all: fix typos

graph/formats/gexf12/gexf.go

@@ -182,7 +182,7 @@ type Edge struct {
 	Weight float64 `xml:"weight,attr,omitempty"`
 }
 
-// AttVlues holds a collection of attribute values.
+// AttValues holds a collection of attribute values.
 type AttValues struct {
 	AttValues []AttValue `xml:"attvalue,omitempty"`
 }

graph/formats/rdf/equi_canonical_test.go

@@ -504,7 +504,7 @@ func TestJoin(t *testing.T) {
 		want       []map[string]string
 	}{
 		{
-			name: "Indentity",
+			name: "Identity",
 			q:    `_:a <ex:p> _:b .`,
 			statements: `
 _:a <ex:p> _:b .

graph/formats/rdf/nquads.rl

@@ -2,7 +2,7 @@
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 
-// Ragel gramar definition derived from http://www.w3.org/TR/n-quads/#sec-grammar.
+// Ragel grammar definition derived from http://www.w3.org/TR/n-quads/#sec-grammar.
 
 %%{
 	machine nquads;

graph/path/dynamic/dumper_test.go

@@ -124,7 +124,7 @@ func (d *dumper) dump(withpath bool) {
 }
 
 // printEdges pretty prints the given edges to the dumper's io.Writer using the provided
-// format string. The edges are first formated to a string, so the format string must use
+// format string. The edges are first formatted to a string, so the format string must use
 // the %s verb to indicate where the edges are to be printed.
 func (d *dumper) printEdges(format string, edges []simple.WeightedEdge) {
 	if d == nil {

graph/path/spanning_tree_test.go

@@ -240,7 +240,7 @@ var spanningTreeTests = []struct {
 	},
 }
 
-func testMinumumSpanning(mst func(dst WeightedBuilder, g spanningGraph) float64, t *testing.T) {
+func testMinimumSpanning(mst func(dst WeightedBuilder, g spanningGraph) float64, t *testing.T) {
 	for _, test := range spanningTreeTests {
 		g := test.graph()
 		for _, e := range test.edges {
@@ -283,14 +283,14 @@ func testMinumumSpanning(mst func(dst WeightedBuilder, g spanningGraph) float64,
 
 func TestKruskal(t *testing.T) {
 	t.Parallel()
-	testMinumumSpanning(func(dst WeightedBuilder, g spanningGraph) float64 {
+	testMinimumSpanning(func(dst WeightedBuilder, g spanningGraph) float64 {
 		return Kruskal(dst, g)
 	}, t)
 }
 
 func TestPrim(t *testing.T) {
 	t.Parallel()
-	testMinumumSpanning(func(dst WeightedBuilder, g spanningGraph) float64 {
+	testMinimumSpanning(func(dst WeightedBuilder, g spanningGraph) float64 {
 		return Prim(dst, g)
 	}, t)
 }

graph/topo/paton_cycles_test.go

@@ -115,7 +115,7 @@ func TestUndirectedCyclesIn(t *testing.T) {
 // the first element has the lowest ID and then conditionally
 // reversed so that the second element has the lowest possible
 // neighbouring ID.
-// c lists each node only onces - the final node must not be a
+// c lists each node only once - the final node must not be a
 // reiteration of the first node.
 func canonicalise(c []graph.Node) []graph.Node {
 	if len(c) < 2 {

graph/topo/topo_test.go

@@ -20,7 +20,7 @@ func TestIsPath(t *testing.T) {
 	}
 	p := []graph.Node{simple.Node(0)}
 	if IsPathIn(dg, p) {
-		t.Error("IsPath returns true on nonexistant node")
+		t.Error("IsPath returns true on nonexistent node")
 	}
 	dg.AddNode(p[0])
 	if !IsPathIn(dg, p) {

lapack/lapack64/lapack64.go

@@ -238,7 +238,7 @@ func Gels(trans blas.Transpose, a blas64.General, b blas64.General, work []float
 //
 // and computing H_i = I - tau[i] * v * vᵀ.
 //
-// The orthonormal matrix Q can be constucted from a product of these elementary
+// The orthonormal matrix Q can be constructed from a product of these elementary
 // reflectors, Q = H_0 * H_1 * ... * H_{k-1}, where k = min(m,n).
 //
 // Work is temporary storage, and lwork specifies the usable memory length.
@@ -804,7 +804,7 @@ func Trtrs(trans blas.Transpose, a blas64.Triangular, b blas64.General) (ok bool
 // larger. On return, optimal value of lwork will be stored in work[0].
 //
 // If lwork == -1, instead of performing Geev, the function only calculates the
-// optimal vaule of lwork and stores it into work[0].
+// optimal value of lwork and stores it into work[0].
 //
 // On return, first will be the index of the first valid eigenvalue.
 // If first == 0, all eigenvalues and eigenvectors have been computed.

lapack/testlapack/drscl.go

@@ -38,7 +38,7 @@ func DrsclTest(t *testing.T, impl Drscler) {
 
 		// Cannot test the scaling directly because of floating point scaling issues
 		// (the purpose of Drscl). Instead, check that scaling and scaling back
-		// yeilds approximately x. If overflow or underflow occurs then the scaling
+		// yields approximately x. If overflow or underflow occurs then the scaling
 		// won't match.
 		impl.Drscl(len(test.x), test.a, xcopy, 1)
 		if floats.Equal(xcopy, test.x) {

lapack/testlapack/dsytrd.go

@@ -130,7 +130,7 @@ func DsytrdTest(t *testing.T, impl Dsytrder) {
 					t.Errorf("%v: Q is not orthogonal; resid=%v, want<=%v", prefix, resid, tol*float64(n))
 				}
 
