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https://github.com/gonum/gonum.git
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6d596f2b27
Currently, we throw sigma away, and recompute it if necessary. This PR keeps sigma. This fixes an issue with concurrent calling of methods. In addition, however, it removes any possible issues with reconstructing a badly-conditioned sigma from its Cholesky decomposition, and avoids an extra n^3 work if sigma does need to be recomputed. The complexity of the implementation and difficulties listed above is not worth the memory savings in some cases, especially since the memory of the type is already O(n^2)
74 lines
1.5 KiB
Go
74 lines
1.5 KiB
Go
// Copyright ©2016 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 distmv
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import (
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"log"
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"math/rand"
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"testing"
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"github.com/gonum/matrix/mat64"
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)
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func BenchmarkMarginalNormal10(b *testing.B) {
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sz := 10
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rnd := rand.New(rand.NewSource(1))
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normal := randomNormal(sz, rnd)
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_ = normal.CovarianceMatrix(nil) // pre-compute sigma
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b.ResetTimer()
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for i := 0; i < b.N; i++ {
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marg, ok := normal.MarginalNormal([]int{1}, nil)
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if !ok {
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b.Error("bad test")
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}
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_ = marg
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}
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}
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func BenchmarkMarginalNormalReset10(b *testing.B) {
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sz := 10
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rnd := rand.New(rand.NewSource(1))
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normal := randomNormal(sz, rnd)
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b.ResetTimer()
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for i := 0; i < b.N; i++ {
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marg, ok := normal.MarginalNormal([]int{1}, nil)
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if !ok {
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b.Error("bad test")
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}
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_ = marg
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}
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}
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func BenchmarkMarginalNormalSingle10(b *testing.B) {
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sz := 10
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rnd := rand.New(rand.NewSource(1))
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normal := randomNormal(sz, rnd)
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b.ResetTimer()
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for i := 0; i < b.N; i++ {
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marg := normal.MarginalNormalSingle(1, nil)
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_ = marg
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}
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}
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func randomNormal(sz int, rnd *rand.Rand) *Normal {
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mu := make([]float64, sz)
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for i := range mu {
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mu[i] = rnd.Float64()
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}
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data := make([]float64, sz*sz)
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for i := range data {
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data[i] = rnd.Float64()
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}
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dM := mat64.NewDense(sz, sz, data)
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var sigma mat64.SymDense
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sigma.SymOuterK(1, dM)
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normal, ok := NewNormal(mu, &sigma, nil)
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if !ok {
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log.Fatal("bad test, not pos def")
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}
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return normal
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}
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