437 lines
18 KiB
Go
437 lines
18 KiB
Go
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// Copyright (c) 2026 Petr Balvín <opensource@petrbalvin.org> (https://petrbalvin.org)
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// SPDX-License-Identifier: MIT
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package stats
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import (
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"math"
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"strings"
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"testing"
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"sourcedock.dev/petrbalvin/tensor/internal/core"
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)
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// TestWeibullClosed pins the Weibull on its closed forms: with k = 2
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// and λ = 1 the density is 2xe^{−x²}, the CDF 1 − e^{−x²} and the
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// median √(ln 2); with k = 1 the law is the exponential of rate 1/λ.
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func TestWeibullClosed(t *testing.T) {
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d, err := WeibullDensity(1, 2, 1)
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if err != nil || math.Abs(d-2/math.E) > 1e-15 {
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t.Fatalf("WeibullDensity(1, 2, 1) = %v, %v, want 2/e", d, err)
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}
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v, err := WeibullCDF(1, 2, 1)
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if err != nil || math.Abs(v-(1-1/math.E)) > 1e-15 {
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t.Fatalf("WeibullCDF(1, 2, 1) = %v, %v, want 1 − 1/e", v, err)
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}
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v, err = WeibullCDF(3, 2, 1)
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if err != nil || math.Abs(v-(1-math.Exp(-9))) > 1e-15 {
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t.Fatalf("WeibullCDF(3, 2, 1) = %v, %v, want 1 − e⁻⁹", v, err)
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}
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q, err := WeibullQuantile(0.5, 2, 1)
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if err != nil || math.Abs(q-math.Sqrt(math.Ln2)) > 1e-14 {
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t.Fatalf("WeibullQuantile(0.5, 2, 1) = %v, %v, want √(ln 2)", q, err)
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}
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// k = 1 reduces to the exponential of rate 1/λ.
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v, err = WeibullCDF(2, 1, 0.5)
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exp, eerr := ExponentialCDF(2, 2)
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if err != nil || eerr != nil || v != exp {
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t.Fatalf("WeibullCDF(2, 1, 0.5) = %v vs ExponentialCDF %v (%v, %v)", v, exp, err, eerr)
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}
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// Support convention and round trips.
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if v, _ = WeibullCDF(-1, 2, 1); v != 0 {
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t.Fatalf("WeibullCDF below the support = %v, want 0", v)
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}
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if d, _ = WeibullDensity(-1, 2, 1); d != 0 {
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t.Fatalf("WeibullDensity below the support = %v, want 0", d)
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}
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for _, q := range []float64{0.001, 0.1, 0.5, 0.9, 0.999} {
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x, err := WeibullQuantile(q, 1.5, 2)
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if err != nil {
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t.Fatalf("WeibullQuantile(%g): %v", q, err)
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}
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back, err := WeibullCDF(x, 1.5, 2)
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if err != nil || math.Abs(back-q) > 1e-13 {
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t.Fatalf("round trip q = %g: CDF(quantile) = %v, %v", q, back, err)
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}
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}
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}
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// TestLognormalClosed pins the lognormal through the normal: the CDF
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// at 1 with μ = 0 is exactly ½, the density there is 1/√(2π), and the
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// 0.975 quantile is e to the normal quantile.
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func TestLognormalClosed(t *testing.T) {
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v, err := LognormalCDF(1, 0, 1)
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if err != nil || v != 0.5 {
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t.Fatalf("LognormalCDF(1, 0, 1) = %v, %v, want exactly 0.5", v, err)
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}
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v, err = LognormalCDF(math.E, 0, 1)
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if err != nil || math.Abs(v-NormalCDF(1)) > 1e-15 {
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t.Fatalf("LognormalCDF(e, 0, 1) = %v, %v, want Φ(1)", v, err)
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}
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d, err := LognormalDensity(1, 0, 1)
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if want := 1 / (math.Sqrt2 * math.SqrtPi); err != nil || math.Abs(d-want) > 1e-15 {
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t.Fatalf("LognormalDensity(1, 0, 1) = %v, %v, want %.16g", d, err, want)
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}
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z, _ := NormalQuantile(0.975)
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q, err := LognormalQuantile(0.975, 0, 1)
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if err != nil || math.Abs(q-math.Exp(z)) > 1e-12*math.Exp(z) {
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t.Fatalf("LognormalQuantile(0.975, 0, 1) = %v, %v, want e^{%.16g}", q, err, z)
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}
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// The CDF is the normal CDF at ln x, pointwise.
