Data.Array.Repa.Algorithms.ColorRamp:rampColorHotToCold from repa-algorithms-3.4.0.1, A

Percentage Accurate: 99.8% → 99.9%

Time: 8.7s

Alternatives: 12

Speedup: 1.4×

• 21.1% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ 1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ 1.0 (/ (* 4.0 (- (+ x (* y 0.75)) z)) y)))

C

double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 1.0d0 + ((4.0d0 * ((x + (y * 0.75d0)) - z)) / y)
end function

Java

public static double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
}

Python

def code(x, y, z):
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y)

Julia

function code(x, y, z)
	return Float64(1.0 + Float64(Float64(4.0 * Float64(Float64(x + Float64(y * 0.75)) - z)) / y))
end

MATLAB

function tmp = code(x, y, z)
	tmp = 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
end

Wolfram

code[x_, y_, z_] := N[(1.0 + N[(N[(4.0 * N[(N[(x + N[(y * 0.75), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ 1.0 (/ (* 4.0 (- (+ x (* y 0.75)) z)) y)))

C

double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 1.0d0 + ((4.0d0 * ((x + (y * 0.75d0)) - z)) / y)
end function

Java

public static double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
}

Python

def code(x, y, z):
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y)

Julia

function code(x, y, z)
	return Float64(1.0 + Float64(Float64(4.0 * Float64(Float64(x + Float64(y * 0.75)) - z)) / y))
end

MATLAB

function tmp = code(x, y, z)
	tmp = 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
end

Wolfram

code[x_, y_, z_] := N[(1.0 + N[(N[(4.0 * N[(N[(x + N[(y * 0.75), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ 4 + \frac{4 \cdot \left(x - z\right)}{y} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ 4.0 (/ (* 4.0 (- x z)) y)))

C

double code(double x, double y, double z) {
	return 4.0 + ((4.0 * (x - z)) / y);
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 4.0d0 + ((4.0d0 * (x - z)) / y)
end function

Java

public static double code(double x, double y, double z) {
	return 4.0 + ((4.0 * (x - z)) / y);
}

Python

def code(x, y, z):
	return 4.0 + ((4.0 * (x - z)) / y)

Julia

function code(x, y, z)
	return Float64(4.0 + Float64(Float64(4.0 * Float64(x - z)) / y))
end

MATLAB

function tmp = code(x, y, z)
	tmp = 4.0 + ((4.0 * (x - z)) / y);
end

Wolfram

code[x_, y_, z_] := N[(4.0 + N[(N[(4.0 * N[(x - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 2: 52.0% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := z \cdot \frac{-4}{y}\\ t_1 := x \cdot \frac{4}{y}\\ \mathbf{if}\;x \leq -2.7 \cdot 10^{+53}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.52 \cdot 10^{-27}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq -1.8 \cdot 10^{-47}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -7.6 \cdot 10^{-84}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -8.5 \cdot 10^{-115}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq -2.2 \cdot 10^{-137}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -1 \cdot 10^{-210}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq -2.8 \cdot 10^{-305}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 1.55 \cdot 10^{-163}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq 2.3 \cdot 10^{-147}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 52000:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq 4.1 \cdot 10^{+108} \lor \neg \left(x \leq 10^{+169}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* z (/ -4.0 y))) (t_1 (* x (/ 4.0 y))))
   (if (<= x -2.7e+53)
     t_1
     (if (<= x -1.52e-27)
       4.0
       (if (<= x -1.8e-47)
         t_1
         (if (<= x -7.6e-84)
           t_0
           (if (<= x -8.5e-115)
             4.0
             (if (<= x -2.2e-137)
               t_0
               (if (<= x -1e-210)
                 4.0
                 (if (<= x -2.8e-305)
                   t_0
                   (if (<= x 1.55e-163)
                     4.0
                     (if (<= x 2.3e-147)
                       t_0
                       (if (<= x 52000.0)
                         4.0
                         (if (or (<= x 4.1e+108) (not (<= x 1e+169)))
                           t_1
                           4.0))))))))))))))

