Example #1
0
func EaseInOutElastic(start, end, value float32) float32 {
	end -= start

	var d float32 = 1.0
	var p float32 = d * 0.3
	var s float32 = 0.0
	var a float32 = 0.0

	if value == 0 {
		return start
	}
	value /= d
	if value == 1 {
		return start + end
	}
	if a == 0 || a < float32(math.Abs(float64(end))) {
		a = end
		s = p / 4
	} else {
		s = p / (2 * float32(math.Pi)) * float32(math.Asin(float64(end/a)))
	}
	if value < 1 {
		return -0.5*(a*float32(math.Pow(2, 10*(float64(value)-1)))*float32(math.Sin(float64((value*d-s)*(2*math.Pi)/p)))) + start
	}
	return a*float32(math.Pow(2, -10*(float64(value)-1)))*float32(math.Sin(float64((value*d-s)*(2*math.Pi)/p)))*0.5 + end + start
}
Example #2
0
func f(x, y float64) float64 {
	v := math.Pow(2.0, math.Sin(y)) * math.Pow(2.0, math.Sin(x)) / 12
	if math.IsNaN(v) {
		return 0
	}
	return v
}
Example #3
0
// Lp Norm of an array, given p >= 1
func LPNorm(vector []float64, p float64) (float64, error) {
	distance := 0.
	for _, jj := range vector {
		distance += math.Pow(math.Abs(jj), p)
	}
	return math.Pow(distance, 1/p), nil
}
Example #4
0
func (r *Richard) SimPearson(v1, v2 InnerStruct) float64 {
	common := r.CommonKeys(v1, v2)

	n := float64(len(common))

	if n == 0 {
		return 0
	}

	var sum1, sum2, sumsq1, sumsq2, sump float64

	for key, _ := range common {
		sum1 += v1[key]
		sumsq1 += math.Pow(v1[key], 2)
		sum2 += v2[key]
		sumsq2 += math.Pow(v2[key], 2)
		sump += v1[key] * v2[key]
	}

	num := sump - ((sum1 * sum2) / n)
	den := math.Sqrt((sumsq1 - (math.Pow(sum1, 2))/n) * (sumsq2 - (math.Pow(sum2, 2))/n))

	if den == 0 {
		return 0
	}

	return num / den
}
Example #5
0
File: p205.go Project: benjgibbs/Go
func p205() {
	// p can score from 9 to 36
	pScoresSum := make(map[int]int)
	throw(4, 9, 0, pScoresSum)
	pScoresProb := make(map[int]float64)
	pTot := math.Pow(4.0, 9.0)
	for id, cnt := range pScoresSum {
		pScoresProb[id] = float64(cnt) / pTot
	}
	cScoresSum := make(map[int]int)
	throw(6, 6, 0, cScoresSum)
	cTot := math.Pow(6.0, 6.0)
	cScoresProb := make(map[int]float64)
	for id, cnt := range cScoresSum {
		cScoresProb[id] = float64(cnt) / cTot
	}

	// P(p wins)
	// = P( p has x ) * P( c has less than x)
	// = P(p has x) * (P(c has x-1) + P(c has x-2) + ... + P(c has 1))

	pWinProb := 0.0
	for pScore := 9; pScore <= 36; pScore++ {
		probCLower := 0.0
		for cScore := 6; cScore < pScore; cScore++ {
			probCLower += cScoresProb[cScore]
		}
		pWinProb += pScoresProb[pScore] * probCLower
	}

