예제 #1
0
func ExampleDistanceMatrix_Symmetric() {
	d1 := cluster.DistanceMatrix{
		{0, 3}, // true
		{3, 0},
	}
	d2 := cluster.DistanceMatrix{
		{0, 3}, // false
		{7, 0},
	}
	d3 := cluster.DistanceMatrix{
		{0, math.NaN()}, // false (NaNs do not compare equal)
		{math.NaN(), 0},
	}
	d4 := cluster.DistanceMatrix{
		{0, 3}, // true (diagonal is not checked)
		{3, math.NaN()},
	}
	fmt.Println(d1.Symmetric())
	fmt.Println(d2.Symmetric())
	fmt.Println(d3.Symmetric())
	fmt.Println(d4.Symmetric())
	// Output:
	// true
	// false
	// false
	// true
}
예제 #2
0
func TestNewPointNaN(t *testing.T) {
	test(t, `cpu value=NaN 1000000000`,
		tsdb.NewPoint(
			"cpu",
			tsdb.Tags{},
			tsdb.Fields{
				"value": math.NaN(),
			},
			time.Unix(1, 0)),
	)

	test(t, `cpu value=nAn 1000000000`,
		tsdb.NewPoint(
			"cpu",
			tsdb.Tags{},
			tsdb.Fields{
				"value": math.NaN(),
			},
			time.Unix(1, 0)),
	)

	test(t, `nan value=NaN`,
		tsdb.NewPoint(
			"nan",
			tsdb.Tags{},
			tsdb.Fields{
				"value": math.NaN(),
			},
			time.Unix(0, 0)),
	)

}
예제 #3
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func TestDot(t *testing.T) {
	for _, test := range []struct {
		n       int
		x, y    []float64
		indices []int

		want float64
	}{
		{
			n:       5,
			x:       []float64{1, 2, 3},
			indices: []int{0, 2, 4},
			y:       []float64{1, math.NaN(), 3, math.NaN(), 5},

			want: 22,
		},
	} {
		x := NewVector(test.n, test.x, test.indices)
		y := mat64.NewVector(len(test.y), test.y)
		got := Dot(x, y)
		if got != test.want {
			t.Errorf("want = %v, got %v\n", test.want, got)
		}
	}
}
예제 #4
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func (d *DatasourceDerive) CalculatePdpPrep(newValue string, interval float64) (float64, error) {
	if float64(d.Heartbeat) < interval {
		d.LastValue = Undefined
	}

	rate := math.NaN()
	newPdp := math.NaN()
	if newValue != Undefined && float64(d.Heartbeat) >= interval {
		newInt := new(big.Int)
		_, err := fmt.Sscan(newValue, newInt)
		if err != nil {
			return math.NaN(), errors.Errorf("not a simple signed integer: %s", newValue)
		}
		if d.LastValue != "U" {
			prevInt := new(big.Int)
			_, err := fmt.Sscan(d.LastValue, prevInt)
			if err != nil {
				return math.NaN(), errors.Wrap(err, 0)
			}
			diff := new(big.Int)
			diff.Sub(newInt, prevInt)

