// test set 3
func Test_randist_3(t *testing.T) {
// test bivariate gaussian distributions
rng_type := Ranlxd2
rng_state := Rng_alloc(rng_type)
// bivariate gaussian
tail := BivariateGaussianSlice(rng_state, 1, 1, 1, numSamples)
first := stats.FloatSlice(make([]float64, numSamples))
second := stats.FloatSlice(make([]float64, numSamples))
for i, v := range tail {
first[i], second[i] = v[0], v[1]
}
if first.Mean(1) > margin {
t.Error("randist: Mean of 1st component of bivariate gaussian is not 0.")
}
if second.Mean(1) > margin {
t.Error("randist: Mean of 2nd component of bivariate gaussian is not 0.")
}
if (first.Sd(1) - 1) > margin {
t.Error("randist: Std of 1st component of bivariate gaussian is not 0.")
}
if (second.Sd(1) - 1) > margin {
t.Error("randist: Std of 2nd component of bivariate gaussian is not 0.")
}
}
// test set 1
func Test_randist_1(t *testing.T) {
// test gaussians
rng_type := Ranlxd2
rng_state := Rng_alloc(rng_type)
// gaussian
gaus := stats.FloatSlice(GaussianSlice(rng_state, 1, numSamples))
if gaus.Mean(1) > margin {
t.Error("randist: Mean of gaussian distribution is not 0.")
}
if (gaus.Sd(1) - 1.0) > margin {
t.Error("randist: Stdev of gaussian distribution is not 1.")
}
// gaussian ziggurat
gaus_zig := stats.FloatSlice(GaussianZigguratSlice(rng_state, 1, numSamples))
if gaus_zig.Mean(1) > margin {
t.Error("randist: Mean of gaussian_zig distribution is not 0.")
}
if (gaus_zig.Sd(1) - 1.0) > margin {
t.Error("randist: Stdev of gaussian_zig distribution is not 1.")
}
// gaussian ratio method
gaus_rat := stats.FloatSlice(GaussianRatioMethodSlice(rng_state, 1, numSamples))
if gaus_rat.Mean(1) > margin {
t.Error("randist: Mean of gaussian_rat distribution is not 0.")
}
if (gaus_rat.Sd(1) - 1.0) > margin {
t.Error("randist: Stdev of gaussian_rat distribution is not 1.")
}
// unit gaussian
ugaus := stats.FloatSlice(UgaussianSlice(rng_state, numSamples))
if ugaus.Mean(1) > margin {
t.Error("randist: Mean of ugaussian distribution is not 0.")
}
if (ugaus.Sd(1) - 1.0) > margin {
t.Error("randist: Stdev of ugaussian distribution is not 1.", ugaus.Sd(1))
}
// unit gaussian ratio method
ugaus_rat := stats.FloatSlice(UgaussianRatioMethodSlice(rng_state, numSamples))
if ugaus_rat.Mean(1) > margin {
t.Error("randist: Mean of ugaussian_rat distribution is not 0.")
}
if (ugaus_rat.Sd(1) - 1.0) > margin {
t.Error("randist: Stdev of ugaussian_rat distribution is not 1.", ugaus_rat.Sd(1))
}
}
// test set 1
func Test_random_1(t *testing.T) {
// test 1
var rng_type RngType
for k, v := range TypesSetup() {
if k == "ranlxd2" {
rng_type = v
}
}
if rng_type.Name() != "ranlxd2" {
t.Error("Test 1: Failed to select ranlxd2 rng type.")
}
rng_state := Rng_alloc(rng_type)
// test 2
nums1 := rng_state.GetSlice(100)
nums1_slice := make(stats.FloatSlice, 100)
for i := 0; i < 100; i++ {
nums1_slice[i] = float64(nums1[i])
}
min, max := nums1_slice.MinMax(1)
if min < float64(rng_state.Min()) || max > float64(rng_state.Max()) {
t.Error("Test 2: Generated random numbers are out of range.")
}
// test 3
nums2 := stats.FloatSlice(rng_state.UniformSlice(100))
min, max = nums2.MinMax(1)
if min < 0 || max >= 1 {
t.Error("Test 3: Generated uniform random numbers are out of range.")
}
// test 4
nums3 := rng_state.UniformIntSlice(900, 100)
nums3_slice := make(stats.FloatSlice, 100)
for i := 0; i < 100; i++ {
nums3_slice[i] = float64(nums3[i])
}
min, max = nums3_slice.MinMax(1)
if min < 0 || max >= 900 {
t.Error("Test 4: Generated uniform int random numbers are out of range.")
}
// test 5
rng_state_1 := rng_state.Clone()
if rng_state_1.Get() != rng_state.Get() {
t.Error("Test 5: Failed to clone rng state.")
}
// test 6
rng_state_2 := Rng_alloc(rng_type)
if rng_state_2.Get() == rng_state.Get() {
t.Error("Test 6: Hmmm, this is very unlikely to happen.")
}
rng_state.Memcpy(rng_state_2)
if rng_state_2.Get() != rng_state.Get() {
t.Error("Test 6: Failed to memcpy rng state.")
}
}