Graphics.Rasterific.CubicBezier:cachedBezierAt from Rasterific-0.6.1

Percentage Accurate: 92.3% → 96.5%

Time: 8.7s

Alternatives: 12

Speedup: 0.9×

• 33.0% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (+ (+ (+ x (* y z)) (* t a)) (* (* a z) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	return ((x + (y * z)) + (t * a)) + ((a * z) * b);
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = ((x + (y * z)) + (t * a)) + ((a * z) * b)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return ((x + (y * z)) + (t * a)) + ((a * z) * b);
}

Python

def code(x, y, z, t, a, b):
	return ((x + (y * z)) + (t * a)) + ((a * z) * b)

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(x + Float64(y * z)) + Float64(t * a)) + Float64(Float64(a * z) * b))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = ((x + (y * z)) + (t * a)) + ((a * z) * b);
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision] + N[(t * a), $MachinePrecision]), $MachinePrecision] + N[(N[(a * z), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]

Initial Program: 92.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(x + y \cdot z\right) + t \cdot a\right) + \left(a \cdot z\right) \cdot b \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (+ (+ (+ x (* y z)) (* t a)) (* (* a z) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	return ((x + (y * z)) + (t * a)) + ((a * z) * b);
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = ((x + (y * z)) + (t * a)) + ((a * z) * b)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return ((x + (y * z)) + (t * a)) + ((a * z) * b);
}

Python

def code(x, y, z, t, a, b):
	return ((x + (y * z)) + (t * a)) + ((a * z) * b)

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(x + Float64(y * z)) + Float64(t * a)) + Float64(Float64(a * z) * b))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = ((x + (y * z)) + (t * a)) + ((a * z) * b);
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision] + N[(t * a), $MachinePrecision]), $MachinePrecision] + N[(N[(a * z), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]

