| // Copyright 2009 The Go 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 rand |
| |
| import ( |
| "math" |
| "fmt" |
| "os" |
| "testing" |
| ) |
| |
| const ( |
| numTestSamples = 10000 |
| ) |
| |
| type statsResults struct { |
| mean float64 |
| stddev float64 |
| closeEnough float64 |
| maxError float64 |
| } |
| |
| func max(a, b float64) float64 { |
| if a > b { |
| return a |
| } |
| return b |
| } |
| |
| func nearEqual(a, b, closeEnough, maxError float64) bool { |
| absDiff := math.Fabs(a - b) |
| if absDiff < closeEnough { // Necessary when one value is zero and one value is close to zero. |
| return true |
| } |
| return absDiff/max(math.Fabs(a), math.Fabs(b)) < maxError |
| } |
| |
| var testSeeds = []int64{1, 1754801282, 1698661970, 1550503961} |
| |
| // checkSimilarDistribution returns success if the mean and stddev of the |
| // two statsResults are similar. |
| func (this *statsResults) checkSimilarDistribution(expected *statsResults) os.Error { |
| if !nearEqual(this.mean, expected.mean, expected.closeEnough, expected.maxError) { |
| s := fmt.Sprintf("mean %v != %v (allowed error %v, %v)", this.mean, expected.mean, expected.closeEnough, expected.maxError) |
| fmt.Println(s) |
| return os.ErrorString(s) |
| } |
| if !nearEqual(this.stddev, expected.stddev, 0, expected.maxError) { |
| s := fmt.Sprintf("stddev %v != %v (allowed error %v, %v)", this.stddev, expected.stddev, expected.closeEnough, expected.maxError) |
| fmt.Println(s) |
| return os.ErrorString(s) |
| } |
| return nil |
| } |
| |
| func getStatsResults(samples []float64) *statsResults { |
| res := new(statsResults) |
| var sum float64 |
| for i := range samples { |
| sum += samples[i] |
| } |
| res.mean = sum / float64(len(samples)) |
| var devsum float64 |
| for i := range samples { |
| devsum += math.Pow(samples[i]-res.mean, 2) |
| } |
| res.stddev = math.Sqrt(devsum / float64(len(samples))) |
| return res |
| } |
| |
| func checkSampleDistribution(t *testing.T, samples []float64, expected *statsResults) { |
| actual := getStatsResults(samples) |
| err := actual.checkSimilarDistribution(expected) |
| if err != nil { |
| t.Errorf(err.String()) |
| } |
| } |
| |
| func checkSampleSliceDistributions(t *testing.T, samples []float64, nslices int, expected *statsResults) { |
| chunk := len(samples) / nslices |
| for i := 0; i < nslices; i++ { |
| low := i * chunk |
| var high int |
| if i == nslices-1 { |
| high = len(samples) - 1 |
| } else { |
| high = (i + 1) * chunk |
| } |
| checkSampleDistribution(t, samples[low:high], expected) |
| } |
| } |
| |
| // |
| // Normal distribution tests |
| // |
| |
| func generateNormalSamples(nsamples int, mean, stddev float64, seed int64) []float64 { |
| r := New(NewSource(seed)) |
| samples := make([]float64, nsamples) |
| for i := range samples { |
| samples[i] = r.NormFloat64()*stddev + mean |
| } |
| return samples |
| } |
| |
| func testNormalDistribution(t *testing.T, nsamples int, mean, stddev float64, seed int64) { |
| //fmt.Printf("testing nsamples=%v mean=%v stddev=%v seed=%v\n", nsamples, mean, stddev, seed); |
| |
| samples := generateNormalSamples(nsamples, mean, stddev, seed) |
| errorScale := max(1.0, stddev) // Error scales with stddev |
| expected := &statsResults{mean, stddev, 0.10 * errorScale, 0.08 * errorScale} |