C

double code(double x, double y, double z) {
	double t_0 = z * (-4.0 / y);
	double t_1 = x * (4.0 / y);
	double tmp;
	if (x <= -2.7e+53) {
		tmp = t_1;
	} else if (x <= -1.52e-27) {
		tmp = 4.0;
	} else if (x <= -1.8e-47) {
		tmp = t_1;
	} else if (x <= -7.6e-84) {
		tmp = t_0;
	} else if (x <= -8.5e-115) {
		tmp = 4.0;
	} else if (x <= -2.2e-137) {
		tmp = t_0;
	} else if (x <= -1e-210) {
		tmp = 4.0;
	} else if (x <= -2.8e-305) {
		tmp = t_0;
	} else if (x <= 1.55e-163) {
		tmp = 4.0;
	} else if (x <= 2.3e-147) {
		tmp = t_0;
	} else if (x <= 52000.0) {
		tmp = 4.0;
	} else if ((x <= 4.1e+108) || !(x <= 1e+169)) {
		tmp = t_1;
	} else {
		tmp = 4.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = z * ((-4.0d0) / y)
    t_1 = x * (4.0d0 / y)
    if (x <= (-2.7d+53)) then
        tmp = t_1
    else if (x <= (-1.52d-27)) then
        tmp = 4.0d0
    else if (x <= (-1.8d-47)) then
        tmp = t_1
    else if (x <= (-7.6d-84)) then
        tmp = t_0
    else if (x <= (-8.5d-115)) then
        tmp = 4.0d0
    else if (x <= (-2.2d-137)) then
        tmp = t_0
    else if (x <= (-1d-210)) then
        tmp = 4.0d0
    else if (x <= (-2.8d-305)) then
        tmp = t_0
    else if (x <= 1.55d-163) then
        tmp = 4.0d0
    else if (x <= 2.3d-147) then
        tmp = t_0
    else if (x <= 52000.0d0) then
        tmp = 4.0d0
    else if ((x <= 4.1d+108) .or. (.not. (x <= 1d+169))) then
        tmp = t_1
    else
        tmp = 4.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = z * (-4.0 / y);
	double t_1 = x * (4.0 / y);
	double tmp;
	if (x <= -2.7e+53) {
		tmp = t_1;
	} else if (x <= -1.52e-27) {
		tmp = 4.0;
	} else if (x <= -1.8e-47) {
		tmp = t_1;
	} else if (x <= -7.6e-84) {
		tmp = t_0;
	} else if (x <= -8.5e-115) {
		tmp = 4.0;
	} else if (x <= -2.2e-137) {
		tmp = t_0;
	} else if (x <= -1e-210) {
		tmp = 4.0;
	} else if (x <= -2.8e-305) {
		tmp = t_0;
	} else if (x <= 1.55e-163) {
		tmp = 4.0;
	} else if (x <= 2.3e-147) {
		tmp = t_0;
	} else if (x <= 52000.0) {
		tmp = 4.0;
	} else if ((x <= 4.1e+108) || !(x <= 1e+169)) {
		tmp = t_1;
	} else {
		tmp = 4.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = z * (-4.0 / y)
	t_1 = x * (4.0 / y)
	tmp = 0
	if x <= -2.7e+53:
		tmp = t_1
	elif x <= -1.52e-27:
		tmp = 4.0
	elif x <= -1.8e-47:
		tmp = t_1
	elif x <= -7.6e-84:
		tmp = t_0
	elif x <= -8.5e-115:
		tmp = 4.0
	elif x <= -2.2e-137:
		tmp = t_0
	elif x <= -1e-210:
		tmp = 4.0
	elif x <= -2.8e-305:
		tmp = t_0
	elif x <= 1.55e-163:
		tmp = 4.0
	elif x <= 2.3e-147:
		tmp = t_0
	elif x <= 52000.0:
		tmp = 4.0
	elif (x <= 4.1e+108) or not (x <= 1e+169):
		tmp = t_1
	else:
		tmp = 4.0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(z * Float64(-4.0 / y))
	t_1 = Float64(x * Float64(4.0 / y))
	tmp = 0.0
	if (x <= -2.7e+53)
		tmp = t_1;
	elseif (x <= -1.52e-27)
		tmp = 4.0;
	elseif (x <= -1.8e-47)
		tmp = t_1;
	elseif (x <= -7.6e-84)
		tmp = t_0;
	elseif (x <= -8.5e-115)
		tmp = 4.0;
	elseif (x <= -2.2e-137)
		tmp = t_0;
	elseif (x <= -1e-210)
		tmp = 4.0;
	elseif (x <= -2.8e-305)
		tmp = t_0;
	elseif (x <= 1.55e-163)
		tmp = 4.0;
	elseif (x <= 2.3e-147)
		tmp = t_0;
	elseif (x <= 52000.0)
		tmp = 4.0;
	elseif ((x <= 4.1e+108) || !(x <= 1e+169))
		tmp = t_1;
	else
		tmp = 4.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = z * (-4.0 / y);
	t_1 = x * (4.0 / y);
	tmp = 0.0;
	if (x <= -2.7e+53)
		tmp = t_1;
	elseif (x <= -1.52e-27)
		tmp = 4.0;
	elseif (x <= -1.8e-47)
		tmp = t_1;
	elseif (x <= -7.6e-84)
		tmp = t_0;
	elseif (x <= -8.5e-115)
		tmp = 4.0;
	elseif (x <= -2.2e-137)
		tmp = t_0;
	elseif (x <= -1e-210)
		tmp = 4.0;
	elseif (x <= -2.8e-305)
		tmp = t_0;
	elseif (x <= 1.55e-163)
		tmp = 4.0;
	elseif (x <= 2.3e-147)
		tmp = t_0;
	elseif (x <= 52000.0)
		tmp = 4.0;
	elseif ((x <= 4.1e+108) || ~((x <= 1e+169)))
		tmp = t_1;
	else
		tmp = 4.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(z * N[(-4.0 / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(x * N[(4.0 / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.7e+53], t$95$1, If[LessEqual[x, -1.52e-27], 4.0, If[LessEqual[x, -1.8e-47], t$95$1, If[LessEqual[x, -7.6e-84], t$95$0, If[LessEqual[x, -8.5e-115], 4.0, If[LessEqual[x, -2.2e-137], t$95$0, If[LessEqual[x, -1e-210], 4.0, If[LessEqual[x, -2.8e-305], t$95$0, If[LessEqual[x, 1.55e-163], 4.0, If[LessEqual[x, 2.3e-147], t$95$0, If[LessEqual[x, 52000.0], 4.0, If[Or[LessEqual[x, 4.1e+108], N[Not[LessEqual[x, 1e+169]], $MachinePrecision]], t$95$1, 4.0]]]]]]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 3: 52.0% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := z \cdot \frac{-4}{y}\\ t_1 := x \cdot \frac{4}{y}\\ t_2 := \frac{z}{\frac{y}{-4}}\\ \mathbf{if}\;x \leq -2.35 \cdot 10^{+53}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -3.9 \cdot 10^{-30}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq -2 \cdot 10^{-47}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.85 \cdot 10^{-84}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -3.1 \cdot 10^{-114}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq -3.4 \cdot 10^{-137}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq -4.8 \cdot 10^{-210}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq -3.1 \cdot 10^{-305}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq 2.1 \cdot 10^{-163}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq 5 \cdot 10^{-146}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 126000000:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq 2.4 \cdot 10^{+109} \lor \neg \left(x \leq 10^{+169}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* z (/ -4.0 y))) (t_1 (* x (/ 4.0 y))) (t_2 (/ z (/ y -4.0))))
   (if (<= x -2.35e+53)
     t_1
     (if (<= x -3.9e-30)
       4.0
       (if (<= x -2e-47)
         t_1
         (if (<= x -1.85e-84)
           t_0
           (if (<= x -3.1e-114)
             4.0
             (if (<= x -3.4e-137)
               t_2
               (if (<= x -4.8e-210)
                 4.0
                 (if (<= x -3.1e-305)
                   t_2
                   (if (<= x 2.1e-163)
                     4.0
                     (if (<= x 5e-146)
                       t_0
                       (if (<= x 126000000.0)
                         4.0
                         (if (or (<= x 2.4e+109) (not (<= x 1e+169)))
                           t_1
                           4.0))))))))))))))

