

@@ -2,29 +2,29 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64_test
+package mat_test
 
 import (
 	"fmt"
 
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 func ExampleCholesky() {
 	// Construct a symmetric positive definite matrix.
-	tmp := mat64.NewDense(4, 4, []float64{
+	tmp := mat.NewDense(4, 4, []float64{
 		2, 6, 8, -4,
 		1, 8, 7, -2,
 		2, 2, 1, 7,
 		8, -2, -2, 1,
 	})
-	var a mat64.SymDense
+	var a mat.SymDense
 	a.SymOuterK(1, tmp)
 
-	fmt.Printf("a = %0.4v\n", mat64.Formatted(&a, mat64.Prefix("    ")))
+	fmt.Printf("a = %0.4v\n", mat.Formatted(&a, mat.Prefix("    ")))
 
 	// Compute the cholesky factorization.
-	var chol mat64.Cholesky
+	var chol mat.Cholesky
 	if ok := chol.Factorize(&a); !ok {
 		fmt.Println("a matrix is not positive semi-definite.")
 	}
@@ -33,21 +33,21 @@ func ExampleCholesky() {
 	fmt.Printf("\nThe determinant of a is %0.4g\n\n", chol.Det())
 
 	// Use the factorization to solve the system of equations a * x = b.
-	b := mat64.NewVector(4, []float64{1, 2, 3, 4})
+	b := mat.NewVector(4, []float64{1, 2, 3, 4})
-	var x mat64.Vector
+	var x mat.Vector
 	if err := x.SolveCholeskyVec(&chol, b); err != nil {
 		fmt.Println("Matrix is near singular: ", err)
 	}
 	fmt.Println("Solve a * x = b")
-	fmt.Printf("x = %0.4v\n", mat64.Formatted(&x, mat64.Prefix("    ")))
+	fmt.Printf("x = %0.4v\n", mat.Formatted(&x, mat.Prefix("    ")))
 
 	// Extract the factorization and check that it equals the original matrix.
-	var t mat64.TriDense
+	var t mat.TriDense
 	t.LFromCholesky(&chol)
-	var test mat64.Dense
+	var test mat.Dense
 	test.Mul(&t, t.T())
 	fmt.Println()
-	fmt.Printf("L * L^T = %0.4v\n", mat64.Formatted(&a, mat64.Prefix("          ")))
+	fmt.Printf("L * L^T = %0.4v\n", mat.Formatted(&a, mat.Prefix("          ")))
 
 	// Output:
 	// a = ⎡120  114   -4  -16⎤
@@ -70,35 +70,35 @@ func ExampleCholesky() {
 }
 
 func ExampleCholeskySymRankOne() {
-	a := mat64.NewSymDense(4, []float64{
+	a := mat.NewSymDense(4, []float64{
 		1, 1, 1, 1,
 		0, 2, 3, 4,
 		0, 0, 6, 10,
 		0, 0, 0, 20,
 	})
-	fmt.Printf("A = %0.4v\n", mat64.Formatted(a, mat64.Prefix("    ")))
+	fmt.Printf("A = %0.4v\n", mat.Formatted(a, mat.Prefix("    ")))
 
 	// Compute the Cholesky factorization.
-	var chol mat64.Cholesky
+	var chol mat.Cholesky
 	if ok := chol.Factorize(a); !ok {
 		fmt.Println("matrix a is not positive definite.")
 	}
 
-	x := mat64.NewVector(4, []float64{0, 0, 0, 1})
+	x := mat.NewVector(4, []float64{0, 0, 0, 1})
-	fmt.Printf("\nx = %0.4v\n", mat64.Formatted(x, mat64.Prefix("    ")))
+	fmt.Printf("\nx = %0.4v\n", mat.Formatted(x, mat.Prefix("    ")))
 
 	// Rank-1 update the factorization.
 	chol.SymRankOne(&chol, 1, x)
 	// Rank-1 update the matrix a.
 	a.SymRankOne(a, 1, x)
 
-	var au mat64.SymDense
+	var au mat.SymDense
 	au.FromCholesky(&chol)
 
 	// Print the matrix that was updated directly.
-	fmt.Printf("\nA' =        %0.4v\n", mat64.Formatted(a, mat64.Prefix("            ")))
+	fmt.Printf("\nA' =        %0.4v\n", mat.Formatted(a, mat.Prefix("            ")))
 	// Print the matrix recovered from the factorization.
-	fmt.Printf("\nU'^T * U' = %0.4v\n", mat64.Formatted(&au, mat64.Prefix("            ")))
+	fmt.Printf("\nU'^T * U' = %0.4v\n", mat.Formatted(&au, mat.Prefix("            ")))
 
 	// Output:
 	// A = ⎡ 1   1   1   1⎤

mat/cholesky_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math"
@@ -10,7 +10,6 @@ import (
 	"testing"
 
 	"gonum.org/v1/gonum/blas/testblas"
-	"gonum.org/v1/gonum/matrix"
 )
 
 func TestCholesky(t *testing.T) {
@@ -269,7 +268,7 @@ func TestCloneCholesky(t *testing.T) {
 
 		// Corrupt chol2 and try again
 		chol2.cond = math.NaN()
-		chol2.chol = NewTriDense(2, matrix.Upper, nil)
+		chol2.chol = NewTriDense(2, Upper, nil)
 		chol2.Clone(&chol)
 		if chol.cond != chol2.cond {
 			t.Errorf("condition number mismatch from non-zero")

mat/cmatrix.go

@@ -2,10 +2,10 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package cmat128
+package mat
 
-// Matrix is the basic matrix interface type.
+// CMatrix is the basic matrix interface type for complex matrices.
-type Matrix interface {
+type CMatrix interface {
 	// Dims returns the dimensions of a Matrix.
 	Dims() (r, c int)
 
@@ -17,11 +17,11 @@ type Matrix interface {
 	// returns a copy of the underlying data is implementation dependent.
 	// This method may be implemented using the Conjugate type, which
 	// provides an implicit matrix conjugate transpose.
-	H() Matrix
+	H() CMatrix
 }
 
 var (
-	_ Matrix       = Conjugate{}
+	_ CMatrix      = Conjugate{}
 	_ Unconjugator = Conjugate{}
 )
 
@@ -29,13 +29,13 @@ var (
 // It implements the Matrix interface, returning values from the conjugate
 // transpose of the matrix within.
 type Conjugate struct {
-	Matrix Matrix
+	CMatrix CMatrix
 }
 
 // At returns the value of the element at row i and column j of the transposed
 // matrix, that is, row j and column i of the Matrix field.
 func (t Conjugate) At(i, j int) complex128 {
-	z := t.Matrix.At(j, i)
+	z := t.CMatrix.At(j, i)
 	return complex(real(z), -imag(z))
 }
 
@@ -43,18 +43,18 @@ func (t Conjugate) At(i, j int) complex128 {
 // is the number of columns in the Matrix field, and the number of columns is
 // the number of rows in the Matrix field.
 func (t Conjugate) Dims() (r, c int) {
-	c, r = t.Matrix.Dims()
+	c, r = t.CMatrix.Dims()
 	return r, c
 }
 
 // H performs an implicit conjugate transpose by returning the Matrix field.
-func (t Conjugate) H() Matrix {
+func (t Conjugate) H() CMatrix {
-	return t.Matrix
+	return t.CMatrix
 }
 
 // Unconjugate returns the Matrix field.
-func (t Conjugate) Unconjugate() Matrix {
+func (t Conjugate) Unconjugate() CMatrix {
-	return t.Matrix
+	return t.CMatrix
 }
 
 // Unconjugator is a type that can undo an implicit conjugate transpose.
@@ -67,5 +67,5 @@ type Unconjugator interface {
 
 	// Unconjugate returns the underlying Matrix stored for the implicit
 	// conjugate transpose.
-	Unconjugate() Matrix
+	Unconjugate() CMatrix
 }

mat/consts.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package matrix
+package mat
 
 // TriKind represents the triangularity of the matrix.
 type TriKind bool

mat/dense.go

@@ -2,12 +2,11 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/blas/blas64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 var (
@@ -45,7 +44,7 @@ type Dense struct {
 // element in the data slice is the {i, j}-th element in the matrix.
 func NewDense(r, c int, data []float64) *Dense {
 	if data != nil && r*c != len(data) {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	if data == nil {
 		data = make([]float64, r*c)
@@ -83,7 +82,7 @@ func (m *Dense) reuseAs(r, c int) {
 		return
 	}
 	if r != m.mat.Rows || c != m.mat.Cols {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 }
 
@@ -109,7 +108,7 @@ func (m *Dense) reuseAsZeroed(r, c int) {
 		return
 	}
 	if r != m.mat.Rows || c != m.mat.Cols {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	for i := 0; i < r; i++ {
 		zero(m.mat.Data[i*m.mat.Stride : i*m.mat.Stride+c])
@@ -206,7 +205,7 @@ func (m *Dense) T() Matrix {
 // See ColViewer for more information.
 func (m *Dense) ColView(j int) *Vector {
 	if j >= m.mat.Cols || j < 0 {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	return &Vector{
 		mat: blas64.Vector{
@@ -221,10 +220,10 @@ func (m *Dense) ColView(j int) *Vector {
 // in src. len(src) must equal the number of rows in the receiver.
 func (m *Dense) SetCol(j int, src []float64) {
 	if j >= m.mat.Cols || j < 0 {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	if len(src) != m.mat.Rows {
-		panic(matrix.ErrColLength)
+		panic(ErrColLength)
 	}
 
 	blas64.Copy(m.mat.Rows,
@@ -237,10 +236,10 @@ func (m *Dense) SetCol(j int, src []float64) {
 // in src. len(src) must equal the number of columns in the receiver.
 func (m *Dense) SetRow(i int, src []float64) {
 	if i >= m.mat.Rows || i < 0 {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	if len(src) != m.mat.Cols {
-		panic(matrix.ErrRowLength)
+		panic(ErrRowLength)
 	}
 
 	copy(m.rawRowView(i), src)
@@ -252,7 +251,7 @@ func (m *Dense) SetRow(i int, src []float64) {
 // See RowViewer for more information.
 func (m *Dense) RowView(i int) *Vector {
 	if i >= m.mat.Rows || i < 0 {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	return &Vector{
 		mat: blas64.Vector{
@@ -267,7 +266,7 @@ func (m *Dense) RowView(i int) *Vector {
 // receiver.
 func (m *Dense) RawRowView(i int) []float64 {
 	if i >= m.mat.Rows || i < 0 {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	return m.rawRowView(i)
 }
@@ -294,7 +293,7 @@ func (m *Dense) View(i, j, r, c int) Matrix {
 func (m *Dense) Slice(i, k, j, l int) Matrix {
 	mr, mc := m.Dims()
 	if i < 0 || mr <= i || j < 0 || mc <= j || k <= i || mr < k || l <= j || mc < l {
-		panic(matrix.ErrIndexOutOfRange)
+		panic(ErrIndexOutOfRange)
 	}
 	t := *m
 	t.mat.Data = t.mat.Data[i*t.mat.Stride+j : (k-1)*t.mat.Stride+l]
@@ -311,7 +310,7 @@ func (m *Dense) Slice(i, k, j, l int) Matrix {
 // during the call to Grow.
 func (m *Dense) Grow(r, c int) Matrix {
 	if r < 0 || c < 0 {
-		panic(matrix.ErrIndexOutOfRange)
+		panic(ErrIndexOutOfRange)
 	}
 	if r == 0 && c == 0 {
 		return m
@@ -514,7 +513,7 @@ func (m *Dense) Stack(a, b Matrix) {
 	ar, ac := a.Dims()
 	br, bc := b.Dims()
 	if ac != bc || m == a || m == b {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	m.reuseAs(ar+br, ac)
@@ -532,7 +531,7 @@ func (m *Dense) Augment(a, b Matrix) {
 	ar, ac := a.Dims()
 	br, bc := b.Dims()
 	if ar != br || m == a || m == b {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	m.reuseAs(ar, ac+bc)

mat/dense_arithmetic.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math"
@@ -10,7 +10,6 @@ import (
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/blas/blas64"
 	"gonum.org/v1/gonum/lapack/lapack64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 // Add adds a and b element-wise, placing the result in the receiver. Add
@@ -19,7 +18,7 @@ func (m *Dense) Add(a, b Matrix) {
 	ar, ac := a.Dims()
 	br, bc := b.Dims()
 	if ar != br || ac != bc {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	aU, _ := untranspose(a)
@@ -66,7 +65,7 @@ func (m *Dense) Sub(a, b Matrix) {
 	ar, ac := a.Dims()
 	br, bc := b.Dims()
 	if ar != br || ac != bc {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	aU, _ := untranspose(a)
@@ -114,7 +113,7 @@ func (m *Dense) MulElem(a, b Matrix) {
 	ar, ac := a.Dims()
 	br, bc := b.Dims()
 	if ar != br || ac != bc {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	aU, _ := untranspose(a)
@@ -162,7 +161,7 @@ func (m *Dense) DivElem(a, b Matrix) {
 	ar, ac := a.Dims()
 	br, bc := b.Dims()
 	if ar != br || ac != bc {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	aU, _ := untranspose(a)
@@ -211,7 +210,7 @@ func (m *Dense) Inverse(a Matrix) error {
 	// TODO(btracey): Special case for RawTriangular, etc.
 	r, c := a.Dims()
 	if r != c {
-		panic(matrix.ErrSquare)
+		panic(ErrSquare)
 	}
 	m.reuseAs(a.Dims())
 	aU, aTrans := untranspose(a)
@@ -234,7 +233,7 @@ func (m *Dense) Inverse(a Matrix) error {
 	defer putInts(ipiv)
 	ok := lapack64.Getrf(m.mat, ipiv)
 	if !ok {
-		return matrix.Condition(math.Inf(1))
+		return Condition(math.Inf(1))
 	}
 	work := getFloats(4*r, false) // must be at least 4*r for cond.
 	lapack64.Getri(m.mat, ipiv, work, -1)
@@ -247,14 +246,14 @@ func (m *Dense) Inverse(a Matrix) error {
 	}
 	defer putFloats(work)
 	lapack64.Getri(m.mat, ipiv, work, len(work))
-	norm := lapack64.Lange(matrix.CondNorm, m.mat, work)
+	norm := lapack64.Lange(CondNorm, m.mat, work)
-	rcond := lapack64.Gecon(matrix.CondNorm, m.mat, norm, work, ipiv) // reuse ipiv
+	rcond := lapack64.Gecon(CondNorm, m.mat, norm, work, ipiv) // reuse ipiv
 	if rcond == 0 {
-		return matrix.Condition(math.Inf(1))
+		return Condition(math.Inf(1))
 	}
 	cond := 1 / rcond
-	if cond > matrix.ConditionTolerance {
+	if cond > ConditionTolerance {
-		return matrix.Condition(cond)
+		return Condition(cond)
 	}
 	return nil
 }
@@ -266,7 +265,7 @@ func (m *Dense) Mul(a, b Matrix) {
 	br, bc := b.Dims()
 
 	if ac != br {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	aU, aTrans := untranspose(a)
@@ -452,7 +451,7 @@ func strictCopy(m *Dense, a Matrix) {
 	if r != m.mat.Rows || c != m.mat.Cols {
 		// Panic with a string since this
 		// is not a user-facing panic.
-		panic(matrix.ErrShape.Error())
+		panic(ErrShape.Error())
 	}
 }
 
@@ -464,7 +463,7 @@ func strictCopy(m *Dense, a Matrix) {
 func (m *Dense) Exp(a Matrix) {
 	r, c := a.Dims()
 	if r != c {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	var w *Dense
@@ -530,7 +529,7 @@ func (m *Dense) Pow(a Matrix, n int) {
 	}
 	r, c := a.Dims()
 	if r != c {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	m.reuseAs(r, c)
@@ -657,10 +656,10 @@ func (m *Dense) Apply(fn func(i, j int, v float64) float64, a Matrix) {
 func (m *Dense) RankOne(a Matrix, alpha float64, x, y *Vector) {
 	ar, ac := a.Dims()
 	if x.Len() != ar {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	if y.Len() != ac {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	m.checkOverlap(x.asGeneral())
@@ -707,7 +706,7 @@ func (m *Dense) Outer(alpha float64, x, y *Vector) {
 		m.capRows = r
 		m.capCols = c
 	} else if r != m.mat.Rows || c != m.mat.Cols {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	} else {
 		m.checkOverlap(x.asGeneral())
 		m.checkOverlap(y.asGeneral())

mat/dense_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"fmt"
@@ -13,7 +13,6 @@ import (
 
 	"gonum.org/v1/gonum/blas/blas64"
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix"
 )
 
 func asBasicMatrix(d *Dense) Matrix            { return (*basicMatrix)(d) }
@@ -188,13 +187,13 @@ func TestAtSet(t *testing.T) {
 		// Check access out of bounds fails
 		for _, row := range []int{-1, rows, rows + 1} {
 			panicked, message := panics(func() { m.At(row, 0) })
-			if !panicked || message != matrix.ErrRowAccess.Error() {
+			if !panicked || message != ErrRowAccess.Error() {
 				t.Errorf("expected panic for invalid row access N=%d r=%d", rows, row)
 			}
 		}
 		for _, col := range []int{-1, cols, cols + 1} {
 			panicked, message := panics(func() { m.At(0, col) })
-			if !panicked || message != matrix.ErrColAccess.Error() {
+			if !panicked || message != ErrColAccess.Error() {
 				t.Errorf("expected panic for invalid column access N=%d c=%d", cols, col)
 			}
 		}
@@ -202,13 +201,13 @@ func TestAtSet(t *testing.T) {
 		// Check Set out of bounds
 		for _, row := range []int{-1, rows, rows + 1} {
 			panicked, message := panics(func() { m.Set(row, 0, 1.2) })
-			if !panicked || message != matrix.ErrRowAccess.Error() {
+			if !panicked || message != ErrRowAccess.Error() {
 				t.Errorf("expected panic for invalid row access N=%d r=%d", rows, row)
 			}
 		}
 		for _, col := range []int{-1, cols, cols + 1} {
 			panicked, message := panics(func() { m.Set(0, col, 1.2) })
-			if !panicked || message != matrix.ErrColAccess.Error() {
+			if !panicked || message != ErrColAccess.Error() {
 				t.Errorf("expected panic for invalid column access N=%d c=%d", cols, col)
 			}
 		}
@@ -293,13 +292,13 @@ func TestRowColView(t *testing.T) {
 
 		for _, row := range []int{-1, rows, rows + 1} {
 			panicked, message := panics(func() { m.At(row, 0) })
-			if !panicked || message != matrix.ErrRowAccess.Error() {
+			if !panicked || message != ErrRowAccess.Error() {
 				t.Errorf("expected panic for invalid row access rows=%d r=%d", rows, row)
 			}
 		}
 		for _, col := range []int{-1, cols, cols + 1} {
 			panicked, message := panics(func() { m.At(0, col) })
-			if !panicked || message != matrix.ErrColAccess.Error() {
+			if !panicked || message != ErrColAccess.Error() {
 				t.Errorf("expected panic for invalid column access cols=%d c=%d", cols, col)
 			}
 		}
@@ -870,7 +869,7 @@ func TestMul(t *testing.T) {
 
 func randDense(size int, rho float64, rnd func() float64) (*Dense, error) {
 	if size == 0 {
-		return nil, matrix.ErrZeroLength
+		return nil, ErrZeroLength
 	}
 	d := &Dense{
 		mat: blas64.General{

mat/doc.go

@@ -1,29 +1,26 @@
-// Generated by running
-//  go generate github.com/gonum/matrix
-// DO NOT EDIT.
-
 // Copyright ©2015 The gonum Authors. All rights reserved.
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-// Package mat64 provides implementations of float64 matrix structures and
+// Package mat provides implementations of float64 and complex128 matrix
-// linear algebra operations on them.
+// structures and linear algebra operations on them.
 //
 // Overview
 //
-// This section provides a quick overview of the mat64 package. The following
+// This section provides a quick overview of the mat package. The following
 // sections provide more in depth commentary.
 //
-// mat64 provides:
+// mat provides:
 //  - Interfaces for Matrix classes (Matrix, Symmetric, Triangular)
 //  - Concrete implementations (Dense, SymDense, TriDense)
 //  - Methods and functions for using matrix data (Add, Trace, SymRankOne)
 //  - Types for constructing and using matrix factorizations (QR, LU)
+//  - The complementary types for complex matrices, CMatrix, CSymDense, etc.
 //
 // A matrix may be constructed through the corresponding New function. If no
 // backing array is provided the matrix will be initialized to all zeros.
-//  // Allocate a zeroed matrix of size 3×5
+//  // Allocate a zeroed real matrix of size 3×5
-//  zero := mat64.NewDense(3, 5, nil)
+//  zero := mat.NewDense(3, 5, nil)
 // If a backing data slice is provided, the matrix will have those elements.
 // Matrices are all stored in row-major format.
 //  // Generate a 6×6 matrix of random values.
@@ -32,7 +29,6 @@
 //  	data[i] = rand.NormFloat64()
 //  }
 //  a := mat64.NewDense(6, 6, data)
-//
 // Operations involving matrix data are implemented as functions when the values
 // of the matrix remain unchanged
 //  tr := mat64.Trace(a)
@@ -42,17 +38,17 @@
 // Receivers must be the correct size for the matrix operations, otherwise the
 // operation will panic. As a special case for convenience, a zero-sized matrix
 // will be modified to have the correct size, allocating data if necessary.
-//  var c mat64.Dense // construct a new zero-sized matrix
+//  var c mat.Dense // construct a new zero-sized matrix
 //  c.Mul(a, a)       // c is automatically adjusted to be 6×6
 //
 // The Matrix Interfaces
 //
-// The Matrix interface is the common link between the concrete types. The Matrix
+// The Matrix interface is the common link between the concrete types of real
-// interface is defined by three functions: Dims, which returns the dimensions
+// matrices, The Matrix interface is defined by three functions: Dims, which
-// of the Matrix, At, which returns the element in the specified location, and
+// returns the dimensions of the Matrix, At, which returns the element in the
-// T for returning a Transpose (discussed later). All of the concrete types can
+// specified location, and T for returning a Transpose (discussed later). All of
-// perform these behaviors and so implement the interface. Methods and functions
+// the concrete types can perform these behaviors and so implement the interface.
-// are designed to use this interface, so in particular the method
+// Methods and functions are designed to use this interface, so in particular the method
 //  func (m *Dense) Mul(a, b Matrix)
 // constructs a *Dense from the result of a multiplication with any Matrix types,
 // not just *Dense. Where more restrictive requirements must be met, there are also the
@@ -60,12 +56,17 @@
 //  func (s *SymDense) AddSym(a, b Symmetric)
 // the Symmetric interface guarantees a symmetric result.
 //
-// Transposes
+// The CMatrix interface plays the same role for complex matrices. The difference
+// is that the CMatrix type has the H method instead T, for returning the conjugate
+// transpose.
 //
-// The T method is used for transposition. For example, c.Mul(a.T(), b) computes
+// (Conjugate) Transposes
-// c = a^T * b. The mat64 types implement this method using an implicit transpose —
+//
-// see the Transpose type for more details. Note that some operations have a
+// The T method is used for transposition on real matrices, and H is used for
-// transpose as part of their definition, as in *SymDense.SymOuterK.
+// conjugate transposition on complex matrices. For example, c.Mul(a.T(), b) computes
+// c = a^T * b. The mat types implement this method implicitly —
+// see the Transpose and Conjugate types for more details. Note that some
+// operations have a transpose as part of their definition, as in *SymDense.SymOuterK.
 //
 // Matrix Factorization
 //
@@ -75,19 +76,20 @@
 //  var lu mat64.LU
 //  lu.Factorize(a)
 // The elements of the factorization can be extracted through methods on the
-// appropriate type, i.e. *TriDense.LFromLU and *TriDense.UFromLU. Alternatively,
+// factorized type, i.e. *LU.UTo. The factorization types can also be used directly,
-// they can be used directly, as in *Dense.SolveLU. Some factorizations can be
+// as in *Dense.SolveCholesky. Some factorizations can be updated directly,
-// updated directly, without needing to update the original matrix and refactorize,
+// without needing to update the original matrix and refactorize,
 // as in *LU.RankOne.
 //
 // BLAS and LAPACK
 //
 // BLAS and LAPACK are the standard APIs for linear algebra routines. Many
-// operations in mat64 are implemented using calls to the wrapper functions
+// operations in mat are implemented using calls to the wrapper functions
-// in gonum/blas/blas64 and gonum/lapack/lapack64. By default, blas64 and
+// in gonum/blas/blas64 and gonum/lapack/lapack64 and their complex equivalents.
-// lapack64 call the native Go implementations of the routines. Alternatively,
+// By default, blas64 and lapack64 call the native Go implementations of the
-// it is possible to use C-based implementations of the APIs through the respective
+// routines. Alternatively, it is possible to use C-based implementations of the
-// cgo packages and "Use" functions. The Go implementation of LAPACK makes calls
+// APIs through the respective cgo packages and "Use" functions. The Go
+// implementation of LAPACK (used by default) makes calls
 // through blas64, so if a cgo BLAS implementation is registered, the lapack64
 // calls will be partially executed in Go and partially executed in C.
 //
@@ -121,7 +123,7 @@
 //
 // Element Aliasing
 //
-// Most methods in mat64 modify receiver data. It is forbidden for the modified
+// Most methods in mat modify receiver data. It is forbidden for the modified
 // data region of the receiver to overlap the used data area of the input
 // arguments. The exception to this rule is when the method receiver is equal to one
 // of the input arguments, as in the a.Pow(a, 6) call above, or its implicit transpose.
@@ -136,7 +138,7 @@
 // your program, you are being clever. Don't be clever. If you must be clever,
 // blas64 and lapack64 may be used to call the behavior directly.
 //
-// mat64 will use the following rules to detect overlap between the receiver and one
+// mat will use the following rules to detect overlap between the receiver and one
 // of the inputs:
 //  - the input implements one of the Raw methods, and
 //  - the Raw type matches that of the receiver or
@@ -150,9 +152,9 @@
 //  - there is pointer identity between the receiver and input values after
 //    the value has been untransposed if necessary.
 //
-// mat64 will not attempt to detect element overlap if the input does not implement a
+// mat will not attempt to detect element overlap if the input does not implement a
 // Raw method, or if the Raw method differs from that of the receiver except when a
-// conversion has occurred through a mat64 API function. Method behavior is undefined
+// conversion has occurred through a mat API function. Method behavior is undefined
 // if there is undetected overlap.
 //
-package mat64 // import "gonum.org/v1/gonum/matrix/mat64"
+package mat // import "gonum.org/v1/gonum/mat"

mat/eigen.go

@@ -3,12 +3,11 @@
 // license that can be found in the LICENSE file.
 // Based on the EigenvalueDecomposition class from Jama 1.0.3.
 
-package mat64
+package mat
 
 import (
 	"gonum.org/v1/gonum/lapack"
 	"gonum.org/v1/gonum/lapack/lapack64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 const (
@@ -82,7 +81,7 @@ func (e *EigenSym) Values(dst []float64) []float64 {
 		dst = make([]float64, len(e.values))
 	}
 	if len(dst) != len(e.values) {
-		panic(matrix.ErrSliceLengthMismatch)
+		panic(ErrSliceLengthMismatch)
 	}
 	copy(dst, e.values)
 	return dst
@@ -149,7 +148,7 @@ func (e *Eigen) Factorize(a Matrix, left, right bool) (ok bool) {
 	// Copy a because it is modified during the Lapack call.
 	r, c := a.Dims()
 	if r != c {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	var sd Dense
 	sd.Clone(a)
@@ -209,7 +208,7 @@ func (e *Eigen) Values(dst []complex128) []complex128 {
 		dst = make([]complex128, e.n)
 	}
 	if len(dst) != e.n {
-		panic(matrix.ErrSliceLengthMismatch)
+		panic(ErrSliceLengthMismatch)
 	}
 	copy(dst, e.values)
 	return dst

mat/eigen_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math/rand"

mat/errors.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package matrix
+package mat
 
 import (
 	"fmt"

mat/errors_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package matrix
+package mat
 
 import "testing"
 

mat/fao_data_test.go

@@ -2,20 +2,20 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64_test
+package mat_test
 
-import "gonum.org/v1/gonum/matrix/mat64"
+import "gonum.org/v1/gonum/mat"
 
 // FAO is a dataset extracted from Food and Agriculture Organization of the
 // United Nations "FAO Statistical Pocketbook: World Food and Agriculture 2015".
 // pp49-52.
 var FAO = struct {
-	Africa                *mat64.Dense
+	Africa                *mat.Dense
-	Asia                  *mat64.Dense
+	Asia                  *mat.Dense
-	LatinAmericaCaribbean *mat64.Dense
+	LatinAmericaCaribbean *mat.Dense
-	Oceania               *mat64.Dense
+	Oceania               *mat.Dense
 }{
-	Africa: mat64.NewDense(21, 3, []float64{
+	Africa: mat.NewDense(21, 3, []float64{
 		// 1990, 2000, 2014
 		35.3, 38, 30.7, // Employment in agriculture (%)
 		9.2, 20.3, 25.2, // Employment in agriculture, female (%)
@@ -43,7 +43,7 @@ var FAO = struct {
 		72, 92, 119, // Fish
 	}),
 
-	Asia: mat64.NewDense(21, 3, []float64{
+	Asia: mat.NewDense(21, 3, []float64{
 		// 1990, 2000, 2014
 		30.9, 24.5, 27.6, // Employment in agriculture (%)
 		40.9, 29.4, 31.1, // Employment in agriculture, female (%)
@@ -71,7 +71,7 @@ var FAO = struct {
 		65, 90, 119, // Fish
 	}),
 
-	LatinAmericaCaribbean: mat64.NewDense(14, 3, []float64{
+	LatinAmericaCaribbean: mat.NewDense(14, 3, []float64{
 		// 1990, 2000, 2014
 		19.5, 14.2, 15.8, // Employment in agriculture (%)
 		13.7, 6.2, 7.6, // Employment in agriculture, female (%)
@@ -92,7 +92,7 @@ var FAO = struct {
 		82, 107, 71, // Fish
 	}),
 
-	Oceania: mat64.NewDense(21, 3, []float64{
+	Oceania: mat.NewDense(21, 3, []float64{
 		// 1990, 2000, 2014
 		6.2, 17.1, 3.8, // Employment in agriculture (%)
 		4.5, 3.9, 4.4, // Employment in agriculture, female (%)

mat/format.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"fmt"

mat/format_example_test.go

@@ -2,20 +2,20 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64_test
+package mat_test
 
 import (
 	"fmt"
 
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 func ExampleFormatted() {
-	a := mat64.NewDense(3, 3, []float64{1, 2, 3, 0, 4, 5, 0, 0, 6})
+	a := mat.NewDense(3, 3, []float64{1, 2, 3, 0, 4, 5, 0, 0, 6})
 
 	// Create a matrix formatting value with a prefix and calculating each column
 	// width individually...
-	fa := mat64.Formatted(a, mat64.Prefix("    "), mat64.Squeeze())
+	fa := mat.Formatted(a, mat.Prefix("    "), mat.Squeeze())
 
 	// and then print with and without zero value elements.
 	fmt.Printf("with all values:\na = %v\n\n", fa)
@@ -61,27 +61,27 @@ func ExampleExcerpt() {
 	// matrices and vectors.
 