| |
| // Make sure that the entire set matches the expected distribution. |
| checkSampleDistribution(t, samples, expected) |
| |
| // Make sure that each half of the set matches the expected distribution. |
| checkSampleSliceDistributions(t, samples, 2, expected) |
| |
| // Make sure that each 7th of the set matches the expected distribution. |
| checkSampleSliceDistributions(t, samples, 7, expected) |
| } |
| |
| // Actual tests |
| |
| func TestStandardNormalValues(t *testing.T) { |
| for _, seed := range testSeeds { |
| testNormalDistribution(t, numTestSamples, 0, 1, seed) |
| } |
| } |
| |
| func TestNonStandardNormalValues(t *testing.T) { |
| for sd := float64(0.5); sd < 1000; sd *= 2 { |
| for m := float64(0.5); m < 1000; m *= 2 { |
| for _, seed := range testSeeds { |
| testNormalDistribution(t, numTestSamples, m, sd, seed) |
| } |
| } |
| } |
| } |
| |
| // |
| // Exponential distribution tests |
| // |
| |
| func generateExponentialSamples(nsamples int, rate float64, seed int64) []float64 { |
| r := New(NewSource(seed)) |
| samples := make([]float64, nsamples) |
| for i := range samples { |
| samples[i] = r.ExpFloat64() / rate |
| } |
| return samples |
| } |
| |
| func testExponentialDistribution(t *testing.T, nsamples int, rate float64, seed int64) { |
| //fmt.Printf("testing nsamples=%v rate=%v seed=%v\n", nsamples, rate, seed); |
| |
| mean := 1 / rate |
| stddev := mean |
| |
| samples := generateExponentialSamples(nsamples, rate, seed) |
| errorScale := max(1.0, 1/rate) // Error scales with the inverse of the rate |
| expected := &statsResults{mean, stddev, 0.10 * errorScale, 0.20 * errorScale} |
| |
| // Make sure that the entire set matches the expected distribution. |
| checkSampleDistribution(t, samples, expected) |
| |
| // Make sure that each half of the set matches the expected distribution. |
| checkSampleSliceDistributions(t, samples, 2, expected) |
| |
| // Make sure that each 7th of the set matches the expected distribution. |
| checkSampleSliceDistributions(t, samples, 7, expected) |
| } |
| |
| // Actual tests |
| |
| func TestStandardExponentialValues(t *testing.T) { |
| for _, seed := range testSeeds { |
| testExponentialDistribution(t, numTestSamples, 1, seed) |
| } |
| } |
| |
| func TestNonStandardExponentialValues(t *testing.T) { |
| for rate := float64(0.05); rate < 10; rate *= 2 { |
| for _, seed := range testSeeds { |
| testExponentialDistribution(t, numTestSamples, rate, seed) |
| } |
| } |
| } |
| |
| // |
| // Table generation tests |
| // |
| |
| func initNorm() (testKn []uint32, testWn, testFn []float32) { |
| const m1 = 1 << 31 |
| var ( |
| dn float64 = rn |
| tn = dn |
| vn float64 = 9.91256303526217e-3 |
| ) |
| |
| testKn = make([]uint32, 128) |
| testWn = make([]float32, 128) |
| testFn = make([]float32, 128) |
| |
| q := vn / math.Exp(-0.5*dn*dn) |
| testKn[0] = uint32((dn / q) * m1) |
| testKn[1] = 0 |
| testWn[0] = float32(q / m1) |
| testWn[127] = float32(dn / m1) |
| testFn[0] = 1.0 |
| testFn[127] = float32(math.Exp(-0.5 * dn * dn)) |
| for i := 126; i >= 1; i-- { |
| dn = math.Sqrt(-2.0 * math.Log(vn/dn+math.Exp(-0.5*dn*dn))) |
| testKn[i+1] = uint32((dn / tn) * m1) |
| tn = dn |
| testFn[i] = float32(math.Exp(-0.5 * dn * dn)) |
| testWn[i] = float32(dn / m1) |
| } |
| return |
| } |
