all: remove redundant bad prefix and make panic strings const

mat/eigen.go

@@ -11,7 +11,7 @@ import (
 
 const (
 	badFact   = "mat: use without successful factorization"
-	badNoVect = "mat: eigenvectors not computed"
+	noVectors = "mat: eigenvectors not computed"
 )
 
 // EigenSym is a type for creating and manipulating the Eigen decomposition of
@@ -103,7 +103,7 @@ func (e *EigenSym) VectorsTo(dst *Dense) {
 		panic(badFact)
 	}
 	if !e.vectorsComputed {
-		panic(badNoVect)
+		panic(noVectors)
 	}
 	r, c := e.vectors.Dims()
 	if dst.IsEmpty() {
@@ -320,7 +320,7 @@ func (e *Eigen) VectorsTo(dst *CDense) {
 		panic(badFact)
 	}
 	if e.kind&EigenRight == 0 {
-		panic(badNoVect)
+		panic(noVectors)
 	}
 	if dst.IsEmpty() {
 		dst.ReuseAs(e.n, e.n)
@@ -346,7 +346,7 @@ func (e *Eigen) LeftVectorsTo(dst *CDense) {
 		panic(badFact)
 	}
 	if e.kind&EigenLeft == 0 {
-		panic(badNoVect)
+		panic(noVectors)
 	}
 	if dst.IsEmpty() {
 		dst.ReuseAs(e.n, e.n)

mathext/internal/cephes/cephes.go

@@ -15,9 +15,9 @@ See https://github.com/deepmind/torch-cephes/blob/master/LICENSE.txt and
 https://lists.debian.org/debian-legal/2004/12/msg00295.html
 */
 
-var (
+const (
-	badParamOutOfBounds         = "cephes: parameter out of bounds"
+	paramOutOfBounds            = "cephes: parameter out of bounds"
-	badParamFunctionSingularity = "cephes: function singularity"
+	errParamFunctionSingularity = "cephes: function singularity"
 )
 
 const (

mathext/internal/cephes/igam.go

@@ -69,7 +69,7 @@ func Igam(a, x float64) float64 {
 	}
 
 	if x < 0 || a <= 0 {
-		panic(badParamOutOfBounds)
+		panic(paramOutOfBounds)
 	}
 
 	// Asymptotic regime where a ~ x; see [2].
@@ -101,7 +101,7 @@ func IgamC(a, x float64) float64 {
 
 	switch {
 	case x < 0, a <= 0:
-		panic(badParamOutOfBounds)
+		panic(paramOutOfBounds)
 	case x == 0:
 		return 1
 	case math.IsInf(x, 0):

mathext/internal/cephes/igami.go

@@ -26,7 +26,7 @@ func IgamI(a, p float64) float64 {
 	dithresh := 5.0 * machEp
 
 	if p < 0 || p > 1 || a <= 0 {
-		panic(badParamOutOfBounds)
+		panic(paramOutOfBounds)
 	}
 
 	if p == 0 {

mathext/internal/cephes/incbeta.go

@@ -24,7 +24,7 @@ const (
 // Incbet computes the regularized incomplete beta function.
 func Incbet(aa, bb, xx float64) float64 {
 	if aa <= 0 || bb <= 0 {
-		panic(badParamOutOfBounds)
+		panic(paramOutOfBounds)
 	}
 	if xx <= 0 || xx >= 1 {
 		if xx == 0 {
@@ -33,7 +33,7 @@ func Incbet(aa, bb, xx float64) float64 {
 		if xx == 1 {
 			return 1
 		}
-		panic(badParamOutOfBounds)
+		panic(paramOutOfBounds)
 	}
 
 	var flag int

mathext/internal/cephes/ndtri.go

@@ -108,13 +108,13 @@ func Ndtri(y0 float64) float64 {
 
 	if y0 <= 0.0 {
 		if y0 < 0 {
-			panic(badParamOutOfBounds)
+			panic(paramOutOfBounds)
 		}
 		return math.Inf(-1)
 	}
 	if y0 >= 1.0 {
 		if y0 > 1 {
-			panic(badParamOutOfBounds)
+			panic(paramOutOfBounds)
 		}
 		return math.Inf(1)
 	}