 	// The big matrix is too large to properly print...
-	big := mat64.NewDense(100, 100, nil)
+	big := mat.NewDense(100, 100, nil)
 	for i := 0; i < 100; i++ {
 		big.Set(i, i, 1)
 	}
 
 	// so only print corner excerpts of the matrix.
 	fmt.Printf("excerpt big identity matrix: %v\n\n",
-		mat64.Formatted(big, mat64.Prefix(" "), mat64.Excerpt(3)))
+		mat.Formatted(big, mat.Prefix(" "), mat.Excerpt(3)))
 
 	// The long vector is also too large, ...
-	long := mat64.NewVector(100, nil)
+	long := mat.NewVector(100, nil)
 	for i := 0; i < 100; i++ {
 		long.SetVec(i, float64(i))
 	}
 
 	// ... so print end excerpts of the vector,
 	fmt.Printf("excerpt long column vector: %v\n\n",
-		mat64.Formatted(long, mat64.Prefix(" "), mat64.Excerpt(3)))
+		mat.Formatted(long, mat.Prefix(" "), mat.Excerpt(3)))
 	// or its transpose.
 	fmt.Printf("excerpt long row vector: %v\n",
-		mat64.Formatted(long.T(), mat64.Prefix(" "), mat64.Excerpt(3)))
+		mat.Formatted(long.T(), mat.Prefix(" "), mat.Excerpt(3)))
 
 	// Output:
 	// excerpt big identity matrix: Dims(100, 100)

mat/format_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"fmt"
@@ -26,8 +26,8 @@ func TestFormat(t *testing.T) {
 			[]rp{
 				{"%v", "⎡0  0  0⎤\n⎢0  0  0⎥\n⎣0  0  0⎦"},
 				{"% f", "⎡.  .  .⎤\n⎢.  .  .⎥\n⎣.  .  .⎦"},
-				{"%#v", "&mat64.Dense{mat:blas64.General{Rows:3, Cols:3, Stride:3, Data:[]float64{0, 0, 0, 0, 0, 0, 0, 0, 0}}, capRows:3, capCols:3}"},
+				{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:3, Stride:3, Data:[]float64{0, 0, 0, 0, 0, 0, 0, 0, 0}}, capRows:3, capCols:3}"},
-				{"%s", "%!s(*mat64.Dense=Dims(3, 3))"},
+				{"%s", "%!s(*mat.Dense=Dims(3, 3))"},
 			},
 		},
 		{
@@ -35,7 +35,7 @@ func TestFormat(t *testing.T) {
 			[]rp{
 				{"%v", "⎡1  1  1⎤\n⎢1  1  1⎥\n⎣1  1  1⎦"},
 				{"% f", "⎡1  1  1⎤\n⎢1  1  1⎥\n⎣1  1  1⎦"},
-				{"%#v", "&mat64.Dense{mat:blas64.General{Rows:3, Cols:3, Stride:3, Data:[]float64{1, 1, 1, 1, 1, 1, 1, 1, 1}}, capRows:3, capCols:3}"},
+				{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:3, Stride:3, Data:[]float64{1, 1, 1, 1, 1, 1, 1, 1, 1}}, capRows:3, capCols:3}"},
 			},
 		},
 		{
@@ -43,7 +43,7 @@ func TestFormat(t *testing.T) {
 			[]rp{
 				{"%v", "⎡1  1  1⎤\n\t⎢1  1  1⎥\n\t⎣1  1  1⎦"},
 				{"% f", "⎡1  1  1⎤\n\t⎢1  1  1⎥\n\t⎣1  1  1⎦"},
-				{"%#v", "&mat64.Dense{mat:blas64.General{Rows:3, Cols:3, Stride:3, Data:[]float64{1, 1, 1, 1, 1, 1, 1, 1, 1}}, capRows:3, capCols:3}"},
+				{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:3, Stride:3, Data:[]float64{1, 1, 1, 1, 1, 1, 1, 1, 1}}, capRows:3, capCols:3}"},
 			},
 		},
 		{
@@ -51,7 +51,7 @@ func TestFormat(t *testing.T) {
 			[]rp{
 				{"%v", "⎡1  0  0⎤\n⎢0  1  0⎥\n⎣0  0  1⎦"},
 				{"% f", "⎡1  .  .⎤\n⎢.  1  .⎥\n⎣.  .  1⎦"},
-				{"%#v", "&mat64.Dense{mat:blas64.General{Rows:3, Cols:3, Stride:3, Data:[]float64{1, 0, 0, 0, 1, 0, 0, 0, 1}}, capRows:3, capCols:3}"},
+				{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:3, Stride:3, Data:[]float64{1, 0, 0, 0, 1, 0, 0, 0, 1}}, capRows:3, capCols:3}"},
 			},
 		},
 		{
@@ -59,7 +59,7 @@ func TestFormat(t *testing.T) {
 			[]rp{
 				{"%v", "⎡1  2  3⎤\n⎣4  5  6⎦"},
 				{"% f", "⎡1  2  3⎤\n⎣4  5  6⎦"},
-				{"%#v", "&mat64.Dense{mat:blas64.General{Rows:2, Cols:3, Stride:3, Data:[]float64{1, 2, 3, 4, 5, 6}}, capRows:2, capCols:3}"},
+				{"%#v", "&mat.Dense{mat:blas64.General{Rows:2, Cols:3, Stride:3, Data:[]float64{1, 2, 3, 4, 5, 6}}, capRows:2, capCols:3}"},
 			},
 		},
 		{
@@ -67,7 +67,7 @@ func TestFormat(t *testing.T) {
 			[]rp{
 				{"%v", "⎡1  2⎤\n⎢3  4⎥\n⎣5  6⎦"},
 				{"% f", "⎡1  2⎤\n⎢3  4⎥\n⎣5  6⎦"},
-				{"%#v", "&mat64.Dense{mat:blas64.General{Rows:3, Cols:2, Stride:2, Data:[]float64{1, 2, 3, 4, 5, 6}}, capRows:3, capCols:2}"},
+				{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:2, Stride:2, Data:[]float64{1, 2, 3, 4, 5, 6}}, capRows:3, capCols:2}"},
 			},
 		},
 		{
@@ -80,7 +80,7 @@ func TestFormat(t *testing.T) {
 				{"%v", "⎡                 0                   1  1.4142135623730951⎤\n⎣1.7320508075688772                   2    2.23606797749979⎦"},
 				{"%.2f", "⎡0.00  1.00  1.41⎤\n⎣1.73  2.00  2.24⎦"},
 				{"% f", "⎡                 .                   1  1.4142135623730951⎤\n⎣1.7320508075688772                   2    2.23606797749979⎦"},
-				{"%#v", "&mat64.Dense{mat:blas64.General{Rows:2, Cols:3, Stride:3, Data:[]float64{0, 1, 1.4142135623730951, 1.7320508075688772, 2, 2.23606797749979}}, capRows:2, capCols:3}"},
+				{"%#v", "&mat.Dense{mat:blas64.General{Rows:2, Cols:3, Stride:3, Data:[]float64{0, 1, 1.4142135623730951, 1.7320508075688772, 2, 2.23606797749979}}, capRows:2, capCols:3}"},
 			},
 		},
 		{
@@ -93,7 +93,7 @@ func TestFormat(t *testing.T) {
 				{"%v", "⎡                 0                   1⎤\n⎢1.4142135623730951  1.7320508075688772⎥\n⎣                 2    2.23606797749979⎦"},
 				{"%.2f", "⎡0.00  1.00⎤\n⎢1.41  1.73⎥\n⎣2.00  2.24⎦"},
 				{"% f", "⎡                 .                   1⎤\n⎢1.4142135623730951  1.7320508075688772⎥\n⎣                 2    2.23606797749979⎦"},
-				{"%#v", "&mat64.Dense{mat:blas64.General{Rows:3, Cols:2, Stride:2, Data:[]float64{0, 1, 1.4142135623730951, 1.7320508075688772, 2, 2.23606797749979}}, capRows:3, capCols:2}"},
+				{"%#v", "&mat.Dense{mat:blas64.General{Rows:3, Cols:2, Stride:2, Data:[]float64{0, 1, 1.4142135623730951, 1.7320508075688772, 2, 2.23606797749979}}, capRows:3, capCols:2}"},
 			},
 		},
 		{
@@ -106,7 +106,7 @@ func TestFormat(t *testing.T) {
 				{"%v", "⎡                 0  1  1.4142135623730951⎤\n⎣1.7320508075688772  2    2.23606797749979⎦"},
 				{"%.2f", "⎡0.00  1.00  1.41⎤\n⎣1.73  2.00  2.24⎦"},
 				{"% f", "⎡                 .  1  1.4142135623730951⎤\n⎣1.7320508075688772  2    2.23606797749979⎦"},
-				{"%#v", "&mat64.Dense{mat:blas64.General{Rows:2, Cols:3, Stride:3, Data:[]float64{0, 1, 1.4142135623730951, 1.7320508075688772, 2, 2.23606797749979}}, capRows:2, capCols:3}"},
+				{"%#v", "&mat.Dense{mat:blas64.General{Rows:2, Cols:3, Stride:3, Data:[]float64{0, 1, 1.4142135623730951, 1.7320508075688772, 2, 2.23606797749979}}, capRows:2, capCols:3}"},
 			},
 		},
 		{

mat/gsvd.go

@@ -3,14 +3,13 @@
 // license that can be found in the LICENSE file.
 // Based on the SingularValueDecomposition class from Jama 1.0.3.
 
-package mat64
+package mat
 
 import (
 	"gonum.org/v1/gonum/blas/blas64"
 	"gonum.org/v1/gonum/floats"
 	"gonum.org/v1/gonum/lapack"
 	"gonum.org/v1/gonum/lapack/lapack64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 // GSVD is a type for creating and using the Generalized Singular Value Decomposition
@@ -20,7 +19,7 @@ import (
 // variable×sample spaces to reduced and diagonalized "eigenvariable"×"eigensample"
 // spaces.
 type GSVD struct {
-	kind matrix.GSVDKind
+	kind GSVDKind
 
 	r, p, c, k, l int
 	s1, s2        []float64
@@ -50,24 +49,24 @@ type GSVD struct {
 //
 // Factorize returns whether the decomposition succeeded. If the decomposition
 // failed, routines that require a successful factorization will panic.
-func (gsvd *GSVD) Factorize(a, b Matrix, kind matrix.GSVDKind) (ok bool) {
+func (gsvd *GSVD) Factorize(a, b Matrix, kind GSVDKind) (ok bool) {
 	r, c := a.Dims()
 	gsvd.r, gsvd.c = r, c
 	p, c := b.Dims()
 	gsvd.p = p
 	if gsvd.c != c {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	var jobU, jobV, jobQ lapack.GSVDJob
 	switch {
 	default:
 		panic("gsvd: bad input kind")
-	case kind == matrix.GSVDNone:
+	case kind == GSVDNone:
 		jobU = lapack.GSVDNone
 		jobV = lapack.GSVDNone
 		jobQ = lapack.GSVDNone
-	case (matrix.GSVDU|matrix.GSVDV|matrix.GSVDQ)&kind != 0:
+	case (GSVDU|GSVDV|GSVDQ)&kind != 0:
-		if matrix.GSVDU&kind != 0 {
+		if GSVDU&kind != 0 {
 			jobU = lapack.GSVDU
 			gsvd.u = blas64.General{
 				Rows:   r,
@@ -76,7 +75,7 @@ func (gsvd *GSVD) Factorize(a, b Matrix, kind matrix.GSVDKind) (ok bool) {
 				Data:   use(gsvd.u.Data, r*r),
 			}
 		}
-		if matrix.GSVDV&kind != 0 {
+		if GSVDV&kind != 0 {
 			jobV = lapack.GSVDV
 			gsvd.v = blas64.General{
 				Rows:   p,
@@ -85,7 +84,7 @@ func (gsvd *GSVD) Factorize(a, b Matrix, kind matrix.GSVDKind) (ok bool) {
 				Data:   use(gsvd.v.Data, p*p),
 			}
 		}
-		if matrix.GSVDQ&kind != 0 {
+		if GSVDQ&kind != 0 {
 			jobQ = lapack.GSVDQ
 			gsvd.q = blas64.General{
 				Rows:   c,
@@ -119,7 +118,7 @@ func (gsvd *GSVD) Factorize(a, b Matrix, kind matrix.GSVDKind) (ok bool) {
 
 // Kind returns the matrix.GSVDKind of the decomposition. If no decomposition has been
 // computed, Kind returns 0.
-func (gsvd *GSVD) Kind() matrix.GSVDKind {
+func (gsvd *GSVD) Kind() GSVDKind {
 	return gsvd.kind
 }
 
@@ -147,7 +146,7 @@ func (gsvd *GSVD) GeneralizedValues(v []float64) []float64 {
 		v = make([]float64, d-k)
 	}
 	if len(v) != d-k {
-		panic(matrix.ErrSliceLengthMismatch)
+		panic(ErrSliceLengthMismatch)
 	}
 	floats.DivTo(v, gsvd.s1[k:d], gsvd.s2[k:d])
 	return v
@@ -172,7 +171,7 @@ func (gsvd *GSVD) ValuesA(s []float64) []float64 {
 		s = make([]float64, d-k)
 	}
 	if len(s) != d-k {
-		panic(matrix.ErrSliceLengthMismatch)
+		panic(ErrSliceLengthMismatch)
 	}
 	copy(s, gsvd.s1[k:min(r, c)])
 	return s
@@ -197,7 +196,7 @@ func (gsvd *GSVD) ValuesB(s []float64) []float64 {
 		s = make([]float64, d-k)
 	}
 	if len(s) != d-k {
-		panic(matrix.ErrSliceLengthMismatch)
+		panic(ErrSliceLengthMismatch)
 	}
 	copy(s, gsvd.s2[k:d])
 	return s
@@ -300,7 +299,7 @@ func (gsvd *GSVD) SigmaBTo(dst *Dense) *Dense {
 //
 // UTo will panic if the receiver does not contain a successful factorization.
 func (gsvd *GSVD) UTo(dst *Dense) *Dense {
-	if gsvd.kind&matrix.GSVDU == 0 {
+	if gsvd.kind&GSVDU == 0 {
 		panic("mat64: improper GSVD kind")
 	}
 	r := gsvd.u.Rows
@@ -326,7 +325,7 @@ func (gsvd *GSVD) UTo(dst *Dense) *Dense {
 //
 // VTo will panic if the receiver does not contain a successful factorization.
 func (gsvd *GSVD) VTo(dst *Dense) *Dense {
-	if gsvd.kind&matrix.GSVDV == 0 {
+	if gsvd.kind&GSVDV == 0 {
 		panic("mat64: improper GSVD kind")
 	}
 	r := gsvd.v.Rows
@@ -352,7 +351,7 @@ func (gsvd *GSVD) VTo(dst *Dense) *Dense {
 //
 // QTo will panic if the receiver does not contain a successful factorization.
 func (gsvd *GSVD) QTo(dst *Dense) *Dense {
-	if gsvd.kind&matrix.GSVDQ == 0 {
+	if gsvd.kind&GSVDQ == 0 {
 		panic("mat64: improper GSVD kind")
 	}
 	r := gsvd.q.Rows

mat/gsvd_example_test.go

@@ -2,15 +2,14 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64_test
+package mat_test
 
 import (
 	"fmt"
 	"log"
 	"math"
 
-	"gonum.org/v1/gonum/matrix"
+	"gonum.org/v1/gonum/mat"
-	"gonum.org/v1/gonum/matrix/mat64"
 )
 
 func ExampleGSVD() {
@@ -19,8 +18,8 @@ func ExampleGSVD() {
 	//
 	// See Lee et al. doi:10.1371/journal.pone.0030098 and
 	// Alter at al. doi:10.1073/pnas.0530258100 for more details.
-	var gsvd mat64.GSVD
+	var gsvd mat.GSVD
-	ok := gsvd.Factorize(FAO.Africa, FAO.LatinAmericaCaribbean, matrix.GSVDU|matrix.GSVDV|matrix.GSVDQ)
+	ok := gsvd.Factorize(FAO.Africa, FAO.LatinAmericaCaribbean, mat.GSVDU|mat.GSVDV|mat.GSVDQ)
 	if !ok {
 		log.Fatal("GSVD factorization failed")
 	}
@@ -32,14 +31,14 @@ func ExampleGSVD() {
 	s2 := gsvd.ValuesB(nil)
 
 	fmt.Printf("Africa\n\ts1 = %.4f\n\n\tU = %.4f\n\n",
-		s1, mat64.Formatted(u, mat64.Prefix("\t    "), mat64.Excerpt(2)))
+		s1, mat.Formatted(u, mat.Prefix("\t    "), mat.Excerpt(2)))
 	fmt.Printf("Latin America/Caribbean\n\ts2 = %.4f\n\n\tV = %.4f\n",
-		s2, mat64.Formatted(v, mat64.Prefix("\t    "), mat64.Excerpt(2)))
+		s2, mat.Formatted(v, mat.Prefix("\t    "), mat.Excerpt(2)))
 
-	var q mat64.Dense
+	var q mat.Dense
 	q.Mul(gsvd.ZeroRTo(nil), gsvd.QTo(nil))
 	fmt.Printf("\nCommon basis vectors\n\n\tQ^T = %.4f\n",
-		mat64.Formatted(q.T(), mat64.Prefix("\t      ")))
+		mat.Formatted(q.T(), mat.Prefix("\t      ")))
 
 	// Calculate the antisymmetric angular distances for each eigenvariable.
 	fmt.Println("\nSignificance:")

mat/gsvd_test.go

@@ -2,14 +2,13 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math/rand"
 	"testing"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix"
 )
 
 func TestGSVD(t *testing.T) {
@@ -49,7 +48,7 @@ func TestGSVD(t *testing.T) {
 
 			// Test Full decomposition.
 			var gsvd GSVD
-			ok := gsvd.Factorize(a, b, matrix.GSVDU|matrix.GSVDV|matrix.GSVDQ)
+			ok := gsvd.Factorize(a, b, GSVDU|GSVDV|GSVDQ)
 			if !ok {
 				t.Errorf("GSVD factorization failed")
 			}
@@ -83,7 +82,7 @@ func TestGSVD(t *testing.T) {
 			}
 
 			// Test None decomposition.
-			ok = gsvd.Factorize(a, b, matrix.GSVDNone)
+			ok = gsvd.Factorize(a, b, GSVDNone)
 			if !ok {
 				t.Errorf("GSVD factorization failed")
 			}

mat/hogsvd.go

@@ -3,13 +3,12 @@
 // license that can be found in the LICENSE file.
 // Based on the SingularValueDecomposition class from Jama 1.0.3.
 
-package mat64
+package mat
 
 import (
 	"errors"
 
 	"gonum.org/v1/gonum/blas/blas64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 // HOGSVD is a type for creating and using the Higher Order Generalized Singular Value
@@ -58,14 +57,14 @@ func (gsvd *HOGSVD) Factorize(m ...Matrix) (ok bool) {
 	for i, d := range m {
 		rd, cd := d.Dims()
 		if rd < cd {
-			gsvd.err = matrix.ErrShape
+			gsvd.err = ErrShape
 			return false
 		}
 		if rd > r {
 			r = rd
 		}
 		if cd != c {
-			panic(matrix.ErrShape)
+			panic(ErrShape)
 		}
 		ts.Reset()
 		ts.SymOuterK(1, d.T())
@@ -187,7 +186,7 @@ func (gsvd *HOGSVD) Values(s []float64, n int) []float64 {
 	if s == nil {
 		s = make([]float64, c)
 	} else if len(s) != c {
-		panic(matrix.ErrSliceLengthMismatch)
+		panic(ErrSliceLengthMismatch)
 	}
 	for j := 0; j < c; j++ {
 		s[j] = blas64.Nrm2(r, gsvd.b[n].ColView(j).mat)

mat/hogsvd_example_test.go

@@ -2,13 +2,13 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64_test
+package mat_test
 
 import (
 	"fmt"
 	"log"
 
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 func ExampleHOGSVD() {
@@ -17,7 +17,7 @@ func ExampleHOGSVD() {
 	//
 	// See Ponnapalli et al. doi:10.1371/journal.pone.0028072 and
 	// Alter at al. doi:10.1073/pnas.0530258100 for more details.
-	var gsvd mat64.HOGSVD
+	var gsvd mat.HOGSVD
 	ok := gsvd.Factorize(FAO.Africa, FAO.Asia, FAO.LatinAmericaCaribbean, FAO.Oceania)
 	if !ok {
 		log.Fatal("HOGSVD factorization failed: %v", gsvd.Err())
@@ -27,12 +27,12 @@ func ExampleHOGSVD() {
 		u := gsvd.UTo(nil, i)
 		s := gsvd.Values(nil, i)
 		fmt.Printf("%s\n\ts_%d = %.4f\n\n\tU_%[2]d = %.4[4]f\n",
-			n, i, s, mat64.Formatted(u, mat64.Prefix("\t      ")))
+			n, i, s, mat.Formatted(u, mat.Prefix("\t      ")))
 	}
 
 	v := gsvd.VTo(nil)
 	fmt.Printf("\nCommon basis vectors\n\n\tV^T = %.4f",
-		mat64.Formatted(v.T(), mat64.Prefix("\t      ")))
+		mat.Formatted(v.T(), mat.Prefix("\t      ")))
 
 	// Output:
 	//

mat/hogsvd_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math/rand"

mat/index_bound_checks.go

@@ -6,9 +6,7 @@
 
 //+build bounds
 
-package mat64
+package mat
-
-import "gonum.org/v1/gonum/matrix"
 
 // At returns the element at row i, column j.
 func (m *Dense) At(i, j int) float64 {
@@ -17,10 +15,10 @@ func (m *Dense) At(i, j int) float64 {
 
 func (m *Dense) at(i, j int) float64 {
 	if uint(i) >= uint(m.mat.Rows) {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	if uint(j) >= uint(m.mat.Cols) {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	return m.mat.Data[i*m.mat.Stride+j]
 }
@@ -32,10 +30,10 @@ func (m *Dense) Set(i, j int, v float64) {
 
 func (m *Dense) set(i, j int, v float64) {
 	if uint(i) >= uint(m.mat.Rows) {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	if uint(j) >= uint(m.mat.Cols) {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	m.mat.Data[i*m.mat.Stride+j] = v
 }
@@ -44,14 +42,14 @@ func (m *Dense) set(i, j int, v float64) {
 // It panics if i is out of bounds or if j is not zero.
 func (v *Vector) At(i, j int) float64 {
 	if j != 0 {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	return v.at(i)
 }
 
 func (v *Vector) at(i int) float64 {
 	if uint(i) >= uint(v.n) {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	return v.mat.Data[i*v.mat.Inc]
 }
@@ -64,7 +62,7 @@ func (v *Vector) SetVec(i int, val float64) {
 
 func (v *Vector) setVec(i int, val float64) {
 	if uint(i) >= uint(v.n) {
-		panic(matrix.ErrVectorAccess)
+		panic(ErrVectorAccess)
 	}
 	v.mat.Data[i*v.mat.Inc] = val
 }
@@ -76,10 +74,10 @@ func (t *SymDense) At(i, j int) float64 {
 
 func (t *SymDense) at(i, j int) float64 {
 	if uint(i) >= uint(t.mat.N) {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	if uint(j) >= uint(t.mat.N) {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	if i > j {
 		i, j = j, i
@@ -94,10 +92,10 @@ func (t *SymDense) SetSym(i, j int, v float64) {
 
 func (t *SymDense) set(i, j int, v float64) {
 	if uint(i) >= uint(t.mat.N) {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	if uint(j) >= uint(t.mat.N) {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	if i > j {
 		i, j = j, i
@@ -112,10 +110,10 @@ func (t *TriDense) At(i, j int) float64 {
 
 func (t *TriDense) at(i, j int) float64 {
 	if uint(i) >= uint(t.mat.N) {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	if uint(j) >= uint(t.mat.N) {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	isUpper := t.isUpper()
 	if (isUpper && i > j) || (!isUpper && i < j) {
@@ -132,14 +130,14 @@ func (t *TriDense) SetTri(i, j int, v float64) {
 
 func (t *TriDense) set(i, j int, v float64) {
 	if uint(i) >= uint(t.mat.N) {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	if uint(j) >= uint(t.mat.N) {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	isUpper := t.isUpper()
 	if (isUpper && i > j) || (!isUpper && i < j) {
-		panic(matrix.ErrTriangleSet)
+		panic(ErrTriangleSet)
 	}
 	t.mat.Data[i*t.mat.Stride+j] = v
 }

mat/index_no_bound_checks.go

@@ -6,17 +6,15 @@
 
 //+build !bounds
 
-package mat64
+package mat
-
-import "gonum.org/v1/gonum/matrix"
 
 // At returns the element at row i, column j.
 func (m *Dense) At(i, j int) float64 {
 	if uint(i) >= uint(m.mat.Rows) {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	if uint(j) >= uint(m.mat.Cols) {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	return m.at(i, j)
 }
@@ -28,10 +26,10 @@ func (m *Dense) at(i, j int) float64 {
 // Set sets the element at row i, column j to the value v.
 func (m *Dense) Set(i, j int, v float64) {
 	if uint(i) >= uint(m.mat.Rows) {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	if uint(j) >= uint(m.mat.Cols) {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	m.set(i, j, v)
 }
@@ -44,10 +42,10 @@ func (m *Dense) set(i, j int, v float64) {
 // It panics if i is out of bounds or if j is not zero.
 func (v *Vector) At(i, j int) float64 {
 	if uint(i) >= uint(v.n) {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	if j != 0 {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	return v.at(i)
 }
@@ -60,7 +58,7 @@ func (v *Vector) at(i int) float64 {
 // It panics if i is out of bounds.
 func (v *Vector) SetVec(i int, val float64) {
 	if uint(i) >= uint(v.n) {
-		panic(matrix.ErrVectorAccess)
+		panic(ErrVectorAccess)
 	}
 	v.setVec(i, val)
 }
@@ -72,10 +70,10 @@ func (v *Vector) setVec(i int, val float64) {
 // At returns the element at row i and column j.
 func (s *SymDense) At(i, j int) float64 {
 	if uint(i) >= uint(s.mat.N) {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	if uint(j) >= uint(s.mat.N) {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	return s.at(i, j)
 }
@@ -90,10 +88,10 @@ func (s *SymDense) at(i, j int) float64 {
 // SetSym sets the elements at (i,j) and (j,i) to the value v.
 func (s *SymDense) SetSym(i, j int, v float64) {
 	if uint(i) >= uint(s.mat.N) {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	if uint(j) >= uint(s.mat.N) {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	s.set(i, j, v)
 }
@@ -108,10 +106,10 @@ func (s *SymDense) set(i, j int, v float64) {
 // At returns the element at row i, column j.
 func (t *TriDense) At(i, j int) float64 {
 	if uint(i) >= uint(t.mat.N) {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	if uint(j) >= uint(t.mat.N) {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	return t.at(i, j)
 }
@@ -128,14 +126,14 @@ func (t *TriDense) at(i, j int) float64 {
 // It panics if the location is outside the appropriate half of the matrix.
 func (t *TriDense) SetTri(i, j int, v float64) {
 	if uint(i) >= uint(t.mat.N) {
-		panic(matrix.ErrRowAccess)
+		panic(ErrRowAccess)
 	}
 	if uint(j) >= uint(t.mat.N) {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	isUpper := t.isUpper()
 	if (isUpper && i > j) || (!isUpper && i < j) {
-		panic(matrix.ErrTriangleSet)
+		panic(ErrTriangleSet)
 	}
 	t.set(i, j, v)
 }

mat/inner.go

@@ -2,12 +2,11 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/internal/asm/f64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 // Inner computes the generalized inner product
@@ -19,10 +18,10 @@ import (
 func Inner(x *Vector, A Matrix, y *Vector) float64 {
 	m, n := A.Dims()
 	if x.Len() != m {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	if y.Len() != n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	if m == 0 || n == 0 {
 		return 0

mat/inner_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math"

mat/io.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"encoding/binary"

mat/io_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"bytes"

mat/list_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"fmt"
@@ -14,7 +14,6 @@ import (
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/blas/blas64"
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix"
 )
 
 // legalSizeSameRectangular returns whether the two matrices have the same rectangular shape.
@@ -301,7 +300,7 @@ func makeRandOf(a Matrix, m, n int) Matrix {
 		// This is necessary because we are making
 		// a triangle from the zero value, which
 		// always returns upper as true.
-		var triKind matrix.TriKind
+		var triKind TriKind
 		switch t := t.(type) {
 		case *TriDense:
 			triKind = t.triKind()
@@ -310,7 +309,7 @@ func makeRandOf(a Matrix, m, n int) Matrix {
 		}
 
 		mat := NewTriDense(n, triKind, nil)
-		if triKind == matrix.Upper {
+		if triKind == Upper {
 			for i := 0; i < m; i++ {
 				for j := i; j < n; j++ {
 					mat.SetTri(i, j, rand.NormFloat64())

mat/lq.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math"
@@ -10,7 +10,6 @@ import (
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/blas/blas64"
 	"gonum.org/v1/gonum/lapack/lapack64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 // LQ is a type for creating and using the LQ factorization of a matrix.
@@ -27,7 +26,7 @@ func (lq *LQ) updateCond() {
 	work := getFloats(3*m, false)
 	iwork := getInts(m, false)
 	l := lq.lq.asTriDense(m, blas.NonUnit, blas.Lower)
-	v := lapack64.Trcon(matrix.CondNorm, l.mat, work, iwork)
+	v := lapack64.Trcon(CondNorm, l.mat, work, iwork)
 	lq.cond = 1 / v
 	putFloats(work)
 	putInts(iwork)
@@ -42,7 +41,7 @@ func (lq *LQ) updateCond() {
 func (lq *LQ) Factorize(a Matrix) {
 	m, n := a.Dims()
 	if m > n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	k := min(m, n)
 	if lq.lq == nil {
@@ -141,12 +140,12 @@ func (m *Dense) SolveLQ(lq *LQ, trans bool, b Matrix) error {
 	// copy the result into m at the end.
 	if trans {
 		if c != br {
-			panic(matrix.ErrShape)
+			panic(ErrShape)
 		}
 		m.reuseAs(r, bc)
 	} else {
 		if r != br {
-			panic(matrix.ErrShape)
+			panic(ErrShape)
 		}
 		m.reuseAs(c, bc)
 	}
@@ -164,12 +163,12 @@ func (m *Dense) SolveLQ(lq *LQ, trans bool, b Matrix) error {
 
 		ok := lapack64.Trtrs(blas.Trans, t, x.mat)
 		if !ok {
-			return matrix.Condition(math.Inf(1))
+			return Condition(math.Inf(1))
 		}
 	} else {
 		ok := lapack64.Trtrs(blas.NoTrans, t, x.mat)
 		if !ok {
-			return matrix.Condition(math.Inf(1))
+			return Condition(math.Inf(1))
 		}
 		for i := r; i < c; i++ {
 			zero(x.mat.Data[i*x.mat.Stride : i*x.mat.Stride+bc])
@@ -183,8 +182,8 @@ func (m *Dense) SolveLQ(lq *LQ, trans bool, b Matrix) error {
 	// M was set above to be the correct size for the result.
 	m.Copy(x)
 	putWorkspace(x)
-	if lq.cond > matrix.ConditionTolerance {
+	if lq.cond > ConditionTolerance {
-		return matrix.Condition(lq.cond)
+		return Condition(lq.cond)
 	}
 	return nil
 }

mat/lq_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math/rand"

mat/lu.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math"
@@ -11,7 +11,6 @@ import (
 	"gonum.org/v1/gonum/blas/blas64"
 	"gonum.org/v1/gonum/floats"
 	"gonum.org/v1/gonum/lapack/lapack64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 const badSliceLength = "mat64: improper slice length"
@@ -39,11 +38,11 @@ func (lu *LU) updateCond(norm float64) {
 		// update possibilities, e.g. RankOne.
 		u := lu.lu.asTriDense(n, blas.NonUnit, blas.Upper)
 		l := lu.lu.asTriDense(n, blas.Unit, blas.Lower)
-		unorm := lapack64.Lantr(matrix.CondNorm, u.mat, work)
+		unorm := lapack64.Lantr(CondNorm, u.mat, work)
-		lnorm := lapack64.Lantr(matrix.CondNorm, l.mat, work)
+		lnorm := lapack64.Lantr(CondNorm, l.mat, work)
 		norm = unorm * lnorm
 	}
-	v := lapack64.Gecon(matrix.CondNorm, lu.lu.mat, norm, work, iwork)
+	v := lapack64.Gecon(CondNorm, lu.lu.mat, norm, work, iwork)
 	lu.cond = 1 / v
 }
 