C

double code(double x, double y, double z) {
	double t_0 = z * (-4.0 / y);
	double t_1 = x * (4.0 / y);
	double t_2 = z / (y / -4.0);
	double tmp;
	if (x <= -2.35e+53) {
		tmp = t_1;
	} else if (x <= -3.9e-30) {
		tmp = 4.0;
	} else if (x <= -2e-47) {
		tmp = t_1;
	} else if (x <= -1.85e-84) {
		tmp = t_0;
	} else if (x <= -3.1e-114) {
		tmp = 4.0;
	} else if (x <= -3.4e-137) {
		tmp = t_2;
	} else if (x <= -4.8e-210) {
		tmp = 4.0;
	} else if (x <= -3.1e-305) {
		tmp = t_2;
	} else if (x <= 2.1e-163) {
		tmp = 4.0;
	} else if (x <= 5e-146) {
		tmp = t_0;
	} else if (x <= 126000000.0) {
		tmp = 4.0;
	} else if ((x <= 2.4e+109) || !(x <= 1e+169)) {
		tmp = t_1;
	} else {
		tmp = 4.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_0 = z * ((-4.0d0) / y)
    t_1 = x * (4.0d0 / y)
    t_2 = z / (y / (-4.0d0))
    if (x <= (-2.35d+53)) then
        tmp = t_1
    else if (x <= (-3.9d-30)) then
        tmp = 4.0d0
    else if (x <= (-2d-47)) then
        tmp = t_1
    else if (x <= (-1.85d-84)) then
        tmp = t_0
    else if (x <= (-3.1d-114)) then
        tmp = 4.0d0
    else if (x <= (-3.4d-137)) then
        tmp = t_2
    else if (x <= (-4.8d-210)) then
        tmp = 4.0d0
    else if (x <= (-3.1d-305)) then
        tmp = t_2
    else if (x <= 2.1d-163) then
        tmp = 4.0d0
    else if (x <= 5d-146) then
        tmp = t_0
    else if (x <= 126000000.0d0) then
        tmp = 4.0d0
    else if ((x <= 2.4d+109) .or. (.not. (x <= 1d+169))) then
        tmp = t_1
    else
        tmp = 4.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = z * (-4.0 / y);
	double t_1 = x * (4.0 / y);
	double t_2 = z / (y / -4.0);
	double tmp;
	if (x <= -2.35e+53) {
		tmp = t_1;
	} else if (x <= -3.9e-30) {
		tmp = 4.0;
	} else if (x <= -2e-47) {
		tmp = t_1;
	} else if (x <= -1.85e-84) {
		tmp = t_0;
	} else if (x <= -3.1e-114) {
		tmp = 4.0;
	} else if (x <= -3.4e-137) {
		tmp = t_2;
	} else if (x <= -4.8e-210) {
		tmp = 4.0;
	} else if (x <= -3.1e-305) {
		tmp = t_2;
	} else if (x <= 2.1e-163) {
		tmp = 4.0;
	} else if (x <= 5e-146) {
		tmp = t_0;
	} else if (x <= 126000000.0) {
		tmp = 4.0;
	} else if ((x <= 2.4e+109) || !(x <= 1e+169)) {
		tmp = t_1;
	} else {
		tmp = 4.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = z * (-4.0 / y)
	t_1 = x * (4.0 / y)
	t_2 = z / (y / -4.0)
	tmp = 0
	if x <= -2.35e+53:
		tmp = t_1
	elif x <= -3.9e-30:
		tmp = 4.0
	elif x <= -2e-47:
		tmp = t_1
	elif x <= -1.85e-84:
		tmp = t_0
	elif x <= -3.1e-114:
		tmp = 4.0
	elif x <= -3.4e-137:
		tmp = t_2
	elif x <= -4.8e-210:
		tmp = 4.0
	elif x <= -3.1e-305:
		tmp = t_2
	elif x <= 2.1e-163:
		tmp = 4.0
	elif x <= 5e-146:
		tmp = t_0
	elif x <= 126000000.0:
		tmp = 4.0
	elif (x <= 2.4e+109) or not (x <= 1e+169):
		tmp = t_1
	else:
		tmp = 4.0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(z * Float64(-4.0 / y))
	t_1 = Float64(x * Float64(4.0 / y))
	t_2 = Float64(z / Float64(y / -4.0))
	tmp = 0.0
	if (x <= -2.35e+53)
		tmp = t_1;
	elseif (x <= -3.9e-30)
		tmp = 4.0;
	elseif (x <= -2e-47)
		tmp = t_1;
	elseif (x <= -1.85e-84)
		tmp = t_0;
	elseif (x <= -3.1e-114)
		tmp = 4.0;
	elseif (x <= -3.4e-137)
		tmp = t_2;
	elseif (x <= -4.8e-210)
		tmp = 4.0;
	elseif (x <= -3.1e-305)
		tmp = t_2;
	elseif (x <= 2.1e-163)
		tmp = 4.0;
	elseif (x <= 5e-146)
		tmp = t_0;
	elseif (x <= 126000000.0)
		tmp = 4.0;
	elseif ((x <= 2.4e+109) || !(x <= 1e+169))
		tmp = t_1;
	else
		tmp = 4.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = z * (-4.0 / y);
	t_1 = x * (4.0 / y);
	t_2 = z / (y / -4.0);
	tmp = 0.0;
	if (x <= -2.35e+53)
		tmp = t_1;
	elseif (x <= -3.9e-30)
		tmp = 4.0;
	elseif (x <= -2e-47)
		tmp = t_1;
	elseif (x <= -1.85e-84)
		tmp = t_0;
	elseif (x <= -3.1e-114)
		tmp = 4.0;
	elseif (x <= -3.4e-137)
		tmp = t_2;
	elseif (x <= -4.8e-210)
		tmp = 4.0;
	elseif (x <= -3.1e-305)
		tmp = t_2;
	elseif (x <= 2.1e-163)
		tmp = 4.0;
	elseif (x <= 5e-146)
		tmp = t_0;
	elseif (x <= 126000000.0)
		tmp = 4.0;
	elseif ((x <= 2.4e+109) || ~((x <= 1e+169)))
		tmp = t_1;
	else
		tmp = 4.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(z * N[(-4.0 / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(x * N[(4.0 / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(z / N[(y / -4.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.35e+53], t$95$1, If[LessEqual[x, -3.9e-30], 4.0, If[LessEqual[x, -2e-47], t$95$1, If[LessEqual[x, -1.85e-84], t$95$0, If[LessEqual[x, -3.1e-114], 4.0, If[LessEqual[x, -3.4e-137], t$95$2, If[LessEqual[x, -4.8e-210], 4.0, If[LessEqual[x, -3.1e-305], t$95$2, If[LessEqual[x, 2.1e-163], 4.0, If[LessEqual[x, 5e-146], t$95$0, If[LessEqual[x, 126000000.0], 4.0, If[Or[LessEqual[x, 2.4e+109], N[Not[LessEqual[x, 1e+169]], $MachinePrecision]], t$95$1, 4.0]]]]]]]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 4: 52.0% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := z \cdot \frac{-4}{y}\\ t_1 := \frac{4 \cdot x}{y}\\ t_2 := \frac{z}{\frac{y}{-4}}\\ \mathbf{if}\;x \leq -5.2 \cdot 10^{+53}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -3.9 \cdot 10^{-30}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq -1.85 \cdot 10^{-47}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -4.5 \cdot 10^{-85}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -1.3 \cdot 10^{-112}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq -2.9 \cdot 10^{-139}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq -2.9 \cdot 10^{-209}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq -4.8 \cdot 10^{-305}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;x \leq 5.2 \cdot 10^{-166}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq 9 \cdot 10^{-145}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 2300000000000:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq 3.1 \cdot 10^{+109} \lor \neg \left(x \leq 1.3 \cdot 10^{+169}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* z (/ -4.0 y))) (t_1 (/ (* 4.0 x) y)) (t_2 (/ z (/ y -4.0))))
   (if (<= x -5.2e+53)
     t_1
     (if (<= x -3.9e-30)
       4.0
       (if (<= x -1.85e-47)
         t_1
         (if (<= x -4.5e-85)
           t_0
           (if (<= x -1.3e-112)
             4.0
             (if (<= x -2.9e-139)
               t_2
               (if (<= x -2.9e-209)
                 4.0
                 (if (<= x -4.8e-305)
                   t_2
                   (if (<= x 5.2e-166)
                     4.0
                     (if (<= x 9e-145)
                       t_0
                       (if (<= x 2300000000000.0)
                         4.0
                         (if (or (<= x 3.1e+109) (not (<= x 1.3e+169)))
                           t_1
                           4.0))))))))))))))

