func TestRunIncomes(t *testing.T) {
JsonObj := []byte(`{"Age":22, "Retirement_age":65, "Terminal_age":90, "Effective_tax_rate":0.3, "Returns_tax_rate":0.3, "N": 20000,
"Non_Taxable_contribution":17500, "Taxable_contribution": 0, "Non_Taxable_balance":0, "Taxable_balance": 0,
"Yearly_social_security_income":0, "Asset_volatility": 0.15, "Expected_rate_of_return": 0.07, "Inflation_rate":0.035}`)
rc, err := NewRetCalcFromJSON(JsonObj)
if err != nil {
t.Error(err)
}
runIncomes := rc.RunIncomes()
sort.Float64s(runIncomes)
incomePerRun := make([]float64, rc.N, rc.N)
for i := range incomePerRun {
incomePerRun[i] = rc.IncomeOnPath(i)
}
sort.Float64s(incomePerRun)
incomesOk := true
for i := range incomePerRun {
if incomePerRun[i] != runIncomes[i] {
incomesOk = false
fmt.Printf("RunIncomes: %f, IncomeOnPath: %f\n", runIncomes[i], incomePerRun[i])
}
}
if !incomesOk {
t.Errorf("Incomes do not calculate correctly for RunIncomes()")
}
if !sort.Float64sAreSorted(runIncomes) {
t.Errorf("Incomes from RetCalc.RunIncomes() should be sorted on return")
}
}
func (res *Results) med() {
slice := res.copyTook()
if !sort.Float64sAreSorted(slice) {
sort.Float64s(slice)
}
l := len(slice)
switch l {
case 0:
res.TookMed = float64(0)
case 1:
res.TookMed = slice[0]
case 2:
res.TookMed = slice[1]
default:
if math.Mod(float64(l), 2) == 0 {
index := int(math.Floor(float64(l)/2) - 1)
lower := slice[index]
upper := slice[index+1]
res.TookMed = (lower + upper) / 2
} else {
res.TookMed = slice[l/2]
}
}
}
func TestFloat64s(t *testing.T) {
data := float64s
Sort(sort.Float64Slice(data[0:]))
if !sort.Float64sAreSorted(data[0:]) {
t.Errorf("sorted %v", float64s)
t.Errorf(" got %v", data)
}
}
func (res *Results) min() {
slice := res.copyTook()
if !sort.Float64sAreSorted(slice) {
sort.Float64s(slice)
}
res.TookMin = slice[0]
}
func (res *Results) max() {
slice := res.copyTook()
if !sort.Float64sAreSorted(slice) {
sort.Float64s(slice)
}
res.TookMax = slice[len(slice)-1]
}
// sortedCopyDif returns a sorted copy of float64s
// only if the original data isn't sorted.
// Only use this if returned slice won't be manipulated!
func sortedCopyDif(input Float64Data) (copy Float64Data) {
if sort.Float64sAreSorted(input) {
return input
}
copy = copyslice(input)
sort.Float64s(copy)
return
}
func (sf64 *SliceFloat64) Max() (float64, error) {
if len(sf64.Elements) == 0 {
return 0, errors.New("List is empty")
}
if !sort.Float64sAreSorted(sf64.Elements) {
sort.Float64s(sf64.Elements)
}
return sf64.Elements[len(sf64.Elements)-1], nil
}
func TestSort(t *testing.T) {
r := testData()
SortBYOB(r, make([]float64, len(r)))
if !sort.Float64sAreSorted(r) {
log.Printf("Should have sorted slice.\n")
log.Printf("Data was %v", r)
t.FailNow()
}
}
func (sf64 *SliceFloat64) Median() (float64, error) {
if len(sf64.Elements) == 0 {
return 0, errors.New("List is empty")
}
if !sort.Float64sAreSorted(sf64.Elements) {
sort.Float64s(sf64.Elements)
}
mid := int64(float64(len(sf64.Elements)) / 2)
return sf64.Elements[mid], nil
}
func TestSortRand(t *testing.T) {
test := func(r []float64) bool {
SortBYOB(r, make([]float64, len(r)))
return sort.Float64sAreSorted(r)
}
if err := quick.Check(test, nil); err != nil {
t.Error(err)
}
}
// extractXY should return one series with its x values sorted.
func TestExtractXY(t *testing.T) {
vals, _ := seriesTestDefaultData(5)
series := extractXY(vals, "X", "Y")
if len(series) != 1 {
t.Fatalf("extractXY returned > 1 series")
}
s := series[0]
if !sort.Float64sAreSorted(s.xs) {
t.Fatalf("extractXY returned incorrectly sorted series")
}
}
// Sort sorts the samples in place in s and returns s.
