func testDerivParam(t *testing.T, d derivParamTester) {
// Tests that the derivative matches for a number of different quantiles
// along the distribution.
nTest := 10
quantiles := make([]float64, nTest)
floats.Span(quantiles, 0.1, 0.9)
deriv := make([]float64, d.NumParameters())
fdDeriv := make([]float64, d.NumParameters())
initParams := d.parameters(nil)
init := make([]float64, d.NumParameters())
for i, v := range initParams {
init[i] = v.Value
}
for _, v := range quantiles {
d.setParameters(initParams)
x := d.Quantile(v)
d.DLogProbDParam(x, deriv)
f := func(p []float64) float64 {
params := d.parameters(nil)
for i, v := range p {
params[i].Value = v
}
d.setParameters(params)
return d.LogProb(x)
}
fd.Gradient(fdDeriv, f, init, nil)
if !floats.EqualApprox(deriv, fdDeriv, 1e-6) {
t.Fatal("Derivative mismatch. Want", fdDeriv, ", got", deriv, ".")
}
}
}
func ExampleHistogram() {
x := make([]float64, 101)
for i := range x {
x[i] = 1.1 * float64(i) // x data ranges from 0 to 110
}
dividers := []float64{7, 20, 100, 1000}
fmt.Println(`Histogram counts the amount of data in the bins specified by
the dividers. In this data set, there are 7 data points less than 7 (dividers[0]),
12 data points between 7 and 20 (dividers[2] and dividers[1]), and 0 data points
above 1000. Since dividers has length 4, there will be 5 bins.`)
hist := Histogram(nil, dividers, x, nil)
fmt.Printf("Hist = %v\n", hist)
fmt.Println()
fmt.Println("For ease, the floats Span function can be used to set the dividers")
nBins := 10
// Create one fewer divider than bins, but add two to work with Span (see
// note below)
dividers = make([]float64, nBins+1)
min, _ := floats.Min(x)
max, _ := floats.Max(x)
floats.Span(dividers, min, max)
// Span includes the min and the max. Trim the dividers to create 10 buckets
dividers = dividers[1 : len(dividers)-1]
fmt.Println("len dividers = ", len(dividers))
hist = Histogram(nil, dividers, x, nil)
fmt.Printf("Hist = %v\n", hist)
fmt.Println()
fmt.Println(`Histogram also works with weighted data, and allows reusing of
the count field in order to avoid extra garbage`)
weights := make([]float64, len(x))
for i := range weights {
weights[i] = float64(i + 1)
}
Histogram(hist, dividers, x, weights)
fmt.Printf("Weighted Hist = %v\n", hist)
// Output:
// Histogram counts the amount of data in the bins specified by
// the dividers. In this data set, there are 7 data points less than 7 (dividers[0]),
// 12 data points between 7 and 20 (dividers[2] and dividers[1]), and 0 data points
// above 1000. Since dividers has length 4, there will be 5 bins.
// Hist = [7 12 72 10 0]
//
// For ease, the floats Span function can be used to set the dividers
// len dividers = 9
// Hist = [11 10 10 10 9 11 10 10 9 11]
//
// Histogram also works with weighted data, and allows reusing of
// the count field in order to avoid extra garbage
// Weighted Hist = [77 175 275 375 423 627 675 775 783 1067]
}
func ExampleHistogram() {
x := make([]float64, 101)
for i := range x {
x[i] = 1.1 * float64(i) // x data ranges from 0 to 110
}
dividers := []float64{0, 7, 20, 100, 1000}
fmt.Println(`Histogram counts the amount of data in the bins specified by
the dividers. In this data set, there are 7 data points less than 7 (between dividers[0]
and dividers[1]), 12 data points between 7 and 20 (dividers[1] and dividers[2]),
and 0 data points above 1000. Since dividers has length 5, there will be 4 bins.`)
hist := Histogram(nil, dividers, x, nil)
fmt.Printf("Hist = %v\n", hist)
fmt.Println()
fmt.Println("For ease, the floats Span function can be used to set the dividers")
nBins := 10
dividers = make([]float64, nBins+1)
min := floats.Min(x)
max := floats.Max(x)
// Increase the maximum divider so that the maximum value of x is contained
// within the last bucket.
max += 1
floats.Span(dividers, min, max)
// Span includes the min and the max. Trim the dividers to create 10 buckets
hist = Histogram(nil, dividers, x, nil)
fmt.Printf("Hist = %v\n", hist)
fmt.Println()
fmt.Println(`Histogram also works with weighted data, and allows reusing of
the count field in order to avoid extra garbage`)
weights := make([]float64, len(x))
for i := range weights {
weights[i] = float64(i + 1)
}
Histogram(hist, dividers, x, weights)
fmt.Printf("Weighted Hist = %v\n", hist)
// Output:
// Histogram counts the amount of data in the bins specified by
// the dividers. In this data set, there are 7 data points less than 7 (between dividers[0]
// and dividers[1]), 12 data points between 7 and 20 (dividers[1] and dividers[2]),
// and 0 data points above 1000. Since dividers has length 5, there will be 4 bins.