			newPdp = float64(diff.Uint64())
			rate = newPdp / interval
		}
	}

	if !d.checkRateBounds(rate) {
		newPdp = math.NaN()
	}

	d.LastValue = newValue

	return newPdp, nil
}
예제 #5
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/* Min比较一组数字、字符串式的数字中最小的一个
 */
func Min(arg ...interface{}) (res float64, err error) {
	fmt.Println("")
	//多个元素比较大小
	if len(arg) > 1 {
		res, err = GetFloat(arg[0])
		for _, v := range arg {
			v, _ := GetFloat(v)
			if v < res {
				res = v
			}
		}
	} else { //对单个数组中元素进行比较
		t := reflect.TypeOf(arg[0])
		v := reflect.ValueOf(arg[0])
		if t != arrayFloat {
			return math.NaN(), errors.New("Max: 传入一个元素时,必须是[]flat64")
		} else {
			res = math.NaN()
			for i := 1; i < v.Len(); i++ {
				if math.IsNaN(res) {
					res = v.Index(0).Float()
				}
				if v.Index(i).Float() < res {
					res = v.Index(i).Float()
				}
			}
		}
	}
	return res, err
}
예제 #6
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/// <summary>
/// Upside Potential Ratio,compared to Sortino, was a further improvement, extending the
/// measurement of only upside on the numerator, and only downside of the
/// denominator of the ratio equation.
/// (分子只考虑超过MAR部分,分母只考虑DownsideDeviation的下跌风险)
/// </summary>
func UpsidePotentialRatio(Ra *utils.SlidingWindow, MAR float64) (float64, error) {
	//var r = Ra.Where<float64>(singleData => singleData > MAR).ToList<float64>();
	r, err := utils.AboveValue(Ra, MAR)
	if err != nil {
		return math.NaN(), err
	}
	var length int
	method := "subset"
	switch method {
	case "full":
		length = Ra.Count()
		break
	case "subset":
		length = r.Count()
		break
	default:
		return math.NaN(), errors.New("In UpsidePotentialRatio, method is default !!!")
	}
	add_Sliding, err := utils.Add(-MAR, r)
	if err != nil {
		return math.NaN(), err
	}
	dd2Data, err := DownsideDeviation2(Ra, MAR)
	if err != nil {
		return math.NaN(), err
	}
	var result = (add_Sliding.Sum() / float64(length)) / dd2Data
	return result, nil
}
예제 #7
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func TestTimeseries_MarshalJSON(t *testing.T) {
	for _, suite := range []struct {
		input    Timeseries
		expected string
	}{
		{
			Timeseries{
				TagSet: ParseTagSet("foo=bar"),
				Values: []float64{0, 1, -1, math.NaN()},
			},
			`{"tagset":{"foo":"bar"},"values":[0,1,-1,null]}`,
		},
		{
			Timeseries{
				TagSet: NewTagSet(),
				Values: []float64{0, 1, -1, math.NaN()},
			},
			`{"tagset":{},"values":[0,1,-1,null]}`,
		},
	} {
		a := assert.New(t).Contextf("expected=%s", suite.expected)
		encoded, err := json.Marshal(suite.input)
		a.CheckError(err)
		a.Eq(string(encoded), suite.expected)
	}
}
예제 #8
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// Covariance is a measure of how much two sets of data change
func Covariance(data1, data2 Float64Data) (float64, error) {

	l1 := data1.Len()
	l2 := data2.Len()

	if l1 == 0 || l2 == 0 {
		return math.NaN(), EmptyInput
	}

	if l1 != l2 {
		return math.NaN(), SizeErr
	}

	m1, _ := Mean(data1)
	m2, _ := Mean(data2)

	// Calculate sum of squares
	var ss float64
	for i := 0; i < l1; i++ {
		delta1 := (data1.Get(i) - m1)
		delta2 := (data2.Get(i) - m2)
		ss += (delta1*delta2 - ss) / float64(i+1)
	}

	return ss * float64(l1) / float64(l1-1), nil
}
예제 #9
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// CovariancePopulation computes covariance for entire population between two variables.
func CovariancePopulation(data1, data2 Float64Data) (float64, error) {

	l1 := data1.Len()
	l2 := data2.Len()

	if l1 == 0 || l2 == 0 {
		return math.NaN(), EmptyInput
	}

	if l1 != l2 {
		return math.NaN(), SizeErr
	}

	m1, _ := Mean(data1)
	m2, _ := Mean(data2)

	var s float64
	for i := 0; i < l1; i++ {
		delta1 := (data1.Get(i) - m1)
		delta2 := (data2.Get(i) - m2)
		s += delta1 * delta2
	}

	return s / float64(l1), nil
}
예제 #10
0
파일: grid.go 프로젝트: jmptrader/graph
// XY returns the cartesian coordinates of n. If n is not a node
// in the grid, (NaN, NaN) is returned.
func (g *Grid) XY(n graph.Node) (x, y float64) {
	if !g.Has(n) {
		return math.NaN(), math.NaN()
	}
	r, c := g.RowCol(n.ID())
	return float64(c), float64(r)
}
예제 #11
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/** Returns the lowest positive root of the quadric equation given by a* x * x + b * x + c = 0. If no solution is given
 * Float.Nan is returned.
 * @param a the first coefficient of the quadric equation
 * @param b the second coefficient of the quadric equation
 * @param c the third coefficient of the quadric equation
 * @return the lowest positive root or Float.Nan */
func LowestPositiveRoot(a, b, c float32) float32 {
	det := b*b - 4*a*c
	if det < 0 {
		return float32(math.NaN())
	}