//
// A sorted sample improves the performance of some algorithms.
func (s *Sample) Sort() *Sample {
if s.Sorted || sort.Float64sAreSorted(s.Xs) {
// All set
} else if s.Weights == nil {
sort.Float64s(s.Xs)
} else {
sort.Sort(&sampleSorter{s.Xs, s.Weights})
}
s.Sorted = true
return s
}
func TestFloat64s(t *testing.T) {
data := float64s
for _, alg := range algorithms {
sorting.Float64s(data[:], alg)
if !sort.Float64sAreSorted(data[:]) {
t.Errorf("%v\n", util.GetFuncName(alg))
t.Errorf("sorted: %v\n", float64s)
t.Errorf(" got: %v\n", data)
}
}
}
func TestAdjustLeftRight(t *testing.T) {
keys := []float64{1, 2, 3, 4, 9, 5, 6, 7, 8}
counts := []uint32{1, 2, 3, 4, 9, 5, 6, 7, 8}
s := summary{keys: keys, counts: counts}
s.adjustRight(4)
if !sort.Float64sAreSorted(s.keys) || s.counts[4] != 5 {
t.Errorf("adjustRight should have fixed the keys/counts state. %v %v", s.keys, s.counts)
}
keys = []float64{1, 2, 3, 4, 0, 5, 6, 7, 8}
counts = []uint32{1, 2, 3, 4, 0, 5, 6, 7, 8}
s = summary{keys: keys, counts: counts}
s.adjustLeft(4)
if !sort.Float64sAreSorted(s.keys) || s.counts[4] != 4 {
t.Errorf("adjustLeft should have fixed the keys/counts state. %v %v", s.keys, s.counts)
}
}
func Median(list []float64) (float64, error) {
if len(list) == 0 {
return 0.0, errors.New("Empty list given")
}
if !sort.Float64sAreSorted(list) {
sort.Float64s(list)
}
if len(list)%2 == 1 {
return list[(len(list) / 2)], nil
}
return (list[len(list)/2] + list[len(list)/2-1]) / 2, nil
}
func TestSortCopy(t *testing.T) {
x := testData()
y := SortCopy(x)
if !sort.Float64sAreSorted(y) {
log.Printf("Should have sorted slice.\n")
log.Printf("Data was %v", y)
t.FailNow()
}
if x[0] != 3.1415 || x[10] != math.SmallestNonzeroFloat64 {
log.Printf("Slice should have not have been modified.\n")
log.Printf("Data was %v", y)
t.FailNow()
}
}
// Integrate a function from a to b using cubic spline interpolation to
// approximate the function specified at the points xs with values ys.
// xs must be sorted and of the same length as ys, and (a, b) must be
// within (xs[0], xs[len(xs)-1]).
func Spline(xs, ys []float64, a, b float64) (float64, error) {
// check preconditions
N := len(xs)
if N != len(ys) {
return 0.0, fmt.Errorf("xs and ys must have the same length for spline interpolation")
}
if !sort.Float64sAreSorted(xs) {
return 0.0, fmt.Errorf("xs values must be sorted for spline interpolation")
}
if a < xs[0] || b > xs[len(xs)-1] {
return 0.0, fmt.Errorf("Spline integration bounds must be within xs")
}
// do spline integration
val := C.integrate_spline(unsafe.Pointer(&xs), unsafe.Pointer(&ys), C.int(N), C.double(a), C.double(b))
return float64(val), nil
}
//Sorts b by ascending x value
func (b *BiVariateData) Sort() {
//edge case 1
if b.isSorted {
return
}
//edge case 2
if sort.Float64sAreSorted(b.Xs) {
b.isSorted = true
return
}
sort.Sort(b)
b.isSorted = true
}
// Trapezoidal estimates the integral of a function f
// \int_a^b f(x) dx
// from a set of evaluations of the function using the trapezoidal rule.
// The trapezoidal rule makes piecewise linear approximations to the function,
// and estimates
// \int_x[i]^x[i+1] f(x) dx
// as
// (x[i+1] - x[i]) * (f[i] + f[i+1])/2
// where f[i] is the value of the function at x[i].
// More details on the trapezoidal rule can be found at:
// https://en.wikipedia.org/wiki/Trapezoidal_rule
//
// The (x,f) input data points must be sorted along x.
// One can use github.com/gonum/stat.SortWeighted to do that.