	//bruteForce()
	fmt.Printf("Prob P Wins %.7f\n", pWinProb)
}
Example #6
0
func main() {
	var list list.List
	scanner := bufio.NewScanner(os.Stdin)
	p := getNumber(scanner)
	q := getNumber(scanner)
	for i := p; i <= q; i++ {
		square := i * i
		squareStr := strconv.Itoa(square)
		mid := len(squareStr) / 2
		power := float64(len(squareStr) - mid)
		rem := square % int(math.Pow(10, power))
		quo := square / int(math.Pow(10, power))
		sum := rem + quo
		if sum == i {
			list.PushBack(i)
		}
	}
	if list.Len() <= 0 {
		fmt.Println("INVALID RANGE")
	} else {
		for e := list.Front(); e != nil; e = e.Next() {
			fmt.Printf("%d ", e.Value)
		}
	}
}
Example #7
0
// Distance computes the L-norm of s - t. See Norm for special cases.
// A panic will occur if the lengths of s and t do not match.
func Distance(s, t []float64, L float64) float64 {
	if len(s) != len(t) {
		panic("floats: slice lengths do not match")
	}
	if len(s) == 0 {
		return 0
	}
	var norm float64
	if L == 2 {
		for i, v := range s {
			diff := t[i] - v
			norm = math.Hypot(norm, diff)
		}
		return norm
	}
	if L == 1 {
		for i, v := range s {
			norm += math.Abs(t[i] - v)
		}
		return norm
	}
	if math.IsInf(L, 1) {
		for i, v := range s {
			absDiff := math.Abs(t[i] - v)
			if absDiff > norm {
				norm = absDiff
			}
		}
		return norm
	}
	for i, v := range s {
		norm += math.Pow(math.Abs(t[i]-v), L)
	}
	return math.Pow(norm, 1/L)
}
Example #8
0
func PixelDistance(image image.Image, x, y int, r, g, b, a uint32) float64 {
	if x < image.Bounds().Min.X ||
		y < image.Bounds().Min.Y ||
		x > image.Bounds().Max.X ||
		y > image.Bounds().Max.Y {
		log.Printf("Invalid pixel at %d, %d", x, y)
		return 0.0
	}

	targetR, targetG, targetB, targetA := image.At(x, y).RGBA()

	distance := 0.0
	distance += math.Pow(float64(r-targetR), 2)
	if math.IsNaN(distance) {
		log.Printf("Distance is NaN after red at %d, %d", x, y)
	}
	distance += math.Pow(float64(g-targetG), 2)
	if math.IsNaN(distance) {
		log.Printf("Distance is NaN after green at %d, %d", x, y)
	}
	distance += math.Pow(float64(b-targetB), 2)
	if math.IsNaN(distance) {
		log.Printf("Distance is NaN after blue at %d, %d", x, y)
	}
	distance += math.Pow(float64(a-targetA), 2)
	if math.IsNaN(distance) {
		log.Printf("Distance is NaN after alpha at %d, %d", x, y)
	}
	distance = math.Sqrt(distance)
	if math.IsNaN(distance) {
		log.Printf("Distance is NaN after sqrt at %d, %d", x, y)
	}

	return distance
}
Example #9
0
// ScoreFit is used to score the fit based on the Google work published here:
// http://www.columbia.edu/~cs2035/courses/ieor4405.S13/datacenter_scheduling.ppt
// This is equivalent to their BestFit v3
func ScoreFit(node *Node, util *Resources) float64 {
	// Determine the node availability
	nodeCpu := float64(node.Resources.CPU)
	if node.Reserved != nil {
		nodeCpu -= float64(node.Reserved.CPU)
	}
	nodeMem := float64(node.Resources.MemoryMB)
	if node.Reserved != nil {
		nodeMem -= float64(node.Reserved.MemoryMB)
	}

	// Compute the free percentage
	freePctCpu := 1 - (float64(util.CPU) / nodeCpu)
	freePctRam := 1 - (float64(util.MemoryMB) / nodeMem)

	// Total will be "maximized" the smaller the value is.
	// At 100% utilization, the total is 2, while at 0% util it is 20.
	total := math.Pow(10, freePctCpu) + math.Pow(10, freePctRam)

	// Invert so that the "maximized" total represents a high-value
	// score. Because the floor is 20, we simply use that as an anchor.
	// This means at a perfect fit, we return 18 as the score.
	score := 20.0 - total

	// Bound the score, just in case
	// If the score is over 18, that means we've overfit the node.
	if score > 18.0 {
		score = 18.0
	} else if score < 0 {
		score = 0
	}
	return score
}
func main() {

	i := 1

	for i <= 10 {
		fmt.Println(i, math.Pow(2, float64(i)), math.Pow(2, 1/float64(i)))
		i = i + 1
	}