	sqrtD := float32(math.Sqrt(float64(det)))
	invA := 1 / (2 * a)
	r1 := (-b - sqrtD) * invA
	r2 := (-b + sqrtD) * invA

	if r1 > r2 {
		tmp := r2
		r2 = r1
		r1 = tmp
	}

	if r1 > 0 {
		return r1
	}
	if r2 > 0 {
		return r2
	}
	return float32(math.NaN())
}
예제 #12
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// PrioritizeWorkUnits changes the priorities of some number of work
// units.  The actual work units are in options["work_unit_keys"].  A
// higher priority results in the work units being scheduled sooner.
func (jobs *JobServer) PrioritizeWorkUnits(workSpecName string, options map[string]interface{}) (bool, string, error) {
	var (
		err      error
		query    coordinate.WorkUnitQuery
		workSpec coordinate.WorkSpec
	)
	pwuOptions := PrioritizeWorkUnitsOptions{
		Priority:   math.NaN(),
		Adjustment: math.NaN(),
	}
	workSpec, err = jobs.Namespace.WorkSpec(workSpecName)
	if err == nil {
		err = decode(&pwuOptions, options)
	}
	if err == nil && pwuOptions.WorkUnitKeys == nil {
		return false, "missing work_unit_keys", err
	}
	if err == nil {
		query.Names = pwuOptions.WorkUnitKeys
		if !math.IsNaN(pwuOptions.Priority) {
			err = workSpec.SetWorkUnitPriorities(query, pwuOptions.Priority)
		} else if !math.IsNaN(pwuOptions.Adjustment) {
			err = workSpec.AdjustWorkUnitPriorities(query, pwuOptions.Adjustment)
		}
	}
	return err == nil, "", err
}
예제 #13
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func (b *Bisection) Init(f, g float64, step float64) EvaluationType {
	if step <= 0 {
		panic("bisection: bad step size")
	}
	if g >= 0 {
		panic("bisection: initial derivative is non-negative")
	}

	if b.GradConst == 0 {
		b.GradConst = 0.9
	}
	if b.GradConst <= 0 || b.GradConst >= 1 {
		panic("bisection: GradConst not between 0 and 1")
	}

	b.minStep = 0
	b.maxStep = math.Inf(1)
	b.currStep = step

	b.initF = f
	b.minF = f
	b.maxF = math.NaN()

	b.initGrad = g
	b.minGrad = g
	b.maxGrad = math.NaN()

	return FuncEvaluation | GradEvaluation
}
예제 #14
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// PercentileNearestRank finds the relative standing in a slice of floats using the Nearest Rank method
func PercentileNearestRank(input Float64Data, percent float64) (percentile float64, err error) {

	// Find the length of items in the slice
	il := input.Len()

	// Return an error for empty slices
	if il == 0 {
		return math.NaN(), EmptyInput
	}

	// Return error for less than 0 or greater than 100 percentages
	if percent < 0 || percent > 100 {
		return math.NaN(), BoundsErr
	}

	// Start by sorting a copy of the slice
	c := sortedCopy(input)

	// Return the last item
	if percent == 100.0 {
		return c[il-1], nil
	}

	// Find ordinal ranking
	or := int(math.Ceil(float64(il) * percent / 100))

	// Return the item that is in the place of the ordinal rank
	if or == 0 {
		return c[0], nil
	}
	return c[or-1], nil