Alternative 1: 96.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(z \cdot a\right) \cdot b + \left(\left(x + y \cdot z\right) + t \cdot a\right)\\ \mathbf{if}\;t_1 \leq \infty:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;a \cdot \left(t + z \cdot b\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ (* (* z a) b) (+ (+ x (* y z)) (* t a)))))
   (if (<= t_1 INFINITY) t_1 (* a (+ t (* z b))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((z * a) * b) + ((x + (y * z)) + (t * a));
	double tmp;
	if (t_1 <= ((double) INFINITY)) {
		tmp = t_1;
	} else {
		tmp = a * (t + (z * b));
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = ((z * a) * b) + ((x + (y * z)) + (t * a));
	double tmp;
	if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = t_1;
	} else {
		tmp = a * (t + (z * b));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = ((z * a) * b) + ((x + (y * z)) + (t * a))
	tmp = 0
	if t_1 <= math.inf:
		tmp = t_1
	else:
		tmp = a * (t + (z * b))
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(Float64(z * a) * b) + Float64(Float64(x + Float64(y * z)) + Float64(t * a)))
	tmp = 0.0
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = Float64(a * Float64(t + Float64(z * b)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = ((z * a) * b) + ((x + (y * z)) + (t * a));
	tmp = 0.0;
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = a * (t + (z * b));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[(z * a), $MachinePrecision] * b), $MachinePrecision] + N[(N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision] + N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, Infinity], t$95$1, N[(a * N[(t + N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 2: 38.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(z \cdot b\right)\\ \mathbf{if}\;x \leq -3 \cdot 10^{+144}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq -2.1 \cdot 10^{+108}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -4.45 \cdot 10^{+74}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq -5 \cdot 10^{-191}:\\ \;\;\;\;t \cdot a\\ \mathbf{elif}\;x \leq -3.5 \cdot 10^{-212}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;x \leq 2 \cdot 10^{-232}:\\ \;\;\;\;t \cdot a\\ \mathbf{elif}\;x \leq 1.02 \cdot 10^{-93}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;x \leq 0.031:\\ \;\;\;\;t \cdot a\\ \mathbf{elif}\;x \leq 2.8 \cdot 10^{+68}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* a (* z b))))
   (if (<= x -3e+144)
     x
     (if (<= x -2.1e+108)
       t_1
       (if (<= x -4.45e+74)
         x
         (if (<= x -5e-191)
           (* t a)
           (if (<= x -3.5e-212)
             (* y z)
             (if (<= x 2e-232)
               (* t a)
               (if (<= x 1.02e-93)
                 (* y z)
                 (if (<= x 0.031) (* t a) (if (<= x 2.8e+68) t_1 x)))))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (z * b);
	double tmp;
	if (x <= -3e+144) {
		tmp = x;
	} else if (x <= -2.1e+108) {
		tmp = t_1;
	} else if (x <= -4.45e+74) {
		tmp = x;
	} else if (x <= -5e-191) {
		tmp = t * a;
	} else if (x <= -3.5e-212) {
		tmp = y * z;
	} else if (x <= 2e-232) {
		tmp = t * a;
	} else if (x <= 1.02e-93) {
		tmp = y * z;
	} else if (x <= 0.031) {
		tmp = t * a;
	} else if (x <= 2.8e+68) {
		tmp = t_1;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = a * (z * b)
    if (x <= (-3d+144)) then
        tmp = x
    else if (x <= (-2.1d+108)) then
        tmp = t_1
    else if (x <= (-4.45d+74)) then
        tmp = x
    else if (x <= (-5d-191)) then
        tmp = t * a
    else if (x <= (-3.5d-212)) then
        tmp = y * z
    else if (x <= 2d-232) then
        tmp = t * a
    else if (x <= 1.02d-93) then
        tmp = y * z
    else if (x <= 0.031d0) then
        tmp = t * a
    else if (x <= 2.8d+68) then
        tmp = t_1
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (z * b);
	double tmp;
	if (x <= -3e+144) {
		tmp = x;
	} else if (x <= -2.1e+108) {
		tmp = t_1;
	} else if (x <= -4.45e+74) {
		tmp = x;
	} else if (x <= -5e-191) {
		tmp = t * a;
	} else if (x <= -3.5e-212) {
		tmp = y * z;
	} else if (x <= 2e-232) {
		tmp = t * a;
	} else if (x <= 1.02e-93) {
		tmp = y * z;
	} else if (x <= 0.031) {
		tmp = t * a;
	} else if (x <= 2.8e+68) {
		tmp = t_1;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (z * b)
	tmp = 0
	if x <= -3e+144:
		tmp = x
	elif x <= -2.1e+108:
		tmp = t_1
	elif x <= -4.45e+74:
		tmp = x
	elif x <= -5e-191:
		tmp = t * a
	elif x <= -3.5e-212:
		tmp = y * z
	elif x <= 2e-232:
		tmp = t * a
	elif x <= 1.02e-93:
		tmp = y * z
	elif x <= 0.031:
		tmp = t * a
	elif x <= 2.8e+68:
		tmp = t_1
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a * Float64(z * b))
	tmp = 0.0
	if (x <= -3e+144)
		tmp = x;
	elseif (x <= -2.1e+108)
		tmp = t_1;
	elseif (x <= -4.45e+74)
		tmp = x;
	elseif (x <= -5e-191)
		tmp = Float64(t * a);
	elseif (x <= -3.5e-212)
		tmp = Float64(y * z);
	elseif (x <= 2e-232)
		tmp = Float64(t * a);
	elseif (x <= 1.02e-93)
		tmp = Float64(y * z);
	elseif (x <= 0.031)
		tmp = Float64(t * a);
	elseif (x <= 2.8e+68)
		tmp = t_1;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a * (z * b);
	tmp = 0.0;
	if (x <= -3e+144)
		tmp = x;
	elseif (x <= -2.1e+108)
		tmp = t_1;
	elseif (x <= -4.45e+74)
		tmp = x;
	elseif (x <= -5e-191)
		tmp = t * a;
	elseif (x <= -3.5e-212)
		tmp = y * z;
	elseif (x <= 2e-232)
		tmp = t * a;
	elseif (x <= 1.02e-93)
		tmp = y * z;
	elseif (x <= 0.031)
		tmp = t * a;
	elseif (x <= 2.8e+68)
		tmp = t_1;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(a * N[(z * b), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -3e+144], x, If[LessEqual[x, -2.1e+108], t$95$1, If[LessEqual[x, -4.45e+74], x, If[LessEqual[x, -5e-191], N[(t * a), $MachinePrecision], If[LessEqual[x, -3.5e-212], N[(y * z), $MachinePrecision], If[LessEqual[x, 2e-232], N[(t * a), $MachinePrecision], If[LessEqual[x, 1.02e-93], N[(y * z), $MachinePrecision], If[LessEqual[x, 0.031], N[(t * a), $MachinePrecision], If[LessEqual[x, 2.8e+68], t$95$1, x]]]]]]]]]]