	// A more standard initial condition, iterate to final condition loop

	for j := 3; j <= 9; j++ {
		fmt.Println(j * j)
	}

	k := 1

	for {
		if k > 8 {
			fmt.Println("k equals", k, "and the loop stops NOW!")
			break
		} else if k == 5 {
			fmt.Println("k equals", k, "?! That's just swell!")
			k++
		} else {
			fmt.Println("k equals", k, "and the loop goes around again...")
			k++
		}
	}
}
Example #11
0
// Estimate runtime for job
func (m *Machine) ETA() time.Duration {
	var eta time.Duration
	var lx, ly, lz float64
	for _, pos := range m.Positions {
		feed := pos.State.Feedrate
		if feed <= 0 {
			// Just to use something...
			feed = 300
		}

		// Convert from minutes to microseconds
		feed /= 60000000

		switch pos.State.MoveMode {
		case MoveModeNone:
			continue
		case MoveModeRapid:
			// This is silly, but it gives something to calculate with
			feed *= 8
		case MoveModeDwell:
			eta += time.Duration(pos.State.DwellTime) * time.Second
			continue
		}
		dx, dy, dz := pos.X-lx, pos.Y-ly, pos.Z-lz
		lx, ly, lz = pos.X, pos.Y, pos.Z

		dist := math.Sqrt(math.Pow(dx, 2) + math.Pow(dy, 2) + math.Pow(dz, 2))
		eta += time.Duration(dist/feed) * time.Microsecond
	}
	return eta
}
Example #12
0
// This method finds the closest color for a given RGB tuple and returns the name of the color in given mode
func FindClosestColor(RequestedColor []int, mode string) string {
	MinColors := make(map[int]string)
	var ColorMap map[string]string

	// css3 gives the shades while css21 gives the primary or base colors
	if mode == "css3" {
		ColorMap = gwc.CSS3NamesToHex
	} else {
		ColorMap = gwc.HTML4NamesToHex
	}

	for name, hexcode := range ColorMap {
		rgb_triplet := gwc.HexToRGB(hexcode)
		rd := math.Pow(float64(rgb_triplet[0]-RequestedColor[0]), float64(2))
		gd := math.Pow(float64(rgb_triplet[1]-RequestedColor[1]), float64(2))
		bd := math.Pow(float64(rgb_triplet[2]-RequestedColor[2]), float64(2))
		MinColors[int(rd+gd+bd)] = name
	}

	keys := make([]int, 0, len(MinColors))
	for key := range MinColors {
		keys = append(keys, key)
	}
	sort.Ints(keys)
	return MinColors[keys[0]]
}
Example #13
0
File: pmc.go Project: RayRacine/pmc
func qk(k, n, p float64) float64 {
	result := 1.0
	for i := 1.0; i <= k; i++ {
		result *= (1.0 - math.Pow(1.0-math.Pow(2, -i), n)*(1.0-p))
	}
	return result
}
Example #14
0
func TestGoertzel(t *testing.T) {
	samplerate := 1024
	blocksize := 1024
	freq := 128
	samples := make([]float64, blocksize)
	w := 2 * math.Pi / float64(samplerate)
	for i := 0; i < blocksize; i++ {
		samples[i] = math.Sin(float64(i) * float64(freq) * w)
	}
	g := NewGoertzel([]uint64{128, 129}, samplerate, blocksize)
	g.Feed(samples)
	m := g.Magnitude()
	if e := math.Pow(float64(blocksize)/2, 2); !approxEqual(m[0], e, 1e-8) {
		t.Errorf("Goertzel magnitude = %f. Want %f", m[0], e)
	}
	if !approxEqual(float64(m[1]), 0.0, 1e-10) {
		t.Errorf("Foertzel magnitude = %f. Want 0.0", m[1])
	}
	c := g.Complex()
	if e, m := math.Sqrt(math.Pow(float64(blocksize)/2, 2)), cmplx.Abs(complex128(c[0])); !approxEqual(m, e, 1e-8) {
		t.Errorf("Goertzel magnitude = %f. Want %f", m, e)
	}
	if e, p := -math.Pi/2, cmplx.Phase(complex128(c[0])); !approxEqual(p, e, 1e-12) {
		t.Errorf("Goertzel phase = %f. Want %f", p, e)
	}
}
Example #15
0
// ExponentialRegression returns an exponential regression on data series
func ExponentialRegression(s Series) (regressions Series, err error) {