@@ -57,7 +56,7 @@ func (lu *LU) updateCond(norm float64) {
 func (lu *LU) Factorize(a Matrix) {
 	r, c := a.Dims()
 	if r != c {
-		panic(matrix.ErrSquare)
+		panic(ErrSquare)
 	}
 	if lu.lu == nil {
 		lu.lu = NewDense(r, r, nil)
@@ -71,7 +70,7 @@ func (lu *LU) Factorize(a Matrix) {
 	}
 	lu.pivot = lu.pivot[:r]
 	work := getFloats(r, false)
-	anorm := lapack64.Lange(matrix.CondNorm, lu.lu.mat, work)
+	anorm := lapack64.Lange(CondNorm, lu.lu.mat, work)
 	putFloats(work)
 	lapack64.Getrf(lu.lu.mat, lu.pivot)
 	lu.updateCond(anorm)
@@ -152,10 +151,10 @@ func (lu *LU) RankOne(orig *LU, alpha float64, x, y *Vector) {
 	// http://web.stanford.edu/group/SOL/dissertations/Linzhong-Deng-thesis.pdf
 	_, n := orig.lu.Dims()
 	if x.Len() != n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	if y.Len() != n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	if orig != lu {
 		if lu.isZero() {
@@ -169,7 +168,7 @@ func (lu *LU) RankOne(orig *LU, alpha float64, x, y *Vector) {
 				lu.lu.reuseAs(n, n)
 			}
 		} else if len(lu.pivot) != n {
-			panic(matrix.ErrShape)
+			panic(ErrShape)
 		}
 		copy(lu.pivot, orig.pivot)
 		lu.lu.Copy(orig.lu)
@@ -215,9 +214,9 @@ func (lu *LU) RankOne(orig *LU, alpha float64, x, y *Vector) {
 func (lu *LU) LTo(dst *TriDense) *TriDense {
 	_, n := lu.lu.Dims()
 	if dst == nil {
-		dst = NewTriDense(n, matrix.Lower, nil)
+		dst = NewTriDense(n, Lower, nil)
 	} else {
-		dst.reuseAs(n, matrix.Lower)
+		dst.reuseAs(n, Lower)
 	}
 	// Extract the lower triangular elements.
 	for i := 0; i < n; i++ {
@@ -237,9 +236,9 @@ func (lu *LU) LTo(dst *TriDense) *TriDense {
 func (lu *LU) UTo(dst *TriDense) *TriDense {
 	_, n := lu.lu.Dims()
 	if dst == nil {
-		dst = NewTriDense(n, matrix.Upper, nil)
+		dst = NewTriDense(n, Upper, nil)
 	} else {
-		dst.reuseAs(n, matrix.Upper)
+		dst.reuseAs(n, Upper)
 	}
 	// Extract the upper triangular elements.
 	for i := 0; i < n; i++ {
@@ -260,7 +259,7 @@ func (m *Dense) Permutation(r int, swaps []int) {
 		zero(m.mat.Data[i*m.mat.Stride : i*m.mat.Stride+r])
 		v := swaps[i]
 		if v < 0 || v >= r {
-			panic(matrix.ErrRowAccess)
+			panic(ErrRowAccess)
 		}
 		m.mat.Data[i*m.mat.Stride+v] = 1
 	}
@@ -279,12 +278,12 @@ func (m *Dense) SolveLU(lu *LU, trans bool, b Matrix) error {
 	_, n := lu.lu.Dims()
 	br, bc := b.Dims()
 	if br != n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	// TODO(btracey): Should test the condition number instead of testing that
 	// the determinant is exactly zero.
 	if lu.Det() == 0 {
-		return matrix.Condition(math.Inf(1))
+		return Condition(math.Inf(1))
 	}
 
 	m.reuseAs(n, bc)
@@ -303,8 +302,8 @@ func (m *Dense) SolveLU(lu *LU, trans bool, b Matrix) error {
 		t = blas.Trans
 	}
 	lapack64.Getrs(t, lu.lu.mat, m.mat, lu.pivot)
-	if lu.cond > matrix.ConditionTolerance {
+	if lu.cond > ConditionTolerance {
-		return matrix.Condition(lu.cond)
+		return Condition(lu.cond)
 	}
 	return nil
 }
@@ -322,7 +321,7 @@ func (v *Vector) SolveLUVec(lu *LU, trans bool, b *Vector) error {
 	_, n := lu.lu.Dims()
 	bn := b.Len()
 	if bn != n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	if v != b {
 		v.checkOverlap(b.mat)
@@ -330,7 +329,7 @@ func (v *Vector) SolveLUVec(lu *LU, trans bool, b *Vector) error {
 	// TODO(btracey): Should test the condition number instead of testing that
 	// the determinant is exactly zero.
 	if lu.Det() == 0 {
-		return matrix.Condition(math.Inf(1))
+		return Condition(math.Inf(1))
 	}
 
 	v.reuseAs(n)
@@ -351,8 +350,8 @@ func (v *Vector) SolveLUVec(lu *LU, trans bool, b *Vector) error {
 		t = blas.Trans
 	}
 	lapack64.Getrs(t, lu.lu.mat, vMat, lu.pivot)
-	if lu.cond > matrix.ConditionTolerance {
+	if lu.cond > ConditionTolerance {
-		return matrix.Condition(lu.cond)
+		return Condition(lu.cond)
 	}
 	return nil
 }

mat/lu_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math/rand"

mat/matrix.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math"
@@ -12,7 +12,6 @@ import (
 	"gonum.org/v1/gonum/floats"
 	"gonum.org/v1/gonum/lapack"
 	"gonum.org/v1/gonum/lapack/lapack64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 // Matrix is the basic matrix interface type.
@@ -196,13 +195,13 @@ type RawVectorer interface {
 func Col(dst []float64, j int, a Matrix) []float64 {
 	r, c := a.Dims()
 	if j < 0 || j >= c {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	if dst == nil {
 		dst = make([]float64, r)
 	} else {
 		if len(dst) != r {
-			panic(matrix.ErrColLength)
+			panic(ErrColLength)
 		}
 	}
 	aU, aTrans := untranspose(a)
@@ -230,13 +229,13 @@ func Col(dst []float64, j int, a Matrix) []float64 {
 func Row(dst []float64, i int, a Matrix) []float64 {
 	r, c := a.Dims()
 	if i < 0 || i >= r {
-		panic(matrix.ErrColAccess)
+		panic(ErrColAccess)
 	}
 	if dst == nil {
 		dst = make([]float64, c)
 	} else {
 		if len(dst) != c {
-			panic(matrix.ErrRowLength)
+			panic(ErrRowLength)
 		}
 	}
 	aU, aTrans := untranspose(a)
@@ -270,7 +269,7 @@ func Row(dst []float64, i int, a Matrix) []float64 {
 func Cond(a Matrix, norm float64) float64 {
 	m, n := a.Dims()
 	if m == 0 || n == 0 {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	var lnorm lapack.MatrixNorm
 	switch norm {
@@ -280,7 +279,7 @@ func Cond(a Matrix, norm float64) float64 {
 		lnorm = lapack.MaxColumnSum
 	case 2:
 		var svd SVD
-		ok := svd.Factorize(a, matrix.SVDNone)
+		ok := svd.Factorize(a, SVDNone)
 		if !ok {
 			return math.Inf(1)
 		}
@@ -360,7 +359,7 @@ func Dot(a, b *Vector) float64 {
 	la := a.Len()
 	lb := b.Len()
 	if la != lb {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	return blas64.Dot(la, a.mat, b.mat)
 }
@@ -523,7 +522,7 @@ func LogDet(a Matrix) (det float64, sign float64) {
 func Max(a Matrix) float64 {
 	r, c := a.Dims()
 	if r == 0 || c == 0 {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	// Max(A) = Max(A^T)
 	aU, _ := untranspose(a)
@@ -598,7 +597,7 @@ func Max(a Matrix) float64 {
 func Min(a Matrix) float64 {
 	r, c := a.Dims()
 	if r == 0 || c == 0 {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	// Min(A) = Min(A^T)
 	aU, _ := untranspose(a)
@@ -680,7 +679,7 @@ func Min(a Matrix) float64 {
 func Norm(a Matrix, norm float64) float64 {
 	r, c := a.Dims()
 	if r == 0 || c == 0 {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	aU, aTrans := untranspose(a)
 	var work []float64
@@ -787,7 +786,7 @@ func normLapack(norm float64, aTrans bool) lapack.MatrixNorm {
 		}
 		return n
 	default:
-		panic(matrix.ErrNormOrder)
+		panic(ErrNormOrder)
 	}
 }
 
@@ -820,7 +819,7 @@ func Sum(a Matrix) float64 {
 func Trace(a Matrix) float64 {
 	r, c := a.Dims()
 	if r != c {
-		panic(matrix.ErrSquare)
+		panic(ErrSquare)
 	}
 
 	aU, _ := untranspose(a)

mat/matrix_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"fmt"
@@ -13,7 +13,6 @@ import (
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/blas/blas64"
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix"
 )
 
 func panics(fn func()) (panicked bool, message string) {
@@ -335,7 +334,7 @@ func TestDet(t *testing.T) {
 	f = func(a Matrix) interface{} {
 		ar, ac := a.Dims()
 		if !isWide(ar, ac) {
-			panic(matrix.ErrShape)
+			panic(ErrShape)
 		}
 		var tmp Dense
 		tmp.Mul(a, a.T())
@@ -344,7 +343,7 @@ func TestDet(t *testing.T) {
 	denseComparison = func(a *Dense) interface{} {
 		ar, ac := a.Dims()
 		if !isWide(ar, ac) {
-			panic(matrix.ErrShape)
+			panic(ErrShape)
 		}
 		var tmp SymDense
 		tmp.SymOuterK(1, a)
@@ -366,7 +365,7 @@ func TestDot(t *testing.T) {
 		ra, ca := a.Dims()
 		rb, cb := b.Dims()
 		if ra != rb || ca != cb {
-			panic(matrix.ErrShape)
+			panic(ErrShape)
 		}
 		var sum float64
 		for i := 0; i < ra; i++ {
@@ -481,9 +480,9 @@ func TestNormZero(t *testing.T) {
 			if !panicked {
 				t.Errorf("expected panic for Norm(&%T{}, %v)", a, norm)
 			}
-			if message != matrix.ErrShape.Error() {
+			if message != ErrShape.Error() {
 				t.Errorf("unexpected panic string for Norm(&%T{}, %v): got:%s want:%s",
-					a, norm, message, matrix.ErrShape.Error())
+					a, norm, message, ErrShape.Error())
 			}
 		}
 	}

mat/mul_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math/rand"
@@ -11,7 +11,6 @@ import (
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/blas/blas64"
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix"
 )
 
 // TODO: Need to add tests where one is overwritten.
@@ -247,7 +246,7 @@ func (m *basicTriangular) T() Matrix {
 	return Transpose{m}
 }
 
-func (m *basicTriangular) Triangle() (int, matrix.TriKind) {
+func (m *basicTriangular) Triangle() (int, TriKind) {
 	return (*TriDense)(m).Triangle()
 }
 

mat/offset.go

@@ -4,7 +4,7 @@
 
 //+build !appengine
 
-package mat64
+package mat
 
 import "unsafe"
 

mat/offset_appengine.go

@@ -4,7 +4,7 @@
 
 //+build appengine
 
-package mat64
+package mat
 
 import "reflect"
 

mat/pool.go

@@ -2,14 +2,13 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"sync"
 
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/blas/blas64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 var tab64 = [64]byte{
@@ -148,7 +147,7 @@ func putWorkspaceSym(s *SymDense) {
 // getWorkspaceTri returns a *TriDense of size n and a cap that
 // is less than 2*n. If clear is true, the data slice visible
 // through the Matrix interface is zeroed.
-func getWorkspaceTri(n int, kind matrix.TriKind, clear bool) *TriDense {
+func getWorkspaceTri(n int, kind TriKind, clear bool) *TriDense {
 	l := uint64(n)
 	l *= l
 	t := poolTri[bits(l)].Get().(*TriDense)
@@ -158,12 +157,12 @@ func getWorkspaceTri(n int, kind matrix.TriKind, clear bool) *TriDense {
 	}
 	t.mat.N = n
 	t.mat.Stride = n
-	if kind == matrix.Upper {
+	if kind == Upper {
 		t.mat.Uplo = blas.Upper
-	} else if kind == matrix.Lower {
+	} else if kind == Lower {
 		t.mat.Uplo = blas.Lower
 	} else {
-		panic(matrix.ErrTriangle)
+		panic(ErrTriangle)
 	}
 	t.mat.Diag = blas.NonUnit
 	t.cap = n

mat/pool_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math"

mat/product.go

@@ -2,13 +2,9 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
-import (
+import "fmt"
-	"fmt"
-
-	"gonum.org/v1/gonum/matrix"
-)
 
 // Product calculates the product of the given factors and places the result in
 // the receiver. The order of multiplication operations is optimized to minimize
@@ -31,7 +27,7 @@ func (m *Dense) Product(factors ...Matrix) {
 	switch len(factors) {
 	case 0:
 		if r != 0 || c != 0 {
-			panic(matrix.ErrShape)
+			panic(ErrShape)
 		}
 		return
 	case 1:
@@ -77,10 +73,10 @@ func newMultiplier(m *Dense, factors []Matrix) *multiplier {
 	fr, fc := factors[0].Dims() // newMultiplier is only called with len(factors) > 2.
 	if !m.isZero() {
 		if fr != r {
-			panic(matrix.ErrShape)
+			panic(ErrShape)
 		}
 		if _, lc := factors[len(factors)-1].Dims(); lc != c {
-			panic(matrix.ErrShape)
+			panic(ErrShape)
 		}
 	}
 
@@ -92,7 +88,7 @@ func newMultiplier(m *Dense, factors []Matrix) *multiplier {
 		cr, cc := f.Dims()
 		dims[i+1] = cr
 		if pc != cr {
-			panic(matrix.ErrShape)
+			panic(ErrShape)
 		}
 		pc = cc
 	}
@@ -150,7 +146,7 @@ func (p *multiplier) multiplySubchain(i, j int) (m Matrix, intermediate bool) {
 	if ac != br {
 		// Panic with a string since this
 		// is not a user-facing panic.
-		panic(matrix.ErrShape.Error())
+		panic(ErrShape.Error())
 	}
 
 	if debugProductWalk {

mat/product_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"fmt"

mat/qr.go

@@ -3,7 +3,7 @@
 // license that can be found in the LICENSE file.
 // Based on the QRDecomposition class from Jama 1.0.3.
 
-package mat64
+package mat
 
 import (
 	"math"
@@ -11,7 +11,6 @@ import (
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/blas/blas64"
 	"gonum.org/v1/gonum/lapack/lapack64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 // QR is a type for creating and using the QR factorization of a matrix.
@@ -28,7 +27,7 @@ func (qr *QR) updateCond() {
 	work := getFloats(3*n, false)
 	iwork := getInts(n, false)
 	r := qr.qr.asTriDense(n, blas.NonUnit, blas.Upper)
-	v := lapack64.Trcon(matrix.CondNorm, r.mat, work, iwork)
+	v := lapack64.Trcon(CondNorm, r.mat, work, iwork)
 	putFloats(work)
 	putInts(iwork)
 	qr.cond = 1 / v
@@ -43,7 +42,7 @@ func (qr *QR) updateCond() {
 func (qr *QR) Factorize(a Matrix) {
 	m, n := a.Dims()
 	if m < n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	k := min(m, n)
 	if qr.qr == nil {
@@ -138,12 +137,12 @@ func (m *Dense) SolveQR(qr *QR, trans bool, b Matrix) error {
 	// copy the result into m at the end.
 	if trans {
 		if c != br {
-			panic(matrix.ErrShape)
+			panic(ErrShape)
 		}
 		m.reuseAs(r, bc)
 	} else {
 		if r != br {
-			panic(matrix.ErrShape)
+			panic(ErrShape)
 		}
 		m.reuseAs(c, bc)
 	}
@@ -155,7 +154,7 @@ func (m *Dense) SolveQR(qr *QR, trans bool, b Matrix) error {
 	if trans {
 		ok := lapack64.Trtrs(blas.Trans, t, x.mat)
 		if !ok {
-			return matrix.Condition(math.Inf(1))
+			return Condition(math.Inf(1))
 		}
 		for i := c; i < r; i++ {
 			zero(x.mat.Data[i*x.mat.Stride : i*x.mat.Stride+bc])
@@ -174,14 +173,14 @@ func (m *Dense) SolveQR(qr *QR, trans bool, b Matrix) error {
 
 		ok := lapack64.Trtrs(blas.NoTrans, t, x.mat)
 		if !ok {
-			return matrix.Condition(math.Inf(1))
+			return Condition(math.Inf(1))
 		}
 	}
 	// M was set above to be the correct size for the result.
 	m.Copy(x)
 	putWorkspace(x)
-	if qr.cond > matrix.ConditionTolerance {
+	if qr.cond > ConditionTolerance {
-		return matrix.Condition(qr.cond)
+		return Condition(qr.cond)
 	}
 	return nil
 }

mat/qr_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math"

mat/shadow.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"gonum.org/v1/gonum/blas"

mat/shadow_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math/rand"
@@ -10,7 +10,6 @@ import (
 
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/blas/blas64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 func TestDenseOverlaps(t *testing.T) {
@@ -94,7 +93,7 @@ func TestTriDenseOverlaps(t *testing.T) {
 
 	rnd := rand.New(rand.NewSource(1))
 
-	for _, parentKind := range []matrix.TriKind{matrix.Upper, matrix.Lower} {
+	for _, parentKind := range []TriKind{Upper, Lower} {
 		for n := 1; n < 20; n++ {
 			data := make([]float64, n*n)
 			for i := range data {
@@ -120,7 +119,7 @@ func TestTriDenseOverlaps(t *testing.T) {
 					} else {
 						views[k].n = 1
 					}
-					viewKind := []matrix.TriKind{matrix.Upper, matrix.Lower}[rnd.Intn(2)]
+					viewKind := []TriKind{Upper, Lower}[rnd.Intn(2)]
 					views[k].TriDense = denseAsTriDense(
 						m.Slice(views[k].i, views[k].i+views[k].n, views[k].j, views[k].j+views[k].n).(*Dense),
 						viewKind)
@@ -172,21 +171,21 @@ func intervalsOverlap(a, b interval) bool {
 	return a.to > b.from && b.to > a.from
 }
 
-func overlapsParentTriangle(i, j, n int, parent, view matrix.TriKind) bool {
+func overlapsParentTriangle(i, j, n int, parent, view TriKind) bool {
 	switch parent {
-	case matrix.Upper:
+	case Upper:
 		if i <= j {
 			return true
 		}
-		if view == matrix.Upper {
+		if view == Upper {
 			return i < j+n
 		}
 
-	case matrix.Lower:
+	case Lower:
 		if i >= j {
 			return true
 		}
-		if view == matrix.Lower {
+		if view == Lower {
 			return i+n > j
 		}
 	}
@@ -194,17 +193,17 @@ func overlapsParentTriangle(i, j, n int, parent, view matrix.TriKind) bool {
 	return false
 }
 
-func overlapSiblingTriangles(ai, aj, an int, aKind matrix.TriKind, bi, bj, bn int, bKind matrix.TriKind) bool {
+func overlapSiblingTriangles(ai, aj, an int, aKind TriKind, bi, bj, bn int, bKind TriKind) bool {
 	for i := max(ai, bi); i < min(ai+an, bi+bn); i++ {
 		var a, b interval
 
-		if aKind == matrix.Upper {
+		if aKind == Upper {
 			a = interval{from: aj - ai + i, to: aj + an}
 		} else {
 			a = interval{from: aj, to: aj - ai + i + 1}
 		}
 
-		if bKind == matrix.Upper {
+		if bKind == Upper {
 			b = interval{from: bj - bi + i, to: bj + bn}
 		} else {
 			b = interval{from: bj, to: bj - bi + i + 1}
@@ -217,8 +216,8 @@ func overlapSiblingTriangles(ai, aj, an int, aKind matrix.TriKind, bi, bj, bn in
 	return false
 }
 
-func kindString(k matrix.TriKind) string {
+func kindString(k TriKind) string {
-	if k == matrix.Upper {
+	if k == Upper {
 		return "U"
 	}
 	return "L"
@@ -252,14 +251,14 @@ func TestIssue359(t *testing.T) {
 
 // denseAsTriDense returns a triangular matrix derived from the
 // square matrix m, with the orientation specified by kind.
-func denseAsTriDense(m *Dense, kind matrix.TriKind) *TriDense {
+func denseAsTriDense(m *Dense, kind TriKind) *TriDense {
 	r, c := m.Dims()
 	if r != c {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	n := r
 	uplo := blas.Lower
-	if kind == matrix.Upper {
+	if kind == Upper {
 		uplo = blas.Upper
 	}
 	return &TriDense{

mat/solve.go

@@ -2,13 +2,12 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/blas/blas64"
 	"gonum.org/v1/gonum/lapack/lapack64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 // Solve finds a minimum-norm solution to a system of linear equations defined
@@ -23,7 +22,7 @@ func (m *Dense) Solve(a, b Matrix) error {
 	ar, ac := a.Dims()
 	br, bc := b.Dims()
 	if ar != br {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	m.reuseAs(ac, bc)
 
@@ -66,11 +65,11 @@ func (m *Dense) Solve(a, b Matrix) error {
 		blas64.Trsm(side, tA, 1, rm, m.mat)
 		work := getFloats(3*rm.N, false)
 		iwork := getInts(rm.N, false)
-		cond := lapack64.Trcon(matrix.CondNorm, rm, work, iwork)
+		cond := lapack64.Trcon(CondNorm, rm, work, iwork)
 		putFloats(work)
 		putInts(iwork)
-		if cond > matrix.ConditionTolerance {
+		if cond > ConditionTolerance {
-			return matrix.Condition(cond)
+			return Condition(cond)
 		}
 		return nil
 	}

mat/solve_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math/rand"

mat/svd.go

@@ -3,19 +3,18 @@
 // license that can be found in the LICENSE file.
 // Based on the SingularValueDecomposition class from Jama 1.0.3.
 
-package mat64
+package mat
 
 import (
 	"gonum.org/v1/gonum/blas/blas64"
 	"gonum.org/v1/gonum/lapack"
 	"gonum.org/v1/gonum/lapack/lapack64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 // SVD is a type for creating and using the Singular Value Decomposition (SVD)
 // of a matrix.
 type SVD struct {
-	kind matrix.SVDKind
+	kind SVDKind
 
 	s  []float64
 	u  blas64.General
@@ -40,16 +39,16 @@ type SVD struct {
 //
 // Factorize returns whether the decomposition succeeded. If the decomposition
 // failed, routines that require a successful factorization will panic.
-func (svd *SVD) Factorize(a Matrix, kind matrix.SVDKind) (ok bool) {
+func (svd *SVD) Factorize(a Matrix, kind SVDKind) (ok bool) {
 	m, n := a.Dims()
 	var jobU, jobVT lapack.SVDJob
 	switch kind {
 	default:
 		panic("svd: bad input kind")
-	case matrix.SVDNone:
+	case SVDNone:
 		jobU = lapack.SVDNone
 		jobVT = lapack.SVDNone
-	case matrix.SVDFull:
+	case SVDFull:
 		// TODO(btracey): This code should be modified to have the smaller
 		// matrix written in-place into aCopy when the lapack/native/dgesvd
 		// implementation is complete.
@@ -67,7 +66,7 @@ func (svd *SVD) Factorize(a Matrix, kind matrix.SVDKind) (ok bool) {
 		}
 		jobU = lapack.SVDAll
 		jobVT = lapack.SVDAll
-	case matrix.SVDThin:
+	case SVDThin:
 		// TODO(btracey): This code should be modified to have the larger
 		// matrix written in-place into aCopy when the lapack/native/dgesvd
 		// implementation is complete.
@@ -105,7 +104,7 @@ func (svd *SVD) Factorize(a Matrix, kind matrix.SVDKind) (ok bool) {
 
 // Kind returns the matrix.SVDKind of the decomposition. If no decomposition has been
 // computed, Kind returns 0.
-func (svd *SVD) Kind() matrix.SVDKind {
+func (svd *SVD) Kind() SVDKind {
 	return svd.kind
 }
 
@@ -133,7 +132,7 @@ func (svd *SVD) Values(s []float64) []float64 {
 		s = make([]float64, len(svd.s))
 	}
 	if len(s) != len(svd.s) {
-		panic(matrix.ErrSliceLengthMismatch)
+		panic(ErrSliceLengthMismatch)
 	}
 	copy(s, svd.s)
 	return s
@@ -144,7 +143,7 @@ func (svd *SVD) Values(s []float64) []float64 {
 // of size m×min(m,n) if svd.Kind() == SVDThin, and UTo panics otherwise.
 func (svd *SVD) UTo(dst *Dense) *Dense {
 	kind := svd.kind
-	if kind != matrix.SVDFull && kind != matrix.SVDThin {
+	if kind != SVDFull && kind != SVDThin {
 		panic("mat64: improper SVD kind")
 	}
 	r := svd.u.Rows
@@ -170,7 +169,7 @@ func (svd *SVD) UTo(dst *Dense) *Dense {
 // of size n×min(m,n) if svd.Kind() == SVDThin, and VTo panics otherwise.
 func (svd *SVD) VTo(dst *Dense) *Dense {
 	kind := svd.kind
-	if kind != matrix.SVDFull && kind != matrix.SVDThin {
+	if kind != SVDFull && kind != SVDThin {
 		panic("mat64: improper SVD kind")
 	}
 	r := svd.vt.Rows

mat/svd_test.go

@@ -2,14 +2,13 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math/rand"
 	"testing"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix"
 )
 
 func TestSVD(t *testing.T) {
@@ -63,7 +62,7 @@ func TestSVD(t *testing.T) {
 		},
 	} {
 		var svd SVD
-		ok := svd.Factorize(test.a, matrix.SVDThin)
+		ok := svd.Factorize(test.a, SVDThin)
 		if !ok {
 			t.Errorf("SVD failed")
 		}
@@ -111,7 +110,7 @@ func TestSVD(t *testing.T) {
 
 			// Test Full decomposition.
 			var svd SVD
-			ok := svd.Factorize(a, matrix.SVDFull)
+			ok := svd.Factorize(a, SVDFull)
 			if !ok {
 				t.Errorf("SVD factorization failed")
 			}
@@ -130,7 +129,7 @@ func TestSVD(t *testing.T) {
 			}
 
 			// Test Thin decomposition.
-			ok = svd.Factorize(a, matrix.SVDThin)
+			ok = svd.Factorize(a, SVDThin)
 			if !ok {
 				t.Errorf("SVD factorization failed")
 			}
@@ -152,7 +151,7 @@ func TestSVD(t *testing.T) {
 			}
 
 			// Test None decomposition.
-			ok = svd.Factorize(a, matrix.SVDNone)
+			ok = svd.Factorize(a, SVDNone)
 			if !ok {
 				t.Errorf("SVD factorization failed")
 			}

mat/symmetric.go

@@ -2,14 +2,13 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"math"
 
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/blas/blas64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 var (
@@ -64,7 +63,7 @@ func NewSymDense(n int, data []float64) *SymDense {
 		panic("mat64: negative dimension")
 	}
 	if data != nil && n*n != len(data) {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	if data == nil {
 		data = make([]float64, n*n)
@@ -147,7 +146,7 @@ func (s *SymDense) reuseAs(n int) {
 		panic(badSymTriangle)
 	}
 	if s.mat.N != n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 }
 
@@ -163,7 +162,7 @@ func (s *SymDense) isolatedWorkspace(a Symmetric) (w *SymDense, restore func())
 func (s *SymDense) AddSym(a, b Symmetric) {
 	n := a.Symmetric()
 	if n != b.Symmetric() {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	s.reuseAs(n)
 
@@ -227,7 +226,7 @@ func (s *SymDense) CopySym(a Symmetric) int {
 func (s *SymDense) SymRankOne(a Symmetric, alpha float64, x *Vector) {
 	n := x.Len()
 	if a.Symmetric() != n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	s.reuseAs(n)
 	if s != a {
@@ -246,7 +245,7 @@ func (s *SymDense) SymRankK(a Symmetric, alpha float64, x Matrix) {
 	n := a.Symmetric()
 	r, _ := x.Dims()
 	if r != n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	xMat, aTrans := untranspose(x)
 	var g blas64.General
@@ -307,7 +306,7 @@ func (s *SymDense) SymOuterK(alpha float64, x Matrix) {
 			s.SymRankK(s, alpha, x)
 		}
 	default:
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 }
 
@@ -317,10 +316,10 @@ func (s *SymDense) SymOuterK(alpha float64, x Matrix) {
 func (s *SymDense) RankTwo(a Symmetric, alpha float64, x, y *Vector) {
 	n := s.mat.N
 	if x.Len() != n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	if y.Len() != n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	var w SymDense
 	if s == a {
@@ -419,7 +418,7 @@ func (s *SymDense) ViewSquare(i, n int) Matrix {
 func (s *SymDense) SliceSquare(i, k int) Matrix {
 	sz := s.Symmetric()
 	if i < 0 || sz < i || k < i || sz < k {
-		panic(matrix.ErrIndexOutOfRange)
+		panic(ErrIndexOutOfRange)
 	}
 	v := *s
 	v.mat.Data = s.mat.Data[i*s.mat.Stride+i : (k-1)*s.mat.Stride+k]
@@ -434,7 +433,7 @@ func (s *SymDense) SliceSquare(i, k int) Matrix {
 // not modified during the call to GrowSquare.
 func (s *SymDense) GrowSquare(n int) Matrix {
 	if n < 0 {
-		panic(matrix.ErrIndexOutOfRange)
+		panic(ErrIndexOutOfRange)
 	}
 	if n == 0 {
 		return s
@@ -483,12 +482,12 @@ func (s *SymDense) PowPSD(a Symmetric, pow float64) error {
 	var eigen EigenSym
 	ok := eigen.Factorize(a, true)
 	if !ok {
-		return matrix.ErrFailedEigen
+		return ErrFailedEigen
 	}
 	values := eigen.Values(nil)
 	for i, v := range values {
 		if v <= 0 {
-			return matrix.ErrNotPSD
+			return ErrNotPSD
 		}
 		values[i] = math.Pow(v, pow)
 	}

mat/symmetric_example_test.go

@@ -1,14 +1,14 @@
-package mat64_test
+package mat_test
 
 import (
 	"fmt"
 