C

double code(double x, double y, double z) {
	double t_0 = z * (-4.0 / y);
	double t_1 = (4.0 * x) / y;
	double t_2 = z / (y / -4.0);
	double tmp;
	if (x <= -5.2e+53) {
		tmp = t_1;
	} else if (x <= -3.9e-30) {
		tmp = 4.0;
	} else if (x <= -1.85e-47) {
		tmp = t_1;
	} else if (x <= -4.5e-85) {
		tmp = t_0;
	} else if (x <= -1.3e-112) {
		tmp = 4.0;
	} else if (x <= -2.9e-139) {
		tmp = t_2;
	} else if (x <= -2.9e-209) {
		tmp = 4.0;
	} else if (x <= -4.8e-305) {
		tmp = t_2;
	} else if (x <= 5.2e-166) {
		tmp = 4.0;
	} else if (x <= 9e-145) {
		tmp = t_0;
	} else if (x <= 2300000000000.0) {
		tmp = 4.0;
	} else if ((x <= 3.1e+109) || !(x <= 1.3e+169)) {
		tmp = t_1;
	} else {
		tmp = 4.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_0 = z * ((-4.0d0) / y)
    t_1 = (4.0d0 * x) / y
    t_2 = z / (y / (-4.0d0))
    if (x <= (-5.2d+53)) then
        tmp = t_1
    else if (x <= (-3.9d-30)) then
        tmp = 4.0d0
    else if (x <= (-1.85d-47)) then
        tmp = t_1
    else if (x <= (-4.5d-85)) then
        tmp = t_0
    else if (x <= (-1.3d-112)) then
        tmp = 4.0d0
    else if (x <= (-2.9d-139)) then
        tmp = t_2
    else if (x <= (-2.9d-209)) then
        tmp = 4.0d0
    else if (x <= (-4.8d-305)) then
        tmp = t_2
    else if (x <= 5.2d-166) then
        tmp = 4.0d0
    else if (x <= 9d-145) then
        tmp = t_0
    else if (x <= 2300000000000.0d0) then
        tmp = 4.0d0
    else if ((x <= 3.1d+109) .or. (.not. (x <= 1.3d+169))) then
        tmp = t_1
    else
        tmp = 4.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = z * (-4.0 / y);
	double t_1 = (4.0 * x) / y;
	double t_2 = z / (y / -4.0);
	double tmp;
	if (x <= -5.2e+53) {
		tmp = t_1;
	} else if (x <= -3.9e-30) {
		tmp = 4.0;
	} else if (x <= -1.85e-47) {
		tmp = t_1;
	} else if (x <= -4.5e-85) {
		tmp = t_0;
	} else if (x <= -1.3e-112) {
		tmp = 4.0;
	} else if (x <= -2.9e-139) {
		tmp = t_2;
	} else if (x <= -2.9e-209) {
		tmp = 4.0;
	} else if (x <= -4.8e-305) {
		tmp = t_2;
	} else if (x <= 5.2e-166) {
		tmp = 4.0;
	} else if (x <= 9e-145) {
		tmp = t_0;
	} else if (x <= 2300000000000.0) {
		tmp = 4.0;
	} else if ((x <= 3.1e+109) || !(x <= 1.3e+169)) {
		tmp = t_1;
	} else {
		tmp = 4.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = z * (-4.0 / y)
	t_1 = (4.0 * x) / y
	t_2 = z / (y / -4.0)
	tmp = 0
	if x <= -5.2e+53:
		tmp = t_1
	elif x <= -3.9e-30:
		tmp = 4.0
	elif x <= -1.85e-47:
		tmp = t_1
	elif x <= -4.5e-85:
		tmp = t_0
	elif x <= -1.3e-112:
		tmp = 4.0
	elif x <= -2.9e-139:
		tmp = t_2
	elif x <= -2.9e-209:
		tmp = 4.0
	elif x <= -4.8e-305:
		tmp = t_2
	elif x <= 5.2e-166:
		tmp = 4.0
	elif x <= 9e-145:
		tmp = t_0
	elif x <= 2300000000000.0:
		tmp = 4.0
	elif (x <= 3.1e+109) or not (x <= 1.3e+169):
		tmp = t_1
	else:
		tmp = 4.0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(z * Float64(-4.0 / y))
	t_1 = Float64(Float64(4.0 * x) / y)
	t_2 = Float64(z / Float64(y / -4.0))
	tmp = 0.0
	if (x <= -5.2e+53)
		tmp = t_1;
	elseif (x <= -3.9e-30)
		tmp = 4.0;
	elseif (x <= -1.85e-47)
		tmp = t_1;
	elseif (x <= -4.5e-85)
		tmp = t_0;
	elseif (x <= -1.3e-112)
		tmp = 4.0;
	elseif (x <= -2.9e-139)
		tmp = t_2;
	elseif (x <= -2.9e-209)
		tmp = 4.0;
	elseif (x <= -4.8e-305)
		tmp = t_2;
	elseif (x <= 5.2e-166)
		tmp = 4.0;
	elseif (x <= 9e-145)
		tmp = t_0;
	elseif (x <= 2300000000000.0)
		tmp = 4.0;
	elseif ((x <= 3.1e+109) || !(x <= 1.3e+169))
		tmp = t_1;
	else
		tmp = 4.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = z * (-4.0 / y);
	t_1 = (4.0 * x) / y;
	t_2 = z / (y / -4.0);
	tmp = 0.0;
	if (x <= -5.2e+53)
		tmp = t_1;
	elseif (x <= -3.9e-30)
		tmp = 4.0;
	elseif (x <= -1.85e-47)
		tmp = t_1;
	elseif (x <= -4.5e-85)
		tmp = t_0;
	elseif (x <= -1.3e-112)
		tmp = 4.0;
	elseif (x <= -2.9e-139)
		tmp = t_2;
	elseif (x <= -2.9e-209)
		tmp = 4.0;
	elseif (x <= -4.8e-305)
		tmp = t_2;
	elseif (x <= 5.2e-166)
		tmp = 4.0;
	elseif (x <= 9e-145)
		tmp = t_0;
	elseif (x <= 2300000000000.0)
		tmp = 4.0;
	elseif ((x <= 3.1e+109) || ~((x <= 1.3e+169)))
		tmp = t_1;
	else
		tmp = 4.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(z * N[(-4.0 / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(4.0 * x), $MachinePrecision] / y), $MachinePrecision]}, Block[{t$95$2 = N[(z / N[(y / -4.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -5.2e+53], t$95$1, If[LessEqual[x, -3.9e-30], 4.0, If[LessEqual[x, -1.85e-47], t$95$1, If[LessEqual[x, -4.5e-85], t$95$0, If[LessEqual[x, -1.3e-112], 4.0, If[LessEqual[x, -2.9e-139], t$95$2, If[LessEqual[x, -2.9e-209], 4.0, If[LessEqual[x, -4.8e-305], t$95$2, If[LessEqual[x, 5.2e-166], 4.0, If[LessEqual[x, 9e-145], t$95$0, If[LessEqual[x, 2300000000000.0], 4.0, If[Or[LessEqual[x, 3.1e+109], N[Not[LessEqual[x, 1.3e+169]], $MachinePrecision]], t$95$1, 4.0]]]]]]]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 5: 53.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{z}{\frac{y}{-4}}\\ t_1 := 1 + 4 \cdot \frac{x}{y}\\ \mathbf{if}\;x \leq -8.8 \cdot 10^{+33}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -0.027:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -6.7 \cdot 10^{-49}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.1 \cdot 10^{-112}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq -5 \cdot 10^{-139}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -4.8 \cdot 10^{-210}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq -2.7 \cdot 10^{-305}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 3.9 \cdot 10^{-165}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq 1.2 \cdot 10^{-137}:\\ \;\;\;\;z \cdot \frac{-4}{y}\\ \mathbf{elif}\;x \leq 14600000:\\ \;\;\;\;4\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ z (/ y -4.0))) (t_1 (+ 1.0 (* 4.0 (/ x y)))))
   (if (<= x -8.8e+33)
     t_1
     (if (<= x -0.027)
       t_0
       (if (<= x -6.7e-49)
         t_1
         (if (<= x -1.1e-112)
           4.0
           (if (<= x -5e-139)
             t_0
             (if (<= x -4.8e-210)
               4.0
               (if (<= x -2.7e-305)
                 t_0
                 (if (<= x 3.9e-165)
                   4.0
                   (if (<= x 1.2e-137)
                     (* z (/ -4.0 y))
                     (if (<= x 14600000.0) 4.0 t_1))))))))))))

