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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
100 lines
2.5 KiB
Go
100 lines
2.5 KiB
Go
// Copyright ©2015 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 sampleuv
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import (
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"math"
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"sort"
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"testing"
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"gonum.org/v1/gonum/stat"
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"gonum.org/v1/gonum/stat/distuv"
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)
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type lhDist interface {
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Quantile(float64) float64
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CDF(float64) float64
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}
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func TestLatinHypercube(t *testing.T) {
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for _, nSamples := range []int{1, 2, 5, 10, 20} {
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samples := make([]float64, nSamples)
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for _, dist := range []lhDist{
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distuv.Uniform{Min: 0, Max: 1},
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distuv.Uniform{Min: 0, Max: 10},
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distuv.Normal{Mu: 5, Sigma: 3},
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} {
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LatinHypercube(samples, dist, nil)
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sort.Float64s(samples)
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for i, v := range samples {
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p := dist.CDF(v)
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if p < float64(i)/float64(nSamples) || p > float64(i+1)/float64(nSamples) {
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t.Errorf("probability out of bounds")
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}
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}
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}
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}
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}
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func TestImportance(t *testing.T) {
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// Test by finding the expected value of a Normal.
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trueMean := 3.0
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target := distuv.Normal{Mu: trueMean, Sigma: 2}
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proposal := distuv.Normal{Mu: 0, Sigma: 5}
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nSamples := 100000
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x := make([]float64, nSamples)
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weights := make([]float64, nSamples)
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Importance(x, weights, target, proposal)
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ev := stat.Mean(x, weights)
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if math.Abs(ev-trueMean) > 1e-2 {
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t.Errorf("Mean mismatch: Want %v, got %v", trueMean, ev)
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}
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}
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func TestRejection(t *testing.T) {
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// Test by finding the expected value of a Normal.
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trueMean := 3.0
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target := distuv.Normal{Mu: trueMean, Sigma: 2}
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proposal := distuv.Normal{Mu: 0, Sigma: 5}
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nSamples := 20000
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x := make([]float64, nSamples)
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Rejection(x, target, proposal, 100, nil)
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ev := stat.Mean(x, nil)
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if math.Abs(ev-trueMean) > 2e-2 {
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t.Errorf("Mean mismatch: Want %v, got %v", trueMean, ev)
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}
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}
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type condNorm struct {
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Sigma float64
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}
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func (c condNorm) ConditionalRand(y float64) float64 {
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return distuv.Normal{Mu: y, Sigma: c.Sigma}.Rand()
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}
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func (c condNorm) ConditionalLogProb(x, y float64) float64 {
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return distuv.Normal{Mu: y, Sigma: c.Sigma}.LogProb(x)
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}
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func TestMetropolisHastings(t *testing.T) {
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// Test by finding the expected value of a Normal.
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trueMean := 3.0
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target := distuv.Normal{Mu: trueMean, Sigma: 2}
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proposal := condNorm{Sigma: 5}
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burnin := 500
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nSamples := 100000 + burnin
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x := make([]float64, nSamples)
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MetropolisHastings(x, 100, target, proposal, nil)
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// Remove burnin
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x = x[burnin:]
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ev := stat.Mean(x, nil)
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if math.Abs(ev-trueMean) > 1e-2 {
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t.Errorf("Mean mismatch: Want %v, got %v", trueMean, ev)
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}
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}
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