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for _, x := range []float64{0.05, 0.5, 2, 20} {
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got, _ := LognormalCDF(x, 0.3, 0.8)
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want := NormalCDF((math.Log(x) - 0.3) / 0.8)
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if math.Abs(got-want) > 1e-15 {
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t.Fatalf("LognormalCDF(%g, 0.3, 0.8) = %v, want %v", x, got, want)
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}
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}
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if v, _ = LognormalCDF(0, 0, 1); v != 0 {
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t.Fatalf("LognormalCDF at 0 = %v, want 0", v)
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}
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if d, _ = LognormalDensity(0, 0, 1); d != 0 {
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t.Fatalf("LognormalDensity at 0 = %v, want 0", d)
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}
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// A subnormal x underflows the closed form's numerator and
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// denominator together: the log-space tail must answer 0, not the
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// NaN their division would report.
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for _, sigma := range []float64{0.1, 0.01, 1} {
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if d, err = LognormalDensity(5e-324, 0, sigma); err != nil || d != 0 {
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t.Fatalf("LognormalDensity(5e-324, 0, %g) = %v, %v, want 0", sigma, d, err)
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}
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}
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}
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// TestParetoClosed pins the Pareto on its closed forms with x_m = 1
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// and α = 3: the CDF at 2 is 1 − 2^{−3} = 7/8, the density there
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// 3/16, and the 7/8 quantile exactly 2.
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func TestParetoClosed(t *testing.T) {
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v, err := ParetoCDF(2, 1, 3)
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if err != nil || math.Abs(v-0.875) > 1e-15 {
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t.Fatalf("ParetoCDF(2, 1, 3) = %v, %v, want 0.875", v, err)
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}
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d, err := ParetoDensity(2, 1, 3)
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if err != nil || math.Abs(d-3.0/16) > 1e-15 {
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t.Fatalf("ParetoDensity(2, 1, 3) = %v, %v, want 3/16", d, err)
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}
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q, err := ParetoQuantile(0.875, 1, 3)
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if err != nil || math.Abs(q-2) > 1e-12 {
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t.Fatalf("ParetoQuantile(0.875, 1, 3) = %v, %v, want 2", q, err)
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}
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if v, _ = ParetoCDF(0.5, 1, 3); v != 0 {
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t.Fatalf("ParetoCDF below the support = %v, want 0", v)
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}
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if d, _ = ParetoDensity(0.5, 1, 3); d != 0 {
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t.Fatalf("ParetoDensity below the support = %v, want 0", d)
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}
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// The Expm1 form keeps the digits just above the support. The
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// exact probability of the representable input is 3u for the
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// offset u the double actually carries.
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input := 1 + 1e-12
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u := input - 1
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v, err = ParetoCDF(input, 1, 3)
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if err != nil || math.Abs(v-3*u) > 1e-10*3*u {
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t.Fatalf("ParetoCDF just above the support = %v, %v, want ≈ %.17g", v, err, 3*u)
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}
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}
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// TestNegativeBinomialClosed pins the negative binomial on exact
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// fractions with r = 3 and p = ½, and holds the summation CDF against
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// the exact identity P(X ≤ k) = I_p(r, k+1).
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func TestNegativeBinomialClosed(t *testing.T) {
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d, err := NegativeBinomialPMF(0, 3, 0.5)
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if err != nil || math.Abs(d-math.Pow(0.5, 3)) > 1e-16 {
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t.Fatalf("NegativeBinomialPMF(0, 3, 0.5) = %v, %v, want 1/8", d, err)
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}
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d, err = NegativeBinomialPMF(1, 3, 0.5)
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if err != nil || math.Abs(d-3*math.Pow(0.5, 4)) > 1e-16 {
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t.Fatalf("NegativeBinomialPMF(1, 3, 0.5) = %v, %v, want 3/16", d, err)
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}
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v, err := NegativeBinomialCDF(1, 3, 0.5)
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if err != nil || math.Abs(v-0.3125) > 1e-15 {
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t.Fatalf("NegativeBinomialCDF(1, 3, 0.5) = %v, %v, want 5/16", v, err)
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}
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// Exact identity against the regularised beta.