C

double code(double x, double y, double z) {
	double t_0 = z / (y / -4.0);
	double t_1 = 1.0 + (4.0 * (x / y));
	double tmp;
	if (x <= -8.8e+33) {
		tmp = t_1;
	} else if (x <= -0.027) {
		tmp = t_0;
	} else if (x <= -6.7e-49) {
		tmp = t_1;
	} else if (x <= -1.1e-112) {
		tmp = 4.0;
	} else if (x <= -5e-139) {
		tmp = t_0;
	} else if (x <= -4.8e-210) {
		tmp = 4.0;
	} else if (x <= -2.7e-305) {
		tmp = t_0;
	} else if (x <= 3.9e-165) {
		tmp = 4.0;
	} else if (x <= 1.2e-137) {
		tmp = z * (-4.0 / y);
	} else if (x <= 14600000.0) {
		tmp = 4.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = z / (y / (-4.0d0))
    t_1 = 1.0d0 + (4.0d0 * (x / y))
    if (x <= (-8.8d+33)) then
        tmp = t_1
    else if (x <= (-0.027d0)) then
        tmp = t_0
    else if (x <= (-6.7d-49)) then
        tmp = t_1
    else if (x <= (-1.1d-112)) then
        tmp = 4.0d0
    else if (x <= (-5d-139)) then
        tmp = t_0
    else if (x <= (-4.8d-210)) then
        tmp = 4.0d0
    else if (x <= (-2.7d-305)) then
        tmp = t_0
    else if (x <= 3.9d-165) then
        tmp = 4.0d0
    else if (x <= 1.2d-137) then
        tmp = z * ((-4.0d0) / y)
    else if (x <= 14600000.0d0) then
        tmp = 4.0d0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = z / (y / -4.0);
	double t_1 = 1.0 + (4.0 * (x / y));
	double tmp;
	if (x <= -8.8e+33) {
		tmp = t_1;
	} else if (x <= -0.027) {
		tmp = t_0;
	} else if (x <= -6.7e-49) {
		tmp = t_1;
	} else if (x <= -1.1e-112) {
		tmp = 4.0;
	} else if (x <= -5e-139) {
		tmp = t_0;
	} else if (x <= -4.8e-210) {
		tmp = 4.0;
	} else if (x <= -2.7e-305) {
		tmp = t_0;
	} else if (x <= 3.9e-165) {
		tmp = 4.0;
	} else if (x <= 1.2e-137) {
		tmp = z * (-4.0 / y);
	} else if (x <= 14600000.0) {
		tmp = 4.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = z / (y / -4.0)
	t_1 = 1.0 + (4.0 * (x / y))
	tmp = 0
	if x <= -8.8e+33:
		tmp = t_1
	elif x <= -0.027:
		tmp = t_0
	elif x <= -6.7e-49:
		tmp = t_1
	elif x <= -1.1e-112:
		tmp = 4.0
	elif x <= -5e-139:
		tmp = t_0
	elif x <= -4.8e-210:
		tmp = 4.0
	elif x <= -2.7e-305:
		tmp = t_0
	elif x <= 3.9e-165:
		tmp = 4.0
	elif x <= 1.2e-137:
		tmp = z * (-4.0 / y)
	elif x <= 14600000.0:
		tmp = 4.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(z / Float64(y / -4.0))
	t_1 = Float64(1.0 + Float64(4.0 * Float64(x / y)))
	tmp = 0.0
	if (x <= -8.8e+33)
		tmp = t_1;
	elseif (x <= -0.027)
		tmp = t_0;
	elseif (x <= -6.7e-49)
		tmp = t_1;
	elseif (x <= -1.1e-112)
		tmp = 4.0;
	elseif (x <= -5e-139)
		tmp = t_0;
	elseif (x <= -4.8e-210)
		tmp = 4.0;
	elseif (x <= -2.7e-305)
		tmp = t_0;
	elseif (x <= 3.9e-165)
		tmp = 4.0;
	elseif (x <= 1.2e-137)
		tmp = Float64(z * Float64(-4.0 / y));
	elseif (x <= 14600000.0)
		tmp = 4.0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = z / (y / -4.0);
	t_1 = 1.0 + (4.0 * (x / y));
	tmp = 0.0;
	if (x <= -8.8e+33)
		tmp = t_1;
	elseif (x <= -0.027)
		tmp = t_0;
	elseif (x <= -6.7e-49)
		tmp = t_1;
	elseif (x <= -1.1e-112)
		tmp = 4.0;
	elseif (x <= -5e-139)
		tmp = t_0;
	elseif (x <= -4.8e-210)
		tmp = 4.0;
	elseif (x <= -2.7e-305)
		tmp = t_0;
	elseif (x <= 3.9e-165)
		tmp = 4.0;
	elseif (x <= 1.2e-137)
		tmp = z * (-4.0 / y);
	elseif (x <= 14600000.0)
		tmp = 4.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(z / N[(y / -4.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(1.0 + N[(4.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -8.8e+33], t$95$1, If[LessEqual[x, -0.027], t$95$0, If[LessEqual[x, -6.7e-49], t$95$1, If[LessEqual[x, -1.1e-112], 4.0, If[LessEqual[x, -5e-139], t$95$0, If[LessEqual[x, -4.8e-210], 4.0, If[LessEqual[x, -2.7e-305], t$95$0, If[LessEqual[x, 3.9e-165], 4.0, If[LessEqual[x, 1.2e-137], N[(z * N[(-4.0 / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 14600000.0], 4.0, t$95$1]]]]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 6: 56.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 + -4 \cdot \frac{z}{y}\\ t_1 := 1 + 4 \cdot \frac{x}{y}\\ \mathbf{if}\;x \leq -1.7 \cdot 10^{+32}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -0.021:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -2 \cdot 10^{-47}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -2.9 \cdot 10^{-305}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 4.9 \cdot 10^{-166}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq 4.8 \cdot 10^{-137}:\\ \;\;\;\;z \cdot \frac{-4}{y}\\ \mathbf{elif}\;x \leq 330000000:\\ \;\;\;\;4\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ 1.0 (* -4.0 (/ z y)))) (t_1 (+ 1.0 (* 4.0 (/ x y)))))
   (if (<= x -1.7e+32)
     t_1
     (if (<= x -0.021)
       t_0
       (if (<= x -2e-47)
         t_1
         (if (<= x -2.9e-305)
           t_0
           (if (<= x 4.9e-166)
             4.0
             (if (<= x 4.8e-137)
               (* z (/ -4.0 y))
               (if (<= x 330000000.0) 4.0 t_1)))))))))

