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e5448cb39f
* stat/all: reduce random size and test tolerance to decrease testing time. We were generating a lot of random numbers, which is slow. Decrease the size of those random numbers, and in some cases increase the tolerance to compensate. In a couple cases, pull out code from testFullDist to allow for more fine-grained testing. This decrases: distmat from 4.5s to 0.5s distmv from 24.8s to 9s distuv from 65.2s to 13s samplemv from 2.8s to 1.2s sampleuv from 3.5s to 2.1s
102 lines
1.7 KiB
Go
102 lines
1.7 KiB
Go
// Copyright ©2017 The gonum Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style
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// license that can be found in the LICENSE file.
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package distuv
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import (
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"math"
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"math/rand"
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"sort"
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"testing"
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)
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func TestTriangleConstraint(t *testing.T) {
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defer func() {
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if r := recover(); r == nil {
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t.Errorf("The constraints were violated, but not caught")
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}
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}()
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// test b < a
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NewTriangle(3, 1, 2)
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// test c > b
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NewTriangle(1, 2, 3)
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}
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func TestTriangle(t *testing.T) {
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src := rand.New(rand.NewSource(1))
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for i, test := range []struct {
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a, b, c float64
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}{
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{
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a: 0.0,
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b: 1.0,
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c: 0.5,
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},
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{
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a: 0.1,
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b: 0.3,
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c: 0.2,
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},
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{
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a: 1.0,
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b: 2.0,
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c: 1.5,
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},
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{
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a: 0.0,
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b: 1.0,
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c: 0.0,
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},
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} {
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f := NewTriangle(test.a, test.b, test.c)
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f.Source = src
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tol := 1e-2
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const n = 1e5
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x := make([]float64, n)
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generateSamples(x, f)
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sort.Float64s(x)
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checkMean(t, i, x, f, tol)
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checkVarAndStd(t, i, x, f, tol)
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checkEntropy(t, i, x, f, tol)
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checkExKurtosis(t, i, x, f, tol)
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checkSkewness(t, i, x, f, 5e-2)
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checkMedian(t, i, x, f, tol)
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checkQuantileCDFSurvival(t, i, x, f, tol)
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checkProbContinuous(t, i, x, f, 1e-10)
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checkProbQuantContinuous(t, i, x, f, tol)
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}
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}
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func TestTriangleProb(t *testing.T) {
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pts := []univariateProbPoint{
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{
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loc: 0.5,
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prob: 0,
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cumProb: 0,
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logProb: math.Inf(-1),
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},
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{
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loc: 1,
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prob: 0,
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cumProb: 0,
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logProb: math.Inf(-1),
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},
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{
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loc: 2,
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prob: 1.0,
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cumProb: 0.5,
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logProb: 0,
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},
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{
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loc: 3,
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prob: 0,
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cumProb: 1,
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logProb: math.Inf(-1),
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},
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}
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testDistributionProbs(t, NewTriangle(1, 3, 2), "Standard 1,2,3 Triangle", pts)
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}
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