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for _, r := range []int{1, 2, 5, 12} {
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for _, p := range []float64{0.2, 0.5, 0.8} {
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for k := 0; k <= 25; k++ {
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got, err := NegativeBinomialCDF(k, r, p)
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if err != nil {
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t.Fatalf("NegativeBinomialCDF(%d, %d, %g): %v", k, r, p, err)
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}
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want, err := BetaIncomplete(p, float64(r), float64(k+1))
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if err != nil {
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t.Fatalf("BetaIncomplete: %v", err)
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}
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if math.Abs(got-want) > 1e-14 {
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t.Fatalf("NegativeBinomialCDF(%d, %d, %g) = %.16g, want I_p identity %.16g",
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k, r, p, got, want)
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}
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}
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}
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}
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// The CDF at k = 2 is exactly ½, so both sides of the smallest-k
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// rule are pinned.
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q, err := NegativeBinomialQuantile(0.5, 0.5, 3)
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if err != nil || q != 2 {
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t.Fatalf("NegativeBinomialQuantile(0.5, 0.5, 3) = %v, %v, want 2", q, err)
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}
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q, err = NegativeBinomialQuantile(0.3125, 0.5, 3)
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if err != nil || q != 1 {
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t.Fatalf("NegativeBinomialQuantile(0.3125, 0.5, 3) = %v, %v, want 1", q, err)
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}
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// q = 1 brackets once the summed tail underflows below a half ulp
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// of 1, at a few hundred failures for these parameters.
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qEnd, err := NegativeBinomialQuantile(1, 0.5, 3)
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if err != nil || qEnd < 10 {
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t.Fatalf("NegativeBinomialQuantile(1, 0.5, 3) = %v, %v", qEnd, err)
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}
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if d, err = NegativeBinomialPMF(-1, 3, 0.5); err != nil || d != 0 {
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t.Fatalf("NegativeBinomialPMF below the support = %v, %v", d, err)
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}
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}
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// TestDirichletDensityMoments pins the density on an exact rational
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// value, the boundary conventions, and the mean and mode helpers.
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// With α = (2, 3, 4) the normalising constant is
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// B(α) = Γ2Γ3Γ4/Γ9 = 12/40320 = 1/3360, so the uniform point carries
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// 3360·3^{−6} = 3360/729.
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func TestDirichletDensityMoments(t *testing.T) {
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third := 1.0 / 3
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d, err := DirichletDensity([]float64{2, 3, 4}, []float64{third, third, third})
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if err != nil || math.Abs(d-3360.0/729) > 1e-12*3360.0/729 {
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t.Fatalf("DirichletDensity uniform = %v, %v, want %.16g", d, err, 3360.0/729)
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}
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// Boundary: α_i > 1 kills the density, α_i < 1 blows it up, and an
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// α_i of exactly 1 contributes nothing.
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if d, _ = DirichletDensity([]float64{2, 2}, []float64{0, 1}); d != 0 {
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t.Fatalf("boundary density with α > 1 = %v, want 0", d)
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}
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if d, _ = DirichletDensity([]float64{0.5, 0.5}, []float64{0, 1}); !math.IsInf(d, 1) {
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t.Fatalf("boundary density with α < 1 = %v, want +Inf", d)
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}
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d, err = DirichletDensity([]float64{1, 3}, []float64{0, 1})
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if err != nil || math.Abs(d-3) > 1e-14 {
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t.Fatalf("boundary density with α = 1 = %v, %v, want 3", d, err)
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}
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mean, err := DirichletMean([]float64{2, 3, 4})
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if err != nil {
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t.Fatalf("DirichletMean: %v", err)
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}
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for i, want := range []float64{2.0 / 9, 1.0 / 3, 4.0 / 9} {
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if math.Abs(mean[i]-want) > 1e-15 {
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t.Fatalf("DirichletMean[%d] = %.16g, want %.16g", i, mean[i], want)
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}
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}
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mode, err := DirichletMode([]float64{2, 3, 4})
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if err != nil {
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t.Fatalf("DirichletMode: %v", err)
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}
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for i, want := range []float64{1.0 / 6, 1.0 / 3, 0.5} {
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if math.Abs(mode[i]-want) > 1e-15 {
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t.Fatalf("DirichletMode[%d] = %.16g, want %.16g", i, mode[i], want)
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}
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}
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if _, err := DirichletMode([]float64{1, 2}); err == nil {
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t.Fatal("an α of 1 leaves no interior mode: want an error")
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}
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}
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// TestDirichletDraws checks the sampler statistically: every row is a
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// probability vector, the column means track α/α₀, and the seed makes
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// the run reproducible.