mathext/internal/cephes/zeta.go

@@ -48,15 +48,15 @@ func Zeta(x, q float64) float64 {
 	}
 
 	if x < 1 {
-		panic(badParamOutOfBounds)
+		panic(paramOutOfBounds)
 	}
 
 	if q <= 0 {
 		if q == math.Floor(q) {
-			panic(badParamFunctionSingularity)
+			panic(errParamFunctionSingularity)
 		}
 		if x != math.Floor(x) {
-			panic(badParamOutOfBounds) // Because q^-x not defined
+			panic(paramOutOfBounds) // Because q^-x not defined
 		}
 	}
 

optimize/convex/lp/simplex.go

@@ -33,9 +33,7 @@ var (
 	ErrZeroRow    = errors.New("lp: A has a row of all zeros")
 )
 
-var (
+const badShape = "lp: size mismatch"
-	badShape = "lp: size mismatch"
-)
 
 // TODO(btracey): Should these tolerances be part of a settings struct?
 

optimize/errors.go

@@ -75,6 +75,4 @@ func (err ErrGrad) Error() string {
 }
 
 // List of shared panic strings
-var (
+const badProblem = "optimize: objective function is undefined"
-	badProblem = "optimize: objective function is undefined"
-)

optimize/functions/doc.go

@@ -10,6 +10,4 @@
 // significance due to prior use as benchmark cases.
 package functions // import "gonum.org/v1/gonum/optimize/functions"
 
-const (
+const badInputDim = "functions: wrong input dimension"
-	badInputDim = "functions: wrong input dimension"
-)

stat/combin/combin.go

@@ -10,10 +10,10 @@ import (
 )
 
 const (
-	badNegInput          = "combin: negative input"
+	errNegInput             = "combin: negative input"
 	badSetSize              = "combin: n < k"
 	badInput                = "combin: wrong input slice length"
-	nonpositiveDimension = "combin: non-positive dimension"
+	errNonpositiveDimension = "combin: non-positive dimension"
 )
 
 // Binomial returns the binomial coefficient of (n,k), also commonly referred to
@@ -28,7 +28,7 @@ const (
 // No check is made for overflow.
 func Binomial(n, k int) int {
 	if n < 0 || k < 0 {
-		panic(badNegInput)
+		panic(errNegInput)
 	}
 	if n < k {
 		panic(badSetSize)
@@ -61,7 +61,7 @@ func GeneralizedBinomial(n, k float64) float64 {
 // See GeneralizedBinomial for more information.
 func LogGeneralizedBinomial(n, k float64) float64 {
 	if n < 0 || k < 0 {
-		panic(badNegInput)
+		panic(errNegInput)
 	}
 	if n < k {
 		panic(badSetSize)
@@ -193,7 +193,7 @@ func nextCombination(s []int, n, k int) {
 // [0,n) integers, if n or k are non-negative, or if k is greater than n.
 func CombinationIndex(comb []int, n, k int) int {
 	if n < 0 || k < 0 {
-		panic(badNegInput)
+		panic(errNegInput)
 	}
 	if n < k {
 		panic(badSetSize)
@@ -359,7 +359,7 @@ func IdxFor(sub, dims []int) int {
 		v := sub[i]
 		d := dims[i]
 		if d <= 0 {
-			panic(nonpositiveDimension)
+			panic(errNonpositiveDimension)
 		}
 		if v < 0 || v >= d {
 			panic("combin: invalid subscript")
@@ -386,7 +386,7 @@ func SubFor(sub []int, idx int, dims []int) []int {
 		panic(badInput)
 	}
 	if idx < 0 {
-		panic(badNegInput)
+		panic(errNegInput)
 	}
 	stride := 1
 	for i := len(dims) - 1; i >= 1; i-- {
@@ -396,7 +396,7 @@ func SubFor(sub []int, idx int, dims []int) []int {
 		v := idx / stride
 		d := dims[i]
 		if d < 0 {
-			panic(nonpositiveDimension)
+			panic(errNonpositiveDimension)
 		}
 		if v >= dims[i] {
 			panic("combin: index too large")
@@ -528,7 +528,7 @@ func (p *PermutationGenerator) Permutation(dst []int) []int {
 // [0,n) integers, if n or k are non-negative, or if k is greater than n.
 func PermutationIndex(perm []int, n, k int) int {
 	if n < 0 || k < 0 {
-		panic(badNegInput)
+		panic(errNegInput)
 	}
 	if n < k {
 		panic(badSetSize)

