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 // Copyright ©2017 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 distuv import ( "math" "sort" "testing" "golang.org/x/exp/rand" "gonum.org/v1/gonum/floats/scalar" ) func TestFProb(t *testing.T) { t.Parallel() for _, test := range []struct { x, d1, d2, want float64 }{ // Values calculated with scipy.stats.f {0.0001, 4, 6, 0.00053315559110558126}, {0.1, 1, 1, 0.91507658371794609}, {0.5, 11, 7, 0.66644660411410883}, {0.9, 20, 15, 0.88293424959522437}, {1, 1, 1, 0.15915494309189535}, {2, 15, 12, 0.16611971273429088}, {5, 4, 8, 0.013599775603702537}, {10, 12, 9, 0.00032922887567957289}, {100, 7, 7, 6.08037637806889e-08}, {1000, 2, 1, 1.1171959870312232e-05}, } { pdf := F{test.d1, test.d2, nil}.Prob(test.x) if !scalar.EqualWithinAbsOrRel(pdf, test.want, 1e-10, 1e-10) { t.Errorf("Prob mismatch, x = %v, d1 = %v, d2 = %v. Got %v, want %v", test.x, test.d1, test.d2, pdf, test.want) } } } func TestFCDF(t *testing.T) { t.Parallel() for _, test := range []struct { x, d1, d2, want float64 }{ // Values calculated with scipy.stats.f {0.0001, 4, 6, 2.6660741629519019e-08}, {0.1, 1, 1, 0.19498222904213672}, {0.5, 11, 7, 0.14625028471336987}, {0.9, 20, 15, 0.40567939897287852}, {1, 1, 1, 0.50000000000000011}, {2, 15, 12, 0.8839384428956264}, {5, 4, 8, 0.97429642410900219}, {10, 12, 9, 0.99915733385467187}, {100, 7, 7, 0.99999823560259171}, {1000, 2, 1, 0.97764490829950534}, } { cdf := F{test.d1, test.d2, nil}.CDF(test.x) if !scalar.EqualWithinAbsOrRel(cdf, test.want, 1e-10, 1e-10) { t.Errorf("CDF mismatch, x = %v, d1 = %v, d2 = %v. Got %v, want %v", test.x, test.d1, test.d2, cdf, test.want) } } } func TestF(t *testing.T) { t.Parallel() src := rand.New(rand.NewSource(1)) for i, f := range []F{ {13, 16, src}, {42, 31, src}, {77, 92, src}, } { testF(t, f, i) } } func testF(t *testing.T, f F, i int) { const ( tol = 1e-2 n = 1e6 bins = 50 ) x := make([]float64, n) generateSamples(x, f) sort.Float64s(x) testRandLogProbContinuous(t, i, 0, x, f, tol, bins) checkProbContinuous(t, i, x, 0, math.Inf(1), f, 1e-4) checkMean(t, i, x, f, tol) checkVarAndStd(t, i, x, f, tol) checkExKurtosis(t, i, x, f, 1e-1) checkSkewness(t, i, x, f, 5e-2) checkQuantileCDFSurvival(t, i, x, f, 5e-3) checkMode(t, i, x, f, 2e-2, 3e-2) if f.NumParameters() != 2 { t.Errorf("Wrong number of parameters. Got %v, want 2", f.NumParameters()) } } func TestFUndefined(t *testing.T) { t.Parallel() for _, d1 := range []float64{1, 100} { for _, d2 := range []float64{4, 8} { f := F{d1, d2, nil} exKurt := f.ExKurtosis() if !math.IsNaN(exKurt) { t.Errorf("Expected NaN excess kurtosis for D1 = %g and D2 = %g, got %v", d1, d2, exKurt) } } } for _, d1 := range []float64{1, 100} { for _, d2 := range []float64{1, 2} { f := F{d1, d2, nil} mean := f.Mean() if !math.IsNaN(mean) { t.Errorf("Expected NaN mean for D1 = %g and D2 = %g, got %v", d1, d2, mean) } } } for _, d1 := range []float64{1, 2} { for _, d2 := range []float64{1, 100} { f := F{d1, d2, nil} mode := f.Mode() if !math.IsNaN(mode) { t.Errorf("Expected NaN mode for D1 = %g and D2 = %g, got %v", d1, d2, mode) } } } for _, d1 := range []float64{1, 100} { for _, d2 := range []float64{3, 6} { f := F{d1, d2, nil} skewness := f.Skewness() if !math.IsNaN(skewness) { t.Errorf("Expected NaN skewness for D1 = %g and D2 = %g, got %v", d1, d2, skewness) } } } for _, d1 := range []float64{1, 100} { for _, d2 := range []float64{2, 4} { f := F{d1, d2, nil} variance := f.Variance() if !math.IsNaN(variance) { t.Errorf("Expected NaN variance for D1 = %g and D2 = %g, got %v", d1, d2, variance) } stdDev := f.StdDev() if !math.IsNaN(stdDev) { t.Errorf("Expected NaN standard deviation for D1 = %g and D2 = %g, got %v", d1, d2, variance) } } } }