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func TestDirichletDraws(t *testing.T) {
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g := core.NewGenerator(7)
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alpha := []float64{2, 3, 4}
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const n = 200000
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draws, err := DirichletDraws(g, n, alpha)
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if err != nil {
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t.Fatalf("DirichletDraws: %v", err)
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}
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if draws.Shape()[0] != n || draws.Shape()[1] != len(alpha) {
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t.Fatalf("draw shape %v, want (%d, %d)", draws.Shape(), n, len(alpha))
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}
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colSum := make([]float64, len(alpha))
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for r := range n {
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sum := 0.0
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for c := range alpha {
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v := draws.FloatAt(r*len(alpha) + c)
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if v < 0 {
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t.Fatalf("draw (%d, %d) = %v is negative", r, c, v)
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}
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sum += v
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colSum[c] += v
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}
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if math.Abs(sum-1) > 1e-12 {
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t.Fatalf("row %d sums to %.17g, want 1", r, sum)
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}
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}
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mean, _ := DirichletMean(alpha)
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for c := range alpha {
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m := colSum[c] / n
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if math.Abs(m-mean[c]) > 0.005 {
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t.Fatalf("column %d mean = %g, want ≈ %g", c, m, mean[c])
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}
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}
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again, _ := DirichletDraws(core.NewGenerator(7), n, alpha)
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for i := range n * len(alpha) {
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if again.FloatAt(i) != draws.FloatAt(i) {
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t.Fatalf("seeded run not deterministic at %d", i)
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}
|
|||
|
|
}
|
|||
|
|
// Concentrations below 1 take the boosted gamma branch of the
|
|||
|
|
// sampler; the mean still tracks α/α₀.
|
|||
|
|
sparse, err := DirichletDraws(core.NewGenerator(3), n, []float64{0.5, 0.7})
|
|||
|
|
if err != nil {
|
|||
|
|
t.Fatalf("DirichletDraws: %v", err)
|
|||
|
|
}
|
|||
|
|
sums := []float64{0, 0}
|
|||
|
|
for r := range n {
|
|||
|
|
for c := range 2 {
|
|||
|
|
v := sparse.FloatAt(r*2 + c)
|
|||
|
|
sums[c] += v
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
for c, a := range []float64{0.5, 0.7} {
|
|||
|
|
if m := sums[c] / n; math.Abs(m-a/1.2) > 0.005 {
|
|||
|
|
t.Fatalf("sparse column %d mean = %g, want ≈ %g", c, m, a/1.2)
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
if _, err := DirichletDraws(core.NewGenerator(1), 1, []float64{2, -1}); err == nil {
|
|||
|
|
t.Fatal("a negative concentration: want an error")
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// TestDistribution2Errors pins the parameter contracts of the new
|
|||
|
|
// distributions.