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 func ExampleSymDense_SubsetSym() {
 	n := 5
-	s := mat64.NewSymDense(5, nil)
+	s := mat.NewSymDense(5, nil)
 	count := 1.0
 	for i := 0; i < n; i++ {
 		for j := i; j < n; j++ {
@@ -17,18 +17,18 @@ func ExampleSymDense_SubsetSym() {
 		}
 	}
 	fmt.Println("Original matrix:")
-	fmt.Printf("%0.4v\n\n", mat64.Formatted(s))
+	fmt.Printf("%0.4v\n\n", mat.Formatted(s))
 
 	// Take the subset {0, 2, 4}
-	var sub mat64.SymDense
+	var sub mat.SymDense
 	sub.SubsetSym(s, []int{0, 2, 4})
 	fmt.Println("Subset {0, 2, 4}")
-	fmt.Printf("%0.4v\n\n", mat64.Formatted(&sub))
+	fmt.Printf("%0.4v\n\n", mat.Formatted(&sub))
 
 	// Take the subset {0, 0, 4}
 	sub.SubsetSym(s, []int{0, 0, 4})
 	fmt.Println("Subset {0, 0, 4}")
-	fmt.Printf("%0.4v\n\n", mat64.Formatted(&sub))
+	fmt.Printf("%0.4v\n\n", mat.Formatted(&sub))
 
 	// Output:
 	// Original matrix:

mat/symmetric_test.go

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"fmt"
@@ -14,7 +14,6 @@ import (
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/blas/blas64"
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix"
 )
 
 func TestNewSymmetric(t *testing.T) {
@@ -61,7 +60,7 @@ func TestNewSymmetric(t *testing.T) {
 	}
 
 	panicked, message := panics(func() { NewSymDense(3, []float64{1, 2}) })
-	if !panicked || message != matrix.ErrShape.Error() {
+	if !panicked || message != ErrShape.Error() {
 		t.Error("expected panic for invalid data slice length")
 	}
 }
@@ -81,13 +80,13 @@ func TestSymAtSet(t *testing.T) {
 	// Check At out of bounds
 	for _, row := range []int{-1, rows, rows + 1} {
 		panicked, message := panics(func() { sym.At(row, 0) })
-		if !panicked || message != matrix.ErrRowAccess.Error() {
+		if !panicked || message != ErrRowAccess.Error() {
 			t.Errorf("expected panic for invalid row access N=%d r=%d", rows, row)
 		}
 	}
 	for _, col := range []int{-1, cols, cols + 1} {
 		panicked, message := panics(func() { sym.At(0, col) })
-		if !panicked || message != matrix.ErrColAccess.Error() {
+		if !panicked || message != ErrColAccess.Error() {
 			t.Errorf("expected panic for invalid column access N=%d c=%d", cols, col)
 		}
 	}
@@ -95,13 +94,13 @@ func TestSymAtSet(t *testing.T) {
 	// Check Set out of bounds
 	for _, row := range []int{-1, rows, rows + 1} {
 		panicked, message := panics(func() { sym.SetSym(row, 0, 1.2) })
-		if !panicked || message != matrix.ErrRowAccess.Error() {
+		if !panicked || message != ErrRowAccess.Error() {
 			t.Errorf("expected panic for invalid row access N=%d r=%d", rows, row)
 		}
 	}
 	for _, col := range []int{-1, cols, cols + 1} {
 		panicked, message := panics(func() { sym.SetSym(0, col, 1.2) })
-		if !panicked || message != matrix.ErrColAccess.Error() {
+		if !panicked || message != ErrColAccess.Error() {
 			t.Errorf("expected panic for invalid column access N=%d c=%d", cols, col)
 		}
 	}

mat/triangular.go

@@ -1,4 +1,4 @@
-package mat64
+package mat
 
 import (
 	"math"
@@ -6,7 +6,6 @@ import (
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/blas/blas64"
 	"gonum.org/v1/gonum/lapack/lapack64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 var (
@@ -29,7 +28,7 @@ type Triangular interface {
 	Matrix
 	// Triangular returns the number of rows/columns in the matrix and its
 	// orientation.
-	Triangle() (n int, kind matrix.TriKind)
+	Triangle() (n int, kind TriKind)
 
 	// TTri is the equivalent of the T() method in the Matrix interface but
 	// guarantees the transpose is of triangular type.
@@ -72,7 +71,7 @@ func (t TransposeTri) T() Matrix {
 }
 
 // Triangle returns the number of rows/columns in the matrix and its orientation.
-func (t TransposeTri) Triangle() (int, matrix.TriKind) {
+func (t TransposeTri) Triangle() (int, TriKind) {
 	n, upper := t.Triangular.Triangle()
 	return n, !upper
 }
@@ -99,18 +98,18 @@ func (t TransposeTri) UntransposeTri() Triangular {
 // The data must be arranged in row-major order, i.e. the (i*c + j)-th
 // element in the data slice is the {i, j}-th element in the matrix.
 // Only the values in the triangular portion corresponding to kind are used.
-func NewTriDense(n int, kind matrix.TriKind, data []float64) *TriDense {
+func NewTriDense(n int, kind TriKind, data []float64) *TriDense {
 	if n < 0 {
 		panic("mat64: negative dimension")
 	}
 	if data != nil && len(data) != n*n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	if data == nil {
 		data = make([]float64, n*n)
 	}
 	uplo := blas.Lower
-	if kind == matrix.Upper {
+	if kind == Upper {
 		uplo = blas.Upper
 	}
 	return &TriDense{
@@ -131,16 +130,16 @@ func (t *TriDense) Dims() (r, c int) {
 
 // Triangle returns the dimension of t and its orientation. The returned
 // orientation is only valid when n is not zero.
-func (t *TriDense) Triangle() (n int, kind matrix.TriKind) {
+func (t *TriDense) Triangle() (n int, kind TriKind) {
-	return t.mat.N, matrix.TriKind(!t.isZero()) && t.triKind()
+	return t.mat.N, TriKind(!t.isZero()) && t.triKind()
 }
 
 func (t *TriDense) isUpper() bool {
 	return isUpperUplo(t.mat.Uplo)
 }
 
-func (t *TriDense) triKind() matrix.TriKind {
+func (t *TriDense) triKind() TriKind {
-	return matrix.TriKind(isUpperUplo(t.mat.Uplo))
+	return TriKind(isUpperUplo(t.mat.Uplo))
 }
 
 func isUpperUplo(u blas.Uplo) bool {
@@ -216,9 +215,9 @@ func untransposeTri(a Triangular) (Triangular, bool) {
 // reuseAs resizes a zero receiver to an n×n triangular matrix with the given
 // orientation. If the receiver is non-zero, reuseAs checks that the receiver
 // is the correct size and orientation.
-func (t *TriDense) reuseAs(n int, kind matrix.TriKind) {
+func (t *TriDense) reuseAs(n int, kind TriKind) {
 	ul := blas.Lower
-	if kind == matrix.Upper {
+	if kind == Upper {
 		ul = blas.Upper
 	}
 	if t.mat.N > t.cap {
@@ -236,10 +235,10 @@ func (t *TriDense) reuseAs(n int, kind matrix.TriKind) {
 		return
 	}
 	if t.mat.N != n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	if t.mat.Uplo != ul {
-		panic(matrix.ErrTriangle)
+		panic(ErrTriangle)
 	}
 }
 
@@ -333,18 +332,18 @@ func (t *TriDense) InverseTri(a Triangular) error {
 	t.Copy(a)
 	work := getFloats(3*n, false)
 	iwork := getInts(n, false)
-	cond := lapack64.Trcon(matrix.CondNorm, t.mat, work, iwork)
+	cond := lapack64.Trcon(CondNorm, t.mat, work, iwork)
 	putFloats(work)
 	putInts(iwork)
 	if math.IsInf(cond, 1) {
-		return matrix.Condition(cond)
+		return Condition(cond)
 	}
 	ok := lapack64.Trtri(t.mat)
 	if !ok {
-		return matrix.Condition(math.Inf(1))
+		return Condition(math.Inf(1))
 	}
-	if cond > matrix.ConditionTolerance {
+	if cond > ConditionTolerance {
-		return matrix.Condition(cond)
+		return Condition(cond)
 	}
 	return nil
 }
@@ -356,10 +355,10 @@ func (t *TriDense) MulTri(a, b Triangular) {
 	n, kind := a.Triangle()
 	nb, kindb := b.Triangle()
 	if n != nb {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	if kind != kindb {
-		panic(matrix.ErrTriangle)
+		panic(ErrTriangle)
 	}
 
 	aU, _ := untransposeTri(a)
@@ -375,7 +374,7 @@ func (t *TriDense) MulTri(a, b Triangular) {
 	}
 
 	// TODO(btracey): Improve the set of fast-paths.
-	if kind == matrix.Upper {
+	if kind == Upper {
 		for i := 0; i < n; i++ {
 			for j := i; j < n; j++ {
 				var v float64

mat/triangular_test.go

@@ -1,4 +1,4 @@
-package mat64
+package mat
 
 import (
 	"math"
@@ -8,14 +8,13 @@ import (
 
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/blas/blas64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 func TestNewTriangular(t *testing.T) {
 	for i, test := range []struct {
 		data []float64
 		n    int
-		kind matrix.TriKind
+		kind TriKind
 		mat  *TriDense
 	}{
 		{
@@ -25,7 +24,7 @@ func TestNewTriangular(t *testing.T) {
 				7, 8, 9,
 			},
 			n:    3,
-			kind: matrix.Upper,
+			kind: Upper,
 			mat: &TriDense{
 				mat: blas64.Triangular{
 					N:      3,
@@ -52,9 +51,9 @@ func TestNewTriangular(t *testing.T) {
 		}
 	}
 
-	for _, kind := range []matrix.TriKind{matrix.Lower, matrix.Upper} {
+	for _, kind := range []TriKind{Lower, Upper} {
 		panicked, message := panics(func() { NewTriDense(3, kind, []float64{1, 2}) })
-		if !panicked || message != matrix.ErrShape.Error() {
+		if !panicked || message != ErrShape.Error() {
 			t.Errorf("expected panic for invalid data slice length for upper=%t", kind)
 		}
 	}
@@ -77,13 +76,13 @@ func TestTriAtSet(t *testing.T) {
 	// Check At out of bounds
 	for _, row := range []int{-1, rows, rows + 1} {
 		panicked, message := panics(func() { tri.At(row, 0) })
-		if !panicked || message != matrix.ErrRowAccess.Error() {
+		if !panicked || message != ErrRowAccess.Error() {
 			t.Errorf("expected panic for invalid row access N=%d r=%d", rows, row)
 		}
 	}
 	for _, col := range []int{-1, cols, cols + 1} {
 		panicked, message := panics(func() { tri.At(0, col) })
-		if !panicked || message != matrix.ErrColAccess.Error() {
+		if !panicked || message != ErrColAccess.Error() {
 			t.Errorf("expected panic for invalid column access N=%d c=%d", cols, col)
 		}
 	}
@@ -91,13 +90,13 @@ func TestTriAtSet(t *testing.T) {
 	// Check Set out of bounds
 	for _, row := range []int{-1, rows, rows + 1} {
 		panicked, message := panics(func() { tri.SetTri(row, 0, 1.2) })
-		if !panicked || message != matrix.ErrRowAccess.Error() {
+		if !panicked || message != ErrRowAccess.Error() {
 			t.Errorf("expected panic for invalid row access N=%d r=%d", rows, row)
 		}
 	}
 	for _, col := range []int{-1, cols, cols + 1} {
 		panicked, message := panics(func() { tri.SetTri(0, col, 1.2) })
-		if !panicked || message != matrix.ErrColAccess.Error() {
+		if !panicked || message != ErrColAccess.Error() {
 			t.Errorf("expected panic for invalid column access N=%d c=%d", cols, col)
 		}
 	}
@@ -111,7 +110,7 @@ func TestTriAtSet(t *testing.T) {
 	} {
 		tri.mat.Uplo = st.uplo
 		panicked, message := panics(func() { tri.SetTri(st.row, st.col, 1.2) })
-		if !panicked || message != matrix.ErrTriangleSet.Error() {
+		if !panicked || message != ErrTriangleSet.Error() {
 			t.Errorf("expected panic for %+v", st)
 		}
 	}
@@ -140,8 +139,8 @@ func TestTriDenseCopy(t *testing.T) {
 		size := rand.Intn(100)
 		r, err := randDense(size, 0.9, rand.NormFloat64)
 		if size == 0 {
-			if err != matrix.ErrZeroLength {
+			if err != ErrZeroLength {
-				t.Fatalf("expected error %v: got: %v", matrix.ErrZeroLength, err)
+				t.Fatalf("expected error %v: got: %v", ErrZeroLength, err)
 			}
 			continue
 		}
@@ -190,8 +189,8 @@ func TestTriTriDenseCopy(t *testing.T) {
 		size := rand.Intn(100)
 		r, err := randDense(size, 1, rand.NormFloat64)
 		if size == 0 {
-			if err != matrix.ErrZeroLength {
+			if err != ErrZeroLength {
-				t.Fatalf("expected error %v: got: %v", matrix.ErrZeroLength, err)
+				t.Fatalf("expected error %v: got: %v", ErrZeroLength, err)
 			}
 			continue
 		}
@@ -238,7 +237,7 @@ func TestTriTriDenseCopy(t *testing.T) {
 }
 
 func TestTriInverse(t *testing.T) {
-	for _, kind := range []matrix.TriKind{matrix.Upper, matrix.Lower} {
+	for _, kind := range []TriKind{Upper, Lower} {
 		for _, n := range []int{1, 3, 5, 9} {
 			data := make([]float64, n*n)
 			for i := range data {
@@ -297,10 +296,10 @@ func TestTriMul(t *testing.T) {
 			return false
 		}
 		_, kind := a.(Triangular).Triangle()
-		r := kind == matrix.Lower
+		r := kind == Lower
 		return r
 	}
-	receiver := NewTriDense(3, matrix.Lower, nil)
+	receiver := NewTriDense(3, Lower, nil)
 	testTwoInput(t, "TriMul", receiver, method, denseComparison, legalTypesLower, legalSizeTriMul, 1e-14)
 
 	legalTypesUpper := func(a, b Matrix) bool {
@@ -309,10 +308,10 @@ func TestTriMul(t *testing.T) {
 			return false
 		}
 		_, kind := a.(Triangular).Triangle()
-		r := kind == matrix.Upper
+		r := kind == Upper
 		return r
 	}
-	receiver = NewTriDense(3, matrix.Upper, nil)
+	receiver = NewTriDense(3, Upper, nil)
 	testTwoInput(t, "TriMul", receiver, method, denseComparison, legalTypesUpper, legalSizeTriMul, 1e-14)
 }
 
@@ -323,7 +322,7 @@ func TestCopySymIntoTriangle(t *testing.T) {
 		sUplo blas.Uplo
 		s     []float64
 
-		tUplo matrix.TriKind
+		tUplo TriKind
 		want  []float64
 	}{
 		{
@@ -334,7 +333,7 @@ func TestCopySymIntoTriangle(t *testing.T) {
 				nan, 4, 5,
 				nan, nan, 6,
 			},
-			tUplo: matrix.Upper,
+			tUplo: Upper,
 			want: []float64{
 				1, 2, 3,
 				0, 4, 5,
@@ -349,7 +348,7 @@ func TestCopySymIntoTriangle(t *testing.T) {
 				2, 3, nan,
 				4, 5, 6,
 			},
-			tUplo: matrix.Upper,
+			tUplo: Upper,
 			want: []float64{
 				1, 2, 4,
 				0, 3, 5,
@@ -364,7 +363,7 @@ func TestCopySymIntoTriangle(t *testing.T) {
 				nan, 4, 5,
 				nan, nan, 6,
 			},
-			tUplo: matrix.Lower,
+			tUplo: Lower,
 			want: []float64{
 				1, 0, 0,
 				2, 4, 0,
@@ -379,7 +378,7 @@ func TestCopySymIntoTriangle(t *testing.T) {
 				2, 3, nan,
 				4, 5, 6,
 			},
-			tUplo: matrix.Lower,
+			tUplo: Lower,
 			want: []float64{
 				1, 0, 0,
 				2, 3, 0,

mat/vector.go

@@ -2,13 +2,12 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-package mat64
+package mat
 
 import (
 	"gonum.org/v1/gonum/blas"
 	"gonum.org/v1/gonum/blas/blas64"
 	"gonum.org/v1/gonum/internal/asm/f64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 var (
@@ -34,7 +33,7 @@ type Vector struct {
 // will be reflected in data. If neither of these is true, NewVector will panic.
 func NewVector(n int, data []float64) *Vector {
 	if len(data) != n && data != nil {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 	if data == nil {
 		data = make([]float64, n)
@@ -64,7 +63,7 @@ func (v *Vector) ViewVec(i, n int) *Vector {
 // of the receiver.
 func (v *Vector) SliceVec(i, k int) *Vector {
 	if i < 0 || k <= i || v.n < k {
-		panic(matrix.ErrIndexOutOfRange)
+		panic(ErrIndexOutOfRange)
 	}
 	return &Vector{
 		n: k - i,
@@ -168,7 +167,7 @@ func (v *Vector) AddScaledVec(a *Vector, alpha float64, b *Vector) {
 	br := b.Len()
 
 	if ar != br {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	if v != a {
@@ -211,7 +210,7 @@ func (v *Vector) AddVec(a, b *Vector) {
 	br := b.Len()
 
 	if ar != br {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	if v != a {
@@ -239,7 +238,7 @@ func (v *Vector) SubVec(a, b *Vector) {
 	br := b.Len()
 
 	if ar != br {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	if v != a {
@@ -268,7 +267,7 @@ func (v *Vector) MulElemVec(a, b *Vector) {
 	br := b.Len()
 
 	if ar != br {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	if v != a {
@@ -293,7 +292,7 @@ func (v *Vector) DivElemVec(a, b *Vector) {
 	br := b.Len()
 
 	if ar != br {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	if v != a {
@@ -317,7 +316,7 @@ func (v *Vector) MulVec(a Matrix, b *Vector) {
 	r, c := a.Dims()
 	br := b.Len()
 	if c != br {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 
 	if v != b {
@@ -427,7 +426,7 @@ func (v *Vector) reuseAs(r int) {
 		return
 	}
 	if r != v.n {
-		panic(matrix.ErrShape)
+		panic(ErrShape)
 	}
 }
 

mat/vector_test.go

@@ -1,4 +1,4 @@
-package mat64
+package mat
 
 import (
 	"math/rand"
@@ -6,7 +6,6 @@ import (
 	"testing"
 
 	"gonum.org/v1/gonum/blas/blas64"
-	"gonum.org/v1/gonum/matrix"
 )
 
 func TestNewVector(t *testing.T) {
@@ -80,13 +79,13 @@ func TestVectorAtSet(t *testing.T) {
 
 		for _, row := range []int{-1, n} {
 			panicked, message := panics(func() { v.At(row, 0) })
-			if !panicked || message != matrix.ErrRowAccess.Error() {
+			if !panicked || message != ErrRowAccess.Error() {
 				t.Errorf("expected panic for invalid row access for test %d n=%d r=%d", i, n, row)
 			}
 		}
 		for _, col := range []int{-1, 1} {
 			panicked, message := panics(func() { v.At(0, col) })
-			if !panicked || message != matrix.ErrColAccess.Error() {
+			if !panicked || message != ErrColAccess.Error() {
 				t.Errorf("expected panic for invalid column access for test %d n=%d c=%d", i, n, col)
 			}
 		}
@@ -99,7 +98,7 @@ func TestVectorAtSet(t *testing.T) {
 
 		for _, row := range []int{-1, n} {
 			panicked, message := panics(func() { v.SetVec(row, 100) })
-			if !panicked || message != matrix.ErrVectorAccess.Error() {
+			if !panicked || message != ErrVectorAccess.Error() {
 				t.Errorf("expected panic for invalid row access for test %d n=%d r=%d", i, n, row)
 			}
 		}

matrix/cmat128/doc.go

@@ -1,95 +0,0 @@
-// Generated by running
-//  go generate github.com/gonum/matrix
-// DO NOT EDIT.
-
-// Copyright ©2015 The gonum Authors. All rights reserved.
-// Use of this source code is governed by a BSD-style
-// license that can be found in the LICENSE file.
-
-// Package cmat128 provides implementations of complex128 matrix structures and
-// linear algebra operations on them.
-//
-// Overview
-//
-// This section provides a quick overview of the cmat128 package. The following
-// sections provide more in depth commentary.
-//
-// cmat128 provides:
-//  - Interfaces for a complex Matrix
-//
-// BLAS and LAPACK
-//
-// BLAS and LAPACK are the standard APIs for linear algebra routines. Many
-// operations in cmat128 are implemented using calls to the wrapper functions
-// in gonum/blas/cblas128 and gonum/lapack/clapack128. By default, cblas128 and
-// clapack128 call the native Go implementations of the routines. Alternatively,
-// it is possible to use C-based implementations of the APIs through the respective
-// cgo packages and "Use" functions. The Go implementation of LAPACK makes calls
-// through cblas128, so if a cgo BLAS implementation is registered, the clapack128
-// calls will be partially executed in Go and partially executed in C.
-//
-// Type Switching
-//
-// The Matrix abstraction enables efficiency as well as interoperability. Go's
-// type reflection capabilities are used to choose the most efficient routine
-// given the specific concrete types. For example, in
-//  c.Mul(a, b)
-// if a and b both implement RawMatrixer, that is, they can be represented as a
-// cblas128.General, cblas128.Gemm (general matrix multiplication) is called, while
-// instead if b is a RawSymmetricer cblas128.Symm is used (general-symmetric
-// multiplication), and if b is a *Vector cblas128.Gemv is used.
-//
-// There are many possible type combinations and special cases. No specific guarantees
-// are made about the performance of any method, and in particular, note that an
-// abstract matrix type may be copied into a concrete type of the corresponding
-// value. If there are specific special cases that are needed, please submit a
-// pull-request or file an issue.
-//
-// Invariants
-//
-// Matrix input arguments to functions are never directly modified. If an operation
-// changes Matrix data, the mutated matrix will be the receiver of a function.
-//
-// For convenience, a matrix may be used as both a receiver and as an input, e.g.
-//  a.Pow(a, 6)
-//  v.SolveVec(a.T(), v)
-// though in many cases this will cause an allocation (see Element Aliasing).
-// An exception to this rule is Copy, which does not allow a.Copy(a.T()).
-//
-// Element Aliasing
-//
-// Most methods in cmat128 modify receiver data. It is forbidden for the modified
-// data region of the receiver to overlap the used data area of the input
-// arguments. The exception to this rule is when the method receiver is equal to one
-// of the input arguments, as in the a.Pow(a, 6) call above, or its implicit transpose.
-//
-// This prohibition is to help avoid subtle mistakes when the method needs to read
-// from and write to the same data region. There are ways to make mistakes using the
-// cmat128 API, and cmat128 functions will detect and complain about those.
-// There are many ways to make mistakes by excursion from the cmat128 API via
-// interaction with raw matrix values.
-//
-// If you need to read the rest of this section to understand the behavior of
-// your program, you are being clever. Don't be clever. If you must be clever,
-// cblas128 and clapack128 may be used to call the behavior directly.
-//
-// cmat128 will use the following rules to detect overlap between the receiver and one
-// of the inputs:
-//  - the input implements one of the Raw methods, and
-//  - the Raw type matches that of the receiver or
-//    one is a RawMatrixer and the other is a RawVectorer, and
-//  - the address ranges of the backing data slices overlap, and
-//  - the strides differ or there is an overlap in the used data elements.
-// If such an overlap is detected, the method will panic.
-//
-// The following cases will not panic:
-//  - the data slices do not overlap,
-//  - there is pointer identity between the receiver and input values after
-//    the value has been untransposed if necessary.
-//
-// cmat128 will not attempt to detect element overlap if the input does not implement a
-// Raw method, or if the Raw method differs from that of the receiver except when a
-// conversion has occurred through a cmat128 API function. Method behavior is undefined
-// if there is undetected overlap.
-//
-package cmat128 // import "gonum.org/v1/gonum/matrix/cmat128"

matrix/conv/conv.go

@@ -1,142 +0,0 @@
-// Copyright ©2015 The gonum Authors. All rights reserved.
-// Use of this source code is governed by a BSD-style
-// license that can be found in the LICENSE file.
-
-// Package conv provides matrix type interconversion utilities.
-package conv // import "gonum.org/v1/gonum/matrix/conv"
-
-import (
-	"gonum.org/v1/gonum/matrix"
-	"gonum.org/v1/gonum/matrix/cmat128"
-	"gonum.org/v1/gonum/matrix/mat64"
-)
-
-// Complex is a complex matrix constructed from two real matrices.
-type Complex struct {
-	// r and i are not exposed to ensure that
-	// their dimensions can not be altered by
-	// clients behind our back.
-	r, i mat64.Matrix
-
-	// imagSign holds the sign of the imaginary
-	// part of the Complex. Valid values are 1 and -1.
-	imagSign float64
-}
-
-var (
-	_ Realer = Complex{}
-	_ Imager = Complex{}
-)
-
-// NewComplex returns a complex matrix constructed from r and i. At least one of
-// r or i must be non-nil otherwise NewComplex will panic. If one of the inputs
-// is nil, that part of the complex number will be zero when returned by At.
-// If both are non-nil but differ in their sizes, NewComplex will panic.
-func NewComplex(r, i mat64.Matrix) Complex {
-	if r == nil && i == nil {
-		panic("conv: no matrix")
-	} else if r != nil && i != nil {
-		rr, rc := r.Dims()
-		ir, ic := i.Dims()
-		if rr != ir || rc != ic {
-			panic(matrix.ErrShape)
-		}
-	}
-	return Complex{r: r, i: i, imagSign: 1}
-}
-
-// Dims returns the number of rows and columns in the matrix.
-func (m Complex) Dims() (r, c int) {
-	if m.r == nil {
-		return m.i.Dims()
-	}
-	return m.r.Dims()
-}
-
-// At returns the element at row i, column j.
-func (m Complex) At(i, j int) complex128 {
-	if m.i == nil {
-		return complex(m.r.At(i, j), 0)
-	}
-	if m.r == nil {
-		return complex(0, m.imagSign*m.i.At(i, j))
-	}
-	return complex(m.r.At(i, j), m.imagSign*m.i.At(i, j))
-}
-
-// H performs an implicit transpose.
-func (m Complex) H() cmat128.Matrix {
-	if m.i == nil {
-		return Complex{r: m.r.T()}
-	}
-	if m.r == nil {
-		return Complex{i: m.i.T(), imagSign: -m.imagSign}
-	}
-	return Complex{r: m.r.T(), i: m.i.T(), imagSign: -m.imagSign}
-}
-
-// Real returns the real part of the receiver.
-func (m Complex) Real() mat64.Matrix { return m.r }
-
-// Imag returns the imaginary part of the receiver.
-func (m Complex) Imag() mat64.Matrix { return m.i }
-
-// Realer is a complex matrix that can return its real part.
-type Realer interface {
-	Real() mat64.Matrix
-}
-
-// Imager is a complex matrix that can return its imaginary part.
-type Imager interface {
-	Imag() mat64.Matrix
-}
-
-// Real is the real part of a complex matrix.
-type Real struct {
-	matrix cmat128.Matrix
-}
-
-// NewReal returns a mat64.Matrix representing the real part of m. If m is a Realer,
-// the real part is returned.
-func NewReal(m cmat128.Matrix) mat64.Matrix {
-	if m, ok := m.(Realer); ok {
-		return m.Real()
-	}
-	return Real{m}
-}
-
-// Dims returns the number of rows and columns in the matrix.
-func (m Real) Dims() (r, c int) { return m.matrix.Dims() }
-
-// At returns the element at row i, column j.
-func (m Real) At(i, j int) float64 { return real(m.matrix.At(i, j)) }
-
-// T performs an implicit transpose.
-func (m Real) T() mat64.Matrix { return Real{m.matrix.H()} }
-
-// Imag is the imaginary part of a complex matrix.
-type Imag struct {
-	matrix cmat128.Matrix
-
-	// conjSign holds the sign of the matrix.
-	// Valid values are 1 and -1.
-	conjSign float64
-}
-
-// NewImag returns a mat64.Matrix representing the imaginary part of m. If m is an Imager,
-// the imaginary part is returned.
-func NewImag(m cmat128.Matrix) mat64.Matrix {
-	if m, ok := m.(Imager); ok {
-		return m.Imag()
-	}
-	return Imag{matrix: m, conjSign: 1}
-}
-
-// Dims returns the number of rows and columns in the matrix.
-func (m Imag) Dims() (r, c int) { return m.matrix.Dims() }
-
-// At returns the element at row i, column j.
-func (m Imag) At(i, j int) float64 { return m.conjSign * imag(m.matrix.At(i, j)) }
-
-// T performs an implicit transpose.
-func (m Imag) T() mat64.Matrix { return Imag{matrix: m.matrix.H(), conjSign: -m.conjSign} }

matrix/doc.go

@@ -1,103 +0,0 @@
-// Generated by running
-//  go generate github.com/gonum/matrix
-// DO NOT EDIT.
-
-// Copyright ©2015 The gonum Authors. All rights reserved.
-// Use of this source code is governed by a BSD-style
-// license that can be found in the LICENSE file.
-
-// Package matrix provides common error handling mechanisms for matrix operations
-// in mat64 and cmat128.
-//
-// Overview
-//
-// This section provides a quick overview of the matrix package. The following
-// sections provide more in depth commentary.
-//
-// matrix provides:
-//  - Error type definitions
-//  - Error recovery mechanisms
-//  - Common constants used by mat64 and cmat128
-//
-// Errors
-//
-// The mat64 and cmat128 matrix packages share a common set of errors
-// provided by matrix via the matrix.Error type.
-//
-// Errors are either returned directly or used as the parameter of a panic
-// depending on the class of error encountered. Returned errors indicate
-// that a call was not able to complete successfully while panics generally
-// indicate a programmer or unrecoverable error.
-//
-// Examples of each type are found in the mat64 Solve methods, which find
-// x such that A*x = b.
-//
-// An error value is returned from the function or method when the operation
-// can meaningfully fail. The Solve operation cannot complete if A is
-// singular. However, determining the singularity of A is most easily
-// discovered during the Solve procedure itself and is a valid result from
-// the operation, so in this case an error is returned.
-//
-// A function will panic when the input parameters are inappropriate for
-// the function. In Solve, for example, the number of rows of each input
-// matrix must be equal because of the rules of matrix multiplication.
-// Similarly, for solving A*x = b, a non-zero receiver must have the same
-// number of rows as A has columns and must have the same number of columns
-// as b. In all cases where a function will panic, conditions that would
-// lead to a panic can easily be checked prior to a call.
-//
-// Error Recovery
-//
-// When a matrix.Error is the parameter of a panic, the panic can be
-// recovered by a Maybe function, which will then return the error.
-// Panics that are not of type matrix.Error are re-panicked by the
-// Maybe functions.
-//
-// Invariants
-//
-// Matrix input arguments to functions are never directly modified. If an operation
-// changes Matrix data, the mutated matrix will be the receiver of a function.
-//
-// For convenience, a matrix may be used as both a receiver and as an input, e.g.
-//  a.Pow(a, 6)
-//  v.SolveVec(a.T(), v)
-// though in many cases this will cause an allocation (see Element Aliasing).
-// An exception to this rule is Copy, which does not allow a.Copy(a.T()).
-//
-// Element Aliasing
-//
-// Most methods in the matrix packages modify receiver data. It is forbidden for the modified
-// data region of the receiver to overlap the used data area of the input
-// arguments. The exception to this rule is when the method receiver is equal to one
-// of the input arguments, as in the a.Pow(a, 6) call above, or its implicit transpose.
-//
-// This prohibition is to help avoid subtle mistakes when the method needs to read
-// from and write to the same data region. There are ways to make mistakes using the
-// matrix API, and matrix functions will detect and complain about those.
-// There are many ways to make mistakes by excursion from the matrix API via
-// interaction with raw matrix values.
-//
-// If you need to read the rest of this section to understand the behavior of
-// your program, you are being clever. Don't be clever. If you must be clever,
-// blas64/cblas128 and lapack64/clapack128 may be used to call the behavior directly.
-//
-// The matrix packages will use the following rules to detect overlap between the receiver and one
-// of the inputs:
-//  - the input implements one of the Raw methods, and
-//  - the Raw type matches that of the receiver or
-//    one is a RawMatrixer and the other is a RawVectorer, and
-//  - the address ranges of the backing data slices overlap, and
-//  - the strides differ or there is an overlap in the used data elements.
-// If such an overlap is detected, the method will panic.
-//
-// The following cases will not panic:
-//  - the data slices do not overlap,
-//  - there is pointer identity between the receiver and input values after
-//    the value has been untransposed if necessary.
-//
-// The matrix packages will not attempt to detect element overlap if the input does not implement a
-// Raw method, or if the Raw method differs from that of the receiver except when a
-// conversion has occurred through a matrix API function. Method behavior is undefined
-// if there is undetected overlap.
-//
-package matrix // import "gonum.org/v1/gonum/matrix"