C

double code(double x, double y, double z) {
	double t_0 = 1.0 + (-4.0 * (z / y));
	double t_1 = 1.0 + (4.0 * (x / y));
	double tmp;
	if (x <= -1.7e+32) {
		tmp = t_1;
	} else if (x <= -0.021) {
		tmp = t_0;
	} else if (x <= -2e-47) {
		tmp = t_1;
	} else if (x <= -2.9e-305) {
		tmp = t_0;
	} else if (x <= 4.9e-166) {
		tmp = 4.0;
	} else if (x <= 4.8e-137) {
		tmp = z * (-4.0 / y);
	} else if (x <= 330000000.0) {
		tmp = 4.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = 1.0d0 + ((-4.0d0) * (z / y))
    t_1 = 1.0d0 + (4.0d0 * (x / y))
    if (x <= (-1.7d+32)) then
        tmp = t_1
    else if (x <= (-0.021d0)) then
        tmp = t_0
    else if (x <= (-2d-47)) then
        tmp = t_1
    else if (x <= (-2.9d-305)) then
        tmp = t_0
    else if (x <= 4.9d-166) then
        tmp = 4.0d0
    else if (x <= 4.8d-137) then
        tmp = z * ((-4.0d0) / y)
    else if (x <= 330000000.0d0) then
        tmp = 4.0d0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = 1.0 + (-4.0 * (z / y));
	double t_1 = 1.0 + (4.0 * (x / y));
	double tmp;
	if (x <= -1.7e+32) {
		tmp = t_1;
	} else if (x <= -0.021) {
		tmp = t_0;
	} else if (x <= -2e-47) {
		tmp = t_1;
	} else if (x <= -2.9e-305) {
		tmp = t_0;
	} else if (x <= 4.9e-166) {
		tmp = 4.0;
	} else if (x <= 4.8e-137) {
		tmp = z * (-4.0 / y);
	} else if (x <= 330000000.0) {
		tmp = 4.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 1.0 + (-4.0 * (z / y))
	t_1 = 1.0 + (4.0 * (x / y))
	tmp = 0
	if x <= -1.7e+32:
		tmp = t_1
	elif x <= -0.021:
		tmp = t_0
	elif x <= -2e-47:
		tmp = t_1
	elif x <= -2.9e-305:
		tmp = t_0
	elif x <= 4.9e-166:
		tmp = 4.0
	elif x <= 4.8e-137:
		tmp = z * (-4.0 / y)
	elif x <= 330000000.0:
		tmp = 4.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(1.0 + Float64(-4.0 * Float64(z / y)))
	t_1 = Float64(1.0 + Float64(4.0 * Float64(x / y)))
	tmp = 0.0
	if (x <= -1.7e+32)
		tmp = t_1;
	elseif (x <= -0.021)
		tmp = t_0;
	elseif (x <= -2e-47)
		tmp = t_1;
	elseif (x <= -2.9e-305)
		tmp = t_0;
	elseif (x <= 4.9e-166)
		tmp = 4.0;
	elseif (x <= 4.8e-137)
		tmp = Float64(z * Float64(-4.0 / y));
	elseif (x <= 330000000.0)
		tmp = 4.0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 1.0 + (-4.0 * (z / y));
	t_1 = 1.0 + (4.0 * (x / y));
	tmp = 0.0;
	if (x <= -1.7e+32)
		tmp = t_1;
	elseif (x <= -0.021)
		tmp = t_0;
	elseif (x <= -2e-47)
		tmp = t_1;
	elseif (x <= -2.9e-305)
		tmp = t_0;
	elseif (x <= 4.9e-166)
		tmp = 4.0;
	elseif (x <= 4.8e-137)
		tmp = z * (-4.0 / y);
	elseif (x <= 330000000.0)
		tmp = 4.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(1.0 + N[(-4.0 * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(1.0 + N[(4.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.7e+32], t$95$1, If[LessEqual[x, -0.021], t$95$0, If[LessEqual[x, -2e-47], t$95$1, If[LessEqual[x, -2.9e-305], t$95$0, If[LessEqual[x, 4.9e-166], 4.0, If[LessEqual[x, 4.8e-137], N[(z * N[(-4.0 / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 330000000.0], 4.0, t$95$1]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 7: 85.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -6.4 \cdot 10^{+34} \lor \neg \left(x \leq -0.0175 \lor \neg \left(x \leq -1.6 \cdot 10^{-47}\right) \land x \leq 3.1 \cdot 10^{-47}\right):\\ \;\;\;\;4 + x \cdot \frac{4}{y}\\ \mathbf{else}:\\ \;\;\;\;4 + \frac{z \cdot -4}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -6.4e+34)
         (not (or (<= x -0.0175) (and (not (<= x -1.6e-47)) (<= x 3.1e-47)))))
   (+ 4.0 (* x (/ 4.0 y)))
   (+ 4.0 (/ (* z -4.0) y))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -6.4e+34) || !((x <= -0.0175) || (!(x <= -1.6e-47) && (x <= 3.1e-47)))) {
		tmp = 4.0 + (x * (4.0 / y));
	} else {
		tmp = 4.0 + ((z * -4.0) / y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((x <= (-6.4d+34)) .or. (.not. (x <= (-0.0175d0)) .or. (.not. (x <= (-1.6d-47))) .and. (x <= 3.1d-47))) then
        tmp = 4.0d0 + (x * (4.0d0 / y))
    else
        tmp = 4.0d0 + ((z * (-4.0d0)) / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -6.4e+34) || !((x <= -0.0175) || (!(x <= -1.6e-47) && (x <= 3.1e-47)))) {
		tmp = 4.0 + (x * (4.0 / y));