}
예제 #15
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/// <summary>
/// Kappa is a generalized downside risk-adjusted performance measure.
/// To calculate it, we take the difference of the mean of the distribution
/// to the target and we divide it by the l-root of the lth lower partial
/// moment. To calculate the lth lower partial moment we take the subset of
/// returns below the target and we sum the differences of the target to
/// these returns. We then return return this sum divided by the length of
/// the whole distribution.
/// (非年化的超MAR平均收益率通过l阶根的低于MAR的收益率序列的l阶矩)
/// </summary>
func Kappa(Ra *utils.SlidingWindow, MAR float64, l float64) (float64, error) {
	undervalues, err := utils.NewSlidingWindow(Ra.Count())
	if err != nil {
		return math.NaN(), err
	}
	for i := 0; i < Ra.Count(); i++ {
		if Ra.Data()[i] < MAR {
			undervalues.Add(Ra.Data()[i])
		}
	}

	var n = float64(Ra.Count())
	var m = float64(Ra.Average())
	neg_Sliding, err := utils.Negative(undervalues)
	if err != nil {
		return math.NaN(), err
	}
	add_Sliding, err := utils.Add(MAR, neg_Sliding)
	if err != nil {
		return math.NaN(), err
	}
	pow_Sliding, err := utils.Power(add_Sliding, float64(l))
	if err != nil {
		return math.NaN(), err
	}
	var temp = pow_Sliding.Sum() / n
	return (m - MAR) / math.Pow(temp, (1.0/float64(l))), nil
}
// Convert database.sql types to float64s for Prometheus consumption. Null types are mapped to NaN. string and []byte
// types are mapped as NaN and !ok
func dbToFloat64(t interface{}) (float64, bool) {
	switch v := t.(type) {
	case int64:
		return float64(v), true
	case float64:
		return v, true
	case time.Time:
		return float64(v.Unix()), true
	case []byte:
		// Try and convert to string and then parse to a float64
		strV := string(v)
		result, err := strconv.ParseFloat(strV, 64)
		if err != nil {
			return math.NaN(), false
		}
		return result, true
	case string:
		result, err := strconv.ParseFloat(v, 64)
		if err != nil {
			log.Infoln("Could not parse string:", err)
			return math.NaN(), false
		}
		return result, true
	case nil:
		return math.NaN(), true
	default:
		return math.NaN(), false
	}
}
예제 #17
0
/// <summary>
/// To calculate Burke ratio we take the difference between the portfolio
/// return and the risk free rate and we divide it by the square root of the
/// sum of the square of the drawdowns. To calculate the modified Burke ratio
/// we just multiply the Burke ratio by the square root of the number of datas.
/// (一种调整收益率的计算方式,调整是通过drawdown的平方和进行的)
/// </summary>
func BurkeRatio(Ra *utils.SlidingWindow, Rf float64, scale float64) (float64, error) {
	var len = Ra.Count()
	var in_drawdown = false
	var peak = 1
	var temp = 0.0
	drawdown, err := utils.NewSlidingWindow(len)
	if err != nil {
		return math.NaN(), err
	}
	for i := 1; i < len; i++ {
		if Ra.Data()[i] < 0 {
			if !in_drawdown {
				peak = i - 1
				in_drawdown = true
			}
		} else {
			if in_drawdown {
				temp = 1.0
				for j := peak + 1; j < i; j++ {
					temp = temp * (1.0 + Ra.Data()[j])
				}
				drawdown.Add(temp - 1.0) //Source
				in_drawdown = false
			}
		}
	}

	if in_drawdown {
		temp = 1.0
		for j := peak + 1; j < len; j++ {
			temp = temp * (1.0 + Ra.Data()[j])
		}
		drawdown.Add(temp - 1.0) //Source
		//drawdown.Add((temp - 1.0) * 100.0)
		in_drawdown = false
	}
	//var Rp = Annualized(Ra, scale, true) - 1.0--->Source
	Rp, err := Annualized(Ra, scale, true)
	if err != nil {
		return math.NaN(), err
	}
	var result float64

	if drawdown.Count() != 0 {
		pow_Sliding, err := utils.Power(drawdown, 2)
		if err != nil {
			return math.NaN(), err
		}
		Rf = Rf * scale
		result = (Rp - Rf) / math.Sqrt(pow_Sliding.Sum())
	} else {
		result = 0
	}