// The x and f slices must be of equal length and have length > 1.
func Trapezoidal(x, f []float64) float64 {
switch {
case len(x) != len(f):
panic("integrate: slice length mismatch")
case len(x) < 2:
panic("integrate: input data too small")
case !sort.Float64sAreSorted(x):
panic("integrate: input must be sorted")
}
integral := 0.0
for i := 0; i < len(x)-1; i++ {
integral += 0.5 * (x[i+1] - x[i]) * (f[i+1] + f[i])
}
return integral
}
// Within returns the first index i where s[i] <= v < s[i+1]. Within panics if:
// - len(s) < 2
// - s is not sorted
func Within(s []float64, v float64) int {
if len(s) < 2 {
panic("floats: slice length less than 2")
}
if !sort.Float64sAreSorted(s) {
panic("floats: input slice not sorted")
}
if v < s[0] || v >= s[len(s)-1] || math.IsNaN(v) {
return -1
}
for i, f := range s[1:] {
if v < f {
return i
}
}
return -1
}
// ExtractSeries should return a []Series with length equal to the number of
// values of z passed in to it. Individual series should have their x values
// sorted.
func TestExtractSeries(t *testing.T) {
vals, errs := seriesTestDefaultData(6)
series, zVals := ExtractSeries(vals, errs, []string{"X", "Y", "Z"}, nil, nil, nil, false)
if zVals[0] != 0.0 || zVals[1] != 1.0 {
t.Fatalf("ExtractSeries returned incorrect z values")
}
expectedLength := 2
if len(series) != expectedLength {
t.Fatalf("ExtractSeries returned incorrect number of series")
}
for i := 0; i < expectedLength; i++ {
s := series[i]
if !sort.Float64sAreSorted(s.xs) {
t.Fatalf("ExtractSeries returned incorrectly sorted series")
}
}
}
// Median returns the median value of the data.
func Median(x []float64) float64 {
if len(x) == 0 {
return 0
}
if sort.Float64sAreSorted(x) {
return x[len(x)/2]
}
h := new(floats)
heap.Init(h)
for _, f := range x {
heap.Push(h, f)
}
for i := 0; i < len(x)/2; i++ {
heap.Pop(h)
}
return heap.Pop(h).(float64)
}
func Median(arr []float64) float64 {
n := len(arr)
var thisarr []float64
if !sort.Float64sAreSorted(arr) {
thisarr = make([]float64, n)
copy(arr, thisarr)
sort.Float64s(thisarr)
} else {
thisarr = arr
}
if n%2 == 0 {
j := n / 2
return (thisarr[j] + thisarr[j+1]) / 2
} else {
return thisarr[(n+1)/2]
}
}
// CDF returns the empirical cumulative distribution function value of x, that is
// the fraction of the samples less than or equal to q. The
// exact behavior is determined by the CumulantKind. CDF is theoretically
// the inverse of the Quantile function, though it may not be the actual inverse
// for all values q and CumulantKinds.
//
// The x data must be sorted in increasing order. If weights is nil then all
// of the weights are 1. If weights is not nil, then len(x) must equal len(weights).
//
// CumulantKind behaviors:
// - Empirical: Returns the lowest fraction for which q is greater than or equal
// to that fraction of samples
func CDF(q float64, c CumulantKind, x, weights []float64) float64 {
if weights != nil && len(x) != len(weights) {
panic("stat: slice length mismatch")
}
if floats.HasNaN(x) {
return math.NaN()
}
if !sort.Float64sAreSorted(x) {
panic("x data are not sorted")
}
if q < x[0] {
return 0
}
if q >= x[len(x)-1] {
return 1
}
var sumWeights float64
if weights == nil {
sumWeights = float64(len(x))
} else {
sumWeights = floats.Sum(weights)
}
// Calculate the index
switch c {
case Empirical:
// Find the smallest value that is greater than that percent of the samples
var w float64
for i, v := range x {
if v > q {
return w / sumWeights
}
if weights == nil {
w++
} else {
w += weights[i]
}
}
panic("impossible")
default:
panic("stat: bad cumulant kind")
}
}
func MedianFloat64(data []float64) float64 {
if sort.Float64sAreSorted(data) {
if len(data)%2 == 1 {
return data[len(data)/2]
} else {
return (data[len(data)/2] + data[len(data)/2-1]) / 2.0
}
} else {
sort.Float64s(data)
if len(data)%2 == 1 {
return data[len(data)/2]
} else {
return (data[len(data)/2] + data[len(data)/2-1]) / 2.0
}
}
panic("Error in MedianInt")
return -1
}
// Fit sets the parameters of the probability distribution from the
// data samples x with relative weights w.
// If weights is nil, then all the weights are 1.
// If weights is not nil, then the len(weights) must equal len(samples).
