func S1(r *rng.GslRng, xp interface{}, stepSize float64) {
ptr := xp.(*float64)
oldX := *ptr
u := rng.Uniform(r)
newX := u*2*stepSize - stepSize + oldX
*ptr = newX
}
func TestRng(t *testing.T) {
var n int = 10
rng.EnvSetup()
T := rng.DefaultRngType()
r := rng.RngAlloc(T)
for i := 0; i < n; i++ {
u := rng.Uniform(r)
fmt.Printf("%.5f\n", u)
}
fmt.Println()
}
func TestHistogram(t *testing.T) {
h := histogram.Histogram2dAlloc(10, 10)
histogram.Histogram2dSetRangesUniform(h, 0.0, 1.0, 0.0, 1.0)
histogram.Histogram2dAccumulate(h, 0.3, 0.3, 1)
histogram.Histogram2dAccumulate(h, 0.8, 0.1, 5)
histogram.Histogram2dAccumulate(h, 0.7, 0.9, 0.5)
rng.EnvSetup()
T := rng.DefaultRngType()
r := rng.RngAlloc(T)
hDim := h.Dim()
p := histogram.Histogram2dPdfAlloc(hDim[0], hDim[1])
histogram.Histogram2dPdfInit(p, h)
for i := 0; i < 1000; i++ {
u := rng.Uniform(r)
v := rng.Uniform(r)
_, x, y := histogram.Histogram2dPdfSample(p, u, v)
fmt.Printf("%g %g\n", x, y)
}
}
func TestSort(t *testing.T) {
var n int = 100000
var k int = 5
x := make([]float64, n)
small := make([]float64, k)
rng.EnvSetup()
T := rng.DefaultRngType()
r := rng.RngAlloc(T)
for i := 0; i < n; i++ {
x[i] = rng.Uniform(r)
}
sort.SortSmallest(small, k, x, 1, n)
fmt.Printf("%d smallest values from %d\n", k, n)
for i := 0; i < k; i++ {
fmt.Printf("%d: %.18f\n", i, small[i])
}
}
func TestSortVectorIndex(t *testing.T) {
var n int = 10000
var k int = 5
v := vector.VectorAlloc(n)
p := permutation.PermutationAlloc(n)
rng.EnvSetup()
T := rng.DefaultRngType()
r := rng.RngAlloc(T)
for i := 0; i < n; i++ {
vector.Set(v, i, rng.Uniform(r))
}
sort.SortVectorIndex(p, v)
pData := p.Slice_().([]int)
for i := 0; i < k; i++ {
vpi := vector.Get(v, pData[i])
fmt.Printf("order = %d, value = %g\n", i, vpi)
}
}
func TestSiman(t *testing.T) {
params := &siman.GslSimanParams{
NumTries: N_TRIES,
ItersFixed: ITERS_FIXED_T,
StepSize: STEP_SIZE,
K: K,
TInitial: T_INITIAL,
Mu: MU_T,
TMin: T_MIN,
}
siman.InitializeGslSimanParams(params)
var xInitial float64 = 15.5
rng.EnvSetup()
T := rng.DefaultRngType()
r := rng.RngAlloc(T)
fmt.Println(rng.Uniform(r))
siman.Solve(r, &xInitial, E1, S1, M1, P1, nil, nil, nil, params)
}
func TestRobust(t *testing.T) {
var p int = 2 // linear fit
var a float64 = 1.45 // data slope
var b float64 = 3.88 // data intercept
var n int = 20
X := matrix.MatrixAlloc(n, p)
x := vector.VectorAlloc(n)
y := vector.VectorAlloc(n)
c := vector.VectorAlloc(p)
cOls := vector.VectorAlloc(p)
cov := matrix.MatrixAlloc(p, p)
r := rng.RngAlloc(rng.DefaultRngType())
// generate linear dataset
for i := 0; i < n-3; i++ {
dx := 10.0 / (float64(n) - 1.0)
ei := rng.Uniform(r)
xi := -5.0 + float64(i)*dx
yi := a*xi + b
vector.Set(x, i, xi)
vector.Set(y, i, yi+ei)
}
// add a few outliers
vector.Set(x, n-3, 4.7)
vector.Set(y, n-3, -8.3)
vector.Set(x, n-2, 3.5)
vector.Set(y, n-2, -6.7)
vector.Set(x, n-1, 4.1)
vector.Set(y, n-1, -6.0)
// construct design matrix X for linear fit
for i := 0; i < n; i++ {
xi := vector.Get(x, i)
matrix.Set(X, i, 0, 1.0)
matrix.Set(X, i, 1, xi)
}
// perform robust and OLS fit
DoFit(multifit.GSL_MULTIFIT_ROBUST_OLS, X, y, cOls, cov)
DoFit(multifit.GSL_MULTIFIT_ROBUST_BISQUARE, X, y, c, cov)
// output data and model
for i := 0; i < n; i++ {
xi := vector.Get(x, i)
yi := vector.Get(y, i)
v := matrix.Row(X, i).Vector()
_, yRob, _ := multifit.RobustEst(v, c, cov)
_, yOls, _ := multifit.RobustEst(v, cOls, cov)
fmt.Printf("%g %g %g %g\n", xi, yi, yRob, yOls)
}
fmt.Printf("# best fit: Y = %g + %g X\n", vector.Get(c, 0), vector.Get(c, 1))
fmt.Printf("# covariance matrix:\n")
fmt.Printf("# [ %+.5e, %+.5e\n", matrix.Get(cov, 0, 0), matrix.Get(cov, 0, 1))
fmt.Printf("# %+.5e, %+.5e\n", matrix.Get(cov, 1, 0), matrix.Get(cov, 1, 1))
}