	modified := true
	if modified {
		result = result * math.Sqrt(float64(len))
	}
	return result, nil
}
예제 #18
0
func (d *DatasourceAbstract) ProcessPdp(pdpValue float64, elapsed ElapsedPdpSteps, step time.Duration) float64 {
	var preUnknown float64
	if math.IsNaN(pdpValue) {
		preUnknown = elapsed.PreInt
	} else {
		if math.IsNaN(d.PdpValue) {
			d.PdpValue = 0
		}
		d.PdpValue += pdpValue / elapsed.Interval * elapsed.PreInt
	}
	var pdpTemp float64

	if elapsed.Interval > float64(d.Heartbeat) || uint64(step/time.Second/2) < d.UnknownSecCount {
		pdpTemp = math.NaN()
	} else {
		diffPdpSteps := (elapsed.Steps * uint64(step)) / uint64(time.Second)
		pdpTemp = d.PdpValue / (float64(diffPdpSteps-d.UnknownSecCount) - preUnknown)
	}

	if math.IsNaN(pdpValue) {
		d.UnknownSecCount = uint64(elapsed.PostInt)
		d.PdpValue = math.NaN()
	} else {
		d.UnknownSecCount = 0
		d.PdpValue = pdpValue / elapsed.Interval * elapsed.PostInt
	}

	return pdpTemp
}
예제 #19
0
/// <summary>
/// calculate a traditional or modified Sharpe Ratio of Return over StdDev or
/// VaR or ES
///
/// The Sharpe ratio is simply the return per unit of risk (represented by
/// variability).  In the classic case, the unit of risk is the standard
/// deviation of the returns.
/// </summary>
func SharpeRatio(Ra *utils.SlidingWindow, Rf_val float64, scale float64) (float64, error) {
	Rf, err := utils.CreateList(Rf_val, Ra.Count())
	if err != nil {
		return math.NaN(), err
	}
	xR, err := Excess(Ra, Rf)
	if err != nil {
		return math.NaN(), err
	}
	numerator := 0.0
	denominator := 0.0
	annualize := 1
	if annualize == 1 {
		denominator, err = StdDev_Annualized(Ra, scale)
		if err != nil {
			return math.NaN(), err
		}
		numerator, err = Annualized(xR, scale, true)
		if err != nil {
			return math.NaN(), err
		}
	} else {
		denominator, err = StdDev(Ra)
		if err != nil {
			return math.NaN(), err
		}
		numerator = xR.Average()
	}

	return numerator / denominator, nil
}
예제 #20
0
파일: interface.go 프로젝트: bxy09/gfstat
func (this *P1S1F1SWrapper) Process(AssetPriceReturns, AssetPriceBenchMark []float64, date []time.Time) (float64, error) {
	if AssetPriceReturns == nil {
		return math.NaN(), errors.New("The Input RA is Error !!!")
	}
	var err error
	Period := getPeriod(date)
	if Period == 2520 {
		AssetPriceReturns, err = reorganizeInputPrice(date, AssetPriceReturns)
		if err != nil {
			return math.NaN(), errors.New("Reorganize Minutes Price Error !!!")
		}
		Period = 252
	}

	if this.param == 252 {
		this.param = Period
	}

	Price, err := utils.NewSlidingWindow(len(AssetPriceReturns))
	if err != nil {
		return math.NaN(), err
	}
	for _, val := range AssetPriceReturns {
		Price.Add(val)
	}
	Ra, err := Calculate(Price, "discrete")
	if err != nil {
		return math.NaN(), err
	}
	if this.param == 0.03 {
		this.param = this.param / Period
	}
	return this.function(Ra, this.param, this.str)
}
예제 #21
0
func (s *StatsTimer) Percentile(percentile float64) (float64, error) {
	// Nearest rank implementation
	// http://en.wikipedia.org/wiki/Percentile
	histLen := len(s.history)

	if percentile > 100 {
		return math.NaN(), errors.New("Invalid argument")
	}

	in := make([]int64, 0, histLen)
	for i := range s.history {
		if s.history[i] != NOT_INITIALIZED {
			in = append(in, s.history[i])
		}
	}

	filtLen := len(in)

	if filtLen < 1 {
		return math.NaN(), errors.New("No values")
	}

	// Since slices are zero-indexed, we are naturally rounded up
	nearest_rank := int((percentile / 100) * float64(filtLen))

