# gonum/gonum

stat: reduce golint warnings

vthiery authored and kortschak committed Oct 15, 2018
1 parent 056846e commit 9922db829382769e78a4586457372544eddd5a7a
 @@ -293,8 +293,8 @@ func (s *StudentsT) MarginalStudentsT(vars []int, src rand.Source) (dist *Studen return NewStudentsT(newMean, &newSigma, s.nu, src) } // MarginalStudentsT returns the marginal distribution of the given input variable. // That is, MarginalStudentsT returns // MarginalStudentsTSingle returns the marginal distribution of the given input variable. // That is, MarginalStudentsTSingle returns // p(x_i) = \int_{x_o} p(x_i | x_o) p(x_o) dx_o // where i is the input index, and x_o are the remaining dimensions. // See https://en.wikipedia.org/wiki/Marginal_distribution for more information.
 @@ -10,6 +10,7 @@ import ( "golang.org/x/exp/rand" ) // Bound represents [Min, Max] bounds. type Bound struct { Min float64 Max float64
 @@ -65,7 +65,7 @@ func (Hellinger) DistBeta(l, r Beta) float64 { return math.Sqrt(1 - bc) } // DistHellinger computes the Hellinger distance between Normal distributions l and r. // DistNormal computes the Hellinger distance between Normal distributions l and r. // See the documentation of Bhattacharyya.DistNormal for the distance formula. func (Hellinger) DistNormal(l, r Normal) float64 { db := Bhattacharyya{}.DistNormal(l, r)
 @@ -27,9 +27,8 @@ type Weibull struct { func (w Weibull) CDF(x float64) float64 { if x < 0 { return 0 } else { return 1 - cmplx.Abs(cmplx.Exp(w.LogCDF(x))) } return 1 - cmplx.Abs(cmplx.Exp(w.LogCDF(x))) } // Entropy returns the entropy of the distribution. @@ -51,9 +50,8 @@ func (w Weibull) gammaIPow(i, pow float64) float64 { func (w Weibull) LogCDF(x float64) complex128 { if x < 0 { return 0 } else { return cmplx.Log(-1) + complex(-math.Pow(x/w.Lambda, w.K), 0) } return cmplx.Log(-1) + complex(-math.Pow(x/w.Lambda, w.K), 0) } // LogProb computes the natural logarithm of the value of the probability @@ -67,18 +65,16 @@ func (w Weibull) LogCDF(x float64) complex128 { func (w Weibull) LogProb(x float64) float64 { if x < 0 { return 0 } else { return math.Log(w.K) - math.Log(w.Lambda) + (w.K-1)*(math.Log(x)-math.Log(w.Lambda)) - math.Pow(x/w.Lambda, w.K) } return math.Log(w.K) - math.Log(w.Lambda) + (w.K-1)*(math.Log(x)-math.Log(w.Lambda)) - math.Pow(x/w.Lambda, w.K) } // Survival returns the log of the survival function (complementary CDF) at x. // LogSurvival returns the log of the survival function (complementary CDF) at x. func (w Weibull) LogSurvival(x float64) float64 { if x < 0 { return 0 } else { return -math.Pow(x/w.Lambda, w.K) } return -math.Pow(x/w.Lambda, w.K) } // Mean returns the mean of the probability distribution. @@ -114,9 +110,8 @@ func (Weibull) NumParameters() int { func (w Weibull) Prob(x float64) float64 { if x < 0 { return 0 } else { return math.Exp(w.LogProb(x)) } return math.Exp(w.LogProb(x)) } // Quantile returns the inverse of the cumulative probability distribution.
 @@ -69,7 +69,7 @@ func ROC(n int, y []float64, classes []bool, weights []float64) (tpr, fpr []floa } } var bin int = 1 // the initial bin is known to have 0 fpr and 0 tpr bin := 1 // the initial bin is known to have 0 fpr and 0 tpr var nPos, nNeg float64 for i, u := range classes { var posWeight, negWeight float64 = 0, 1
 @@ -35,6 +35,7 @@ func (h Halton) Sample(batch *mat.Dense) { halton(batch, h.Kind, h.Q, h.Src) } // HaltonKind specifies the type of algorithm used to generate Halton samples. type HaltonKind int const (
 @@ -30,7 +30,7 @@ type MHProposal interface { ConditionalRand(x, y []float64) []float64 } // MetropolisHastings is a type for generating samples using the Metropolis Hastings // MetropolisHastingser is a type for generating samples using the Metropolis Hastings // algorithm (http://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm), // with the given target and proposal distributions, starting at the location // specified by Initial. If src != nil, it will be used to generate random
 @@ -359,7 +359,7 @@ func metropolisHastings(batch []float64, initial float64, target distuv.LogProbe } } // IID generates a set of independently and identically distributed samples from // IIDer generates a set of independently and identically distributed samples from // the input distribution. type IIDer struct { Dist distuv.Rander
 @@ -576,7 +576,7 @@ and 0 data points above 1000. Since dividers has length 5, there will be 4 bins. max := floats.Max(x) // Increase the maximum divider so that the maximum value of x is contained // within the last bucket. max += 1 max++ floats.Span(dividers, min, max) // Span includes the min and the max. Trim the dividers to create 10 buckets hist = Histogram(nil, dividers, x, nil)