Derivation

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

Alternative 3: 81.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + a \cdot \left(t + z \cdot b\right)\\ \mathbf{if}\;a \leq -4.3 \cdot 10^{-68}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 2.1 \cdot 10^{-88}:\\ \;\;\;\;x + y \cdot z\\ \mathbf{elif}\;a \leq 9.8 \cdot 10^{-20}:\\ \;\;\;\;z \cdot \left(y + a \cdot b\right)\\ \mathbf{elif}\;a \leq 1350 \lor \neg \left(a \leq 4.2 \cdot 10^{+18}\right):\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t \cdot a + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ x (* a (+ t (* z b))))))
   (if (<= a -4.3e-68)
     t_1
     (if (<= a 2.1e-88)
       (+ x (* y z))
       (if (<= a 9.8e-20)
         (* z (+ y (* a b)))
         (if (or (<= a 1350.0) (not (<= a 4.2e+18)))
           t_1
           (+ (* t a) (* y z))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (a * (t + (z * b)));
	double tmp;
	if (a <= -4.3e-68) {
		tmp = t_1;
	} else if (a <= 2.1e-88) {
		tmp = x + (y * z);
	} else if (a <= 9.8e-20) {
		tmp = z * (y + (a * b));
	} else if ((a <= 1350.0) || !(a <= 4.2e+18)) {
		tmp = t_1;
	} else {
		tmp = (t * a) + (y * z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + (a * (t + (z * b)))
    if (a <= (-4.3d-68)) then
        tmp = t_1
    else if (a <= 2.1d-88) then
        tmp = x + (y * z)
    else if (a <= 9.8d-20) then
        tmp = z * (y + (a * b))
    else if ((a <= 1350.0d0) .or. (.not. (a <= 4.2d+18))) then
        tmp = t_1
    else
        tmp = (t * a) + (y * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = x + (a * (t + (z * b)));
	double tmp;
	if (a <= -4.3e-68) {
		tmp = t_1;
	} else if (a <= 2.1e-88) {
		tmp = x + (y * z);
	} else if (a <= 9.8e-20) {
		tmp = z * (y + (a * b));
	} else if ((a <= 1350.0) || !(a <= 4.2e+18)) {
		tmp = t_1;
	} else {
		tmp = (t * a) + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = x + (a * (t + (z * b)))
	tmp = 0
	if a <= -4.3e-68:
		tmp = t_1
	elif a <= 2.1e-88:
		tmp = x + (y * z)
	elif a <= 9.8e-20:
		tmp = z * (y + (a * b))
	elif (a <= 1350.0) or not (a <= 4.2e+18):
		tmp = t_1
	else:
		tmp = (t * a) + (y * z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(x + Float64(a * Float64(t + Float64(z * b))))
	tmp = 0.0
	if (a <= -4.3e-68)
		tmp = t_1;
	elseif (a <= 2.1e-88)
		tmp = Float64(x + Float64(y * z));
	elseif (a <= 9.8e-20)
		tmp = Float64(z * Float64(y + Float64(a * b)));
	elseif ((a <= 1350.0) || !(a <= 4.2e+18))
		tmp = t_1;
	else
		tmp = Float64(Float64(t * a) + Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = x + (a * (t + (z * b)));
	tmp = 0.0;
	if (a <= -4.3e-68)
		tmp = t_1;
	elseif (a <= 2.1e-88)
		tmp = x + (y * z);
	elseif (a <= 9.8e-20)
		tmp = z * (y + (a * b));
	elseif ((a <= 1350.0) || ~((a <= 4.2e+18)))
		tmp = t_1;
	else
		tmp = (t * a) + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(x + N[(a * N[(t + N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -4.3e-68], t$95$1, If[LessEqual[a, 2.1e-88], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 9.8e-20], N[(z * N[(y + N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Or[LessEqual[a, 1350.0], N[Not[LessEqual[a, 4.2e+18]], $MachinePrecision]], t$95$1, N[(N[(t * a), $MachinePrecision] + N[(y * z), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