	if nearest_rank == filtLen {
		nearest_rank = filtLen - 1
	}

	sort.Sort(Int64Slice(in))
	ret := float64(in[nearest_rank]) / float64(s.timeUnit.Nanoseconds())

	return ret, nil
}
예제 #22
0
파일: proj.go 프로젝트: pebbe/go-proj-4
func Transform2(srcpj, dstpj *Proj, x, y float64) (float64, float64, error) {
	xx, yy, _, err := transform(srcpj, dstpj, []float64{x}, []float64{y}, nil)
	if err != nil {
		return math.NaN(), math.NaN(), err
	}
	return xx[0], yy[0], err
}
예제 #23
0
func TestVfltu8(t *testing.T) {
	input := []byte{4, 127, 250, 190}
	output := make([]float32, 8)

	for i := 0; i < len(output); i++ {
		output[i] = float32(math.NaN())
	}

	Vfltu8(input, 1, output, 1)
	for i, x := range input {
		expected := float32(x)
		if expected != output[i] {
			t.Errorf("Vfltu8 strides == 1 : output %f != expected %f for index %d", output[i], expected, i)
		}
	}

	for i := 0; i < len(output); i++ {
		output[i] = float32(math.NaN())
	}

	Vfltu8(input, 2, output, 1)
	for i := 0; i < len(input); i += 2 {
		expected := float32(input[i])
		if expected != output[i/2] {
			t.Errorf("Vfltu8 in stride = 2 out stride = 1 : output %f != expected %f for index %d", output[i/2], expected, i)
		}
	}
	for i := len(input) / 2; i < len(output); i++ {
		if !math.IsNaN(float64(output[i])) {
			t.Errorf("Vfltu8 wrote too far for input stride 2 (%d=%f)", i, output[i])
		}
	}
}
예제 #24
0
파일: proj.go 프로젝트: pebbe/go-proj-4
func Transform3(srcpj, dstpj *Proj, x, y, z float64) (float64, float64, float64, error) {
	xx, yy, zz, err := transform(srcpj, dstpj, []float64{x}, []float64{y}, []float64{z})
	if err != nil {
		return math.NaN(), math.NaN(), math.NaN(), err
	}
	return xx[0], yy[0], zz[0], err
}
예제 #25
0
func TestInDelta(t *testing.T) {
	mockT := new(testing.T)

	True(t, InDelta(mockT, 1.001, 1, 0.01), "|1.001 - 1| <= 0.01")
	True(t, InDelta(mockT, 1, 1.001, 0.01), "|1 - 1.001| <= 0.01")
	True(t, InDelta(mockT, 1, 2, 1), "|1 - 2| <= 1")
	False(t, InDelta(mockT, 1, 2, 0.5), "Expected |1 - 2| <= 0.5 to fail")
	False(t, InDelta(mockT, 2, 1, 0.5), "Expected |2 - 1| <= 0.5 to fail")
	False(t, InDelta(mockT, "", nil, 1), "Expected non numerals to fail")
	False(t, InDelta(mockT, 42, math.NaN(), 0.01), "Expected NaN for actual to fail")
	False(t, InDelta(mockT, math.NaN(), 42, 0.01), "Expected NaN for expected to fail")

	cases := []struct {
		a, b  interface{}
		delta float64
	}{
		{uint8(2), uint8(1), 1},
		{uint16(2), uint16(1), 1},
		{uint32(2), uint32(1), 1},
		{uint64(2), uint64(1), 1},

		{int(2), int(1), 1},
		{int8(2), int8(1), 1},
		{int16(2), int16(1), 1},
		{int32(2), int32(1), 1},
		{int64(2), int64(1), 1},