	if len(s) == 0 {
		return nil, errors.New("Input must not be empty")
	}

	var sum [6]float64

	for i := 0; i < len(s); i++ {
		sum[0] += s[i].X
		sum[1] += s[i].Y
		sum[2] += s[i].X * s[i].X * s[i].Y
		sum[3] += s[i].Y * math.Log(s[i].Y)
		sum[4] += s[i].X * s[i].Y * math.Log(s[i].Y)
		sum[5] += s[i].X * s[i].Y
	}

	denominator := (sum[1]*sum[2] - sum[5]*sum[5])
	a := math.Pow(math.E, (sum[2]*sum[3]-sum[5]*sum[4])/denominator)
	b := (sum[1]*sum[4] - sum[5]*sum[3]) / denominator

	for j := 0; j < len(s); j++ {
		regressions = append(regressions, Coordinate{
			X: s[j].X,
			Y: a * math.Pow(2.718281828459045, b*s[j].X),
		})
	}

	return regressions, nil

}
Example #16
0
// ElasticInOut Acceleration until halfway, then deceleration
func ElasticInOut(t, b, c, d float64) float64 {
	if t > d {
		return c
	}

	s := math.SqrtPi
	p := d * (0.3 * 1.5)
	a := c

	if t == 0 {
		return b
	}

	t /= d / 2

	if t == 2 {
		return b + c
	}

	if a < math.Abs(c) {
		s = p / 4
	} else {
		s = p / DoublePi * math.Asin(c/a)
	}

	t--

	if t < 0 {
		return -0.5*(a*math.Pow(2, 10*t)*math.Sin((t*d-s)*DoublePi/p)) + b
	}

	return a*math.Pow(2, -10*t)*math.Sin((t*d-s)*DoublePi/p)*0.5 + c + b
}
Example #17
0
func (a *activeSet) pruneLinearlyDependent(grad linalg.Vector) bool {
	var biggestViolation float64
	var violationI, violationJ int

	for i := 0; i < len(a.Constraints)-1; i++ {
		iConstraint := float64(a.Constraints[i])
		for j := i + 1; j < len(a.Constraints); j++ {
			jConstraint := float64(a.Constraints[j])

			signVec := [2]float64{a.SignVec[i], a.SignVec[j]}
			gradVec := [2]float64{grad[i], grad[j]}

			gradDot := gradVec[0]*signVec[0] + gradVec[1]*signVec[1]
			k := -gradDot / 2.0
			gradProj := [2]float64{gradVec[0] + k*signVec[0], gradVec[1] + k*signVec[1]}

			if gradProj[0]*iConstraint < 0 && gradProj[1]*jConstraint < 0 {
				gradProjMag := math.Pow(gradProj[0], 2) + math.Pow(gradProj[1], 2)
				if gradProjMag >= biggestViolation || violationJ == 0 {
					biggestViolation = gradProjMag
					violationI = i
					violationJ = j
				}
			}
		}
	}
	if violationJ > 0 {
		a.Constraints[violationI] = 0
		a.Constraints[violationJ] = 0
		return true
	}
	return false
}
/*
 * matrix is the input matrix of integers
 *
 */
func (ts *TravellingSalesman) Init(matrix [][]int) {
	// Find locations and coordinates
	coords := map[int][2]int{}
	for i := range matrix {
		for j := range matrix[i] {
			if matrix[i][j] != 0 {
				value := matrix[i][j]
				coords[value] = [2]int{i + 1, j + 1}
			}
		}
	}

	// Gene length equals the number of coordinates
	ts.geneLength = len(coords)