// Hist = [7 12 72 10]
//
// For ease, the floats Span function can be used to set the dividers
// Hist = [11 10 10 10 10 10 10 10 10 10]
//
// Histogram also works with weighted data, and allows reusing of
// the count field in order to avoid extra garbage
// Weighted Hist = [66 165 265 365 465 565 665 765 865 965]
}
func ExampleSingle() {
fmt.Println("Create a grid using Span")
grid := make([]float64, 3)
floats.Span(grid, 0, 1)
fmt.Println("grid =", grid)
pts := meshgrid.Single(3, grid)
fmt.Println("Generated points:")
for _, v := range pts {
fmt.Println(v)
}
// Output:
// Create a grid using Span
// grid = [0 0.5 1]
// Generated points:
// [0 0 0]
// [0 0 0.5]
// [0 0 1]
// [0 0.5 0]
// [0 0.5 0.5]
// [0 0.5 1]
// [0 1 0]
// [0 1 0.5]
// [0 1 1]
// [0.5 0 0]
// [0.5 0 0.5]
// [0.5 0 1]
// [0.5 0.5 0]
// [0.5 0.5 0.5]
// [0.5 0.5 1]
// [0.5 1 0]
// [0.5 1 0.5]
// [0.5 1 1]
// [1 0 0]
// [1 0 0.5]
// [1 0 1]
// [1 0.5 0]
// [1 0.5 0.5]
// [1 0.5 1]
// [1 1 0]
// [1 1 0.5]
// [1 1 1]
}
// checkProbQuantContinuous checks that the Prob, Rand, and Quantile are all consistent.
// checkProbContinuous only checks that Prob is a valid distribution (integrates
// to 1 and greater than 0). However, this is also true if the PDF of a different
// distribution is used. This checks that PDF is also consistent with the
// CDF implementation and the random samples.
func checkProbQuantContinuous(t *testing.T, i int, xs []float64, c cumulantProber, tol float64) {
ps := make([]float64, 101)
floats.Span(ps, 0, 1)
var xp, x float64
for i, p := range ps {
x = c.Quantile(p)
if p == 0 {
xp = x
if floats.Min(xs) < x {
t.Errorf("Sample of x less than Quantile(0). Case %v.", i)
break
}
continue
}
if p == 1 {
if floats.Max(xs) > x {
t.Errorf("Sample of x greater than Quantile(1). Case %v.", i)
break
}
}
// The integral of the PDF between xp and x should be the difference in
// the quantiles.
q := quad.Fixed(c.Prob, xp, x, 1000, nil, 0)
if math.Abs(q-(p-ps[i-1])) > 1e-10 {
t.Errorf("Integral of PDF doesn't match quantile. Case %v. Want %v, got %v.", i, p-ps[i-1], q)
break
}
pEst := stat.CDF(x, stat.Empirical, xs, nil)
if math.Abs(pEst-p) > tol {
t.Errorf("Empirical CDF doesn't match quantile. Case %v.", i)
}
xp = x
}
}
// testFullDist tests all of the functions of a fullDist.
func testFullDist(t *testing.T, f fullDist, i int) {
tol := 1e-1
const n = 1e6
xs := make([]float64, n)
for i := range xs {
xs[i] = f.Rand()
}
sortedXs := make([]float64, n)
copy(sortedXs, xs)
sort.Float64s(sortedXs)
tmp := make([]float64, n)
// Mean check.
mean := stat.Mean(xs, nil)
if !floats.EqualWithinAbsOrRel(mean, f.Mean(), tol, tol) {
t.Errorf("Mean mismatch case %v: want: %v, got: %v", i, mean, f.Mean())
} else {
mean = f.Mean()
}
// Median check.
median := stat.Quantile(0.5, stat.Empirical, sortedXs, nil)
if !floats.EqualWithinAbsOrRel(median, f.Median(), tol, tol) {
t.Errorf("Median mismatch case %v: want: %v, got: %v", i, median, f.Median())
}
// Variance check.
variance := stat.Variance(xs, nil)
if !floats.EqualWithinAbsOrRel(variance, f.Variance(), tol, tol) {
t.Errorf("Variance mismatch case %v: want: %v, got: %v", i, mean, f.Variance())
} else {
variance = f.Variance()
}
std := math.Sqrt(variance)
if !floats.EqualWithinAbsOrRel(std, f.StdDev(), tol, tol) {
t.Errorf("StdDev mismatch case %v: want: %v, got: %v", i, mean, f.StdDev())
} else {
std = f.StdDev()
}
// Entropy check.