matrix/gendoc.go

@@ -1,343 +0,0 @@
-// Copyright ©2015 The gonum Authors. All rights reserved.
-// Use of this source code is governed by a BSD-style
-// license that can be found in the LICENSE file.
-
-//+build ignore
-
-// gendoc creates the matrix, mat64 and cmat128 package doc comments.
-package main
-
-import (
-	"fmt"
-	"log"
-	"os"
-	"path/filepath"
-	"strings"
-	"text/template"
-	"unicode/utf8"
-)
-
-var docs = template.Must(template.New("docs").Funcs(funcs).Parse(`{{define "common"}}// Generated by running
-//  go generate github.com/gonum/matrix
-// DO NOT EDIT.
-
-// Copyright ©2015 The gonum Authors. All rights reserved.
-// Use of this source code is governed by a BSD-style
-// license that can be found in the LICENSE file.
-
-// Package {{.Name}} provides {{.Provides}}
-//
-// Overview
-//
-// This section provides a quick overview of the {{.Name}} package. The following
-// sections provide more in depth commentary.
-//
-{{.Overview}}
-//{{end}}
-{{define "interfaces"}}// The Matrix Interfaces
-//
-// The Matrix interface is the common link between the concrete types. The Matrix
-// interface is defined by three functions: Dims, which returns the dimensions
-// of the Matrix, At, which returns the element in the specified location, and
-// T for returning a Transpose (discussed later). All of the concrete types can
-// perform these behaviors and so implement the interface. Methods and functions
-// are designed to use this interface, so in particular the method
-//  func (m *Dense) Mul(a, b Matrix)
-// constructs a *Dense from the result of a multiplication with any Matrix types,
-// not just *Dense. Where more restrictive requirements must be met, there are also the
-// Symmetric and Triangular interfaces. For example, in
-//  func (s *SymDense) AddSym(a, b Symmetric)
-// the Symmetric interface guarantees a symmetric result.
-//
-// Transposes
-//
-// The T method is used for transposition. For example, c.Mul(a.T(), b) computes
-// c = a^T * b. The {{if .ExamplePackage}}{{.ExamplePackage}}{{else}}{{.Name}}{{end}} types implement this method using an implicit transpose —
-// see the Transpose type for more details. Note that some operations have a
-// transpose as part of their definition, as in *SymDense.SymOuterK.
-//{{end}}
-{{define "factorization"}}// Matrix Factorization
-//
-// Matrix factorizations, such as the LU decomposition, typically have their own
-// specific data storage, and so are each implemented as a specific type. The
-// factorization can be computed through a call to Factorize
-//  var lu {{if .ExamplePackage}}{{.ExamplePackage}}{{else}}{{.Name}}{{end}}.LU
-//  lu.Factorize(a)
-// The elements of the factorization can be extracted through methods on the
-// appropriate type, i.e. *TriDense.LFromLU and *TriDense.UFromLU. Alternatively,
-// they can be used directly, as in *Dense.SolveLU. Some factorizations can be
-// updated directly, without needing to update the original matrix and refactorize,
-// as in *LU.RankOne.
-//{{end}}
-{{define "blas"}}// BLAS and LAPACK
-//
-// BLAS and LAPACK are the standard APIs for linear algebra routines. Many
-// operations in {{if .Description}}{{.Description}}{{else}}{{.Name}}{{end}} are implemented using calls to the wrapper functions
-// in gonum/blas/{{.BLAS|alts}} and gonum/lapack/{{.LAPACK|alts}}. By default, {{.BLAS|join "/"}} and
-// {{.LAPACK|join "/"}} call the native Go implementations of the routines. Alternatively,
-// it is possible to use C-based implementations of the APIs through the respective
-// cgo packages and "Use" functions. The Go implementation of LAPACK makes calls
-// through {{.BLAS|join "/"}}, so if a cgo BLAS implementation is registered, the {{.LAPACK|join "/"}}
-// calls will be partially executed in Go and partially executed in C.
-//{{end}}
-{{define "switching"}}// Type Switching
-//
-// The Matrix abstraction enables efficiency as well as interoperability. Go's
-// type reflection capabilities are used to choose the most efficient routine
-// given the specific concrete types. For example, in
-//  c.Mul(a, b)
-// if a and b both implement RawMatrixer, that is, they can be represented as a
-// {{.BLAS|alts}}.General, {{.BLAS|alts}}.Gemm (general matrix multiplication) is called, while
-// instead if b is a RawSymmetricer {{.BLAS|alts}}.Symm is used (general-symmetric
-// multiplication), and if b is a *Vector {{.BLAS|alts}}.Gemv is used.
-//
-// There are many possible type combinations and special cases. No specific guarantees
-// are made about the performance of any method, and in particular, note that an
-// abstract matrix type may be copied into a concrete type of the corresponding
-// value. If there are specific special cases that are needed, please submit a
-// pull-request or file an issue.
-//{{end}}
-{{define "invariants"}}// Invariants
-//
-// Matrix input arguments to functions are never directly modified. If an operation
-// changes Matrix data, the mutated matrix will be the receiver of a function.
-//
-// For convenience, a matrix may be used as both a receiver and as an input, e.g.
-//  a.Pow(a, 6)
-//  v.SolveVec(a.T(), v)
-// though in many cases this will cause an allocation (see Element Aliasing).
-// An exception to this rule is Copy, which does not allow a.Copy(a.T()).
-//{{end}}
-{{define "aliasing"}}// Element Aliasing
-//
-// Most methods in {{if .Description}}{{.Description}}{{else}}{{.Name}}{{end}} modify receiver data. It is forbidden for the modified
-// data region of the receiver to overlap the used data area of the input
-// arguments. The exception to this rule is when the method receiver is equal to one
-// of the input arguments, as in the a.Pow(a, 6) call above, or its implicit transpose.
-//
-// This prohibition is to help avoid subtle mistakes when the method needs to read
-// from and write to the same data region. There are ways to make mistakes using the
-// {{.Name}} API, and {{.Name}} functions will detect and complain about those.
-// There are many ways to make mistakes by excursion from the {{.Name}} API via
-// interaction with raw matrix values.
-//
-// If you need to read the rest of this section to understand the behavior of
-// your program, you are being clever. Don't be clever. If you must be clever,
-// {{.BLAS|join "/"}} and {{.LAPACK|join "/"}} may be used to call the behavior directly.
-//
-// {{if .Description}}{{.Description|sentence}}{{else}}{{.Name}}{{end}} will use the following rules to detect overlap between the receiver and one
-// of the inputs:
-//  - the input implements one of the Raw methods, and
-//  - the Raw type matches that of the receiver or
-//    one is a RawMatrixer and the other is a RawVectorer, and
-//  - the address ranges of the backing data slices overlap, and
-//  - the strides differ or there is an overlap in the used data elements.
-// If such an overlap is detected, the method will panic.
-//
-// The following cases will not panic:
-//  - the data slices do not overlap,
-//  - there is pointer identity between the receiver and input values after
-//    the value has been untransposed if necessary.
-//
-// {{if .Description}}{{.Description|sentence}}{{else}}{{.Name}}{{end}} will not attempt to detect element overlap if the input does not implement a
-// Raw method, or if the Raw method differs from that of the receiver except when a
-// conversion has occurred through a {{.Name}} API function. Method behavior is undefined
-// if there is undetected overlap.
-//{{end}}`))
-
-type Package struct {
-	path string
-
-	Name           string
-	Provides       string
-	Description    string
-	ExamplePackage string
-	Overview       string
-
-	BLAS   []string
-	LAPACK []string
-
-	template string
-}
-
-var pkgs = []Package{
-	{
-		path: ".",
-
-		Name:        "matrix",
-		Description: "the matrix packages",
-		Provides: `common error handling mechanisms for matrix operations
-// in mat64 and cmat128.`,
-		ExamplePackage: "mat64",
-
-		Overview: `// matrix provides:
-//  - Error type definitions
-//  - Error recovery mechanisms
-//  - Common constants used by mat64 and cmat128
-//
-// Errors
-//
-// The mat64 and cmat128 matrix packages share a common set of errors
-// provided by matrix via the matrix.Error type.
-//
-// Errors are either returned directly or used as the parameter of a panic
-// depending on the class of error encountered. Returned errors indicate
-// that a call was not able to complete successfully while panics generally
-// indicate a programmer or unrecoverable error.
-//
-// Examples of each type are found in the mat64 Solve methods, which find
-// x such that A*x = b.
-//
-// An error value is returned from the function or method when the operation
-// can meaningfully fail. The Solve operation cannot complete if A is
-// singular. However, determining the singularity of A is most easily
-// discovered during the Solve procedure itself and is a valid result from
-// the operation, so in this case an error is returned.
-//
-// A function will panic when the input parameters are inappropriate for
-// the function. In Solve, for example, the number of rows of each input
-// matrix must be equal because of the rules of matrix multiplication.
-// Similarly, for solving A*x = b, a non-zero receiver must have the same
-// number of rows as A has columns and must have the same number of columns
-// as b. In all cases where a function will panic, conditions that would
-// lead to a panic can easily be checked prior to a call.
-//
-// Error Recovery
-//
-// When a matrix.Error is the parameter of a panic, the panic can be
-// recovered by a Maybe function, which will then return the error.
-// Panics that are not of type matrix.Error are re-panicked by the
-// Maybe functions.`,
-		BLAS:   []string{"blas64", "cblas128"},
-		LAPACK: []string{"lapack64", "clapack128"},
-
-		template: `{{template "common" .}}
-{{template "invariants" .}}
-{{template "aliasing" .}}
-package {{.Name}} // import "gonum.org/v1/gonum/{{.Name}}"
-`,
-	},
-	{
-		path: "mat64",
-
-		Name: "mat64",
-		Provides: `implementations of float64 matrix structures and
-// linear algebra operations on them.`,
-
-		Overview: `// mat64 provides:
-//  - Interfaces for Matrix classes (Matrix, Symmetric, Triangular)
-//  - Concrete implementations (Dense, SymDense, TriDense)
-//  - Methods and functions for using matrix data (Add, Trace, SymRankOne)
-//  - Types for constructing and using matrix factorizations (QR, LU)
-//
-// A matrix may be constructed through the corresponding New function. If no
-// backing array is provided the matrix will be initialized to all zeros.
-//  // Allocate a zeroed matrix of size 3×5
-//  zero := mat64.NewDense(3, 5, nil)
-// If a backing data slice is provided, the matrix will have those elements.
-// Matrices are all stored in row-major format.
-//  // Generate a 6×6 matrix of random values.
-//  data := make([]float64, 36)
-//  for i := range data {
-//  	data[i] = rand.NormFloat64()
-//  }
-//  a := mat64.NewDense(6, 6, data)
-//
-// Operations involving matrix data are implemented as functions when the values
-// of the matrix remain unchanged
-//  tr := mat64.Trace(a)
-// and are implemented as methods when the operation modifies the receiver.
-//  zero.Copy(a)
-//
-// Receivers must be the correct size for the matrix operations, otherwise the
-// operation will panic. As a special case for convenience, a zero-sized matrix
-// will be modified to have the correct size, allocating data if necessary.
-//  var c mat64.Dense // construct a new zero-sized matrix
-//  c.Mul(a, a)       // c is automatically adjusted to be 6×6`,
-
-		BLAS:   []string{"blas64"},
-		LAPACK: []string{"lapack64"},
-
-		template: `{{template "common" .}}
-{{template "interfaces" .}}
-{{template "factorization" .}}
-{{template "blas" .}}
-{{template "switching" .}}
-{{template "invariants" .}}
-{{template "aliasing" .}}
-package {{.Name}} // import "gonum.org/v1/gonum/matrix/{{.Name}}"
-`,
-	},
-	{
-		path: "cmat128",
-
-		Name: "cmat128",
-		Provides: `implementations of complex128 matrix structures and
-// linear algebra operations on them.`,
-
-		Overview: `// cmat128 provides:
-//  - Interfaces for a complex Matrix`,
-
-		BLAS:   []string{"cblas128"},
-		LAPACK: []string{"clapack128"},
-
-		template: `{{template "common" . }}
-{{template "blas" .}}
-{{template "switching" .}}
-{{template "invariants" .}}
-{{template "aliasing" .}}
-package {{.Name}} // import "gonum.org/v1/gonum/matrix/{{.Name}}"
-`,
-	},
-}
-
-var funcs = template.FuncMap{
-	"sentence": sentence,
-	"alts":     alts,
-	"join":     join,
-}
-
-// sentence converts a string to sentence case where the string is the prefix of the sentence.
-func sentence(s string) string {
-	if len(s) == 0 {
-		return ""
-	}
-	_, size := utf8.DecodeRune([]byte(s))
-	return strings.ToUpper(s[:size]) + s[size:]
-}
-
-// alts renders a []string as a glob alternatives list.
-func alts(s []string) string {
-	switch len(s) {
-	case 0:
-		return ""
-	case 1:
-		return s[0]
-	default:
-		return fmt.Sprintf("{%s}", strings.Join(s, ","))
-	}
-}
-
-// join is strings.Join with the parameter order changed.
-func join(sep string, s []string) string {
-	return strings.Join(s, sep)
-}
-
-func main() {
-	for _, pkg := range pkgs {
-		t, err := template.Must(docs.Clone()).Parse(pkg.template)
-		if err != nil {
-			log.Fatalf("failed to parse template: %v", err)
-		}
-		file := filepath.Join(pkg.path, "doc.go")
-		f, err := os.Create(file)
-		if err != nil {
-			log.Fatalf("failed to create %q: %v", file, err)
-		}
-		err = t.Execute(f, pkg)
-		if err != nil {
-			log.Fatalf("failed to execute template: %v", err)
-		}
-		f.Close()
-	}
-}

matrix/generate.go

@@ -1,7 +0,0 @@
-// Copyright ©2015 The gonum Authors. All rights reserved.
-// Use of this source code is governed by a BSD-style
-// license that can be found in the LICENSE file.
-
-//go:generate go run gendoc.go
-
-package matrix

optimize/bfgs.go

@@ -7,7 +7,7 @@ package optimize
 import (
 	"math"
 
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 // BFGS implements the Broyden–Fletcher–Goldfarb–Shanno optimization method. It
@@ -24,13 +24,13 @@ type BFGS struct {
 	ls *LinesearchMethod
 
 	dim  int
-	x    mat64.Vector // Location of the last major iteration.
+	x    mat.Vector // Location of the last major iteration.
-	grad mat64.Vector // Gradient at the last major iteration.
+	grad mat.Vector // Gradient at the last major iteration.
-	s    mat64.Vector // Difference between locations in this and the previous iteration.
+	s    mat.Vector // Difference between locations in this and the previous iteration.
-	y    mat64.Vector // Difference between gradients in this and the previous iteration.
+	y    mat.Vector // Difference between gradients in this and the previous iteration.
-	tmp  mat64.Vector
+	tmp  mat.Vector
 
-	invHess *mat64.SymDense
+	invHess *mat.SymDense
 
 	first bool // Indicator of the first iteration.
 }
@@ -57,8 +57,8 @@ func (b *BFGS) InitDirection(loc *Location, dir []float64) (stepSize float64) {
 	b.dim = dim
 	b.first = true
 
-	x := mat64.NewVector(dim, loc.X)
+	x := mat.NewVector(dim, loc.X)
-	grad := mat64.NewVector(dim, loc.Gradient)
+	grad := mat.NewVector(dim, loc.Gradient)
 	b.x.CloneVec(x)
 	b.grad.CloneVec(grad)
 
@@ -67,18 +67,18 @@ func (b *BFGS) InitDirection(loc *Location, dir []float64) (stepSize float64) {
 	b.tmp.Reset()
 
 	if b.invHess == nil || cap(b.invHess.RawSymmetric().Data) < dim*dim {
-		b.invHess = mat64.NewSymDense(dim, nil)
+		b.invHess = mat.NewSymDense(dim, nil)
 	} else {
-		b.invHess = mat64.NewSymDense(dim, b.invHess.RawSymmetric().Data[:dim*dim])
+		b.invHess = mat.NewSymDense(dim, b.invHess.RawSymmetric().Data[:dim*dim])
 	}
 	// The values of the inverse Hessian are initialized in the first call to
 	// NextDirection.
 
 	// Initial direction is just negative of the gradient because the Hessian
 	// is an identity matrix.
-	d := mat64.NewVector(dim, dir)
+	d := mat.NewVector(dim, dir)
 	d.ScaleVec(-1, grad)
-	return 1 / mat64.Norm(d, 2)
+	return 1 / mat.Norm(d, 2)
 }
 
 func (b *BFGS) NextDirection(loc *Location, dir []float64) (stepSize float64) {
@@ -93,21 +93,21 @@ func (b *BFGS) NextDirection(loc *Location, dir []float64) (stepSize float64) {
 		panic("bfgs: unexpected size mismatch")
 	}
 
-	x := mat64.NewVector(dim, loc.X)
+	x := mat.NewVector(dim, loc.X)
-	grad := mat64.NewVector(dim, loc.Gradient)
+	grad := mat.NewVector(dim, loc.Gradient)
 
 	// s = x_{k+1} - x_{k}
 	b.s.SubVec(x, &b.x)
 	// y = g_{k+1} - g_{k}
 	b.y.SubVec(grad, &b.grad)
 
-	sDotY := mat64.Dot(&b.s, &b.y)
+	sDotY := mat.Dot(&b.s, &b.y)
 
 	if b.first {
 		// Rescale the initial Hessian.
 		// From: Nocedal, J., Wright, S.: Numerical Optimization (2nd ed).
 		//       Springer (2006), page 143, eq. 6.20.
-		yDotY := mat64.Dot(&b.y, &b.y)
+		yDotY := mat.Dot(&b.y, &b.y)
 		scale := sDotY / yDotY
 		for i := 0; i < dim; i++ {
 			for j := i; j < dim; j++ {
@@ -130,7 +130,7 @@ func (b *BFGS) NextDirection(loc *Location, dir []float64) (stepSize float64) {
 		//
 		// Note that y_k^T B_k^-1 y_k is a scalar, and that the third term is a
 		// rank-two update where B_k^-1 y_k is one vector and s_k is the other.
-		yBy := mat64.Inner(&b.y, b.invHess, &b.y)
+		yBy := mat.Inner(&b.y, b.invHess, &b.y)
 		b.tmp.MulVec(b.invHess, &b.y)
 		scale := (1 + yBy/sDotY) / sDotY
 		b.invHess.SymRankOne(b.invHess, scale, &b.s)
@@ -142,7 +142,7 @@ func (b *BFGS) NextDirection(loc *Location, dir []float64) (stepSize float64) {
 	b.grad.CopyVec(grad)
 
 	// New direction is stored in dir.
-	d := mat64.NewVector(dim, dir)
+	d := mat.NewVector(dim, dir)
 	d.MulVec(b.invHess, grad)
 	d.ScaleVec(-1, d)
 

optimize/convex/lp/convert.go

@@ -6,7 +6,7 @@ package lp
 
 import (
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 // TODO(btracey): Have some sort of preprocessing step for helping to fix A to make it
@@ -27,7 +27,7 @@ import (
 //  s.t      aNew * x = bNew
 //           x >= 0
 // If there are no constraints of the given type, the inputs may be nil.
-func Convert(c []float64, g mat64.Matrix, h []float64, a mat64.Matrix, b []float64) (cNew []float64, aNew *mat64.Dense, bNew []float64) {
+func Convert(c []float64, g mat.Matrix, h []float64, a mat.Matrix, b []float64) (cNew []float64, aNew *mat.Dense, bNew []float64) {
 	nVar := len(c)
 	nIneq := len(h)
 
@@ -120,21 +120,21 @@ func Convert(c []float64, g mat64.Matrix, h []float64, a mat64.Matrix, b []float
 	copy(bNew[nIneq:], b)
 
 	// Construct aNew = [G, -G, I; A, -A, 0].
-	aNew = mat64.NewDense(nNewEq, nNewVar, nil)
+	aNew = mat.NewDense(nNewEq, nNewVar, nil)
 	if nIneq != 0 {
-		aView := (aNew.View(0, 0, nIneq, nVar)).(*mat64.Dense)
+		aView := (aNew.View(0, 0, nIneq, nVar)).(*mat.Dense)
 		aView.Copy(g)
-		aView = (aNew.View(0, nVar, nIneq, nVar)).(*mat64.Dense)
+		aView = (aNew.View(0, nVar, nIneq, nVar)).(*mat.Dense)
 		aView.Scale(-1, g)
-		aView = (aNew.View(0, 2*nVar, nIneq, nIneq)).(*mat64.Dense)
+		aView = (aNew.View(0, 2*nVar, nIneq, nIneq)).(*mat.Dense)
 		for i := 0; i < nIneq; i++ {
 			aView.Set(i, i, 1)
 		}
 	}
 	if nEq != 0 {
-		aView := (aNew.View(nIneq, 0, nEq, nVar)).(*mat64.Dense)
+		aView := (aNew.View(nIneq, 0, nEq, nVar)).(*mat.Dense)
 		aView.Copy(a)
-		aView = (aNew.View(nIneq, nVar, nEq, nVar)).(*mat64.Dense)
+		aView = (aNew.View(nIneq, nVar, nEq, nVar)).(*mat.Dense)
 		aView.Scale(-1, a)
 	}
 	return cNew, aNew, bNew

optimize/convex/lp/simplex.go

@@ -11,7 +11,7 @@ import (
 	"math"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 // TODO(btracey): Could have a solver structure with an abstract factorizer. With
@@ -85,12 +85,12 @@ const (
 //  Strang, Gilbert. "Linear Algebra and Applications." Academic, New York (1976).
 // For a detailed video introduction, see lectures 11-13 of UC Math 352
 //  https://www.youtube.com/watch?v=ESzYPFkY3og&index=11&list=PLh464gFUoJWOmBYla3zbZbc4nv2AXez6X.
-func Simplex(c []float64, A mat64.Matrix, b []float64, tol float64, initialBasic []int) (optF float64, optX []float64, err error) {
+func Simplex(c []float64, A mat.Matrix, b []float64, tol float64, initialBasic []int) (optF float64, optX []float64, err error) {
 	ans, x, _, err := simplex(initialBasic, c, A, b, tol)
 	return ans, x, err
 }
 
-func simplex(initialBasic []int, c []float64, A mat64.Matrix, b []float64, tol float64) (float64, []float64, []int, error) {
+func simplex(initialBasic []int, c []float64, A mat.Matrix, b []float64, tol float64) (float64, []float64, []int, error) {
 	err := verifyInputs(initialBasic, c, A, b)
 	if err != nil {
 		if err == ErrUnbounded {
@@ -123,7 +123,7 @@ func simplex(initialBasic []int, c []float64, A mat64.Matrix, b []float64, tol f
 	// solution.
 
 	var basicIdxs []int // The indices of the non-zero x values.
-	var ab *mat64.Dense // The subset of columns of A listed in basicIdxs.
+	var ab *mat.Dense   // The subset of columns of A listed in basicIdxs.
 	var xb []float64    // The non-zero elements of x. xb = ab^-1 b
 
 	if initialBasic != nil {
@@ -131,7 +131,7 @@ func simplex(initialBasic []int, c []float64, A mat64.Matrix, b []float64, tol f
 		if len(initialBasic) != m {
 			panic("lp: incorrect number of initial vectors")
 		}
-		ab = mat64.NewDense(m, len(initialBasic), nil)
+		ab = mat.NewDense(m, len(initialBasic), nil)
 		extractColumns(ab, A, initialBasic)
 		xb = make([]float64, m)
 		err = initializeFromBasic(xb, ab, b)
@@ -175,11 +175,11 @@ func simplex(initialBasic []int, c []float64, A mat64.Matrix, b []float64, tol f
 	for i, idx := range nonBasicIdx {
 		cn[i] = c[idx]
 	}
-	an := mat64.NewDense(m, len(nonBasicIdx), nil)
+	an := mat.NewDense(m, len(nonBasicIdx), nil)
 	extractColumns(an, A, nonBasicIdx)
 
-	bVec := mat64.NewVector(len(b), b)
+	bVec := mat.NewVector(len(b), b)
-	cbVec := mat64.NewVector(len(cb), cb)
+	cbVec := mat.NewVector(len(cb), cb)
 
 	// Temporary data needed each iteration. (Described later)
 	r := make([]float64, n-m)
@@ -214,13 +214,13 @@ func simplex(initialBasic []int, c []float64, A mat64.Matrix, b []float64, tol f
 	// of the rule in step 4 to avoid cycling.
 	for {
 		// Compute reduced costs -- r = cn - an^T ab^-T cb
-		var tmp mat64.Vector
+		var tmp mat.Vector
 		err = tmp.SolveVec(ab.T(), cbVec)
 		if err != nil {
 			break
 		}
 		data := make([]float64, n-m)
-		tmp2 := mat64.NewVector(n-m, data)
+		tmp2 := mat.NewVector(n-m, data)
 		tmp2.MulVec(an.T(), &tmp)
 		floats.SubTo(r, cn, data)
 
@@ -261,13 +261,13 @@ func simplex(initialBasic []int, c []float64, A mat64.Matrix, b []float64, tol f
 		// Replace the constrained basicIdx with the newIdx.
 		basicIdxs[replace], nonBasicIdx[minIdx] = nonBasicIdx[minIdx], basicIdxs[replace]
 		cb[replace], cn[minIdx] = cn[minIdx], cb[replace]
-		tmpCol1 := mat64.Col(nil, replace, ab)
+		tmpCol1 := mat.Col(nil, replace, ab)
-		tmpCol2 := mat64.Col(nil, minIdx, an)
+		tmpCol2 := mat.Col(nil, minIdx, an)
 		ab.SetCol(replace, tmpCol2)
 		an.SetCol(minIdx, tmpCol1)
 
 		// Compute the new xb.
-		xbVec := mat64.NewVector(len(xb), xb)
+		xbVec := mat.NewVector(len(xb), xb)
 		err = xbVec.SolveVec(ab, bVec)
 		if err != nil {
 			break
@@ -285,15 +285,15 @@ func simplex(initialBasic []int, c []float64, A mat64.Matrix, b []float64, tol f
 
 // computeMove computes how far can be moved replacing each index. The results
 // are stored into move.
-func computeMove(move []float64, minIdx int, A mat64.Matrix, ab *mat64.Dense, xb []float64, nonBasicIdx []int) error {
+func computeMove(move []float64, minIdx int, A mat.Matrix, ab *mat.Dense, xb []float64, nonBasicIdx []int) error {
 	// Find ae.
-	col := mat64.Col(nil, nonBasicIdx[minIdx], A)
+	col := mat.Col(nil, nonBasicIdx[minIdx], A)
-	aCol := mat64.NewVector(len(col), col)
+	aCol := mat.NewVector(len(col), col)
 
 	// d = - Ab^-1 Ae
 	nb, _ := ab.Dims()
 	d := make([]float64, nb)
-	dVec := mat64.NewVector(nb, d)
+	dVec := mat.NewVector(nb, d)
 	err := dVec.SolveVec(ab, aCol)
 	if err != nil {
 		return ErrLinSolve
@@ -326,7 +326,7 @@ func computeMove(move []float64, minIdx int, A mat64.Matrix, ab *mat64.Dense, xb
 // replaceBland uses the Bland rule to find the indices to swap if the minimum
 // move is 0. The indices to be swapped are replace and minIdx (following the
 // nomenclature in the main routine).
-func replaceBland(A mat64.Matrix, ab *mat64.Dense, xb []float64, basicIdxs, nonBasicIdx []int, r, move []float64) (replace, minIdx int, err error) {
+func replaceBland(A mat.Matrix, ab *mat.Dense, xb []float64, basicIdxs, nonBasicIdx []int, r, move []float64) (replace, minIdx int, err error) {
 	m, _ := A.Dims()
 	// Use the traditional bland rule, except don't replace a constraint which
 	// causes the new ab to be singular.
@@ -353,10 +353,10 @@ func replaceBland(A mat64.Matrix, ab *mat64.Dense, xb []float64, basicIdxs, nonB
 			}
 			copy(biCopy, basicIdxs)
 			biCopy[replace] = nonBasicIdx[minIdx]
-			abTmp := mat64.NewDense(m, len(biCopy), nil)
+			abTmp := mat.NewDense(m, len(biCopy), nil)
 			extractColumns(abTmp, A, biCopy)
 			// If the condition number is reasonable, use this index.
-			if mat64.Cond(abTmp, 1) < 1e16 {
+			if mat.Cond(abTmp, 1) < 1e16 {
 				return replace, minIdx, nil
 			}
 		}
@@ -364,7 +364,7 @@ func replaceBland(A mat64.Matrix, ab *mat64.Dense, xb []float64, basicIdxs, nonB
 	return -1, -1, ErrBland
 }
 
-func verifyInputs(initialBasic []int, c []float64, A mat64.Matrix, b []float64) error {
+func verifyInputs(initialBasic []int, c []float64, A mat.Matrix, b []float64) error {
 	m, n := A.Dims()
 	if len(c) != n {
 		panic("lp: c vector incorrect length")
@@ -426,14 +426,14 @@ func verifyInputs(initialBasic []int, c []float64, A mat64.Matrix, b []float64)
 //
 // If the columns of A are not linearly independent or if the initial set is not
 // feasible, an error is returned.
-func initializeFromBasic(xb []float64, ab *mat64.Dense, b []float64) error {
+func initializeFromBasic(xb []float64, ab *mat.Dense, b []float64) error {
 	m, _ := ab.Dims()
 	if len(xb) != m {
 		panic("simplex: bad xb length")
 	}
-	xbMat := mat64.NewVector(m, xb)
+	xbMat := mat.NewVector(m, xb)
 