//
// Note: Laplace distribution has no FitPrior because it has no sufficient
// statistics.
func (l *Laplace) Fit(samples, weights []float64) {
if len(samples) != len(weights) {
panic(badLength)
}
if len(samples) == 0 {
panic(badNoSamples)
}
if len(samples) == 1 {
l.Mu = samples[0]
l.Scale = 0
return
}
var (
sortedSamples []float64
sortedWeights []float64
)
if sort.Float64sAreSorted(samples) {
sortedSamples = samples
sortedWeights = weights
} else {
// Need to copy variables so the input variables aren't effected by the sorting
sortedSamples = make([]float64, len(samples))
copy(sortedSamples, samples)
sortedWeights := make([]float64, len(samples))
copy(sortedWeights, weights)
stat.SortWeighted(sortedSamples, sortedWeights)
}
// The (weighted) median of the samples is the maximum likelihood estimate
// of the mean parameter
// TODO: Rethink quantile type when stat has more options
l.Mu = stat.Quantile(0.5, stat.Empirical, sortedSamples, sortedWeights)
sumWeights := floats.Sum(weights)
// The scale parameter is the average absolute distance
// between the sample and the mean
absError := stat.MomentAbout(1, samples, l.Mu, weights)
l.Scale = absError / sumWeights
}
func TestNormalizeExpenses(t *testing.T) {
var list []float64 = []float64{4.0, 1.0, 2.0, 3.0, 2.00, 2.0, 5.0}
normalized := normalizeExpenses(list)
if !sort.Float64sAreSorted(normalized) {
t.Fatalf("Expected normalized list to be sorted.")
}
var count float64 = 0
for _, x := range normalized {
if x == 2.0 {
count++
}
}
if count > 1 {
t.Fatalf("Expected one instance of 2.0. Actual: %v", count)
}
}
func main() {
// スライス
is := make([]int, 0, 4) // len(is) = 0, cap(is) = 4
fmt.Println("len(is)=", len(is), ", cap(is)=", cap(is))
for i := 0; i < 10; i++ {
is = append(is, 10-i-1)
}
fmt.Println("len(is)=", len(is), ", cap(is)=", cap(is))
for i, x := range is {
fmt.Println(i, ":", x)
}
sort.Ints(is)
for i, x := range is {
fmt.Println(i, ":", x)
}
fmt.Println(sort.IntsAreSorted(is))
fs := make([]float64, 0, 4)
for i := 0.0; i < 1.0; i += 0.1 {
fs = append(fs, 1.0-i-0.1)
}
for i, x := range fs {
fmt.Println(i, ":", x)
}
sort.Float64s(fs)
for i, x := range fs {
fmt.Println(i, ":", x)
}
fmt.Println(sort.Float64sAreSorted(fs))
// 連想配列
m := make(map[int]string, 3)
m[1] = "hoge"
m[2] = "fuga"
m[3] = "bar"
fmt.Println(m)
delete(m, 2)
for k, v := range m {
fmt.Println(k, ":", v)
}
}
// Quantile returns the sample of x such that x is greater than or
// equal to the fraction p of samples. The exact behavior is determined by the
// CumulantKind, and p should be a number between 0 and 1. Quantile is theoretically
// the inverse of the CDF function, though it may not be the actual inverse
// for all values p and CumulantKinds.
//
// The x data must be sorted in increasing order. If weights is nil then all
// of the weights are 1. If weights is not nil, then len(x) must equal len(weights).
//
// CumulantKind behaviors:
// - Empirical: Returns the lowest value q for which q is greater than or equal
// to the fraction p of samples
func Quantile(p float64, c CumulantKind, x, weights []float64) float64 {
if !(p >= 0 && p <= 1) {
panic("stat: percentile out of bounds")
}
if weights != nil && len(x) != len(weights) {
panic("stat: slice length mismatch")
}
if floats.HasNaN(x) {
return math.NaN() // This is needed because the algorithm breaks otherwise
}
if !sort.Float64sAreSorted(x) {
panic("x data are not sorted")
}
var sumWeights float64
if weights == nil {
sumWeights = float64(len(x))
} else {
sumWeights = floats.Sum(weights)
}
switch c {
case Empirical:
var cumsum float64
fidx := p * sumWeights
for i := range x {
if weights == nil {
cumsum++
} else {
cumsum += weights[i]
}
if cumsum >= fidx {
return x[i]
}
}
panic("impossible")
default:
panic("stat: bad cumulant kind")
}
}
// Helper method for (*Results).calculatePercentiles which actually
// finds the percentile from the passed slice of connection times.
func percentile(times []float64, pct int) float64 {
length := len(times)
if length == 1 {
return times[0]
}
if length == 2 {
return times[1]
}
var times64 []float64
for _, float := range times {
times64 = append(times64, float64(float))
}
if !sort.Float64sAreSorted(times64) {
sort.Float64s(times64)
}
index := int(math.Floor(((float64(length)/100)*float64(pct))+0.5)) - 1
return float64(times64[index])
}