-				// Contruct symmetric tridiagonal T from d and e.
+				// Construct symmetric tridiagonal T from d and e.
 				tMat := zeros(n, n, n)
 				for i := 0; i < n; i++ {
 					tMat.Data[i*tMat.Stride+i] = d[i]

mat/dense_test.go

@@ -520,7 +520,7 @@ func TestDenseAdd(t *testing.T) {
 		temp.mat.Data = nil
 		panicked, message := panics(func() { temp.Add(a, b) })
 		if !panicked || !strings.HasPrefix(message, "runtime error: index out of range") {
-			t.Error("exected runtime panic for nil data slice")
+			t.Error("expected runtime panic for nil data slice")
 		}
 
 		a.Add(a, b)
@@ -608,7 +608,7 @@ func TestDenseSub(t *testing.T) {
 		temp.mat.Data = nil
 		panicked, message := panics(func() { temp.Sub(a, b) })
 		if !panicked || !strings.HasPrefix(message, "runtime error: index out of range") {
-			t.Error("exected runtime panic for nil data slice")
+			t.Error("expected runtime panic for nil data slice")
 		}
 
 		a.Sub(a, b)
@@ -696,7 +696,7 @@ func TestDenseMulElem(t *testing.T) {
 		temp.mat.Data = nil
 		panicked, message := panics(func() { temp.MulElem(a, b) })
 		if !panicked || !strings.HasPrefix(message, "runtime error: index out of range") {
-			t.Error("exected runtime panic for nil data slice")
+			t.Error("expected runtime panic for nil data slice")
 		}
 
 		a.MulElem(a, b)
@@ -800,7 +800,7 @@ func TestDenseDivElem(t *testing.T) {
 		temp.mat.Data = nil
 		panicked, message := panics(func() { temp.DivElem(a, b) })
 		if !panicked || !strings.HasPrefix(message, "runtime error: index out of range") {
-			t.Error("exected runtime panic for nil data slice")
+			t.Error("expected runtime panic for nil data slice")
 		}
 
 		a.DivElem(a, b)

optimize/convex/lp/simplex.go

@@ -612,7 +612,7 @@ func findInitialBasic(A mat.Matrix, b []float64) ([]int, *mat.Dense, []float64,
 	return nil, nil, nil, ErrInfeasible
 }
 
-// findLinearlyIndependnt finds a set of linearly independent columns of A, and
+// findLinearlyIndependent finds a set of linearly independent columns of A, and
 // returns the column indexes of the linearly independent columns.
 func findLinearlyIndependent(A mat.Matrix) []int {
 	m, n := A.Dims()

stat/distuv/binomial.go

@@ -140,7 +140,7 @@ func (b Binomial) Rand() float64 {
 	// appropriate expected value. However, the Poisson approximation is
 	// asymptotic such that the absolute deviation in probability is O(1/n).
 	// Rejection sampling produces exact variates with at worst less than 3%
-	// rejection with miminal additional computation.
+	// rejection with minimal additional computation.
 
 	// Use rejection method with Cauchy proposal.
 	g, _ := math.Lgamma(b.N + 1)

stat/distuv/norm.go

@@ -17,7 +17,7 @@ import (
 // UnitNormal is an instantiation of the normal distribution with Mu = 0 and Sigma = 1.
 var UnitNormal = Normal{Mu: 0, Sigma: 1}
 
-// Normal respresents a normal (Gaussian) distribution (https://en.wikipedia.org/wiki/Normal_distribution).
+// Normal represents a normal (Gaussian) distribution (https://en.wikipedia.org/wiki/Normal_distribution).
 type Normal struct {
 	Mu    float64 // Mean of the normal distribution
 	Sigma float64 // Standard deviation of the normal distribution

stat/spatial/spatial_areal_example_test.go

@@ -13,7 +13,7 @@ import (
 	"gonum.org/v1/gonum/stat/spatial"
 )
 
-// Euclid is a mat.Matrix whose elements refects the Euclidean
+// Euclid is a mat.Matrix whose elements reflects the Euclidean
 // distance between a series of unit-separated points strided
 // to be arranged in an x by y grid.
 type Euclid struct{ x, y int }

stat/stat_test.go

@@ -1116,7 +1116,7 @@ func TestBhattacharyya(t *testing.T) {
 			t.Errorf("Bhattacharyya distance mismatch in case %d. Expected %v, Found %v", i, test.res, resultpq)
 		}
 		if math.Abs(resultpq-resultqp) > 1e-10 {
-			t.Errorf("Bhattacharyya distance is assymmetric in case %d.", i)
+			t.Errorf("Bhattacharyya distance is asymmetric in case %d.", i)
 		}
 	}
 	// Bhattacharyya should panic if the inputs have different length
@@ -1154,7 +1154,7 @@ func TestHellinger(t *testing.T) {
 			t.Errorf("Hellinger distance mismatch in case %d. Expected %v, Found %v", i, test.res, resultpq)
 		}
 		if math.Abs(resultpq-resultqp) > 1e-10 {
-			t.Errorf("Hellinger distance is assymmetric in case %d.", i)
+			t.Errorf("Hellinger distance is asymmetric in case %d.", i)
 		}
 	}
 	if !panics(func() { Hellinger(make([]float64, 2), make([]float64, 3)) }) {