-	err := xbMat.SolveVec(ab, mat64.NewVector(m, b))
+	err := xbMat.SolveVec(ab, mat.NewVector(m, b))
 	if err != nil {
 		return errors.New("lp: subcolumns of A for supplied initial basic singular")
 	}
@@ -453,7 +453,7 @@ func initializeFromBasic(xb []float64, ab *mat64.Dense, b []float64) error {
 }
 
 // extractColumns copies the columns specified by cols into the columns of dst.
-func extractColumns(dst *mat64.Dense, A mat64.Matrix, cols []int) {
+func extractColumns(dst *mat.Dense, A mat.Matrix, cols []int) {
 	r, c := dst.Dims()
 	ra, _ := A.Dims()
 	if ra != r {
@@ -464,14 +464,14 @@ func extractColumns(dst *mat64.Dense, A mat64.Matrix, cols []int) {
 	}
 	col := make([]float64, r)
 	for j, idx := range cols {
-		mat64.Col(col, idx, A)
+		mat.Col(col, idx, A)
 		dst.SetCol(j, col)
 	}
 }
 
 // findInitialBasic finds an initial basic solution, and returns the basic
 // indices, ab, and xb.
-func findInitialBasic(A mat64.Matrix, b []float64) ([]int, *mat64.Dense, []float64, error) {
+func findInitialBasic(A mat.Matrix, b []float64) ([]int, *mat.Dense, []float64, error) {
 	m, n := A.Dims()
 	basicIdxs := findLinearlyIndependent(A)
 	if len(basicIdxs) != m {
@@ -480,7 +480,7 @@ func findInitialBasic(A mat64.Matrix, b []float64) ([]int, *mat64.Dense, []float
 
 	// It may be that this linearly independent basis is also a feasible set. If
 	// so, the Phase I problem can be avoided.
-	ab := mat64.NewDense(m, len(basicIdxs), nil)
+	ab := mat.NewDense(m, len(basicIdxs), nil)
 	extractColumns(ab, A, basicIdxs)
 	xb := make([]float64, m)
 	err := initializeFromBasic(xb, ab, b)
@@ -519,7 +519,7 @@ func findInitialBasic(A mat64.Matrix, b []float64) ([]int, *mat64.Dense, []float
 		if i == minIdx {
 			continue
 		}
-		mat64.Col(col, v, A)
+		mat.Col(col, v, A)
 		floats.Sub(aX1, col)
 	}
 
@@ -527,7 +527,7 @@ func findInitialBasic(A mat64.Matrix, b []float64) ([]int, *mat64.Dense, []float
 	// aNew = [A, a_{n+1}]
 	// bNew = b
 	// cNew = 1 for x_{n+1}
-	aNew := mat64.NewDense(m, n+1, nil)
+	aNew := mat.NewDense(m, n+1, nil)
 	aNew.Copy(A)
 	aNew.SetCol(n, aX1)
 	basicIdxs[minIdx] = n // swap minIdx with n in the basic set.
@@ -574,7 +574,7 @@ func findInitialBasic(A mat64.Matrix, b []float64) ([]int, *mat64.Dense, []float
 		}
 		newBasic[addedIdx] = i
 		if set {
-			mat64.Col(col, i, A)
+			mat.Col(col, i, A)
 			ab.SetCol(addedIdx, col)
 		} else {
 			extractColumns(ab, A, newBasic)
@@ -590,10 +590,10 @@ func findInitialBasic(A mat64.Matrix, b []float64) ([]int, *mat64.Dense, []float
 
 // findLinearlyIndependnt finds a set of linearly independent columns of A, and
 // returns the column indexes of the linearly independent columns.
-func findLinearlyIndependent(A mat64.Matrix) []int {
+func findLinearlyIndependent(A mat.Matrix) []int {
 	m, n := A.Dims()
 	idxs := make([]int, 0, m)
-	columns := mat64.NewDense(m, m, nil)
+	columns := mat.NewDense(m, m, nil)
 	newCol := make([]float64, m)
 	// Walk in reverse order because slack variables are typically the last columns
 	// of A.
@@ -601,7 +601,7 @@ func findLinearlyIndependent(A mat64.Matrix) []int {
 		if len(idxs) == m {
 			break
 		}
-		mat64.Col(newCol, i, A)
+		mat.Col(newCol, i, A)
 		columns.SetCol(len(idxs), newCol)
 		if len(idxs) == 0 {
 			// A column is linearly independent from the null set.
@@ -609,7 +609,7 @@ func findLinearlyIndependent(A mat64.Matrix) []int {
 			idxs = append(idxs, i)
 			continue
 		}
-		if mat64.Cond(columns.View(0, 0, m, len(idxs)+1), 1) > 1e12 {
+		if mat.Cond(columns.View(0, 0, m, len(idxs)+1), 1) > 1e12 {
 			// Not linearly independent.
 			continue
 		}

optimize/convex/lp/simplexexample_test.go

@@ -8,13 +8,13 @@ import (
 	"fmt"
 	"log"
 
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 	"gonum.org/v1/gonum/optimize/convex/lp"
 )
 
 func ExampleSimplex() {
 	c := []float64{-1, -2, 0, 0}
-	A := mat64.NewDense(2, 4, []float64{-1, 2, 1, 0, 3, 1, 0, 1})
+	A := mat.NewDense(2, 4, []float64{-1, 2, 1, 0, 3, 1, 0, 1})
 	b := []float64{4, 9}
 
 	opt, x, err := lp.Simplex(c, A, b, 0, nil)

optimize/functions/functions.go

@@ -8,7 +8,7 @@ import (
 	"math"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 // Beale implements the Beale's function.
@@ -56,7 +56,7 @@ func (Beale) Grad(grad, x []float64) {
 	grad[1] = 2 * x[0] * (f1 + 2*f2*x[1] + 3*f3*x[1]*x[1])
 }
 
-func (Beale) Hess(hess mat64.MutableSymmetric, x []float64) {
+func (Beale) Hess(hess mat.MutableSymmetric, x []float64) {
 	if len(x) != 2 {
 		panic("dimension of the problem must be 2")
 	}
@@ -558,7 +558,7 @@ func (BrownBadlyScaled) Grad(grad, x []float64) {
 	grad[1] = 2*f2 + 2*f3*x[0]
 }
 
-func (BrownBadlyScaled) Hess(hess mat64.MutableSymmetric, x []float64) {
+func (BrownBadlyScaled) Hess(hess mat.MutableSymmetric, x []float64) {
 	if len(x) != 2 {
 		panic("dimension of the problem must be 2")
 	}
@@ -635,7 +635,7 @@ func (BrownAndDennis) Grad(grad, x []float64) {
 	}
 }
 
-func (BrownAndDennis) Hess(hess mat64.MutableSymmetric, x []float64) {
+func (BrownAndDennis) Hess(hess mat.MutableSymmetric, x []float64) {
 	if len(x) != 4 {
 		panic("dimension of the problem must be 4")
 	}
@@ -1268,7 +1268,7 @@ func (PowellBadlyScaled) Grad(grad, x []float64) {
 	grad[1] = 2 * (1e4*f1*x[0] - f2*math.Exp(-x[1]))
 }
 
-func (PowellBadlyScaled) Hess(hess mat64.MutableSymmetric, x []float64) {
+func (PowellBadlyScaled) Hess(hess mat.MutableSymmetric, x []float64) {
 	if len(x) != 2 {
 		panic("dimension of the problem must be 2")
 	}
@@ -1518,7 +1518,7 @@ func (Watson) Grad(grad, x []float64) {
 	grad[1] += 2 * t
 }
 
-func (Watson) Hess(hess mat64.MutableSymmetric, x []float64) {
+func (Watson) Hess(hess mat.MutableSymmetric, x []float64) {
 	dim := len(x)
 	if dim != hess.Symmetric() {
 		panic("incorrect size of the Hessian")
@@ -1638,7 +1638,7 @@ func (Wood) Grad(grad, x []float64) {
 	grad[3] = 2 * (90*f3 + 10*f5 - 0.1*f6)
 }
 
-func (Wood) Hess(hess mat64.MutableSymmetric, x []float64) {
+func (Wood) Hess(hess mat.MutableSymmetric, x []float64) {
 	if len(x) != 4 {
 		panic("dimension of the problem must be 4")
 	}

optimize/guessandcheck_test.go

@@ -7,7 +7,7 @@ package optimize
 import (
 	"testing"
 
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 	"gonum.org/v1/gonum/optimize/functions"
 	"gonum.org/v1/gonum/stat/distmv"
 )
@@ -18,7 +18,7 @@ func TestGuessAndCheck(t *testing.T) {
 		Func: functions.ExtendedRosenbrock{}.Func,
 	}
 	mu := make([]float64, dim)
-	sigma := mat64.NewSymDense(dim, nil)
+	sigma := mat.NewSymDense(dim, nil)
 	for i := 0; i < dim; i++ {
 		sigma.SetSym(i, i, 1)
 	}

optimize/minimize.go

@@ -10,7 +10,7 @@ import (
 	"time"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 // newLocation allocates a new locatian structure of the appropriate size. It
@@ -26,7 +26,7 @@ func newLocation(dim int, method Needser) *Location {
 		loc.Gradient = make([]float64, dim)
 	}
 	if method.Needs().Hessian {
-		loc.Hessian = mat64.NewSymDense(dim, nil)
+		loc.Hessian = mat.NewSymDense(dim, nil)
 	}
 	return loc
 }
@@ -42,7 +42,7 @@ func copyLocation(dst, src *Location) {
 
 	if src.Hessian != nil {
 		if dst.Hessian == nil || dst.Hessian.Symmetric() != len(src.X) {
-			dst.Hessian = mat64.NewSymDense(len(src.X), nil)
+			dst.Hessian = mat.NewSymDense(len(src.X), nil)
 		}
 		dst.Hessian.CopySym(src.Hessian)
 	}

optimize/newton.go

@@ -7,7 +7,7 @@ package optimize
 import (
 	"math"
 
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 const maxNewtonModifications = 20
@@ -48,8 +48,8 @@ type Newton struct {
 
 	ls *LinesearchMethod
 
-	hess *mat64.SymDense // Storage for a copy of the Hessian matrix.
+	hess *mat.SymDense // Storage for a copy of the Hessian matrix.
-	chol mat64.Cholesky  // Storage for the Cholesky factorization.
+	chol mat.Cholesky  // Storage for the Cholesky factorization.
 	tau  float64
 }
 
@@ -88,8 +88,8 @@ func (n *Newton) NextDirection(loc *Location, dir []float64) (stepSize float64)
 	// the Identity) from Nocedal, Wright (2006), 2nd edition.
 
 	dim := len(loc.X)
-	d := mat64.NewVector(dim, dir)
+	d := mat.NewVector(dim, dir)
-	grad := mat64.NewVector(dim, loc.Gradient)
+	grad := mat.NewVector(dim, loc.Gradient)
 	n.hess.CopySym(loc.Hessian)
 
 	// Find the smallest diagonal entry of the Hessian.

optimize/types.go

@@ -10,7 +10,7 @@ import (
 	"math"
 	"time"
 
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 const defaultGradientAbsTol = 1e-6
@@ -83,7 +83,7 @@ type Location struct {
 	X        []float64
 	F        float64
 	Gradient []float64
-	Hessian  *mat64.SymDense
+	Hessian  *mat.SymDense
 }
 
 // Result represents the answer of an optimization run. It contains the optimum
@@ -131,7 +131,7 @@ type Problem struct {
 
 	// Hess evaluates the Hessian at x and stores the result in-place in hess.
 	// Hess must not modify x.
-	Hess func(hess mat64.MutableSymmetric, x []float64)
+	Hess func(hess mat.MutableSymmetric, x []float64)
 
 	// Status reports the status of the objective function being optimized and any
 	// error. This can be used to terminate early, for example when the function is
@@ -164,7 +164,7 @@ type Settings struct {
 	UseInitialData  bool          // Use supplied information about the conditions at the initial x.
 	InitialValue    float64       // Function value at the initial x.
 	InitialGradient []float64     // Gradient at the initial x.
-	InitialHessian  *mat64.SymDense // Hessian at the initial x.
+	InitialHessian  *mat.SymDense // Hessian at the initial x.
 
 	// FunctionThreshold is the threshold for acceptably small values of the
 	// objective function. FunctionThreshold status is returned if
@@ -254,9 +254,9 @@ func resize(x []float64, dim int) []float64 {
 	return x[:dim]
 }
 
-func resizeSymDense(m *mat64.SymDense, dim int) *mat64.SymDense {
+func resizeSymDense(m *mat.SymDense, dim int) *mat.SymDense {
 	if m == nil || cap(m.RawSymmetric().Data) < dim*dim {
-		return mat64.NewSymDense(dim, nil)
+		return mat.NewSymDense(dim, nil)
 	}
-	return mat64.NewSymDense(dim, m.RawSymmetric().Data[:dim*dim])
+	return mat.NewSymDense(dim, m.RawSymmetric().Data[:dim*dim])
 }

optimize/unconstrained_test.go

@@ -10,7 +10,7 @@ import (
 	"testing"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 	"gonum.org/v1/gonum/optimize/functions"
 )
 
@@ -1237,7 +1237,7 @@ func testLocal(t *testing.T, tests []unconstrainedTest, method Method) {
 			test.p.Grad(settings.InitialGradient, test.x)
 		}
 		if method.Needs().Hessian {
-			settings.InitialHessian = mat64.NewSymDense(len(test.x), nil)
+			settings.InitialHessian = mat.NewSymDense(len(test.x), nil)
 			test.p.Hess(settings.InitialHessian, test.x)
 		}
 

stat/boston_data_test.go

@@ -4,7 +4,7 @@
 
 package stat_test
 
-import "gonum.org/v1/gonum/matrix/mat64"
+import "gonum.org/v1/gonum/mat"
 
 // Boston Housing Data of Harrison and Rubinfeld (1978)
 // http://dx.doi.org/10.1016/0095-0696(78)90006-2
@@ -21,7 +21,7 @@ import "gonum.org/v1/gonum/matrix/mat64"
 //  proportion of owner-occupied units built prior to 1940,
 //  full-value property-tax rate per $10000,
 //  median value of owner-occupied homes in $1000s.
-var bostonData = mat64.NewDense(506, 11, []float64{
+var bostonData = mat.NewDense(506, 11, []float64{
 	0.00632, 2.31000, 0.53800, 4.09000, 1.00000, 15.30000, 396.90000, 6.57500, 65.20000, 296.00000, 24.00000,
 	0.02731, 7.07000, 0.46900, 4.96710, 2.00000, 17.80000, 396.90000, 6.42100, 78.90000, 242.00000, 21.60000,
 	0.02729, 7.07000, 0.46900, 4.96710, 2.00000, 17.80000, 392.83000, 7.18500, 61.10000, 242.00000, 34.70000,

stat/car_data_test.go

@@ -4,13 +4,13 @@
 
 package stat_test
 
-import "gonum.org/v1/gonum/matrix/mat64"
+import "gonum.org/v1/gonum/mat"
 
 // ASA Car Exposition Data of Ramos and Donoho (1983)
 // http://lib.stat.cmu.edu/datasets/cars.desc
 // http://lib.stat.cmu.edu/datasets/cars.data
 // Columns are: displacement, horsepower, weight, acceleration, MPG.
-var carData = mat64.NewDense(392, 5, []float64{
+var carData = mat.NewDense(392, 5, []float64{
 	307.0, 130.0, 3504.0, 12.0, 18.0,
 	350.0, 165.0, 3693.0, 11.5, 15.0,
 	318.0, 150.0, 3436.0, 11.0, 18.0,

stat/cca_example_test.go

@@ -9,13 +9,13 @@ import (
 	"log"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 	"gonum.org/v1/gonum/stat"
 )
 
 // symView is a helper for getting a View of a SymDense.
 type symView struct {
-	sym *mat64.SymDense
+	sym *mat.SymDense
 
 	i, j, r, c int
 }
@@ -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() mat64.Matrix { return mat64.Transpose{s} }
+func (s symView) T() mat.Matrix { return mat.Transpose{s} }
 
 func ExampleCC() {
 	// This example is directly analogous to Example 3.5 on page 87 of
@@ -65,7 +65,7 @@ func ExampleCC() {
 	ydata := bostonData.Slice(0, n, xd, xd+yd)
 
 	// For comparison, calculate the correlation matrix for the original data.
-	var cor mat64.SymDense
+	var cor mat.SymDense
 	stat.CorrelationMatrix(&cor, bostonData, nil)
 
 	// Extract just those correlations that are between xdata and ydata.
@@ -75,7 +75,7 @@ func ExampleCC() {
 	// between the 5th variable of xdata (index of accessibility to radial
 	// highways) and the 3rd variable of ydata (full-value property-tax rate per
 	// $10000).
-	fmt.Printf("corRaw = %.4f", mat64.Formatted(corRaw, mat64.Prefix("         ")))
+	fmt.Printf("corRaw = %.4f", mat.Formatted(corRaw, mat.Prefix("         ")))
 
 	// Calculate the canonical correlations.
 	var cc stat.CC
@@ -93,16 +93,16 @@ func ExampleCC() {
 
 	// Canonical Correlation Matrix, or the correlations between the sphered
 	// data.
-	var corSph mat64.Dense
+	var corSph mat.Dense
 	corSph.Clone(pVecs)
 	col := make([]float64, xd)
 	for j := 0; j < yd; j++ {
-		mat64.Col(col, j, &corSph)
+		mat.Col(col, j, &corSph)
 		floats.Scale(ccors[j], col)
 		corSph.SetCol(j, col)
 	}
 	corSph.Product(&corSph, qVecs.T())
-	fmt.Printf("\n\ncorSph = %.4f", mat64.Formatted(&corSph, mat64.Prefix("         ")))
+	fmt.Printf("\n\ncorSph = %.4f", mat.Formatted(&corSph, mat.Prefix("         ")))
 
 	// Canonical Correlations. Note that the first canonical correlation is
 	// 0.95, stronger than the greatest correlation in the original data, and
@@ -110,13 +110,13 @@ func ExampleCC() {
 	fmt.Printf("\n\nccors = %.4f", ccors)
 
 	// Left and right eigenvectors of the canonical correlation matrix.
-	fmt.Printf("\n\npVecs = %.4f", mat64.Formatted(pVecs, mat64.Prefix("        ")))
+	fmt.Printf("\n\npVecs = %.4f", mat.Formatted(pVecs, mat.Prefix("        ")))
-	fmt.Printf("\n\nqVecs = %.4f", mat64.Formatted(qVecs, mat64.Prefix("        ")))
+	fmt.Printf("\n\nqVecs = %.4f", mat.Formatted(qVecs, mat.Prefix("        ")))
 
 	// Canonical Correlation Transforms. These can be useful as they represent
 	// the canonical variables as linear combinations of the original variables.
-	fmt.Printf("\n\nphiVs = %.4f", mat64.Formatted(phiVs, mat64.Prefix("        ")))
+	fmt.Printf("\n\nphiVs = %.4f", mat.Formatted(phiVs, mat.Prefix("        ")))
-	fmt.Printf("\n\npsiVs = %.4f", mat64.Formatted(psiVs, mat64.Prefix("        ")))
+	fmt.Printf("\n\npsiVs = %.4f", mat.Formatted(psiVs, mat.Prefix("        ")))
 
 	// Output:
 	// corRaw = ⎡-0.2192   0.3527   0.5828  -0.3883⎤

stat/cca_test.go

@@ -8,26 +8,26 @@ import (
 	"testing"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 	"gonum.org/v1/gonum/stat"
 )
 
 func TestCanonicalCorrelations(t *testing.T) {
 tests:
 	for i, test := range []struct {
-		xdata     mat64.Matrix
+		xdata     mat.Matrix
-		ydata     mat64.Matrix
+		ydata     mat.Matrix
 		weights   []float64
 		wantCorrs []float64
-		wantpVecs *mat64.Dense
+		wantpVecs *mat.Dense
-		wantqVecs *mat64.Dense
+		wantqVecs *mat.Dense
-		wantphiVs *mat64.Dense
+		wantphiVs *mat.Dense
-		wantpsiVs *mat64.Dense
+		wantpsiVs *mat.Dense
 		epsilon   float64
 	}{
 		// Test results verified using R.
 		{ // Truncated iris data, Sepal vs Petal measurements.
-			xdata: mat64.NewDense(10, 2, []float64{
+			xdata: mat.NewDense(10, 2, []float64{
 				5.1, 3.5,
 				4.9, 3.0,
 				4.7, 3.2,
@@ -39,7 +39,7 @@ tests:
 				4.4, 2.9,
 				4.9, 3.1,
 			}),
-			ydata: mat64.NewDense(10, 2, []float64{
+			ydata: mat.NewDense(10, 2, []float64{
 				1.4, 0.2,
 				1.4, 0.2,
 				1.3, 0.2,
@@ -52,19 +52,19 @@ tests:
 				1.5, 0.1,
 			}),
 			wantCorrs: []float64{0.7250624174504773, 0.5547679185730191},
-			wantpVecs: mat64.NewDense(2, 2, []float64{
+			wantpVecs: mat.NewDense(2, 2, []float64{
 				0.0765914610875867, 0.9970625597666721,
 				0.9970625597666721, -0.0765914610875868,
 			}),
-			wantqVecs: mat64.NewDense(2, 2, []float64{
+			wantqVecs: mat.NewDense(2, 2, []float64{
 				0.3075184850910837, 0.9515421069649439,
 				0.9515421069649439, -0.3075184850910837,
 			}),
-			wantphiVs: mat64.NewDense(2, 2, []float64{
+			wantphiVs: mat.NewDense(2, 2, []float64{
 				-1.9794877596804641, 5.2016325219025124,
 				4.5211829944066553, -2.7263663170835697,
 			}),
-			wantpsiVs: mat64.NewDense(2, 2, []float64{
+			wantpsiVs: mat.NewDense(2, 2, []float64{
 				-0.0613084818030103, 10.8514169865438941,
 				12.7209032660734298, -7.6793888180353775,
 			}),
@@ -79,21 +79,21 @@ tests:
 			// Acceleration, MPG
 			ydata:     carData.Slice(0, 392, 3, 5),
 			wantCorrs: []float64{0.8782187384352336, 0.6328187219216761},
-			wantpVecs: mat64.NewDense(3, 2, []float64{
+			wantpVecs: mat.NewDense(3, 2, []float64{
 				0.3218296374829181, 0.3947540257657075,
 				0.4162807660635797, 0.7573719053303306,
 				0.8503740401982725, -0.5201509936144236,
 			}),
-			wantqVecs: mat64.NewDense(2, 2, []float64{
+			wantqVecs: mat.NewDense(2, 2, []float64{
 				-0.5161984172278830, -0.8564690269072364,
 				-0.8564690269072364, 0.5161984172278830,
 			}),
-			wantphiVs: mat64.NewDense(3, 2, []float64{
+			wantphiVs: mat.NewDense(3, 2, []float64{
 				0.0025033152994308, 0.0047795464118615,
 				0.0201923608080173, 0.0409150208725958,
 				-0.0000247374128745, -0.0026766435161875,
 			}),
-			wantpsiVs: mat64.NewDense(2, 2, []float64{
+			wantpsiVs: mat.NewDense(2, 2, []float64{
 				-0.1666196759760772, -0.3637393866139658,
 				-0.0915512109649727, 0.1077863777929168,
 			}),
@@ -116,7 +116,7 @@ tests:
 			// Median value of owner-occupied homes in $1000s
 			ydata:     bostonData.Slice(0, 506, 7, 11),
 			wantCorrs: []float64{0.9451239443886021, 0.6786622733370654, 0.5714338361583764, 0.2009739704710440},
-			wantpVecs: mat64.NewDense(7, 4, []float64{
+			wantpVecs: mat.NewDense(7, 4, []float64{
 				-0.2574391924541903, 0.0158477516621194, 0.2122169934631024, -0.0945733803894706,
 				-0.4836594430018478, 0.3837101908138468, 0.1474448317415911, 0.6597324886718275,
 				-0.0800776365873296, 0.3493556742809252, 0.3287336458109373, -0.2862040444334655,
@@ -125,13 +125,13 @@ tests:
 				-0.0990903250057199, 0.0503411215453873, 0.6384330631742202, 0.1022367136218303,
 				0.4260459963765036, 0.0323334351308141, -0.2289527516030810, 0.6419232947608805,
 			}),
-			wantqVecs: mat64.NewDense(4, 4, []float64{
+			wantqVecs: mat.NewDense(4, 4, []float64{
 				0.0181660502363264, -0.1583489460479038, -0.0066723577642883, -0.9871935400650649,
 				-0.2347699045986119, 0.9483314614936594, -0.1462420505631345, -0.1554470767919033,
 				-0.9700704038477141, -0.2406071741000039, -0.0251838984227037, 0.0209134074358349,
 				0.0593000682318482, -0.1330460003097728, -0.9889057151969489, 0.0291161494720761,
 			}),
-			wantphiVs: mat64.NewDense(7, 4, []float64{
+			wantphiVs: mat.NewDense(7, 4, []float64{
 				-0.0027462234108197, 0.0093444513500898, 0.0489643932714296, -0.0154967189805819,
 				-0.0428564455279537, -0.0241708702119420, 0.0360723472093996, 0.1838983230588095,
 				-1.2248435648802380, 5.6030921364723980, 5.8094144583797025, -4.7926812190419676,
@@ -140,7 +140,7 @@ tests:
 				-0.0233270323101624, 0.1046330818178399, 0.3853045975077387, -0.0160927870102877,
 				0.0001293051387859, 0.0004540746921446, -0.0030296315865440, 0.0081895477974654,
 			}),
-			wantpsiVs: mat64.NewDense(4, 4, []float64{
+			wantpsiVs: mat.NewDense(4, 4, []float64{
 				0.0301593362017375, -0.3002219289647127, 0.0878217377593682, -1.9583226531517062,
 				-0.0065483104073892, 0.0392212086716247, -0.0117570776209991, -0.0061113064481860,
 				-0.0052075523350125, -0.0045770200452960, -0.0022762313289592, 0.0008441873006821,
@@ -151,8 +151,8 @@ tests:
 	} {
 		var cc stat.CC
 		var corrs []float64
-		var pVecs, qVecs *mat64.Dense
+		var pVecs, qVecs *mat.Dense
-		var phiVs, psiVs *mat64.Dense
+		var phiVs, psiVs *mat.Dense
 		for j := 0; j < 2; j++ {
 			err := cc.CanonicalCorrelations(test.xdata, test.ydata, test.weights)
 			if err != nil {
@@ -170,21 +170,21 @@ tests:
 				t.Errorf("%d use %d: unexpected variance result got:%v, want:%v",
 					i, j, corrs, test.wantCorrs)
 			}
-			if !mat64.EqualApprox(pVecs, test.wantpVecs, test.epsilon) {
+			if !mat.EqualApprox(pVecs, test.wantpVecs, test.epsilon) {
 				t.Errorf("%d use %d: unexpected CCA result got:\n%v\nwant:\n%v",
-					i, j, mat64.Formatted(pVecs), mat64.Formatted(test.wantpVecs))
+					i, j, mat.Formatted(pVecs), mat.Formatted(test.wantpVecs))
 			}
-			if !mat64.EqualApprox(qVecs, test.wantqVecs, test.epsilon) {
+			if !mat.EqualApprox(qVecs, test.wantqVecs, test.epsilon) {
 				t.Errorf("%d use %d: unexpected CCA result got:\n%v\nwant:\n%v",
-					i, j, mat64.Formatted(qVecs), mat64.Formatted(test.wantqVecs))
+					i, j, mat.Formatted(qVecs), mat.Formatted(test.wantqVecs))
 			}
-			if !mat64.EqualApprox(phiVs, test.wantphiVs, test.epsilon) {
+			if !mat.EqualApprox(phiVs, test.wantphiVs, test.epsilon) {
 				t.Errorf("%d use %d: unexpected CCA result got:\n%v\nwant:\n%v",
-					i, j, mat64.Formatted(phiVs), mat64.Formatted(test.wantphiVs))
+					i, j, mat.Formatted(phiVs), mat.Formatted(test.wantphiVs))
 			}
-			if !mat64.EqualApprox(psiVs, test.wantpsiVs, test.epsilon) {
+			if !mat.EqualApprox(psiVs, test.wantpsiVs, test.epsilon) {
 				t.Errorf("%d use %d: unexpected CCA result got:\n%v\nwant:\n%v",
-					i, j, mat64.Formatted(psiVs), mat64.Formatted(test.wantpsiVs))
+					i, j, mat.Formatted(psiVs), mat.Formatted(test.wantpsiVs))
 			}
 		}
 	}

stat/distmat/wishart.go

@@ -9,9 +9,8 @@ import (
 	"math/rand"
 	"sync"
 
+	"gonum.org/v1/gonum/mat"
 	"gonum.org/v1/gonum/mathext"
-	"gonum.org/v1/gonum/matrix"
-	"gonum.org/v1/gonum/matrix/mat64"
 	"gonum.org/v1/gonum/stat/distuv"
 )
 
@@ -30,12 +29,12 @@ type Wishart struct {
 	src *rand.Rand
 
 	dim     int
-	cholv   mat64.Cholesky
+	cholv   mat.Cholesky
 	logdetv float64
-	upper   mat64.TriDense
+	upper   mat.TriDense
 
 	once sync.Once
-	v    *mat64.SymDense // only stored if needed
+	v    *mat.SymDense // only stored if needed
 }
 
 // NewWishart returns a new Wishart distribution with the given shape matrix and
@@ -43,18 +42,18 @@ type Wishart struct {
 // successful.
 //
 // NewWishart panics if nu <= d - 1 where d is the order of v.
-func NewWishart(v mat64.Symmetric, nu float64, src *rand.Rand) (*Wishart, bool) {
+func NewWishart(v mat.Symmetric, nu float64, src *rand.Rand) (*Wishart, bool) {
 	dim := v.Symmetric()
 	if nu <= float64(dim-1) {
 		panic("wishart: nu must be greater than dim-1")
 	}
-	var chol mat64.Cholesky
+	var chol mat.Cholesky
 	ok := chol.Factorize(v)
 	if !ok {
 		return nil, false
 	}
 
-	var u mat64.TriDense
+	var u mat.TriDense
 	u.UFromCholesky(&chol)
 
 	w := &Wishart{
@@ -73,9 +72,9 @@ func NewWishart(v mat64.Symmetric, nu float64, src *rand.Rand) (*Wishart, bool)
 // If x is nil, a new matrix is allocated and returned. If x is not nil, the
 // result is stored in-place into x and MeanSym will panic if the order of x
 // is not equal to the order of the receiver.
-func (w *Wishart) MeanSym(x *mat64.SymDense) *mat64.SymDense {
+func (w *Wishart) MeanSym(x *mat.SymDense) *mat.SymDense {
 	if x == nil {
-		x = mat64.NewSymDense(w.dim, nil)
+		x = mat.NewSymDense(w.dim, nil)
 	}
 	d := x.Symmetric()
 	if d != w.dim {
@@ -89,7 +88,7 @@ func (w *Wishart) MeanSym(x *mat64.SymDense) *mat64.SymDense {
 
 // ProbSym returns the probability of the symmetric matrix x. If x is not positive
 // definite (the Cholesky decomposition fails), it has 0 probability.
-func (w *Wishart) ProbSym(x mat64.Symmetric) float64 {
+func (w *Wishart) ProbSym(x mat.Symmetric) float64 {
 	return math.Exp(w.LogProbSym(x))
 }
 
@@ -97,12 +96,12 @@ func (w *Wishart) ProbSym(x mat64.Symmetric) float64 {
 //
 // LogProbSym returns -∞ if the input matrix is not positive definite (the Cholesky
 // decomposition fails).
-func (w *Wishart) LogProbSym(x mat64.Symmetric) float64 {
+func (w *Wishart) LogProbSym(x mat.Symmetric) float64 {
 	dim := x.Symmetric()
 	if dim != w.dim {
 		panic(badDim)
 	}
-	var chol mat64.Cholesky
+	var chol mat.Cholesky
 	ok := chol.Factorize(x)
 	if !ok {
 		return math.Inf(-1)
@@ -112,7 +111,7 @@ func (w *Wishart) LogProbSym(x mat64.Symmetric) float64 {
 
 // LogProbSymChol returns the log of the probability of the input symmetric matrix
 // given its Cholesky decomposition.
-func (w *Wishart) LogProbSymChol(cholX *mat64.Cholesky) float64 {
+func (w *Wishart) LogProbSymChol(cholX *mat.Cholesky) float64 {
 	dim := cholX.Size()
 	if dim != w.dim {
 		panic(badDim)
@@ -120,7 +119,7 @@ func (w *Wishart) LogProbSymChol(cholX *mat64.Cholesky) float64 {
 	return w.logProbSymChol(cholX)
 }
 
-func (w *Wishart) logProbSymChol(cholX *mat64.Cholesky) float64 {
+func (w *Wishart) logProbSymChol(cholX *mat.Cholesky) float64 {
 	// The PDF is
 	//  p(X) = [|X|^((ν-d-1)/2) * exp(-tr(V^-1 * X)/2)] / [2^(ν*d/2) * |V|^(ν/2) * Γ_d(ν/2)]
 	// The LogPDF is thus
@@ -128,16 +127,16 @@ func (w *Wishart) logProbSymChol(cholX *mat64.Cholesky) float64 {
 	logdetx := cholX.LogDet()
 
 	// Compute tr(V^-1 * X), using the fact that X = U^T * U.
-	var u mat64.TriDense
+	var u mat.TriDense
 	u.UFromCholesky(cholX)
 
-	var vinvx mat64.Dense
+	var vinvx mat.Dense
 	err := vinvx.SolveCholesky(&w.cholv, u.T())
 	if err != nil {
 		return math.Inf(-1)
 	}
 	vinvx.Mul(&vinvx, &u)
-	tr := mat64.Trace(&vinvx)
+	tr := mat.Trace(&vinvx)
 
 	fnu := float64(w.nu)
 	fdim := float64(w.dim)
@@ -146,18 +145,18 @@ func (w *Wishart) logProbSymChol(cholX *mat64.Cholesky) float64 {
 }
 
 // RandSym generates a random symmetric matrix from the distribution.
-func (w *Wishart) RandSym(x *mat64.SymDense) *mat64.SymDense {
+func (w *Wishart) RandSym(x *mat.SymDense) *mat.SymDense {
 	if x == nil {
-		x = &mat64.SymDense{}
+		x = &mat.SymDense{}
 	}
-	var c mat64.Cholesky
+	var c mat.Cholesky
 	w.RandChol(&c)
 	x.FromCholesky(&c)
 	return x
 }
 
 // RandChol generates the Cholesky decomposition of a random matrix from the distribution.
-func (w *Wishart) RandChol(c *mat64.Cholesky) *mat64.Cholesky {
+func (w *Wishart) RandChol(c *mat.Cholesky) *mat.Cholesky {
 	// TODO(btracey): Modify the code if the underlying data from c is exposed
 	// to avoid the dim^2 allocation here.
 