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

Alternative 4: 38.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.9 \cdot 10^{+75}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq -3.8 \cdot 10^{-191}:\\ \;\;\;\;t \cdot a\\ \mathbf{elif}\;x \leq -3.4 \cdot 10^{-207}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;x \leq 1.16 \cdot 10^{-231}:\\ \;\;\;\;t \cdot a\\ \mathbf{elif}\;x \leq 1.6 \cdot 10^{-90}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;x \leq 2.2 \cdot 10^{+23}:\\ \;\;\;\;t \cdot a\\ \mathbf{elif}\;x \leq 5.2 \cdot 10^{+68}:\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= x -2.9e+75)
   x
   (if (<= x -3.8e-191)
     (* t a)
     (if (<= x -3.4e-207)
       (* y z)
       (if (<= x 1.16e-231)
         (* t a)
         (if (<= x 1.6e-90)
           (* y z)
           (if (<= x 2.2e+23) (* t a) (if (<= x 5.2e+68) (* y z) x))))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (x <= -2.9e+75) {
		tmp = x;
	} else if (x <= -3.8e-191) {
		tmp = t * a;
	} else if (x <= -3.4e-207) {
		tmp = y * z;
	} else if (x <= 1.16e-231) {
		tmp = t * a;
	} else if (x <= 1.6e-90) {
		tmp = y * z;
	} else if (x <= 2.2e+23) {
		tmp = t * a;
	} else if (x <= 5.2e+68) {
		tmp = y * z;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (x <= (-2.9d+75)) then
        tmp = x
    else if (x <= (-3.8d-191)) then
        tmp = t * a
    else if (x <= (-3.4d-207)) then
        tmp = y * z
    else if (x <= 1.16d-231) then
        tmp = t * a
    else if (x <= 1.6d-90) then
        tmp = y * z
    else if (x <= 2.2d+23) then
        tmp = t * a
    else if (x <= 5.2d+68) then
        tmp = y * z
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (x <= -2.9e+75) {
		tmp = x;
	} else if (x <= -3.8e-191) {
		tmp = t * a;
	} else if (x <= -3.4e-207) {
		tmp = y * z;
	} else if (x <= 1.16e-231) {
		tmp = t * a;
	} else if (x <= 1.6e-90) {
		tmp = y * z;
	} else if (x <= 2.2e+23) {
		tmp = t * a;
	} else if (x <= 5.2e+68) {
		tmp = y * z;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if x <= -2.9e+75:
		tmp = x
	elif x <= -3.8e-191:
		tmp = t * a
	elif x <= -3.4e-207:
		tmp = y * z
	elif x <= 1.16e-231:
		tmp = t * a
	elif x <= 1.6e-90:
		tmp = y * z
	elif x <= 2.2e+23:
		tmp = t * a
	elif x <= 5.2e+68:
		tmp = y * z
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (x <= -2.9e+75)
		tmp = x;
	elseif (x <= -3.8e-191)
		tmp = Float64(t * a);
	elseif (x <= -3.4e-207)
		tmp = Float64(y * z);
	elseif (x <= 1.16e-231)
		tmp = Float64(t * a);
	elseif (x <= 1.6e-90)
		tmp = Float64(y * z);
	elseif (x <= 2.2e+23)
		tmp = Float64(t * a);
	elseif (x <= 5.2e+68)
		tmp = Float64(y * z);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (x <= -2.9e+75)
		tmp = x;
	elseif (x <= -3.8e-191)
		tmp = t * a;
	elseif (x <= -3.4e-207)
		tmp = y * z;
	elseif (x <= 1.16e-231)
		tmp = t * a;
	elseif (x <= 1.6e-90)
		tmp = y * z;
	elseif (x <= 2.2e+23)
		tmp = t * a;
	elseif (x <= 5.2e+68)
		tmp = y * z;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[x, -2.9e+75], x, If[LessEqual[x, -3.8e-191], N[(t * a), $MachinePrecision], If[LessEqual[x, -3.4e-207], N[(y * z), $MachinePrecision], If[LessEqual[x, 1.16e-231], N[(t * a), $MachinePrecision], If[LessEqual[x, 1.6e-90], N[(y * z), $MachinePrecision], If[LessEqual[x, 2.2e+23], N[(t * a), $MachinePrecision], If[LessEqual[x, 5.2e+68], N[(y * z), $MachinePrecision], x]]]]]]]