|
|||
|
|
func TestDistribution2Errors(t *testing.T) {
|
|||
|
|
if _, err := WeibullDensity(1, 0, 1); err == nil || !strings.Contains(err.Error(), "shape k") {
|
|||
|
|
t.Fatalf("k = 0: got %v, want the shape refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := WeibullDensity(1, 2, 0); err == nil || !strings.Contains(err.Error(), "scale λ") {
|
|||
|
|
t.Fatalf("λ = 0: got %v, want the scale refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := WeibullDensity(math.NaN(), 2, 1); err == nil || !strings.Contains(err.Error(), "must be a number") {
|
|||
|
|
t.Fatalf("NaN x: got %v, want the x refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := WeibullCDF(1, -1, 1); err == nil || !strings.Contains(err.Error(), "shape k") {
|
|||
|
|
t.Fatalf("negative k: got %v, want the shape refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := WeibullQuantile(1.5, 2, 1); err == nil || !strings.Contains(err.Error(), "q must lie") {
|
|||
|
|
t.Fatalf("q outside [0, 1]: got %v, want the q refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := WeibullQuantile(0, 2, 1); err == nil || !strings.Contains(err.Error(), "no finite quantile") {
|
|||
|
|
t.Fatalf("q = 0: got %v, want the q refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := WeibullCDF(1, 2, math.Inf(1)); err == nil || !strings.Contains(err.Error(), "scale λ") {
|
|||
|
|
t.Fatalf("λ = +Inf: got %v, want the scale refusal", err)
|
|||
|
|
}
|
|||
|
|
if v, err := WeibullCDF(math.Inf(1), 2, 1); err != nil || v != 1 {
|
|||
|
|
t.Fatalf("WeibullCDF(+Inf) = %v, %v, want 1", v, err)
|
|||
|
|
}
|
|||
|
|
if _, err := LognormalQuantile(0.5, math.NaN(), 1); err == nil || !strings.Contains(err.Error(), "location μ") {
|
|||
|
|
t.Fatalf("μ = NaN in the quantile: got %v, want the location refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := LognormalQuantile(0.5, 0, math.Inf(1)); err == nil || !strings.Contains(err.Error(), "log-scale σ") {
|
|||
|
|
t.Fatalf("σ = +Inf in the quantile: got %v, want the log-scale refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := LognormalCDF(math.NaN(), 0, 1); err == nil || !strings.Contains(err.Error(), "must be a number") {
|
|||
|
|
t.Fatalf("NaN x: got %v, want the x refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := ParetoQuantile(0.5, 0, 2); err == nil || !strings.Contains(err.Error(), "scale x_m") {
|
|||
|
|
t.Fatalf("x_m = 0 in the quantile: got %v, want the scale refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := ParetoQuantile(0.5, 1, math.NaN()); err == nil || !strings.Contains(err.Error(), "tail index α") {
|
|||
|
|
t.Fatalf("NaN α: got %v, want the tail-index refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := ParetoCDF(math.NaN(), 1, 2); err == nil || !strings.Contains(err.Error(), "must be a number") {
|
|||
|
|
t.Fatalf("NaN x: got %v, want the x refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := WeibullQuantile(0.5, math.Inf(1), 1); err == nil || !strings.Contains(err.Error(), "shape k") {
|
|||
|
|
t.Fatalf("k = +Inf in the quantile: got %v, want the shape refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := WeibullQuantile(0.5, 2, math.Inf(-1)); err == nil || !strings.Contains(err.Error(), "scale λ") {
|
|||
|
|
t.Fatalf("λ = −Inf in the quantile: got %v, want the scale refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := LognormalDensity(math.NaN(), 0, 1); err == nil || !strings.Contains(err.Error(), "must be a number") {
|
|||
|
|
t.Fatalf("NaN x in the density: got %v, want the x refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := ParetoDensity(math.NaN(), 1, 2); err == nil || !strings.Contains(err.Error(), "must be a number") {
|
|||
|
|
t.Fatalf("NaN x in the Pareto density: got %v, want the x refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := ParetoDensity(1, math.Inf(1), 2); err == nil || !strings.Contains(err.Error(), "scale x_m") {
|
|||
|
|
t.Fatalf("x_m = +Inf: got %v, want the scale refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := NegativeBinomialCDF(0, 0, 0.5); err == nil || !strings.Contains(err.Error(), "r must be") {
|
|||
|
|
t.Fatalf("r = 0 in the CDF: got %v, want the r refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := DirichletMode([]float64{2}); err == nil || !strings.Contains(err.Error(), "at least two components") {
|
|||
|
|
t.Fatalf("one component in the mode: got %v, want the component floor refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := DirichletMode([]float64{2, math.Inf(1)}); err == nil || !strings.Contains(err.Error(), "alpha[1]") {
|
|||
|
|