@@ -179,7 +178,7 @@ func (w *Wishart) RandChol(c *mat64.Cholesky) *mat64.Cholesky {
 		Source: w.src,
 	}
 
-	t := mat64.NewTriDense(w.dim, matrix.Upper, nil)
+	t := mat.NewTriDense(w.dim, mat.Upper, nil)
 	for i := 0; i < w.dim; i++ {
 		v := distuv.ChiSquared{
 			K:   w.nu - float64(i),
@@ -195,7 +194,7 @@ func (w *Wishart) RandChol(c *mat64.Cholesky) *mat64.Cholesky {
 
 	t.MulTri(t, &w.upper)
 	if c == nil {
-		c = &mat64.Cholesky{}
+		c = &mat.Cholesky{}
 	}
 	c.SetFromU(t)
 	return c
@@ -204,7 +203,7 @@ func (w *Wishart) RandChol(c *mat64.Cholesky) *mat64.Cholesky {
 // setV computes and stores the covariance matrix of the distribution.
 func (w *Wishart) setV() {
 	w.once.Do(func() {
-		w.v = mat64.NewSymDense(w.dim, nil)
+		w.v = mat.NewSymDense(w.dim, nil)
 		w.v.FromCholesky(&w.cholv)
 	})
 }

stat/distmat/wishart_test.go

@@ -10,39 +10,39 @@ import (
 	"testing"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 func TestWishart(t *testing.T) {
 	for c, test := range []struct {
-		v   *mat64.SymDense
+		v   *mat.SymDense
 		nu  float64
-		xs  []*mat64.SymDense
+		xs  []*mat.SymDense
 		lps []float64
 	}{
 		// Logprob data compared with scipy.
 		{
-			v:  mat64.NewSymDense(2, []float64{1, 0, 0, 1}),
+			v:  mat.NewSymDense(2, []float64{1, 0, 0, 1}),
 			nu: 4,
-			xs: []*mat64.SymDense{
+			xs: []*mat.SymDense{
-				mat64.NewSymDense(2, []float64{0.9, 0.1, 0.1, 0.9}),
+				mat.NewSymDense(2, []float64{0.9, 0.1, 0.1, 0.9}),
 			},
 			lps: []float64{-4.2357432031863409},
 		},
 		{
-			v:  mat64.NewSymDense(2, []float64{0.8, -0.2, -0.2, 0.7}),
+			v:  mat.NewSymDense(2, []float64{0.8, -0.2, -0.2, 0.7}),
 			nu: 5,
-			xs: []*mat64.SymDense{
+			xs: []*mat.SymDense{
-				mat64.NewSymDense(2, []float64{0.9, 0.1, 0.1, 0.9}),
+				mat.NewSymDense(2, []float64{0.9, 0.1, 0.1, 0.9}),
-				mat64.NewSymDense(2, []float64{0.3, -0.1, -0.1, 0.7}),
+				mat.NewSymDense(2, []float64{0.3, -0.1, -0.1, 0.7}),
 			},
 			lps: []float64{-4.2476495605333575, -4.9993285370378633},
 		},
 		{
-			v:  mat64.NewSymDense(3, []float64{0.8, 0.3, 0.1, 0.3, 0.7, -0.1, 0.1, -0.1, 7}),
+			v:  mat.NewSymDense(3, []float64{0.8, 0.3, 0.1, 0.3, 0.7, -0.1, 0.1, -0.1, 7}),
 			nu: 5,
-			xs: []*mat64.SymDense{
+			xs: []*mat.SymDense{
-				mat64.NewSymDense(3, []float64{1, 0.2, -0.3, 0.2, 0.6, -0.2, -0.3, -0.2, 6}),
+				mat.NewSymDense(3, []float64{1, 0.2, -0.3, 0.2, 0.6, -0.2, -0.3, -0.2, 6}),
 			},
 			lps: []float64{-11.010982249229421},
 		},
@@ -54,7 +54,7 @@ func TestWishart(t *testing.T) {
 		for i, x := range test.xs {
 			lp := w.LogProbSym(x)
 
-			var chol mat64.Cholesky
+			var chol mat.Cholesky
 			ok := chol.Factorize(x)
 			if !ok {
 				panic("bad test")
@@ -80,25 +80,25 @@ func TestWishart(t *testing.T) {
 
 func TestWishartRand(t *testing.T) {
 	for c, test := range []struct {
-		v       *mat64.SymDense
+		v       *mat.SymDense
 		nu      float64
 		samples int
 		tol     float64
 	}{
 		{
-			v:       mat64.NewSymDense(2, []float64{0.8, -0.2, -0.2, 0.7}),
+			v:       mat.NewSymDense(2, []float64{0.8, -0.2, -0.2, 0.7}),
 			nu:      5,
 			samples: 30000,
 			tol:     3e-2,
 		},
 		{
-			v:       mat64.NewSymDense(3, []float64{0.8, 0.3, 0.1, 0.3, 0.7, -0.1, 0.1, -0.1, 7}),
+			v:       mat.NewSymDense(3, []float64{0.8, 0.3, 0.1, 0.3, 0.7, -0.1, 0.1, -0.1, 7}),
 			nu:      5,
 			samples: 300000,
 			tol:     3e-2,
 		},
 		{
-			v: mat64.NewSymDense(4, []float64{
+			v: mat.NewSymDense(4, []float64{
 				0.8, 0.3, 0.1, -0.2,
 				0.3, 0.7, -0.1, 0.4,
 				0.1, -0.1, 7, 1,
@@ -114,16 +114,16 @@ func TestWishartRand(t *testing.T) {
 		if !ok {
 			panic("bad test")
 		}
-		mean := mat64.NewSymDense(dim, nil)
+		mean := mat.NewSymDense(dim, nil)
-		x := mat64.NewSymDense(dim, nil)
+		x := mat.NewSymDense(dim, nil)
 		for i := 0; i < test.samples; i++ {
 			w.RandSym(x)
 			x.ScaleSym(1/float64(test.samples), x)
 			mean.AddSym(mean, x)
 		}
 		trueMean := w.MeanSym(nil)
-		if !mat64.EqualApprox(trueMean, mean, test.tol) {
+		if !mat.EqualApprox(trueMean, mean, test.tol) {
-			t.Errorf("Case %d: Mismatch between estimated and true mean. Got\n%0.4v\nWant\n%0.4v\n", c, mat64.Formatted(mean), mat64.Formatted(trueMean))
+			t.Errorf("Case %d: Mismatch between estimated and true mean. Got\n%0.4v\nWant\n%0.4v\n", c, mat.Formatted(mean), mat.Formatted(trueMean))
 		}
 	}
 }

stat/distmv/dirichlet.go

@@ -9,7 +9,7 @@ import (
 	"math/rand"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 	"gonum.org/v1/gonum/stat/distuv"
 )
 
@@ -61,11 +61,11 @@ func NewDirichlet(alpha []float64, src *rand.Rand) *Dirichlet {
 //  covariance(i, j) = E[(x_i - E[x_i])(x_j - E[x_j])]
 // If the input matrix is nil a new matrix is allocated, otherwise the result
 // is stored in-place into the input.
-func (d *Dirichlet) CovarianceMatrix(cov *mat64.SymDense) *mat64.SymDense {
+func (d *Dirichlet) CovarianceMatrix(cov *mat.SymDense) *mat.SymDense {
 	if cov == nil {
-		cov = mat64.NewSymDense(d.Dim(), nil)
+		cov = mat.NewSymDense(d.Dim(), nil)
 	} else if cov.Symmetric() == 0 {
-		*cov = *(cov.GrowSquare(d.dim).(*mat64.SymDense))
+		*cov = *(cov.GrowSquare(d.dim).(*mat.SymDense))
 	} else if cov.Symmetric() != d.dim {
 		panic("normal: input matrix size mismatch")
 	}

stat/distmv/dirichlet_test.go

@@ -9,7 +9,7 @@ import (
 	"math/rand"
 	"testing"
 
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 func TestDirichlet(t *testing.T) {
@@ -64,7 +64,7 @@ func TestDirichlet(t *testing.T) {
 	} {
 		d := test.Dir
 		dim := d.Dim()
-		x := mat64.NewDense(test.N, dim, nil)
+		x := mat.NewDense(test.N, dim, nil)
 		generateSamples(x, d)
 		checkMean(t, cas, x, d, 1e-3)
 		checkCov(t, cas, x, d, 1e-3)

stat/distmv/general_test.go

@@ -9,7 +9,7 @@ import (
 	"testing"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 	"gonum.org/v1/gonum/stat"
 )
 
@@ -37,7 +37,7 @@ func testProbability(t *testing.T, cases []probCase) {
 	}
 }
 
-func generateSamples(x *mat64.Dense, r Rander) {
+func generateSamples(x *mat.Dense, r Rander) {
 	n, _ := x.Dims()
 	for i := 0; i < n; i++ {
 		r.Rand(x.RawRowView(i))
@@ -48,7 +48,7 @@ type Meaner interface {
 	Mean([]float64) []float64
 }
 
-func checkMean(t *testing.T, cas int, x *mat64.Dense, m Meaner, tol float64) {
+func checkMean(t *testing.T, cas int, x *mat.Dense, m Meaner, tol float64) {
 	mean := m.Mean(nil)
 
 	// Check that the answer is identical when using nil or non-nil.
@@ -63,7 +63,7 @@ func checkMean(t *testing.T, cas int, x *mat64.Dense, m Meaner, tol float64) {
 	col := make([]float64, r)
 	meanEst := make([]float64, len(mean))
 	for i := range meanEst {
-		meanEst[i] = stat.Mean(mat64.Col(col, i, x), nil)
+		meanEst[i] = stat.Mean(mat.Col(col, i, x), nil)
 	}
 	if !floats.EqualApprox(mean, meanEst, tol) {
 		t.Errorf("Returned mean and sample mean mismatch. Case %v. Empirical %v, returned %v", cas, meanEst, mean)
@@ -71,26 +71,26 @@ func checkMean(t *testing.T, cas int, x *mat64.Dense, m Meaner, tol float64) {
 }
 
 type Cover interface {
-	CovarianceMatrix(*mat64.SymDense) *mat64.SymDense
+	CovarianceMatrix(*mat.SymDense) *mat.SymDense
 }
 
-func checkCov(t *testing.T, cas int, x *mat64.Dense, c Cover, tol float64) {
+func checkCov(t *testing.T, cas int, x *mat.Dense, c Cover, tol float64) {
 	cov := c.CovarianceMatrix(nil)
 	n := cov.Symmetric()
-	cov2 := mat64.NewSymDense(n, nil)
+	cov2 := mat.NewSymDense(n, nil)
 	c.CovarianceMatrix(cov2)
-	if !mat64.Equal(cov, cov2) {
+	if !mat.Equal(cov, cov2) {
 		t.Errorf("Cov mismatch when providing nil and matrix. Case %v", cas)
 	}
-	var cov3 mat64.SymDense
+	var cov3 mat.SymDense
 	c.CovarianceMatrix(&cov3)
-	if !mat64.Equal(cov, &cov3) {
+	if !mat.Equal(cov, &cov3) {
 		t.Errorf("Cov mismatch when providing zero matrix. Case %v", cas)
 	}
 
 	// Check that the covariance matrix matches the samples
 	covEst := stat.CovarianceMatrix(nil, x, nil)
-	if !mat64.EqualApprox(covEst, cov, tol) {
+	if !mat.EqualApprox(covEst, cov, tol) {
-		t.Errorf("Return cov and sample cov mismatch. Cas %v.\nGot:\n%0.4v\nWant:\n%0.4v", cas, mat64.Formatted(cov), mat64.Formatted(covEst))
+		t.Errorf("Return cov and sample cov mismatch. Cas %v.\nGot:\n%0.4v\nWant:\n%0.4v", cas, mat.Formatted(cov), mat.Formatted(covEst))
 	}
 }

stat/distmv/normal.go

@@ -9,7 +9,7 @@ import (
 	"math/rand"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 	"gonum.org/v1/gonum/stat"
 	"gonum.org/v1/gonum/stat/distuv"
 )
@@ -26,10 +26,10 @@ var (
 type Normal struct {
 	mu []float64
 
-	sigma mat64.SymDense
+	sigma mat.SymDense
 
-	chol       mat64.Cholesky
+	chol       mat.Cholesky
-	lower      mat64.TriDense
+	lower      mat.TriDense
 	logSqrtDet float64
 	dim        int
 
@@ -39,7 +39,7 @@ type Normal struct {
 // NewNormal creates a new Normal with the given mean and covariance matrix.
 // NewNormal panics if len(mu) == 0, or if len(mu) != sigma.N. If the covariance
 // matrix is not positive-definite, the returned boolean is false.
-func NewNormal(mu []float64, sigma mat64.Symmetric, src *rand.Rand) (*Normal, bool) {
+func NewNormal(mu []float64, sigma mat.Symmetric, src *rand.Rand) (*Normal, bool) {
 	if len(mu) == 0 {
 		panic(badZeroDimension)
 	}
@@ -57,7 +57,7 @@ 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 = *mat.NewSymDense(dim, nil)
 	n.sigma.CopySym(sigma)
 	n.lower.LFromCholesky(&n.chol)
 	n.logSqrtDet = 0.5 * n.chol.LogDet()
@@ -67,7 +67,7 @@ func NewNormal(mu []float64, sigma mat64.Symmetric, src *rand.Rand) (*Normal, bo
 // 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 *mat64.Cholesky, src *rand.Rand) *Normal {
+func NewNormalChol(mu []float64, chol *mat.Cholesky, src *rand.Rand) *Normal {
 	dim := len(mu)
 	if dim != chol.Size() {
 		panic(badSizeMismatch)
@@ -89,7 +89,7 @@ func NewNormalChol(mu []float64, chol *mat64.Cholesky, src *rand.Rand) *Normal {
 // panics if len(mu) is not equal to prec.Symmetric(). If the precision matrix
 // is not positive-definite, NewNormalPrecision returns nil for norm and false
 // for ok.
-func NewNormalPrecision(mu []float64, prec *mat64.SymDense, src *rand.Rand) (norm *Normal, ok bool) {
+func NewNormalPrecision(mu []float64, prec *mat.SymDense, src *rand.Rand) (norm *Normal, ok bool) {
 	if len(mu) == 0 {
 		panic(badZeroDimension)
 	}
@@ -102,12 +102,12 @@ func NewNormalPrecision(mu []float64, prec *mat64.SymDense, src *rand.Rand) (nor
 	// is much better, but this still loses precision. It is worth considering if
 	// instead the precision matrix should be stored explicitly and used instead
 	// of the Cholesky decomposition of the covariance matrix where appropriate.
-	var chol mat64.Cholesky
+	var chol mat.Cholesky
 	ok = chol.Factorize(prec)
 	if !ok {
 		return nil, false
 	}
-	var sigma mat64.SymDense
+	var sigma mat.SymDense
 	sigma.InverseCholesky(&chol)
 	return NewNormal(mu, &sigma, src)
 }
@@ -154,9 +154,9 @@ func (n *Normal) ConditionNormal(observed []int, values []float64, src *rand.Ran
 //  covariance(i, j) = E[(x_i - E[x_i])(x_j - E[x_j])]
 // If the input matrix is nil a new matrix is allocated, otherwise the result
 // is stored in-place into the input.
-func (n *Normal) CovarianceMatrix(s *mat64.SymDense) *mat64.SymDense {
+func (n *Normal) CovarianceMatrix(s *mat.SymDense) *mat.SymDense {
 	if s == nil {
-		s = mat64.NewSymDense(n.Dim(), nil)
+		s = mat.NewSymDense(n.Dim(), nil)
 	}
 	sn := s.Symmetric()
 	if sn != n.Dim() {
@@ -183,7 +183,7 @@ func (n *Normal) LogProb(x []float64) float64 {
 		panic(badSizeMismatch)
 	}
 	c := -0.5*float64(dim)*logTwoPi - n.logSqrtDet
-	dst := stat.Mahalanobis(mat64.NewVector(dim, x), mat64.NewVector(dim, n.mu), &n.chol)
+	dst := stat.Mahalanobis(mat.NewVector(dim, x), mat.NewVector(dim, n.mu), &n.chol)
 	return c - 0.5*dst*dst
 }
 
@@ -199,7 +199,7 @@ func (n *Normal) MarginalNormal(vars []int, src *rand.Rand) (*Normal, bool) {
 	for i, v := range vars {
 		newMean[i] = n.mu[v]
 	}
-	var s mat64.SymDense
+	var s mat.SymDense
 	s.SubsetSym(&n.sigma, vars)
 	return NewNormal(newMean, &s, src)
 }
@@ -308,8 +308,8 @@ func (n *Normal) TransformNormal(dst, normal []float64) []float64 {
 // transformNormal performs the same operation as TransformNormal except no
 // safety checks are performed and both input slices must be non-nil.
 func (n *Normal) transformNormal(dst, normal []float64) []float64 {
-	srcVec := mat64.NewVector(n.dim, normal)
+	srcVec := mat.NewVector(n.dim, normal)
-	dstVec := mat64.NewVector(n.dim, dst)
+	dstVec := mat.NewVector(n.dim, dst)
 	dstVec.MulVec(&n.lower, srcVec)
 	floats.Add(dst, n.mu)
 	return dst

stat/distmv/normal_test.go

@@ -10,24 +10,24 @@ import (
 	"testing"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 	"gonum.org/v1/gonum/stat"
 )
 
 type mvTest struct {
 	Mu      []float64
-	Sigma   *mat64.SymDense
+	Sigma   *mat.SymDense
 	Loc     []float64
 	Logprob float64
 	Prob    float64
 }
 
 func TestNormProbs(t *testing.T) {
-	dist1, ok := NewNormal([]float64{0, 0}, mat64.NewSymDense(2, []float64{1, 0, 0, 1}), nil)
+	dist1, ok := NewNormal([]float64{0, 0}, mat.NewSymDense(2, []float64{1, 0, 0, 1}), nil)
 	if !ok {
 		t.Errorf("bad test")
 	}
-	dist2, ok := NewNormal([]float64{6, 7}, mat64.NewSymDense(2, []float64{8, 2, 0, 4}), nil)
+	dist2, ok := NewNormal([]float64{6, 7}, mat.NewSymDense(2, []float64{8, 2, 0, 4}), nil)
 	if !ok {
 		t.Errorf("bad test")
 	}
@@ -53,14 +53,14 @@ func TestNormProbs(t *testing.T) {
 func TestNewNormalChol(t *testing.T) {
 	for _, test := range []struct {
 		mean []float64
-		cov  *mat64.SymDense
+		cov  *mat.SymDense
 	}{
 		{
 			mean: []float64{2, 3},
-			cov:  mat64.NewSymDense(2, []float64{1, 0.1, 0.1, 1}),
+			cov:  mat.NewSymDense(2, []float64{1, 0.1, 0.1, 1}),
 		},
 	} {
-		var chol mat64.Cholesky
+		var chol mat.Cholesky
 		ok := chol.Factorize(test.cov)
 		if !ok {
 			panic("bad test")
@@ -101,26 +101,26 @@ func TestNormRand(t *testing.T) {
 		},
 	} {
 		dim := len(test.mean)
-		cov := mat64.NewSymDense(dim, test.cov)
+		cov := mat.NewSymDense(dim, test.cov)
 		n, ok := NewNormal(test.mean, cov, nil)
 		if !ok {
 			t.Errorf("bad covariance matrix")
 		}
 
 		nSamples := 1000000
-		samps := mat64.NewDense(nSamples, dim, nil)
+		samps := mat.NewDense(nSamples, dim, nil)
 		for i := 0; i < nSamples; i++ {
 			n.Rand(samps.RawRowView(i))
 		}
 		estMean := make([]float64, dim)
 		for i := range estMean {
-			estMean[i] = stat.Mean(mat64.Col(nil, i, samps), nil)
+			estMean[i] = stat.Mean(mat.Col(nil, i, samps), nil)
 		}
 		if !floats.EqualApprox(estMean, test.mean, 1e-2) {
 			t.Errorf("Mean mismatch: want: %v, got %v", test.mean, estMean)
 		}
 		estCov := stat.CovarianceMatrix(nil, samps, nil)
-		if !mat64.EqualApprox(estCov, cov, 1e-2) {
+		if !mat.EqualApprox(estCov, cov, 1e-2) {
 			t.Errorf("Cov mismatch: want: %v, got %v", cov, estCov)
 		}
 	}
@@ -140,7 +140,7 @@ func TestNormalQuantile(t *testing.T) {
 		},
 	} {
 		dim := len(test.mean)
-		cov := mat64.NewSymDense(dim, test.cov)
+		cov := mat.NewSymDense(dim, test.cov)
 		n, ok := NewNormal(test.mean, cov, nil)
 		if !ok {
 			t.Errorf("bad covariance matrix")
@@ -148,7 +148,7 @@ func TestNormalQuantile(t *testing.T) {
 
 		nSamples := 1000000
 		rnd := rand.New(rand.NewSource(1))
-		samps := mat64.NewDense(nSamples, dim, nil)
+		samps := mat.NewDense(nSamples, dim, nil)
 		tmp := make([]float64, dim)
 		for i := 0; i < nSamples; i++ {
 			for j := range tmp {
@@ -158,13 +158,13 @@ func TestNormalQuantile(t *testing.T) {
 		}
 		estMean := make([]float64, dim)
 		for i := range estMean {
-			estMean[i] = stat.Mean(mat64.Col(nil, i, samps), nil)
+			estMean[i] = stat.Mean(mat.Col(nil, i, samps), nil)
 		}
 		if !floats.EqualApprox(estMean, test.mean, 1e-2) {
 			t.Errorf("Mean mismatch: want: %v, got %v", test.mean, estMean)
 		}
 		estCov := stat.CovarianceMatrix(nil, samps, nil)
-		if !mat64.EqualApprox(estCov, cov, 1e-2) {
+		if !mat.EqualApprox(estCov, cov, 1e-2) {
 			t.Errorf("Cov mismatch: want: %v, got %v", cov, estCov)
 		}
 	}
@@ -174,57 +174,57 @@ func TestConditionNormal(t *testing.T) {
 	// Uncorrelated values shouldn't influence the updated values.
 	for _, test := range []struct {
 		mu       []float64
-		sigma    *mat64.SymDense
+		sigma    *mat.SymDense
 		observed []int
 		values   []float64
 
 		newMu    []float64
-		newSigma *mat64.SymDense
+		newSigma *mat.SymDense
 	}{
 		{
 			mu:       []float64{2, 3},
-			sigma:    mat64.NewSymDense(2, []float64{2, 0, 0, 5}),
+			sigma:    mat.NewSymDense(2, []float64{2, 0, 0, 5}),
 			observed: []int{0},
 			values:   []float64{10},
 
 			newMu:    []float64{3},
-			newSigma: mat64.NewSymDense(1, []float64{5}),
+			newSigma: mat.NewSymDense(1, []float64{5}),
 		},
 		{
 			mu:       []float64{2, 3},
-			sigma:    mat64.NewSymDense(2, []float64{2, 0, 0, 5}),
+			sigma:    mat.NewSymDense(2, []float64{2, 0, 0, 5}),
 			observed: []int{1},
 			values:   []float64{10},
 
 			newMu:    []float64{2},
-			newSigma: mat64.NewSymDense(1, []float64{2}),
+			newSigma: mat.NewSymDense(1, []float64{2}),
 		},
 		{
 			mu:       []float64{2, 3, 4},
-			sigma:    mat64.NewSymDense(3, []float64{2, 0, 0, 0, 5, 0, 0, 0, 10}),
+			sigma:    mat.NewSymDense(3, []float64{2, 0, 0, 0, 5, 0, 0, 0, 10}),
 			observed: []int{1},
 			values:   []float64{10},
 
 			newMu:    []float64{2, 4},
-			newSigma: mat64.NewSymDense(2, []float64{2, 0, 0, 10}),
+			newSigma: mat.NewSymDense(2, []float64{2, 0, 0, 10}),
 		},
 		{
 			mu:       []float64{2, 3, 4},
-			sigma:    mat64.NewSymDense(3, []float64{2, 0, 0, 0, 5, 0, 0, 0, 10}),
+			sigma:    mat.NewSymDense(3, []float64{2, 0, 0, 0, 5, 0, 0, 0, 10}),
 			observed: []int{0, 1},
 			values:   []float64{10, 15},
 
 			newMu:    []float64{4},
-			newSigma: mat64.NewSymDense(1, []float64{10}),
+			newSigma: mat.NewSymDense(1, []float64{10}),
 		},
 		{
 			mu:       []float64{2, 3, 4, 5},
-			sigma:    mat64.NewSymDense(4, []float64{2, 0.5, 0, 0, 0.5, 5, 0, 0, 0, 0, 10, 2, 0, 0, 2, 3}),
+			sigma:    mat.NewSymDense(4, []float64{2, 0.5, 0, 0, 0.5, 5, 0, 0, 0, 0, 10, 2, 0, 0, 2, 3}),
 			observed: []int{0, 1},
 			values:   []float64{10, 15},
 
 			newMu:    []float64{4, 5},
-			newSigma: mat64.NewSymDense(2, []float64{10, 2, 2, 3}),
+			newSigma: mat.NewSymDense(2, []float64{10, 2, 2, 3}),
 		},
 	} {
 		normal, ok := NewNormal(test.mu, test.sigma, nil)
@@ -240,9 +240,9 @@ func TestConditionNormal(t *testing.T) {
 			t.Errorf("Updated mean mismatch. Want %v, got %v.", test.newMu, newNormal.mu)
 		}
 
-		var sigma mat64.SymDense
+		var sigma mat.SymDense
 		sigma.FromCholesky(&newNormal.chol)
-		if !mat64.EqualApprox(test.newSigma, &sigma, 1e-12) {
+		if !mat.EqualApprox(test.newSigma, &sigma, 1e-12) {
 			t.Errorf("Updated sigma mismatch\n.Want:\n% v\nGot:\n% v\n", test.newSigma, sigma)
 		}
 	}
@@ -269,7 +269,7 @@ func TestConditionNormal(t *testing.T) {
 	} {
 		std := test.std
 		rho := test.rho
-		sigma := mat64.NewSymDense(2, []float64{std[0] * std[0], std[0] * std[1] * rho, std[0] * std[1] * rho, std[1] * std[1]})
+		sigma := mat.NewSymDense(2, []float64{std[0] * std[0], std[0] * std[1] * rho, std[0] * std[1] * rho, std[1] * std[1]})
 		normal, ok := NewNormal(test.mu, sigma, nil)
 		if !ok {
 			t.Fatalf("Bad test, original sigma not positive definite")
@@ -278,7 +278,7 @@ func TestConditionNormal(t *testing.T) {
 		if !ok {
 			t.Fatalf("Bad test, update failed")
 		}
-		var newSigma mat64.SymDense
+		var newSigma mat.SymDense
 		newSigma.FromCholesky(&newNormal.chol)
 		trueMean := test.mu[0] + rho*(std[0]/std[1])*(test.value-test.mu[1])
 		if math.Abs(trueMean-newNormal.mu[0]) > 1e-14 {
@@ -293,7 +293,7 @@ func TestConditionNormal(t *testing.T) {
 	// Test via sampling.
 	for _, test := range []struct {
 		mu         []float64
-		sigma      *mat64.SymDense
+		sigma      *mat.SymDense
 		observed   []int
 		unobserved []int
 		value      []float64
@@ -301,7 +301,7 @@ func TestConditionNormal(t *testing.T) {
 		// The indices in unobserved must be in ascending order for this test.
 		{
 			mu:    []float64{2, 3, 4},
-			sigma: mat64.NewSymDense(3, []float64{2, 0.5, 3, 0.5, 1, 0.6, 3, 0.6, 10}),
+			sigma: mat.NewSymDense(3, []float64{2, 0.5, 3, 0.5, 1, 0.6, 3, 0.6, 10}),
 
 			observed:   []int{0},
 			unobserved: []int{1, 2},
@@ -309,7 +309,7 @@ func TestConditionNormal(t *testing.T) {
 		},
 		{
 			mu:    []float64{2, 3, 4, 5},
-			sigma: mat64.NewSymDense(4, []float64{2, 0.5, 3, 0.1, 0.5, 1, 0.6, 0.2, 3, 0.6, 10, 0.3, 0.1, 0.2, 0.3, 3}),
+			sigma: mat.NewSymDense(4, []float64{2, 0.5, 3, 0.1, 0.5, 1, 0.6, 0.2, 3, 0.6, 10, 0.3, 0.1, 0.2, 0.3, 3}),
 
 			observed:   []int{0, 3},
 			unobserved: []int{1, 2},
@@ -318,7 +318,7 @@ func TestConditionNormal(t *testing.T) {
 	} {
 		totalSamp := 4000000
 		var nSamp int
-		samples := mat64.NewDense(totalSamp, len(test.mu), nil)
+		samples := mat.NewDense(totalSamp, len(test.mu), nil)
 		normal, ok := NewNormal(test.mu, test.sigma, nil)
 		if !ok {
 			t.Errorf("bad test")
@@ -343,12 +343,12 @@ func TestConditionNormal(t *testing.T) {
 			t.Errorf("bad test, not enough samples")
 			continue
 		}
-		samples = samples.View(0, 0, nSamp, len(test.mu)).(*mat64.Dense)
+		samples = samples.View(0, 0, nSamp, len(test.mu)).(*mat.Dense)
 