Derivation

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

Alternative 5: 85.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(x + y \cdot z\right) + t \cdot a\\ t_2 := x + a \cdot \left(t + z \cdot b\right)\\ \mathbf{if}\;a \leq -1.55 \cdot 10^{-16}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;a \leq 2.1 \cdot 10^{-88}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 2.85 \cdot 10^{-18}:\\ \;\;\;\;z \cdot \left(y + a \cdot b\right)\\ \mathbf{elif}\;a \leq 9.5 \cdot 10^{+23}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ (+ x (* y z)) (* t a))) (t_2 (+ x (* a (+ t (* z b))))))
   (if (<= a -1.55e-16)
     t_2
     (if (<= a 2.1e-88)
       t_1
       (if (<= a 2.85e-18) (* z (+ y (* a b))) (if (<= a 9.5e+23) t_1 t_2))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + (y * z)) + (t * a);
	double t_2 = x + (a * (t + (z * b)));
	double tmp;
	if (a <= -1.55e-16) {
		tmp = t_2;
	} else if (a <= 2.1e-88) {
		tmp = t_1;
	} else if (a <= 2.85e-18) {
		tmp = z * (y + (a * b));
	} else if (a <= 9.5e+23) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (x + (y * z)) + (t * a)
    t_2 = x + (a * (t + (z * b)))
    if (a <= (-1.55d-16)) then
        tmp = t_2
    else if (a <= 2.1d-88) then
        tmp = t_1
    else if (a <= 2.85d-18) then
        tmp = z * (y + (a * b))
    else if (a <= 9.5d+23) then
        tmp = t_1
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x + (y * z)) + (t * a);
	double t_2 = x + (a * (t + (z * b)));
	double tmp;
	if (a <= -1.55e-16) {
		tmp = t_2;
	} else if (a <= 2.1e-88) {
		tmp = t_1;
	} else if (a <= 2.85e-18) {
		tmp = z * (y + (a * b));
	} else if (a <= 9.5e+23) {
		tmp = t_1;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (x + (y * z)) + (t * a)
	t_2 = x + (a * (t + (z * b)))
	tmp = 0
	if a <= -1.55e-16:
		tmp = t_2
	elif a <= 2.1e-88:
		tmp = t_1
	elif a <= 2.85e-18:
		tmp = z * (y + (a * b))
	elif a <= 9.5e+23:
		tmp = t_1
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x + Float64(y * z)) + Float64(t * a))
	t_2 = Float64(x + Float64(a * Float64(t + Float64(z * b))))
	tmp = 0.0
	if (a <= -1.55e-16)
		tmp = t_2;
	elseif (a <= 2.1e-88)
		tmp = t_1;
	elseif (a <= 2.85e-18)
		tmp = Float64(z * Float64(y + Float64(a * b)));
	elseif (a <= 9.5e+23)
		tmp = t_1;
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (x + (y * z)) + (t * a);
	t_2 = x + (a * (t + (z * b)));
	tmp = 0.0;
	if (a <= -1.55e-16)
		tmp = t_2;
	elseif (a <= 2.1e-88)
		tmp = t_1;
	elseif (a <= 2.85e-18)
		tmp = z * (y + (a * b));
	elseif (a <= 9.5e+23)
		tmp = t_1;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision] + N[(t * a), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(a * N[(t + N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.55e-16], t$95$2, If[LessEqual[a, 2.1e-88], t$95$1, If[LessEqual[a, 2.85e-18], N[(z * N[(y + N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 9.5e+23], t$95$1, t$95$2]]]]]]