		{float32(2), float32(1), 1},
		{float64(2), float64(1), 1},
	}

	for _, tc := range cases {
		True(t, InDelta(mockT, tc.a, tc.b, tc.delta), "Expected |%V - %V| <= %v", tc.a, tc.b, tc.delta)
	}
}
예제 #26
0
/// <summary>
/// M squared excess is the quantity above the standard M.
/// There is a geometric excess return which is better for Bacon and an arithmetic excess return
/// (是与Rb的年化收益率进行的excess比较)
/// </summary>
func MSquaredExcess(Ra *utils.SlidingWindow, Rb *utils.SlidingWindow, scale float64, Rf float64, method string) (float64, error) {
	//var n = Rb.Count() //Ra&Rb等长
	Rbp, err := Annualized(Rb, scale, true)
	if err != nil {
		return math.NaN(), err
	}
	var result float64
	switch method {
	case "geometric":
		msq_data, err := MSquared(Ra, Rb, scale, Rf)
		if err != nil {
			return math.NaN(), err
		}
		result = (1.0+msq_data)/(1.0+Rbp) - 1.0
		break
	case "arithmetic":
		msq_data, err := MSquared(Ra, Rb, scale, Rf)
		if err != nil {
			return math.NaN(), err
		}
		result = msq_data - Rbp
		break
	default:
		return math.NaN(), errors.New("In MSquaredExcess, method default !!!")
	}
	return result, nil
}
예제 #27
0
func (d *DatasourceDDerive) CalculatePdpPrep(newValue string, interval float64) (float64, error) {
	if float64(d.Heartbeat) < interval {
		d.LastValue = Undefined
	}
	rate := math.NaN()
	newPdp := math.NaN()
	if newValue != Undefined && float64(d.Heartbeat) >= interval {
		newval, err := strconv.ParseFloat(newValue, 64)
		if err != nil {
			return math.NaN(), errors.Wrap(err, 0)
		}

		oldval, err := strconv.ParseFloat(d.LastValue, 64)
		if err != nil {
			return math.NaN(), errors.Wrap(err, 0)
		}

		newPdp = newval - oldval
		rate = newPdp / interval
	}

	if !d.checkRateBounds(rate) {
		newPdp = math.NaN()
	}

	d.LastValue = newValue

	return newPdp, nil
}
예제 #28
0
// XY returns the cartesian coordinates of n. If n is not a node
// in the grid, (NaN, NaN) is returned.
func (l *LimitedVisionGrid) XY(n graph.Node) (x, y float64) {
	if !l.Has(n) {
		return math.NaN(), math.NaN()
	}
	r, c := l.RowCol(n.ID())
	return float64(c), float64(r)
}
예제 #29
0
파일: value_number.go 프로젝트: hazbo/otto
func stringToFloat(value string) float64 {
	value = strings.TrimSpace(value)

	if value == "" {
		return 0
	}

	parseFloat := false
	if strings.IndexRune(value, '.') != -1 {
		parseFloat = true
	} else if stringToNumberParseInteger.MatchString(value) {
		parseFloat = false
	} else {
		parseFloat = true
	}

	if parseFloat {
		number, err := strconv.ParseFloat(value, 64)
		if err != nil && err.(*strconv.NumError).Err != strconv.ErrRange {
			return math.NaN()
		}
		return number
	}

	number, err := strconv.ParseInt(value, 0, 64)
	if err != nil {
		return math.NaN()
	}
	return float64(number)
}
예제 #30
0
파일: files.go 프로젝트: zzmjohn/Sia
// redundancy returns the redundancy of the least redundant chunk. A file
// becomes available when this redundancy is >= 1. Assumes that every piece is
// unique within a file contract. -1 is returned if the file has size 0.
func (f *file) redundancy() float64 {
	if f.size == 0 {
		return math.NaN()
	}
	piecesPerChunk := make([]int, f.numChunks())
	// If the file has non-0 size then the number of chunks should also be
	// non-0. Therefore the f.size == 0 conditional block above must appear
	// before this check.
	if len(piecesPerChunk) == 0 {
		build.Critical("cannot get redundancy of a file with 0 chunks")
		return math.NaN()
	}
	for _, fc := range f.contracts {
		for _, p := range fc.Pieces {
			piecesPerChunk[p.Chunk]++
		}
	}
	minPieces := piecesPerChunk[0]
	for _, numPieces := range piecesPerChunk {
		if numPieces < minPieces {
			minPieces = numPieces
		}
	}
	return float64(minPieces) / float64(f.erasureCode.MinPieces())
}