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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 TestUniformProb(t *testing.T) { t.Parallel() for _, test := range []struct { min, max, x, want float64 }{ {0, 1, 1, 1}, {2, 4, 0, 0}, {2, 4, 5, 0}, {2, 4, 3, 0.5}, {0, 100, 1, 0.01}, {-1, 1, -1.5, 0}, {-1, 1, 1.5, 0}, } { u := Uniform{test.min, test.max, nil} pdf := u.Prob(test.x) if !scalar.EqualWithinAbsOrRel(pdf, test.want, 1e-15, 1e-15) { t.Errorf("PDF mismatch, x = %v, min = %v, max = %v. Got %v, want %v", test.x, test.min, test.max, pdf, test.want) } logWant := math.Log(test.want) logPdf := u.LogProb(test.x) if !scalar.EqualWithinAbsOrRel(logPdf, logWant, 1e-15, 1e-15) { t.Errorf("Log PDF mismatch, x = %v, min = %v, max = %v. Got %v, want %v", test.x, test.min, test.max, logPdf, logWant) } } } func TestUniformCDFSurvival(t *testing.T) { t.Parallel() for _, test := range []struct { min, max, x, want float64 }{ {0, 1, 1, 1}, {0, 100, 100, 1}, {0, 100, 0, 0}, {0, 100, 50, 0.5}, {0, 50, 10, 0.2}, {-1, 1, -1.5, 0}, {-1, 1, 1.5, 1}, } { u := Uniform{test.min, test.max, nil} cdf := u.CDF(test.x) if !scalar.EqualWithinAbsOrRel(cdf, test.want, 1e-15, 1e-15) { t.Errorf("CDF mismatch, x = %v, min = %v, max = %v. Got %v, want %v", test.x, test.min, test.max, cdf, test.want) } survival := u.Survival(test.x) if !scalar.EqualWithinAbsOrRel(survival, 1-test.want, 1e-15, 1e-15) { t.Errorf("CDF mismatch, x = %v, min = %v, max = %v. Got %v, want %v", test.x, test.min, test.max, survival, 1-test.want) } } } func TestUniform(t *testing.T) { t.Parallel() src := rand.New(rand.NewSource(1)) for i, b := range []Uniform{ {1, 2, src}, {0, 100, src}, {50, 60, src}, } { testUniform(t, b, i) } } func testUniform(t *testing.T, u Uniform, i int) { const ( tol = 1e-2 n = 1e5 bins = 50 ) x := make([]float64, n) generateSamples(x, u) sort.Float64s(x) testRandLogProbContinuous(t, i, 0, x, u, tol, bins) checkMean(t, i, x, u, tol) checkVarAndStd(t, i, x, u, tol) checkExKurtosis(t, i, x, u, 7e-2) checkProbContinuous(t, i, x, u.Min, u.Max, u, 1e-10) checkQuantileCDFSurvival(t, i, x, u, 1e-2) checkEntropy(t, i, x, u, tol) checkSkewness(t, i, x, u, tol) checkMedian(t, i, x, u, tol) testDerivParam(t, &u) } func TestUniformScore(t *testing.T) { t.Parallel() u := Uniform{0, 1, nil} for _, test := range []struct { x, wantMin, wantMax float64 }{ {-0.001, math.NaN(), math.NaN()}, {0, math.NaN(), -1}, {1, 1, math.NaN()}, {1.001, math.NaN(), math.NaN()}, } { score := u.Score(nil, test.x) if !scalar.Same(score[0], test.wantMin) { t.Errorf("Score[0] mismatch for at %g: got %v, want %g", test.x, score[0], test.wantMin) } if !scalar.Same(score[1], test.wantMax) { t.Errorf("Score[1] mismatch for at %g: got %v, want %g", test.x, score[1], test.wantMax) } } } func TestUniformScoreInput(t *testing.T) { t.Parallel() u := Uniform{0, 1, nil} scoreInput := u.ScoreInput(0.5) if scoreInput != 0 { t.Errorf("Mismatch in input score for U(0, 1) at x == 0.5: got %v, want 0", scoreInput) } xs := []float64{-0.0001, 0, 1, 1.0001} for _, x := range xs { scoreInput = u.ScoreInput(x) if !math.IsNaN(scoreInput) { t.Errorf("Expected NaN score input for U(0, 1) at x == %g, got %v", x, scoreInput) } } }