t.Fatalf("α = +Inf in the mode: got %v, want the concentration refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := DirichletDraws(core.NewGenerator(1), 1, []float64{2}); err == nil || !strings.Contains(err.Error(), "at least two components") {
|
|||
|
|
t.Fatalf("one component in the draws: got %v, want the component floor refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := DirichletMean([]float64{2, math.NaN()}); err == nil || !strings.Contains(err.Error(), "alpha[1]") {
|
|||
|
|
t.Fatalf("NaN α in the mean: got %v, want the concentration refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := LognormalDensity(1, math.Inf(1), 1); err == nil || !strings.Contains(err.Error(), "location μ") {
|
|||
|
|
t.Fatalf("μ = +Inf: got %v, want the location refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := LognormalCDF(1, 0, -1); err == nil || !strings.Contains(err.Error(), "log-scale σ") {
|
|||
|
|
t.Fatalf("σ < 0: got %v, want the log-scale refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := LognormalQuantile(0.5, 0, 0); err == nil || !strings.Contains(err.Error(), "log-scale σ") {
|
|||
|
|
t.Fatalf("σ = 0: got %v, want the log-scale refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := ParetoDensity(1, 0, 2); err == nil || !strings.Contains(err.Error(), "scale x_m") {
|
|||
|
|
t.Fatalf("x_m = 0: got %v, want the scale refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := ParetoCDF(1, 1, math.Inf(1)); err == nil || !strings.Contains(err.Error(), "tail index α") {
|
|||
|
|
t.Fatalf("α = +Inf: got %v, want the tail-index refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := ParetoQuantile(1, 1, 2); err == nil || !strings.Contains(err.Error(), "no finite quantile") {
|
|||
|
|
t.Fatalf("q = 1: got %v, want the q refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := NegativeBinomialPMF(0, 0, 0.5); err == nil || !strings.Contains(err.Error(), "r must be") {
|
|||
|
|
t.Fatalf("r = 0: got %v, want the r refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := NegativeBinomialPMF(0, 3, 1.5); err == nil || !strings.Contains(err.Error(), "p must lie") {
|
|||
|
|
t.Fatalf("p above 1 in the PMF: got %v, want the p refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := NegativeBinomialCDF(0, 3, 0); err == nil || !strings.Contains(err.Error(), "p must lie") {
|
|||
|
|
t.Fatalf("p = 0: got %v, want the p refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := NegativeBinomialCDF(0, 3, 1); err == nil || !strings.Contains(err.Error(), "p must lie") {
|
|||
|
|
t.Fatalf("p = 1: got %v, want the p refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := NegativeBinomialQuantile(0.5, 0.5, 0); err == nil || !strings.Contains(err.Error(), "r must be") {
|
|||
|
|
t.Fatalf("r = 0 in the quantile: got %v, want the r refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := NegativeBinomialQuantile(0.5, 1.5, 3); err == nil || !strings.Contains(err.Error(), "p must lie") {
|
|||
|
|
t.Fatalf("p above 1 in the quantile: got %v, want the p refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := NegativeBinomialQuantile(-0.1, 0.5, 3); err == nil || !strings.Contains(err.Error(), "q must lie") {
|
|||
|
|
t.Fatalf("q < 0: got %v, want the q refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := DirichletDensity([]float64{2}, []float64{1}); err == nil || !strings.Contains(err.Error(), "at least two components") {
|
|||
|
|
t.Fatalf("one component: got %v, want the component floor refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := DirichletDensity([]float64{2, 3}, []float64{0.5}); err == nil || !strings.Contains(err.Error(), "components, the point") {
|
|||
|
|
t.Fatalf("length mismatch: got %v, want the length refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := DirichletDensity([]float64{2, 3}, []float64{0.5, 0.2}); err == nil || !strings.Contains(err.Error(), "must sum to 1") {
|
|||
|
|
t.Fatalf("off-simplex point: got %v, want the simplex refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := DirichletDensity([]float64{-1, 3}, []float64{0.5, 0.5}); err == nil || !strings.Contains(err.Error(), "alpha[0]") {
|
|||
|
|
t.Fatalf("negative α: got %v, want the concentration refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := DirichletMean([]float64{0, 3}); err == nil || !strings.Contains(err.Error(), "alpha[0]") {
|
|||
|
|
t.Fatalf("α = 0 in the mean: got %v, want the concentration refusal", err)
|
|||
|
|
}
|
|||
|
|
if _, err := DirichletDraws(core.NewGenerator(1), 0, []float64{2, 3}); err == nil || !strings.Contains(err.Error(), "n must be") {
|
|||
|
|
t.Fatalf("n = 0: got %v, want the n refusal", err)
|
|||
|
|
}
|
|||
|
|
}
|