	// Calculate distances
	var size = ts.geneLength
	ts.distMatrix = util.NewDistanceMatrix(ts.geneLength)
	for i := 0; i < size*size; i++ {
		row := i % size
		col := i / size
		area1 := coords[row+1] // X coordinate (Row)
		area2 := coords[col+1] // Y coordinate (Column)
		if area1[0] == area2[0] {
			// Same Column so distance eq diff(row2-row1)
			ts.distMatrix.SetDistance(
				row,
				col,
				math.Abs(float64(area2[1]-area1[1])))
		} else if area1[1] == area2[1] {
			// Same Row so distance eq diff(col2-col1)
			ts.distMatrix.SetDistance(
				row,
				col,
				math.Abs(float64(area2[0]-area1[0])))
		} else {
			a := math.Pow(float64(area2[1]-area1[1]), 2)
			b := math.Pow(float64(area2[0]-area1[0]), 2)
			c := math.Sqrt(a + b)
			ts.distMatrix.SetDistance(row, col, c)
		}
		//fmt.Printf("%d -> %d: %f\n", row+1, col+1, ts.distMatrix.GetDistance(row,col))
	}

	// Generate random genes
	ts.genes = make([]ga.Gene, ts.nGenes)
	var proto = make([]int, ts.geneLength)
	var i int = 0
	for key, _ := range coords {
		proto[i] = key
		i++
	}

	// Initialize all len(genes) and shuffle
	for i := 0; i < len(ts.genes); i++ {
		ts.genes[i].Data = make([]int, len(proto))
		copy(ts.genes[i].Data, proto)
		shuffleArray(&(ts.genes[i].Data))
	}
}
Example #19
0
func Test_brent01(tst *testing.T) {

	//verbose()
	chk.PrintTitle("brent01. root finding")

	ffcnA := func(x float64) (res float64, err error) {
		res = math.Pow(x, 3.0) - 0.165*math.Pow(x, 2.0) + 3.993e-4
		return
	}

	ffcnB := func(fx, x []float64) (err error) {
		fx[0], err = ffcnA(x[0])
		return
	}

	JfcnB := func(dfdx [][]float64, x []float64) (err error) {
		dfdx[0][0] = 3.0*x[0]*x[0] - 2.0*0.165*x[0]
		return
	}

	xa, xb := 0.0, 0.11
	//xguess := 0.001 // ===> this one converges to the right-hand solution
	xguess := 0.03
	//save   := true
	save := false
	run_rootsol_test(tst, xa, xb, xguess, 1e-7, ffcnA, ffcnB, JfcnB, "brent01.png", save, false)
}
Example #20
0
func ExampleDrawer_floydSteinberg() {
	const width = 130
	const height = 50

	im := image.NewGray(image.Rectangle{Max: image.Point{X: width, Y: height}})
	for x := 0; x < width; x++ {
		for y := 0; y < height; y++ {
			dist := math.Sqrt(math.Pow(float64(x-width/2), 2)/3+math.Pow(float64(y-height/2), 2)) / (height / 1.5) * 255
			var gray uint8
			if dist > 255 {
				gray = 255
			} else {
				gray = uint8(dist)
			}
			im.SetGray(x, y, color.Gray{Y: 255 - gray})
		}
	}
	pi := image.NewPaletted(im.Bounds(), []color.Color{
		color.Gray{Y: 255},
		color.Gray{Y: 160},
		color.Gray{Y: 70},
		color.Gray{Y: 35},
		color.Gray{Y: 0},
	})