	} else {
		tmp = 4.0 + ((z * -4.0) / y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -6.4e+34) or not ((x <= -0.0175) or (not (x <= -1.6e-47) and (x <= 3.1e-47))):
		tmp = 4.0 + (x * (4.0 / y))
	else:
		tmp = 4.0 + ((z * -4.0) / y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -6.4e+34) || !((x <= -0.0175) || (!(x <= -1.6e-47) && (x <= 3.1e-47))))
		tmp = Float64(4.0 + Float64(x * Float64(4.0 / y)));
	else
		tmp = Float64(4.0 + Float64(Float64(z * -4.0) / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -6.4e+34) || ~(((x <= -0.0175) || (~((x <= -1.6e-47)) && (x <= 3.1e-47)))))
		tmp = 4.0 + (x * (4.0 / y));
	else
		tmp = 4.0 + ((z * -4.0) / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -6.4e+34], N[Not[Or[LessEqual[x, -0.0175], And[N[Not[LessEqual[x, -1.6e-47]], $MachinePrecision], LessEqual[x, 3.1e-47]]]], $MachinePrecision]], N[(4.0 + N[(x * N[(4.0 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(4.0 + N[(N[(z * -4.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 8: 84.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4 \cdot 10^{+59}:\\ \;\;\;\;\frac{4 \cdot \left(x - z\right)}{y}\\ \mathbf{elif}\;x \leq -7.5 \cdot 10^{-7} \lor \neg \left(x \leq -1.9 \cdot 10^{-47}\right) \land x \leq 8.5 \cdot 10^{-47}:\\ \;\;\;\;4 + \frac{z \cdot -4}{y}\\ \mathbf{else}:\\ \;\;\;\;4 + x \cdot \frac{4}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -4e+59)
   (/ (* 4.0 (- x z)) y)
   (if (or (<= x -7.5e-7) (and (not (<= x -1.9e-47)) (<= x 8.5e-47)))
     (+ 4.0 (/ (* z -4.0) y))
     (+ 4.0 (* x (/ 4.0 y))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -4e+59) {
		tmp = (4.0 * (x - z)) / y;
	} else if ((x <= -7.5e-7) || (!(x <= -1.9e-47) && (x <= 8.5e-47))) {
		tmp = 4.0 + ((z * -4.0) / y);
	} else {
		tmp = 4.0 + (x * (4.0 / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-4d+59)) then
        tmp = (4.0d0 * (x - z)) / y
    else if ((x <= (-7.5d-7)) .or. (.not. (x <= (-1.9d-47))) .and. (x <= 8.5d-47)) then
        tmp = 4.0d0 + ((z * (-4.0d0)) / y)
    else
        tmp = 4.0d0 + (x * (4.0d0 / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -4e+59) {
		tmp = (4.0 * (x - z)) / y;
	} else if ((x <= -7.5e-7) || (!(x <= -1.9e-47) && (x <= 8.5e-47))) {
		tmp = 4.0 + ((z * -4.0) / y);
	} else {
		tmp = 4.0 + (x * (4.0 / y));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -4e+59:
		tmp = (4.0 * (x - z)) / y
	elif (x <= -7.5e-7) or (not (x <= -1.9e-47) and (x <= 8.5e-47)):
		tmp = 4.0 + ((z * -4.0) / y)
	else:
		tmp = 4.0 + (x * (4.0 / y))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -4e+59)
		tmp = Float64(Float64(4.0 * Float64(x - z)) / y);
	elseif ((x <= -7.5e-7) || (!(x <= -1.9e-47) && (x <= 8.5e-47)))
		tmp = Float64(4.0 + Float64(Float64(z * -4.0) / y));
	else
		tmp = Float64(4.0 + Float64(x * Float64(4.0 / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -4e+59)
		tmp = (4.0 * (x - z)) / y;
	elseif ((x <= -7.5e-7) || (~((x <= -1.9e-47)) && (x <= 8.5e-47)))
		tmp = 4.0 + ((z * -4.0) / y);
	else
		tmp = 4.0 + (x * (4.0 / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -4e+59], N[(N[(4.0 * N[(x - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision], If[Or[LessEqual[x, -7.5e-7], And[N[Not[LessEqual[x, -1.9e-47]], $MachinePrecision], LessEqual[x, 8.5e-47]]], N[(4.0 + N[(N[(z * -4.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], N[(4.0 + N[(x * N[(4.0 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 9: 51.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -9.5 \cdot 10^{+53} \lor \neg \left(x \leq 7000000\right) \land \left(x \leq 1.15 \cdot 10^{+109} \lor \neg \left(x \leq 5.4 \cdot 10^{+169}\right)\right):\\ \;\;\;\;x \cdot \frac{4}{y}\\ \mathbf{else}:\\ \;\;\;\;4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -9.5e+53)
         (and (not (<= x 7000000.0))
              (or (<= x 1.15e+109) (not (<= x 5.4e+169)))))
   (* x (/ 4.0 y))
   4.0))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -9.5e+53) || (!(x <= 7000000.0) && ((x <= 1.15e+109) || !(x <= 5.4e+169)))) {
		tmp = x * (4.0 / y);
	} else {