| |
| func initExp() (testKe []uint32, testWe, testFe []float32) { |
| const m2 = 1 << 32 |
| var ( |
| de float64 = re |
| te = de |
| ve float64 = 3.9496598225815571993e-3 |
| ) |
| |
| testKe = make([]uint32, 256) |
| testWe = make([]float32, 256) |
| testFe = make([]float32, 256) |
| |
| q := ve / math.Exp(-de) |
| testKe[0] = uint32((de / q) * m2) |
| testKe[1] = 0 |
| testWe[0] = float32(q / m2) |
| testWe[255] = float32(de / m2) |
| testFe[0] = 1.0 |
| testFe[255] = float32(math.Exp(-de)) |
| for i := 254; i >= 1; i-- { |
| de = -math.Log(ve/de + math.Exp(-de)) |
| testKe[i+1] = uint32((de / te) * m2) |
| te = de |
| testFe[i] = float32(math.Exp(-de)) |
| testWe[i] = float32(de / m2) |
| } |
| return |
| } |
| |
| // compareUint32Slices returns the first index where the two slices |
| // disagree, or <0 if the lengths are the same and all elements |
| // are identical. |
| func compareUint32Slices(s1, s2 []uint32) int { |
| if len(s1) != len(s2) { |
| if len(s1) > len(s2) { |
| return len(s2) + 1 |
| } |
| return len(s1) + 1 |
| } |
| for i := range s1 { |
| if s1[i] != s2[i] { |
| return i |
| } |
| } |
| return -1 |
| } |
| |
| // compareFloat32Slices returns the first index where the two slices |
| // disagree, or <0 if the lengths are the same and all elements |
| // are identical. |
| func compareFloat32Slices(s1, s2 []float32) int { |
| if len(s1) != len(s2) { |
| if len(s1) > len(s2) { |
| return len(s2) + 1 |
| } |
| return len(s1) + 1 |
| } |
| for i := range s1 { |
| if !nearEqual(float64(s1[i]), float64(s2[i]), 0, 1e-7) { |
| return i |
| } |
| } |
| return -1 |
| } |
| |
| func TestNormTables(t *testing.T) { |
| testKn, testWn, testFn := initNorm() |
| if i := compareUint32Slices(kn[0:], testKn); i >= 0 { |
| t.Errorf("kn disagrees at index %v; %v != %v\n", i, kn[i], testKn[i]) |
| } |
| if i := compareFloat32Slices(wn[0:], testWn); i >= 0 { |
| t.Errorf("wn disagrees at index %v; %v != %v\n", i, wn[i], testWn[i]) |
| } |
| if i := compareFloat32Slices(fn[0:], testFn); i >= 0 { |
| t.Errorf("fn disagrees at index %v; %v != %v\n", i, fn[i], testFn[i]) |
| } |
| } |
| |
| func TestExpTables(t *testing.T) { |
| testKe, testWe, testFe := initExp() |
| if i := compareUint32Slices(ke[0:], testKe); i >= 0 { |
| t.Errorf("ke disagrees at index %v; %v != %v\n", i, ke[i], testKe[i]) |
| } |
| if i := compareFloat32Slices(we[0:], testWe); i >= 0 { |
| t.Errorf("we disagrees at index %v; %v != %v\n", i, we[i], testWe[i]) |
| } |
| if i := compareFloat32Slices(fe[0:], testFe); i >= 0 { |
| t.Errorf("fe disagrees at index %v; %v != %v\n", i, fe[i], testFe[i]) |
| } |
| } |
| |
| // Benchmarks |
| |
| func BenchmarkInt63Threadsafe(b *testing.B) { |
| for n := b.N; n > 0; n-- { |
| Int63() |
| } |
| } |
| |
| func BenchmarkInt63Unthreadsafe(b *testing.B) { |
| r := New(NewSource(1)) |
| for n := b.N; n > 0; n-- { |
| r.Int63() |
| } |
| } |
| |
| func BenchmarkIntn1000(b *testing.B) { |
| r := New(NewSource(1)) |
| for n := b.N; n > 0; n-- { |
| r.Intn(1000) |
| } |
| } |
| |
| func BenchmarkInt63n1000(b *testing.B) { |
| r := New(NewSource(1)) |
| for n := b.N; n > 0; n-- { |
| r.Int63n(1000) |
| } |
| } |
| |
| func BenchmarkInt31n1000(b *testing.B) { |
| r := New(NewSource(1)) |
| for n := b.N; n > 0; n-- { |
| r.Int31n(1000) |
| } |
| } |