	draw.FloydSteinberg.Draw(pi, im.Bounds(), im, image.ZP)
	shade := []string{" ", "░", "▒", "▓", "█"}
	for i, p := range pi.Pix {
		fmt.Print(shade[p])
		if (i+1)%width == 0 {
			fmt.Print("\n")
		}
	}
}
Example #21
0
// Norm returns the L norm of the slice S, defined as
// (sum_{i=1}^N s[i]^L)^{1/L}
// Special cases:
// L = math.Inf(1) gives the maximum absolute value.
// Does not correctly compute the zero norm (use Count).
func Norm(s []float64, L float64) float64 {
	// Should this complain if L is not positive?
	// Should this be done in log space for better numerical stability?
	//	would be more cost
	//	maybe only if L is high?
	if len(s) == 0 {
		return 0
	}
	if L == 2 {
		twoNorm := math.Abs(s[0])
		for i := 1; i < len(s); i++ {
			twoNorm = math.Hypot(twoNorm, s[i])
		}
		return twoNorm
	}
	var norm float64
	if L == 1 {
		for _, val := range s {
			norm += math.Abs(val)
		}
		return norm
	}
	if math.IsInf(L, 1) {
		for _, val := range s {
			norm = math.Max(norm, math.Abs(val))
		}
		return norm
	}
	for _, val := range s {
		norm += math.Pow(math.Abs(val), L)
	}
	return math.Pow(norm, 1/L)
}
Example #22
0
func (t triangle) getThirdSide() float64 {
	a := t.sides[0]
	b := t.sides[1]
	c2 := math.Pow(a, 2) + math.Pow(b, 2) - 2*a*b*math.Cos(t.angle)
	c := math.Sqrt(c2)
	return c
}
Example #23
0
func ExpOut(value, power float32) Interpolation {
	min := float32(math.Pow(float64(value), float64(-power)))
	scale := float32(1 / (1 - min))
	return func(a float32) float32 {
		return 1 - (float32(math.Pow(float64(value), float64(-power*a-min))))*scale
	}
}
Example #24
0
// StyblinskiTang minimum is -39.16599d reached in (-2.903534, ..., -2.903534)
// Recommended search domain is [-5, 5]
func styblinskiTang(X []float64) float64 {
	sum := 0.0
	for _, x := range X {
		sum += m.Pow(x, 4) - 16*m.Pow(x, 2) + 5*x
	}
	return sum / 2
}
Example #25
0
func (hb *Rectangle) intersects(center *terrain.Position, other Hitbox, otherCenter *terrain.Position) (intersects bool, err error) {
	switch o := other.(type) {
	case *Rectangle:
		b1 := hb.bounds(center)
		b2 := o.bounds(otherCenter)
		intersects = (b1[0].X <= b2[1].X && b1[1].X >= b2[0].X &&
			b1[0].Y <= b2[1].Y && b1[1].Y >= b2[0].Y)
	case *Circle:
		// See http://stackoverflow.com/a/402010
		bounds := hb.bounds(center)

		dx := math.Abs(otherCenter.X - bounds[0].X)
		dy := math.Abs(otherCenter.Y - bounds[0].Y)

		if dx > hb.width/2+o.radius || dy > hb.height/2+o.radius {
			intersects = false
		} else if dx <= hb.width/2 || dy <= hb.height/2 {
			intersects = true
		} else {
			dc := math.Pow(dx-hb.width/2, 2) + math.Pow(dy-hb.height/2, 2)
			intersects = (dc <= math.Pow(o.radius, 2))
		}
	default:
		err = errUnsupportedHitbox
	}
	return
}
Example #26
0
// http://www.easyrgb.com/index.php?X=MATH
func xyzToLabAB(x, y, z float64) (float64, float64, float64) {
	normalizedX := x / 95.047
	normalizedY := y / 100.0
	normalizedZ := z / 108.883

	if normalizedX > 0.008856 {
		normalizedX = math.Pow(normalizedX, (1.0 / 3.0))
	} else {
		normalizedX = (7.787 * normalizedX) + (16.0 / 116.0)
	}

	if normalizedY > 0.008856 {
		normalizedY = math.Pow(normalizedY, (1.0 / 3.0))
	} else {
		normalizedY = (7.787 * normalizedY) + (16.0 / 116.0)
	}

	if normalizedZ > 0.008856 {
		normalizedZ = math.Pow(normalizedZ, (1.0 / 3.0))
	} else {
		normalizedZ = (7.787 * normalizedZ) + (16.0 / 116.0)
	}

	l := (116 * normalizedY) - 16
	a := 500 * (normalizedX - normalizedY)
	b := 200 * (normalizedY - normalizedZ)

	return l, a, b
}
Example #27
0
// Computes the error estimate based on fNew:
func (p *peer) computeErrorModel(in *integration) (errorEstimate float64) {
	var i_n, j_stg uint