		tmp = 4.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((x <= (-9.5d+53)) .or. (.not. (x <= 7000000.0d0)) .and. (x <= 1.15d+109) .or. (.not. (x <= 5.4d+169))) then
        tmp = x * (4.0d0 / y)
    else
        tmp = 4.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -9.5e+53) || (!(x <= 7000000.0) && ((x <= 1.15e+109) || !(x <= 5.4e+169)))) {
		tmp = x * (4.0 / y);
	} else {
		tmp = 4.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -9.5e+53) or (not (x <= 7000000.0) and ((x <= 1.15e+109) or not (x <= 5.4e+169))):
		tmp = x * (4.0 / y)
	else:
		tmp = 4.0
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -9.5e+53) || (!(x <= 7000000.0) && ((x <= 1.15e+109) || !(x <= 5.4e+169))))
		tmp = Float64(x * Float64(4.0 / y));
	else
		tmp = 4.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -9.5e+53) || (~((x <= 7000000.0)) && ((x <= 1.15e+109) || ~((x <= 5.4e+169)))))
		tmp = x * (4.0 / y);
	else
		tmp = 4.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -9.5e+53], And[N[Not[LessEqual[x, 7000000.0]], $MachinePrecision], Or[LessEqual[x, 1.15e+109], N[Not[LessEqual[x, 5.4e+169]], $MachinePrecision]]]], N[(x * N[(4.0 / y), $MachinePrecision]), $MachinePrecision], 4.0]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 10: 80.0% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -8.8 \cdot 10^{+185} \lor \neg \left(z \leq 1.9 \cdot 10^{+160}\right):\\ \;\;\;\;1 + -4 \cdot \frac{z}{y}\\ \mathbf{else}:\\ \;\;\;\;4 + x \cdot \frac{4}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -8.8e+185) (not (<= z 1.9e+160)))
   (+ 1.0 (* -4.0 (/ z y)))
   (+ 4.0 (* x (/ 4.0 y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -8.8e+185) || !(z <= 1.9e+160)) {
		tmp = 1.0 + (-4.0 * (z / y));
	} else {
		tmp = 4.0 + (x * (4.0 / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((z <= (-8.8d+185)) .or. (.not. (z <= 1.9d+160))) then
        tmp = 1.0d0 + ((-4.0d0) * (z / y))
    else
        tmp = 4.0d0 + (x * (4.0d0 / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -8.8e+185) || !(z <= 1.9e+160)) {
		tmp = 1.0 + (-4.0 * (z / y));
	} else {
		tmp = 4.0 + (x * (4.0 / y));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -8.8e+185) or not (z <= 1.9e+160):
		tmp = 1.0 + (-4.0 * (z / y))
	else:
		tmp = 4.0 + (x * (4.0 / y))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -8.8e+185) || !(z <= 1.9e+160))
		tmp = Float64(1.0 + Float64(-4.0 * Float64(z / y)));
	else
		tmp = Float64(4.0 + Float64(x * Float64(4.0 / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -8.8e+185) || ~((z <= 1.9e+160)))
		tmp = 1.0 + (-4.0 * (z / y));
	else
		tmp = 4.0 + (x * (4.0 / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -8.8e+185], N[Not[LessEqual[z, 1.9e+160]], $MachinePrecision]], N[(1.0 + N[(-4.0 * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(4.0 + N[(x * N[(4.0 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 11: 7.5% accurate, 13.0× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 1.0)

C

double code(double x, double y, double z) {
	return 1.0;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 1.0d0
end function

Java

public static double code(double x, double y, double z) {
	return 1.0;
}

Python

def code(x, y, z):
	return 1.0

Julia

function code(x, y, z)
	return 1.0
end

MATLAB

function tmp = code(x, y, z)
	tmp = 1.0;
end

Wolfram

code[x_, y_, z_] := 1.0

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 12: 33.6% accurate, 13.0× speedup

Math

\[\begin{array}{l} \\ 4 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 4.0)

C

double code(double x, double y, double z) {
	return 4.0;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 4.0d0
end function

Java

public static double code(double x, double y, double z) {
	return 4.0;
}

Python

def code(x, y, z):
	return 4.0

Julia

function code(x, y, z)
	return 4.0
end

MATLAB

function tmp = code(x, y, z)
	tmp = 4.0;
end

Wolfram

code[x_, y_, z_] := 4.0

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Reproduce

herbie shell --seed 2023350 
(FPCore (x y z)
  :name "Data.Array.Repa.Algorithms.ColorRamp:rampColorHotToCold from repa-algorithms-3.4.0.1, A"
  :precision binary64
  (+ 1.0 (/ (* 4.0 (- (+ x (* y 0.75)) z)) y)))