stat/distmat/general.go

@@ -4,4 +4,4 @@
 
 package distmat
 
-var badDim = "distmat: dimension mismatch"
+const badDim = "distmat: dimension mismatch"

stat/distmv/general.go

@@ -4,7 +4,7 @@
 
 package distmv
 
-var (
+const (
 	badQuantile      = "distmv: quantile not between 0 and 1"
 	badReceiver      = "distmv: input slice is not nil or the correct length"
 	badSizeMismatch  = "distmv: size mismatch"

stat/distmv/normal.go

@@ -15,9 +15,7 @@ import (
 	"gonum.org/v1/gonum/stat/distuv"
 )
 
-var (
+const badInputLength = "distmv: input slice length mismatch"
-	badInputLength = "distmv: input slice length mismatch"
-)
 
 // Normal is a multivariate normal distribution (also known as the multivariate
 // Gaussian distribution). Its pdf in k dimensions is given by

stat/distuv/general.go

@@ -10,11 +10,11 @@ type Parameter struct {
 	Value float64
 }
 
-var (
+const (
 	badPercentile = "distuv: percentile out of bounds"
 	badLength     = "distuv: slice length mismatch"
 	badSuffStat   = "distuv: wrong suffStat length"
-	badNoSamples  = "distuv: must have at least one sample"
+	errNoSamples  = "distuv: must have at least one sample"
 )
 
 const (

stat/distuv/laplace.go

@@ -50,7 +50,7 @@ func (l *Laplace) Fit(samples, weights []float64) {
 	}
 
 	if len(samples) == 0 {
-		panic(badNoSamples)
+		panic(errNoSamples)
 	}
 	if len(samples) == 1 {
 		l.Mu = samples[0]

stat/samplemv/metropolishastings.go

@@ -205,7 +205,7 @@ func (p *ProposalNormal) ConditionalRand(x, y []float64) []float64 {
 		x = make([]float64, p.normal.Dim())
 	}
 	if len(x) != len(y) {
-		panic(badLengthMismatch)
+		panic(errLengthMismatch)
 	}
 	p.normal.SetMean(y)
 	p.normal.Rand(x)

stat/samplemv/samplemv.go

@@ -14,9 +14,7 @@ import (
 	"gonum.org/v1/gonum/stat/distmv"
 )
 
-var (
+const errLengthMismatch = "samplemv: slice length mismatch"
-	badLengthMismatch = "samplemv: slice length mismatch"
-)
 
 var (
 	_ Sampler = LatinHypercube{}
@@ -62,7 +60,7 @@ type SampleUniformWeighted struct {
 func (w SampleUniformWeighted) SampleWeighted(batch *mat.Dense, weights []float64) {
 	r, _ := batch.Dims()
 	if r != len(weights) {
-		panic(badLengthMismatch)
+		panic(errLengthMismatch)
 	}
 	w.Sample(batch)
 	for i := range weights {
@@ -143,7 +141,7 @@ func (l Importance) SampleWeighted(batch *mat.Dense, weights []float64) {
 func importance(batch *mat.Dense, weights []float64, target distmv.LogProber, proposal distmv.RandLogProber) {
 	r, _ := batch.Dims()
 	if r != len(weights) {
-		panic(badLengthMismatch)
+		panic(errLengthMismatch)
 	}
 	for i := 0; i < r; i++ {
 		v := batch.RawRowView(i)

stat/sampleuv/sample.go

@@ -13,9 +13,7 @@ import (
 	"gonum.org/v1/gonum/stat/distuv"
 )
 
-var (
+const badLengthMismatch = "sample: slice length mismatch"
-	badLengthMismatch = "sample: slice length mismatch"
-)
 
 var (
 	_ Sampler = LatinHypercube{}