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stat/all: reduce random size and test tolerance to decrease testing t… (#181)

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* 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
This commit is contained in:
Brendan Tracey
2017-08-07 18:09:24 -06:00
committed by GitHub
parent 73027c37a4
commit e5448cb39f
12 changed files with 73 additions and 42 deletions
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12
stat/samplemv/sample_test.go
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@@ -82,7 +82,7 @@ func TestImportance(t *testing.T) {
weights := make([]float64, nSamples)
Importance(batch, weights, target, proposal)
compareNormal(t, target, batch, weights)
compareNormal(t, target, batch, weights, 5e-2, 5e-2)
}
func TestRejection(t *testing.T) {
@@ -145,14 +145,14 @@ func TestMetropolisHastings(t *testing.T) {
t.Fatal("bad test, sigma not pos def")
}
nSamples := 1000000
nSamples := 100000
burnin := 5000
batch := mat.NewDense(nSamples, dim, nil)
initial := make([]float64, dim)
MetropolisHastings(batch, initial, target, proposal, nil)
batch = batch.Slice(burnin, nSamples, 0, dim).(*mat.Dense)
compareNormal(t, target, batch, nil)
compareNormal(t, target, batch, nil, 5e-1, 5e-1)
}
// randomNormal constructs a random Normal distribution.
@@ -171,7 +171,7 @@ func randomNormal(dim int) (*distmv.Normal, bool) {
return distmv.NewNormal(mu, &sigma, nil)
}
func compareNormal(t *testing.T, want *distmv.Normal, batch *mat.Dense, weights []float64) {
func compareNormal(t *testing.T, want *distmv.Normal, batch *mat.Dense, weights []float64, meanTol, covTol float64) {
dim := want.Dim()
mu := want.Mean(nil)
sigma := want.CovarianceMatrix(nil)
@@ -185,13 +185,13 @@ func compareNormal(t *testing.T, want *distmv.Normal, batch *mat.Dense, weights
for i := 0; i < dim; i++ {
col := mat.Col(nil, i, batch)
ev := stat.Mean(col, weights)
if math.Abs(ev-mu[i]) > 1e-2 {
if math.Abs(ev-mu[i]) > meanTol {
t.Errorf("Mean mismatch: Want %v, got %v", mu[i], ev)
}
}
cov := stat.CovarianceMatrix(nil, batch, weights)
if !mat.EqualApprox(cov, sigma, 1.5e-1) {
if !mat.EqualApprox(cov, sigma, covTol) {
t.Errorf("Covariance matrix mismatch")
}
}