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 TestRandist(t *testing.T) {
var n int = 10
var mu float64 = 3.0
rng.EnvSetup()
T := rng.DefaultRngType()
r := rng.RngAlloc(T)
for i := 0; i < n; i++ {
k := randist.Poisson(r, mu)
fmt.Printf(" %d", k)
}
fmt.Println()
}
func MakeData(start float64, end float64, inc float64) []float64 {
var data []float64
rng.EnvSetup()
T := rng.DefaultRngType()
r := rng.RngAlloc(T)
for x := start; x < end; x += inc {
y0 := math.Exp(x)
sigma := inc * y0
dy := randist.Gaussian(r, sigma)
data = append(data, []float64{x, y0 + dy, sigma}...)
}
return data
}
func TestNtupleWrite(t *testing.T) {
dataBuf := make([]Data, 1)
nt := ntuple.Create(TEMP_FILE_NAME, dataBuf)
rng.EnvSetup()
T := rng.DefaultRngType()
r := rng.RngAlloc(T)
for i := 0; i < 10000; i++ {
dataBuf[0].x = randist.Ugaussian(r)
dataBuf[0].y = randist.Ugaussian(r)
dataBuf[0].z = randist.Ugaussian(r)
ntuple.Write(nt)
}
ntuple.Close(nt)
}
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 TestPermutation(t *testing.T) {
var N int = 15
p := permutation.PermutationAlloc(N)
q := permutation.PermutationAlloc(N)
rng.EnvSetup()
T := rng.DefaultRngType()
r := rng.RngAlloc(T)
fmt.Printf("initial permutation: ")
permutation.PermutationInit(p)
permutation.Fprintf(os.Stdout, p, " %u")
os.Stdout.Sync()
fmt.Printf("\n")
fmt.Printf(" random permutation: ")
randist.Shuffle(r, p.Slice_(), p.Len())
permutation.Fprintf(os.Stdout, p, " %u")
fmt.Printf("\n")
fmt.Printf(" inverse permutation: ")
permutation.Inverse(q, p)
permutation.Fprintf(os.Stdout, q, " %u")
fmt.Printf("\n")
}
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 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 TestMonte(t *testing.T) {
xl := []float64{0, 0, 0}
xu := []float64{math.Pi, math.Pi, math.Pi}
G := &monte.GslMonteFunction{
Function: g,
Dim: 3,
}
monte.InitializeGslMonteFunction(G)
var calls int = 500000
rng.EnvSetup()
T := rng.DefaultRngType()
r := rng.RngAlloc(T)
sp := monte.PlainAlloc(3)
_, res, err := monte.PlainIntegrate(G, xl, xu, 3, calls, r, sp)
DisplayResults("plain", res, err)
sm := monte.MiserAlloc(3)
_, res, err = monte.MiserIntegrate(G, xl, xu, 3, calls, r, sm)
DisplayResults("miser", res, err)
sv := monte.VegasAlloc(3)
_, res, err = monte.VegasIntegrate(G, xl, xu, 3, 10000, r, sv)
DisplayResults("vegas warm-up", res, err)
fmt.Printf("converging...\n")
for {
_, res, err = monte.VegasIntegrate(G, xl, xu, 3, calls/5, r, sv)
fmt.Printf("result = % .6f sigma = % .6f "+
"chisq/dof = %.1f\n", res, err, monte.VegasChisq(sv))
if math.Abs(monte.VegasChisq(sv))-1.0 <= 0.5 {
break
}
}
}
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))
}
func TestBspline(t *testing.T) {
var n int = 200
var ncoeffs int = 12
var nbreak int = ncoeffs - 2
rng.EnvSetup()
r := rng.RngAlloc(rng.DefaultRngType())
// allocate a cubic bspline workspace (k = 4)
bw := bspline.Alloc(4, nbreak)
B := vector.VectorAlloc(ncoeffs)
x := vector.VectorAlloc(n)
y := vector.VectorAlloc(n)
X := matrix.MatrixAlloc(n, ncoeffs)
c := vector.VectorAlloc(ncoeffs)
w := vector.VectorAlloc(n)
cov := matrix.MatrixAlloc(ncoeffs, ncoeffs)
mw := multifit.LinearAlloc(n, ncoeffs)
fmt.Printf("#m=0,S=0\n")
// this is the data to be fitted
for i := 0; i < n; i++ {
xi := (15.0 / (float64(n) - 1)) * float64(i)
yi := math.Cos(xi) * math.Exp(-0.1*xi)
sigma := 0.1 * yi
dy := randist.Gaussian(r, sigma)
vector.Set(x, i, xi)
vector.Set(y, i, yi+dy)
vector.Set(w, i, 1.0/(sigma*sigma))
fmt.Printf("%f %f\n", xi, yi+dy)
}
// use uniform breakpoints on [0, 15]
bspline.KnotsUniform(0.0, 15.0, bw)
// construct the fit matrix X
for i := 0; i < n; i++ {
xi := vector.Get(x, i)
// compute B_j(xi) for all j
bspline.Eval(xi, B, bw)
// fill in row i of X
for j := 0; j < ncoeffs; j++ {
matrix.Set(X, i, j, vector.Get(B, j))
}
}
// do the fit
_, chisq := multifit.Wlinear(X, w, y, c, cov, mw)
dof := float64(n - ncoeffs)
tss := stats.Wtss(w.Data_(), w.Stride(), y.Data_(), y.Stride(), n)
rsq := 1.0 - chisq/tss
fmt.Printf("chisq/dof = %e, Rsq = %f\n", chisq/dof, rsq)
fmt.Printf("#m=1,S=0\n")
for xi := 0.0; xi < 15.0; xi += 0.1 {
bspline.Eval(xi, B, bw)
_, yi, _ := multifit.LinearEst(B, c, cov)
fmt.Printf("%f %f\n", xi, yi)
}
}