 		// Compute mean and covariance matrix.
 		estMean := make([]float64, len(test.mu))
 		for i := range estMean {
-			estMean[i] = stat.Mean(mat64.Col(nil, i, samples), nil)
+			estMean[i] = stat.Mean(mat.Col(nil, i, samples), nil)
 		}
 		estCov := stat.CovarianceMatrix(nil, samples, nil)
 
@@ -363,7 +363,7 @@ func TestConditionNormal(t *testing.T) {
 
 			subEstMean = append(subEstMean, estMean[v])
 		}
-		subEstCov := mat64.NewSymDense(len(test.unobserved), nil)
+		subEstCov := mat.NewSymDense(len(test.unobserved), nil)
 		for i := 0; i < len(test.unobserved); i++ {
 			for j := i; j < len(test.unobserved); j++ {
 				subEstCov.SetSym(i, j, estCov.At(test.unobserved[i], test.unobserved[j]))
@@ -375,9 +375,9 @@ func TestConditionNormal(t *testing.T) {
 				t.Errorf("Mean mismatch. Want %v, got %v.", newNormal.mu[i], v)
 			}
 		}
-		var sigma mat64.SymDense
+		var sigma mat.SymDense
 		sigma.FromCholesky(&newNormal.chol)
-		if !mat64.EqualApprox(&sigma, subEstCov, 1e-1) {
+		if !mat.EqualApprox(&sigma, subEstCov, 1e-1) {
 			t.Errorf("Covariance mismatch. Want:\n%0.8v\nGot:\n%0.8v\n", subEstCov, sigma)
 		}
 	}
@@ -386,11 +386,11 @@ func TestConditionNormal(t *testing.T) {
 func TestCovarianceMatrix(t *testing.T) {
 	for _, test := range []struct {
 		mu    []float64
-		sigma *mat64.SymDense
+		sigma *mat.SymDense
 	}{
 		{
 			mu:    []float64{2, 3, 4},
-			sigma: mat64.NewSymDense(3, []float64{1, 0.5, 3, 0.5, 8, -1, 3, -1, 15}),
+			sigma: mat.NewSymDense(3, []float64{1, 0.5, 3, 0.5, 8, -1, 3, -1, 15}),
 		},
 	} {
 		normal, ok := NewNormal(test.mu, test.sigma, nil)
@@ -398,13 +398,13 @@ func TestCovarianceMatrix(t *testing.T) {
 			t.Fatalf("Bad test, covariance matrix not positive definite")
 		}
 		cov := normal.CovarianceMatrix(nil)
-		if !mat64.EqualApprox(cov, test.sigma, 1e-14) {
+		if !mat.EqualApprox(cov, test.sigma, 1e-14) {
 			t.Errorf("Covariance mismatch with nil input")
 		}
 		dim := test.sigma.Symmetric()
-		cov = mat64.NewSymDense(dim, nil)
+		cov = mat.NewSymDense(dim, nil)
 		normal.CovarianceMatrix(cov)
-		if !mat64.EqualApprox(cov, test.sigma, 1e-14) {
+		if !mat.EqualApprox(cov, test.sigma, 1e-14) {
 			t.Errorf("Covariance mismatch with supplied input")
 		}
 	}
@@ -413,22 +413,22 @@ func TestCovarianceMatrix(t *testing.T) {
 func TestMarginal(t *testing.T) {
 	for _, test := range []struct {
 		mu       []float64
-		sigma    *mat64.SymDense
+		sigma    *mat.SymDense
 		marginal []int
 	}{
 		{
 			mu:       []float64{2, 3, 4},
-			sigma:    mat64.NewSymDense(3, []float64{2, 0.5, 3, 0.5, 1, 0.6, 3, 0.6, 10}),
+			sigma:    mat.NewSymDense(3, []float64{2, 0.5, 3, 0.5, 1, 0.6, 3, 0.6, 10}),
 			marginal: []int{0},
 		},
 		{
 			mu:       []float64{2, 3, 4},
-			sigma:    mat64.NewSymDense(3, []float64{2, 0.5, 3, 0.5, 1, 0.6, 3, 0.6, 10}),
+			sigma:    mat.NewSymDense(3, []float64{2, 0.5, 3, 0.5, 1, 0.6, 3, 0.6, 10}),
 			marginal: []int{0, 2},
 		},
 		{
 			mu:    []float64{2, 3, 4, 5},
-			sigma: mat64.NewSymDense(4, []float64{2, 0.5, 3, 0.1, 0.5, 1, 0.6, 0.2, 3, 0.6, 10, 0.3, 0.1, 0.2, 0.3, 3}),
+			sigma: mat.NewSymDense(4, []float64{2, 0.5, 3, 0.1, 0.5, 1, 0.6, 0.2, 3, 0.6, 10, 0.3, 0.1, 0.2, 0.3, 3}),
 
 			marginal: []int{0, 3},
 		},
@@ -443,13 +443,13 @@ func TestMarginal(t *testing.T) {
 		}
 		dim := normal.Dim()
 		nSamples := 1000000
-		samps := mat64.NewDense(nSamples, dim, nil)
+		samps := mat.NewDense(nSamples, dim, nil)
 		for i := 0; i < nSamples; i++ {
 			normal.Rand(samps.RawRowView(i))
 		}
 		estMean := make([]float64, dim)
 		for i := range estMean {
-			estMean[i] = stat.Mean(mat64.Col(nil, i, samps), nil)
+			estMean[i] = stat.Mean(mat.Col(nil, i, samps), nil)
 		}
 		for i, v := range test.marginal {
 			if math.Abs(marginal.mu[i]-estMean[v]) > 1e-2 {
@@ -474,15 +474,15 @@ func TestMarginal(t *testing.T) {
 func TestMarginalSingle(t *testing.T) {
 	for _, test := range []struct {
 		mu    []float64
-		sigma *mat64.SymDense
+		sigma *mat.SymDense
 	}{
 		{
 			mu:    []float64{2, 3, 4},
-			sigma: mat64.NewSymDense(3, []float64{2, 0.5, 3, 0.5, 1, 0.6, 3, 0.6, 10}),
+			sigma: mat.NewSymDense(3, []float64{2, 0.5, 3, 0.5, 1, 0.6, 3, 0.6, 10}),
 		},
 		{
 			mu:    []float64{2, 3, 4, 5},
-			sigma: mat64.NewSymDense(4, []float64{2, 0.5, 3, 0.1, 0.5, 1, 0.6, 0.2, 3, 0.6, 10, 0.3, 0.1, 0.2, 0.3, 3}),
+			sigma: mat.NewSymDense(4, []float64{2, 0.5, 3, 0.1, 0.5, 1, 0.6, 0.2, 3, 0.6, 10, 0.3, 0.1, 0.2, 0.3, 3}),
 		},
 	} {
 		normal, ok := NewNormal(test.mu, test.sigma, nil)
@@ -513,9 +513,9 @@ func TestMarginalSingle(t *testing.T) {
 		for i := range x {
 			x[i] = rnd.Float64()
 		}
-		mat := mat64.NewDense(dim, dim, x)
+		matrix := mat.NewDense(dim, dim, x)
-		var sigma mat64.SymDense
+		var sigma mat.SymDense
-		sigma.SymOuterK(1, mat)
+		sigma.SymOuterK(1, matrix)
 
 		normal, ok := NewNormal(mu, &sigma, nil)
 		if !ok {

stat/distmv/normalbench_test.go

@@ -9,7 +9,7 @@ import (
 	"math/rand"
 	"testing"
 
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 func BenchmarkMarginalNormal10(b *testing.B) {
@@ -61,8 +61,8 @@ func randomNormal(sz int, rnd *rand.Rand) *Normal {
 	for i := range data {
 		data[i] = rnd.Float64()
 	}
-	dM := mat64.NewDense(sz, sz, data)
+	dM := mat.NewDense(sz, sz, data)
-	var sigma mat64.SymDense
+	var sigma mat.SymDense
 	sigma.SymOuterK(1, dM)
 
 	normal, ok := NewNormal(mu, &sigma, nil)

stat/distmv/statdist.go

@@ -8,7 +8,7 @@ import (
 	"math"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 	"gonum.org/v1/gonum/stat"
 )
 
@@ -37,14 +37,14 @@ func (Bhattacharyya) DistNormal(l, r *Normal) float64 {
 		panic(badSizeMismatch)
 	}
 
-	var sigma mat64.SymDense
+	var sigma mat.SymDense
 	sigma.AddSym(&l.sigma, &r.sigma)
 	sigma.ScaleSym(0.5, &sigma)
 
-	var chol mat64.Cholesky
+	var chol mat.Cholesky
 	chol.Factorize(&sigma)
 
-	mahalanobis := stat.Mahalanobis(mat64.NewVector(dim, l.mu), mat64.NewVector(dim, r.mu), &chol)
+	mahalanobis := stat.Mahalanobis(mat.NewVector(dim, l.mu), mat.NewVector(dim, r.mu), &chol)
 	mahalanobisSq := mahalanobis * mahalanobis
 
 	dl := l.chol.LogDet()
@@ -154,21 +154,21 @@ func (KullbackLeibler) DistNormal(l, r *Normal) float64 {
 		panic(badSizeMismatch)
 	}
 
-	mahalanobis := stat.Mahalanobis(mat64.NewVector(dim, l.mu), mat64.NewVector(dim, r.mu), &r.chol)
+	mahalanobis := stat.Mahalanobis(mat.NewVector(dim, l.mu), mat.NewVector(dim, r.mu), &r.chol)
 	mahalanobisSq := mahalanobis * mahalanobis
 
 	// TODO(btracey): Optimize where there is a SolveCholeskySym
 	// TODO(btracey): There may be a more efficient way to just compute the trace
 	// Compute tr(Σ_r^-1*Σ_l) using the fact that Σ_l = U^T * U
-	var u mat64.TriDense
+	var u mat.TriDense
 	u.UFromCholesky(&l.chol)
-	var m mat64.Dense
+	var m mat.Dense
 	err := m.SolveCholesky(&r.chol, u.T())
 	if err != nil {
 		return math.NaN()
 	}
 	m.Mul(&m, &u)
-	tr := mat64.Trace(&m)
+	tr := mat.Trace(&m)
 
 	return r.logSqrtDet - l.logSqrtDet + 0.5*(mahalanobisSq+tr-float64(l.dim))
 }
@@ -233,20 +233,20 @@ func (Wasserstein) DistNormal(l, r *Normal) float64 {
 	d = d * d
 
 	// Compute Σ_l^(1/2)
-	var ssl mat64.SymDense
+	var ssl mat.SymDense
 	ssl.PowPSD(&l.sigma, 0.5)
 	// Compute Σ_l^(1/2)*Σ_r*Σ_l^(1/2)
-	var mean mat64.Dense
+	var mean mat.Dense
 	mean.Mul(&ssl, &r.sigma)
 	mean.Mul(&mean, &ssl)
 
 	// Reinterpret as symdense, and take Σ^(1/2)
-	meanSym := mat64.NewSymDense(dim, mean.RawMatrix().Data)
+	meanSym := mat.NewSymDense(dim, mean.RawMatrix().Data)
 	ssl.PowPSD(meanSym, 0.5)
 
-	tr := mat64.Trace(&r.sigma)
+	tr := mat.Trace(&r.sigma)
-	tl := mat64.Trace(&l.sigma)
+	tl := mat.Trace(&l.sigma)
-	tm := mat64.Trace(&ssl)
+	tm := mat.Trace(&ssl)
 
 	return d + tl + tr - 2*tm
 }

stat/distmv/statdist_test.go

@@ -10,21 +10,21 @@ import (
 	"testing"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 )
 
 func TestBhattacharyyaNormal(t *testing.T) {
 	for cas, test := range []struct {
 		am, bm  []float64
-		ac, bc  *mat64.SymDense
+		ac, bc  *mat.SymDense
 		samples int
 		tol     float64
 	}{
 		{
 			am:      []float64{2, 3},
-			ac:      mat64.NewSymDense(2, []float64{3, -1, -1, 2}),
+			ac:      mat.NewSymDense(2, []float64{3, -1, -1, 2}),
 			bm:      []float64{-1, 1},
-			bc:      mat64.NewSymDense(2, []float64{1.5, 0.2, 0.2, 0.9}),
+			bc:      mat.NewSymDense(2, []float64{1.5, 0.2, 0.2, 0.9}),
 			samples: 100000,
 			tol:     1e-2,
 		},
@@ -105,15 +105,15 @@ func bhattacharyyaSample(dim, samples int, l RandLogProber, r LogProber) float64
 func TestCrossEntropyNormal(t *testing.T) {
 	for cas, test := range []struct {
 		am, bm  []float64
-		ac, bc  *mat64.SymDense
+		ac, bc  *mat.SymDense
 		samples int
 		tol     float64
 	}{
 		{
 			am:      []float64{2, 3},
-			ac:      mat64.NewSymDense(2, []float64{3, -1, -1, 2}),
+			ac:      mat.NewSymDense(2, []float64{3, -1, -1, 2}),
 			bm:      []float64{-1, 1},
-			bc:      mat64.NewSymDense(2, []float64{1.5, 0.2, 0.2, 0.9}),
+			bc:      mat.NewSymDense(2, []float64{1.5, 0.2, 0.2, 0.9}),
 			samples: 100000,
 			tol:     1e-2,
 		},
@@ -144,15 +144,15 @@ func TestCrossEntropyNormal(t *testing.T) {
 func TestHellingerNormal(t *testing.T) {
 	for cas, test := range []struct {
 		am, bm  []float64
-		ac, bc  *mat64.SymDense
+		ac, bc  *mat.SymDense
 		samples int
 		tol     float64
 	}{
 		{
 			am:      []float64{2, 3},
-			ac:      mat64.NewSymDense(2, []float64{3, -1, -1, 2}),
+			ac:      mat.NewSymDense(2, []float64{3, -1, -1, 2}),
 			bm:      []float64{-1, 1},
-			bc:      mat64.NewSymDense(2, []float64{1.5, 0.2, 0.2, 0.9}),
+			bc:      mat.NewSymDense(2, []float64{1.5, 0.2, 0.2, 0.9}),
 			samples: 100000,
 			tol:     5e-1,
 		},
@@ -188,15 +188,15 @@ func TestHellingerNormal(t *testing.T) {
 func TestKullbackLeiblerNormal(t *testing.T) {
 	for cas, test := range []struct {
 		am, bm  []float64
-		ac, bc  *mat64.SymDense
+		ac, bc  *mat.SymDense
 		samples int
 		tol     float64
 	}{
 		{
 			am:      []float64{2, 3},
-			ac:      mat64.NewSymDense(2, []float64{3, -1, -1, 2}),
+			ac:      mat.NewSymDense(2, []float64{3, -1, -1, 2}),
 			bm:      []float64{-1, 1},
-			bc:      mat64.NewSymDense(2, []float64{1.5, 0.2, 0.2, 0.9}),
+			bc:      mat.NewSymDense(2, []float64{1.5, 0.2, 0.2, 0.9}),
 			samples: 10000,
 			tol:     1e-2,
 		},

stat/distmv/studentst.go

@@ -12,7 +12,7 @@ import (
 	"golang.org/x/tools/container/intsets"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 	"gonum.org/v1/gonum/stat/distuv"
 )
 
@@ -35,10 +35,10 @@ type StudentsT struct {
 	mu  []float64
 	src *rand.Rand
 
-	sigma mat64.SymDense // only stored if needed
+	sigma mat.SymDense // only stored if needed
 
-	chol       mat64.Cholesky
+	chol       mat.Cholesky
-	lower      mat64.TriDense
+	lower      mat.TriDense
 	logSqrtDet float64
 	dim        int
 }
@@ -48,7 +48,7 @@ type StudentsT struct {
 //
 // NewStudentsT panics if len(mu) == 0, or if len(mu) != sigma.Symmetric(). If
 // the covariance matrix is not positive-definite, nil is returned and ok is false.
-func NewStudentsT(mu []float64, sigma mat64.Symmetric, nu float64, src *rand.Rand) (dist *StudentsT, ok bool) {
+func NewStudentsT(mu []float64, sigma mat.Symmetric, nu float64, src *rand.Rand) (dist *StudentsT, ok bool) {
 	if len(mu) == 0 {
 		panic(badZeroDimension)
 	}
@@ -69,7 +69,7 @@ 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 = *mat.NewSymDense(dim, nil)
 	s.sigma.CopySym(sigma)
 	s.lower.LFromCholesky(&s.chol)
 	s.logSqrtDet = 0.5 * s.chol.LogDet()
@@ -113,7 +113,7 @@ func (s *StudentsT) ConditionStudentsT(observed []int, values []float64, src *ra
 // studentsTConditional updates a Student's T distribution based on the observed samples
 // (see documentation for the public function). The Gaussian conditional update
 // is treated as a special case when  nu == math.Inf(1).
-func studentsTConditional(observed []int, values []float64, nu float64, mu []float64, sigma mat64.Symmetric) (newNu float64, newMean []float64, newSigma *mat64.SymDense) {
+func studentsTConditional(observed []int, values []float64, nu float64, mu []float64, sigma mat.Symmetric) (newNu float64, newMean []float64, newSigma *mat.SymDense) {
 	dim := len(mu)
 	ob := len(observed)
 
@@ -133,11 +133,11 @@ func studentsTConditional(observed []int, values []float64, nu float64, mu []flo
 		mu2[i] = values[i] - mu[v]
 	}
 
-	var sigma11, sigma22 mat64.SymDense
+	var sigma11, sigma22 mat.SymDense
 	sigma11.SubsetSym(sigma, unobserved)
 	sigma22.SubsetSym(sigma, observed)
 
-	sigma21 := mat64.NewDense(ob, unob, nil)
+	sigma21 := mat.NewDense(ob, unob, nil)
 	for i, r := range observed {
 		for j, c := range unobserved {
 			v := sigma.At(r, c)
@@ -145,15 +145,15 @@ func studentsTConditional(observed []int, values []float64, nu float64, mu []flo
 		}
 	}
 
-	var chol mat64.Cholesky
+	var chol mat.Cholesky
 	ok := chol.Factorize(&sigma22)
 	if !ok {
 		return math.NaN(), nil, nil
 	}
 
 	// Compute mu_1 + sigma_{2,1}^T * sigma_{2,2}^-1 (v - mu_2).
-	v := mat64.NewVector(ob, mu2)
+	v := mat.NewVector(ob, mu2)
-	var tmp, tmp2 mat64.Vector
+	var tmp, tmp2 mat.Vector
 	err := tmp.SolveCholeskyVec(&chol, v)
 	if err != nil {
 		return math.NaN(), nil, nil
@@ -166,7 +166,7 @@ func studentsTConditional(observed []int, values []float64, nu float64, mu []flo
 
 	// Compute tmp4 = sigma_{2,1}^T * sigma_{2,2}^-1 * sigma_{2,1}.
 	// TODO(btracey): Should this be a method of SymDense?
-	var tmp3, tmp4 mat64.Dense
+	var tmp3, tmp4 mat.Dense
 	err = tmp3.SolveCholesky(&chol, sigma21)
 	if err != nil {
 		return math.NaN(), nil, nil
@@ -189,7 +189,7 @@ func studentsTConditional(observed []int, values []float64, nu float64, mu []flo
 	}
 
 	// Compute beta = (v - mu_2)^T * sigma_{2,2}^-1 * (v - mu_2)^T
-	beta := mat64.Dot(v, &tmp)
+	beta := mat.Dot(v, &tmp)
 
 	// Scale the covariance matrix
 	sigma11.ScaleSym((nu+beta)/(nu+float64(ob)), &sigma11)
@@ -221,9 +221,9 @@ func findUnob(observed []int, dim int) (unobserved []int) {
 //  covariance(i, j) = E[(x_i - E[x_i])(x_j - E[x_j])]
 // If the input matrix is nil a new matrix is allocated, otherwise the result
 // is stored in-place into the input.
-func (st *StudentsT) CovarianceMatrix(s *mat64.SymDense) *mat64.SymDense {
+func (st *StudentsT) CovarianceMatrix(s *mat.SymDense) *mat.SymDense {
 	if s == nil {
-		s = mat64.NewSymDense(st.dim, nil)
+		s = mat.NewSymDense(st.dim, nil)
 	}
 	sn := s.Symmetric()
 	if sn != st.dim {
@@ -256,12 +256,12 @@ func (s *StudentsT) LogProb(y []float64) float64 {
 	copy(shift, y)
 	floats.Sub(shift, s.mu)
 
-	x := mat64.NewVector(s.dim, shift)
+	x := mat.NewVector(s.dim, shift)
 
-	var tmp mat64.Vector
+	var tmp mat.Vector
 	tmp.SolveCholeskyVec(&s.chol, x)
 
-	dot := mat64.Dot(&tmp, x)
+	dot := mat.Dot(&tmp, x)
 
 	return t1 - ((nu+n)/2)*math.Log(1+dot/nu)
 }
@@ -283,7 +283,7 @@ func (s *StudentsT) MarginalStudentsT(vars []int, src *rand.Rand) (dist *Student
 	for i, v := range vars {
 		newMean[i] = s.mu[v]
 	}
-	var newSigma mat64.SymDense
+	var newSigma mat.SymDense
 	newSigma.SubsetSym(&s.sigma, vars)
 	return NewStudentsT(newMean, &newSigma, s.nu, src)
 }
@@ -342,8 +342,8 @@ func (s *StudentsT) Rand(x []float64) []float64 {
 			tmp[i] = s.src.NormFloat64()
 		}
 	}
-	xVec := mat64.NewVector(s.dim, x)
+	xVec := mat.NewVector(s.dim, x)
-	tmpVec := mat64.NewVector(s.dim, tmp)
+	tmpVec := mat.NewVector(s.dim, tmp)
 	xVec.MulVec(&s.lower, tmpVec)
 
 	u := distuv.ChiSquared{K: s.nu, Src: s.src}.Rand()

stat/distmv/studentst_test.go

@@ -10,7 +10,7 @@ import (
 	"testing"
 
 	"gonum.org/v1/gonum/floats"
-	"gonum.org/v1/gonum/matrix/mat64"
+	"gonum.org/v1/gonum/mat"
 	"gonum.org/v1/gonum/stat"
 )
 
@@ -19,7 +19,7 @@ func TestStudentTProbs(t *testing.T) {
 	for _, test := range []struct {
 		nu    float64
 		mu    []float64
-		sigma *mat64.SymDense
+		sigma *mat.SymDense
 
 		x     [][]float64
 		probs []float64
@@ -27,7 +27,7 @@ func TestStudentTProbs(t *testing.T) {
 		{
 			nu:    3,
 			mu:    []float64{0, 0},
-			sigma: mat64.NewSymDense(2, []float64{1, 0, 0, 1}),
+			sigma: mat.NewSymDense(2, []float64{1, 0, 0, 1}),
 
 			x: [][]float64{
 				{0, 0},
@@ -46,7 +46,7 @@ func TestStudentTProbs(t *testing.T) {
 		{
 			nu:    4,
 			mu:    []float64{2, -3},
-			sigma: mat64.NewSymDense(2, []float64{8, -1, -1, 5}),
+			sigma: mat.NewSymDense(2, []float64{8, -1, -1, 5}),
 
 			x: [][]float64{
 				{0, 0},
@@ -87,25 +87,25 @@ func TestStudentsTRand(t *testing.T) {
 	src := rand.New(rand.NewSource(1))
 	for _, test := range []struct {
 		mean   []float64
-		cov    *mat64.SymDense
+		cov    *mat.SymDense
 		nu     float64
 		tolcov float64
 	}{
 		{
 			mean:   []float64{0, 0},
-			cov:    mat64.NewSymDense(2, []float64{1, 0, 0, 1}),
+			cov:    mat.NewSymDense(2, []float64{1, 0, 0, 1}),
 			nu:     3,
 			tolcov: 1e-2,
 		},
 		{
 			mean:   []float64{3, 4},
-			cov:    mat64.NewSymDense(2, []float64{5, 1.2, 1.2, 6}),
+			cov:    mat.NewSymDense(2, []float64{5, 1.2, 1.2, 6}),
 			nu:     8,
 			tolcov: 1e-2,
 		},
 		{
 			mean:   []float64{3, 4, -2},
-			cov:    mat64.NewSymDense(3, []float64{5, 1.2, -0.8, 1.2, 6, 0.4, -0.8, 0.4, 2}),
+			cov:    mat.NewSymDense(3, []float64{5, 1.2, -0.8, 1.2, 6, 0.4, -0.8, 0.4, 2}),
 			nu:     8,
 			tolcov: 1e-2,
 		},
@@ -116,13 +116,13 @@ func TestStudentsTRand(t *testing.T) {
 		}
 		nSamples := 10000000
 		dim := len(test.mean)
-		samps := mat64.NewDense(nSamples, dim, nil)
+		samps := mat.NewDense(nSamples, dim, nil)
 		for i := 0; i < nSamples; i++ {
 			s.Rand(samps.RawRowView(i))
 		}
 		estMean := make([]float64, dim)
 		for i := range estMean {
-			estMean[i] = stat.Mean(mat64.Col(nil, i, samps), nil)
+			estMean[i] = stat.Mean(mat.Col(nil, i, samps), nil)
 		}
 		mean := s.Mean(nil)
 		if !floats.EqualApprox(estMean, mean, 1e-2) {
@@ -130,7 +130,7 @@ func TestStudentsTRand(t *testing.T) {
 		}
 		cov := s.CovarianceMatrix(nil)
 		estCov := stat.CovarianceMatrix(nil, samps, nil)
-		if !mat64.EqualApprox(estCov, cov, test.tolcov) {
+		if !mat.EqualApprox(estCov, cov, test.tolcov) {
 			t.Errorf("Cov mismatch: want: %v, got %v", cov, estCov)
 		}
 	}
@@ -140,7 +140,7 @@ func TestStudentsTConditional(t *testing.T) {
 	src := rand.New(rand.NewSource(1))
 	for _, test := range []struct {
 		mean []float64
-		cov  *mat64.SymDense
+		cov  *mat.SymDense
 		nu   float64
 
 		idx    []int
@@ -149,7 +149,7 @@ func TestStudentsTConditional(t *testing.T) {
 	}{
 		{
 			mean:  []float64{3, 4, -2},
-			cov:   mat64.NewSymDense(3, []float64{5, 1.2, -0.8, 1.2, 6, 0.4, -0.8, 0.4, 2}),
+			cov:   mat.NewSymDense(3, []float64{5, 1.2, -0.8, 1.2, 6, 0.4, -0.8, 0.4, 2}),
 			nu:    8,
 			idx:   []int{0},
 			value: []float64{6},
@@ -182,11 +182,11 @@ func TestStudentsTConditional(t *testing.T) {
 			muOb[i] = test.mean[v]
 		}
 
-		var sig11, sig22 mat64.SymDense
+		var sig11, sig22 mat.SymDense
 		sig11.SubsetSym(&s.sigma, unob)
 		sig22.SubsetSym(&s.sigma, ob)
 
-		sig12 := mat64.NewDense(len(unob), len(ob), nil)
+		sig12 := mat.NewDense(len(unob), len(ob), nil)
 		for i := range unob {
 			for j := range ob {
 				sig12.Set(i, j, s.sigma.At(unob[i], ob[j]))
@@ -198,9 +198,9 @@ func TestStudentsTConditional(t *testing.T) {
 		floats.Sub(shift, muOb)
 
 		newMu := make([]float64, len(muUnob))
-		newMuVec := mat64.NewVector(len(muUnob), newMu)
+		newMuVec := mat.NewVector(len(muUnob), newMu)
-		shiftVec := mat64.NewVector(len(shift), shift)
+		shiftVec := mat.NewVector(len(shift), shift)
-		var tmp mat64.Vector
+		var tmp mat.Vector
 		tmp.SolveVec(&sig22, shiftVec)
 		newMuVec.MulVec(sig12, &tmp)
 		floats.Add(newMu, muUnob)
@@ -209,16 +209,16 @@ func TestStudentsTConditional(t *testing.T) {
 			t.Errorf("Mu mismatch. Got %v, want %v", sUp.mu, newMu)
 		}
 
-		var tmp2 mat64.Dense
+		var tmp2 mat.Dense
 		tmp2.Solve(&sig22, sig12.T())
 
-		var tmp3 mat64.Dense
+		var tmp3 mat.Dense
 		tmp3.Mul(sig12, &tmp2)
 		tmp3.Sub(&sig11, &tmp3)
 
-		dot := mat64.Dot(shiftVec, &tmp)
+		dot := mat.Dot(shiftVec, &tmp)
 		tmp3.Scale((test.nu+dot)/(test.nu+float64(len(ob))), &tmp3)
-		if !mat64.EqualApprox(&tmp3, &sUp.sigma, 1e-10) {
+		if !mat.EqualApprox(&tmp3, &sUp.sigma, 1e-10) {
 			t.Errorf("Sigma mismatch")
 		}
 	}
@@ -227,17 +227,17 @@ func TestStudentsTConditional(t *testing.T) {
 func TestStudentsTMarginalSingle(t *testing.T) {
 	for _, test := range []struct {
 		mu    []float64
-		sigma *mat64.SymDense
+		sigma *mat.SymDense
 		nu    float64
 	}{
 		{
 			mu:    []float64{2, 3, 4},
-			sigma: mat64.NewSymDense(3, []float64{2, 0.5, 3, 0.5, 1, 0.6, 3, 0.6, 10}),
+			sigma: mat.NewSymDense(3, []float64{2, 0.5, 3, 0.5, 1, 0.6, 3, 0.6, 10}),
 			nu:    5,
 		},
 		{
 			mu:    []float64{2, 3, 4, 5},
-			sigma: mat64.NewSymDense(4, []float64{2, 0.5, 3, 0.1, 0.5, 1, 0.6, 0.2, 3, 0.6, 10, 0.3, 0.1, 0.2, 0.3, 3}),
+			sigma: mat.NewSymDense(4, []float64{2, 0.5, 3, 0.1, 0.5, 1, 0.6, 0.2, 3, 0.6, 10, 0.3, 0.1, 0.2, 0.3, 3}),
 			nu:    6,
 		},
 	} {