	// Loop: EmFactors
	for i_n = 0; i_n < in.n; i_n++ {
		var factor float64 = 0.0
		for j_stg = 0; j_stg < p.Stages; j_stg++ {
			factor += p.errorModelWeights[j_stg] * in.fNew[j_stg][i_n]
		}
		in.errorFactors[i_n] = math.Pow(factor/(in.AbsoluteTolerance+in.RelativeTolerance*math.Abs(in.yOld[p.Stages-1][i_n])), 2.0)
	}

	// compute error quotient/20070803
	// step ratio from error model ((1+a)^p-a^p)/est+a^p)^(1/p)-a, p=order/2:
	errorRelative := 0.0
	for i_n = 0; i_n < in.n; i_n++ {
		errorRelative += in.errorFactors[i_n]
	}

	errorEstimate = in.stepEstimate*math.Sqrt(errorRelative/float64(in.n)) + 1e-8
	errorModelDenom := math.Pow(math.Pow(in.stepRatio, 2.0)+p.errorModelA, float64(p.Order)/2.0) - p.errorModelA0
	errorStepRatio := math.Pow(errorModelDenom/errorEstimate+p.errorModelA0, 2.0/float64(p.Order)) - p.errorModelA
	in.stepEstimate = in.stepPrevious * math.Max(in.stepRatioMin, math.Min(0.95*math.Sqrt(errorStepRatio), p.stepRatioMax)) // safety interval

	return
}
Example #28
0
// http://www.easyrgb.com/index.php?X=MATH
func rgbToXyz(r, g, b uint32) (float64, float64, float64) {
	normalizedR := float64(r) / 0xFFFF
	normalizedG := float64(g) / 0xFFFF
	normalizedB := float64(b) / 0xFFFF

	if normalizedR > 0.04045 {
		normalizedR = math.Pow(((normalizedR + 0.055) / 1.055), 2.4)
	} else {
		normalizedR = normalizedR / 12.92
	}

	if normalizedG > 0.04045 {
		normalizedG = math.Pow(((normalizedG + 0.055) / 1.055), 2.4)
	} else {
		normalizedG = normalizedG / 12.92
	}

	if normalizedB > 0.04045 {
		normalizedB = math.Pow(((normalizedB + 0.055) / 1.055), 2.4)
	} else {
		normalizedB = normalizedB / 12.92
	}

	normalizedR *= 100
	normalizedG *= 100
	normalizedB *= 100

	x := normalizedR*0.4124 + normalizedG*0.3576 + normalizedB*0.1805
	y := normalizedR*0.2126 + normalizedG*0.7152 + normalizedB*0.0722
	z := normalizedR*0.0193 + normalizedG*0.1192 + normalizedB*0.9505

	return x, y, z
}
Example #29
0
// p-norm distance with weights (weighted l_p distance)
func WeightedMinkowskiDistance(firstVector, secondVector, weightVector []float64, p float64) (float64, error) {
	distance := 0.
	for ii := range firstVector {
		distance += weightVector[ii] * math.Pow(math.Abs(firstVector[ii]-secondVector[ii]), p)
	}
	return math.Pow(distance, 1/p), nil
}
Example #30
0
func deltafov(uId uint32, r, dx, dy float64) { //TODO
	unit := Units[uId]
	for i, u := range Units {
		if fovUnits[uId][i] {
			continue
		}
		if math.Pow(u.X.A-unit.X.A-dx, 2)+math.Pow(u.Y.A-unit.Y.A-dy, 2) <= r {
			fovUnits[uId][i] = true
			u.X.SetInform(unit.Owner.A)
			u.Y.SetInform(unit.Owner.A)
			//I see him now
		}
	}
	for k, v := range fovUnits[uId] {
		if !v {
			continue
		}
		if math.Pow(Units[k].X.A-unit.X.A-dx, 2)+math.Pow(Units[k].Y.A-unit.Y.A-dy, 2) > r {
			fovUnits[uId][k] = false
			Units[k].X.inform[unit.Owner.A]--
			if Units[k].X.inform[unit.Owner.A] <= 0 {
				delete(Units[k].X.inform, unit.Owner.A)
			}
			Units[k].Y.inform[unit.Owner.A]--
			if Units[k].Y.inform[unit.Owner.A] <= 0 {
				delete(Units[k].Y.inform, unit.Owner.A)
			}
			//I don't see him anymore
		}
	}
}