for i, x := range xs {
tmp[i] = -f.LogProb(x)
}
entropy := stat.Mean(tmp, nil)
if !floats.EqualWithinAbsOrRel(entropy, f.Entropy(), tol, tol) {
t.Errorf("Entropy mismatch case %v: want: %v, got: %v", i, entropy, f.Entropy())
}
// Excess Kurtosis check.
for i, x := range xs {
tmp[i] = math.Pow(x-mean, 4)
}
mu4 := stat.Mean(tmp, nil)
kurtosis := mu4/(variance*variance) - 3
if !floats.EqualWithinAbsOrRel(kurtosis, f.ExKurtosis(), tol, tol) {
t.Errorf("ExKurtosis mismatch case %v: want: %v, got: %v", i, kurtosis, f.ExKurtosis())
}
// Skewness check.
for i, x := range xs {
tmp[i] = math.Pow(x-mean, 3)
}
mu3 := stat.Mean(tmp, nil)
skewness := mu3 / math.Pow(std, 3)
if !floats.EqualWithinAbsOrRel(skewness, f.Skewness(), tol, tol) {
t.Errorf("ExKurtosis mismatch case %v: want: %v, got: %v", i, skewness, f.Skewness())
}
// Quantile, CDF, and survival check.
for i, p := range []float64{0.1, 0.25, 0.5, 0.75, 0.9} {
x := f.Quantile(p)
cdf := f.CDF(x)
estCDF := stat.CDF(x, stat.Empirical, sortedXs, nil)
if !floats.EqualWithinAbsOrRel(cdf, estCDF, tol, tol) {
t.Errorf("CDF mismatch case %v: want: %v, got: %v", i, estCDF, cdf)
}
if !floats.EqualWithinAbsOrRel(cdf, p, tol, tol) {
t.Errorf("Quantile/CDF mismatch case %v: want: %v, got: %v", i, p, cdf)
}
if math.Abs(1-cdf-f.Survival(x)) > 1e-14 {
t.Errorf("Survival/CDF mismatch case %v: want: %v, got: %v", i, 1-cdf, f.Survival(x))
}
}
// Prob and LogProb check.
m := 1001
bins := make([]float64, m)
dividers := make([]float64, m)
floats.Span(bins, 0, 1)
for i, v := range bins {
dividers[i] = f.Quantile(v)
}
counts := stat.Histogram(nil, dividers, sortedXs, nil)
// Test PDf against normalized count
for i, v := range counts {
v /= float64(n)
at := f.Quantile((bins[i] + bins[i+1]) / 2)
prob := f.Prob(at)
if !floats.EqualWithinAbsOrRel(skewness, f.Skewness(), tol, tol) {
t.Errorf("Prob mismatch case %v at %v: want: %v, got: %v", i, at, v, prob)
break
}
if math.Abs(math.Log(prob)-f.LogProb(at)) > 1e-14 {
t.Errorf("Prob and LogProb mismatch case %v at %v: want %v, got %v", i, at, math.Log(prob), f.LogProb(at))
break
}
}
}
func TestTrapezoidal(t *testing.T) {
const N = 1e6
x := floats.Span(make([]float64, N), 0, 1)
for i, test := range []struct {
x []float64
f func(x float64) float64
want float64
}{
{
x: x,
f: func(x float64) float64 { return x },
want: 0.5,
},
{
x: floats.Span(make([]float64, N), -1, 1),
f: func(x float64) float64 { return x },
want: 0,
},
{
x: x,
f: func(x float64) float64 { return x + 10 },
want: 10.5,
},
{
x: x,
f: func(x float64) float64 { return 3*x*x + 10 },
want: 11,
},
{
x: x,
f: func(x float64) float64 { return math.Exp(x) },
want: 1.7182818284591876,
},
{
x: floats.Span(make([]float64, N), 0, math.Pi),
f: func(x float64) float64 { return math.Cos(x) },
want: 0,
},
{
x: floats.Span(make([]float64, N), 0, 2*math.Pi),
f: func(x float64) float64 { return math.Cos(x) },
want: 0,
},
{
x: floats.Span(make([]float64, N*10), 0, math.Pi),
f: func(x float64) float64 { return math.Sin(x) },
want: 2,
},
{
x: floats.Span(make([]float64, N*10), 0, 0.5*math.Pi),
f: func(x float64) float64 { return math.Sin(x) },
want: 1,
},
{
x: floats.Span(make([]float64, N), 0, 2*math.Pi),
f: func(x float64) float64 { return math.Sin(x) },
want: 0,
},
} {
y := make([]float64, len(test.x))
for i, v := range test.x {
y[i] = test.f(v)
}
v := Trapezoidal(test.x, y)
if !floats.EqualWithinAbs(v, test.want, 1e-12) {
t.Errorf("test #%d: got=%v want=%v\n", i, v, test.want)
}
}
}