Derivation

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

Alternative 6: 92.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -2.05 \cdot 10^{+101}:\\ \;\;\;\;x + a \cdot \left(t + z \cdot b\right)\\ \mathbf{else}:\\ \;\;\;\;\left(x + y \cdot z\right) + \left(a \cdot \left(z \cdot b\right) + t \cdot a\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= a -2.05e+101)
   (+ x (* a (+ t (* z b))))
   (+ (+ x (* y z)) (+ (* a (* z b)) (* t a)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (a <= -2.05e+101) {
		tmp = x + (a * (t + (z * b)));
	} else {
		tmp = (x + (y * z)) + ((a * (z * b)) + (t * a));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (a <= (-2.05d+101)) then
        tmp = x + (a * (t + (z * b)))
    else
        tmp = (x + (y * z)) + ((a * (z * b)) + (t * a))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (a <= -2.05e+101) {
		tmp = x + (a * (t + (z * b)));
	} else {
		tmp = (x + (y * z)) + ((a * (z * b)) + (t * a));
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if a <= -2.05e+101:
		tmp = x + (a * (t + (z * b)))
	else:
		tmp = (x + (y * z)) + ((a * (z * b)) + (t * a))
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (a <= -2.05e+101)
		tmp = Float64(x + Float64(a * Float64(t + Float64(z * b))));
	else
		tmp = Float64(Float64(x + Float64(y * z)) + Float64(Float64(a * Float64(z * b)) + Float64(t * a)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (a <= -2.05e+101)
		tmp = x + (a * (t + (z * b)));
	else
		tmp = (x + (y * z)) + ((a * (z * b)) + (t * a));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[a, -2.05e+101], N[(x + N[(a * N[(t + N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision] + N[(N[(a * N[(z * b), $MachinePrecision]), $MachinePrecision] + N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 7: 73.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := a \cdot \left(t + z \cdot b\right)\\ \mathbf{if}\;a \leq -3.2 \cdot 10^{+41}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;a \leq 2.1 \cdot 10^{-88}:\\ \;\;\;\;x + y \cdot z\\ \mathbf{elif}\;a \leq 4.5 \cdot 10^{-18}:\\ \;\;\;\;z \cdot \left(y + a \cdot b\right)\\ \mathbf{elif}\;a \leq 1.72 \cdot 10^{+24}:\\ \;\;\;\;x + t \cdot a\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* a (+ t (* z b)))))
   (if (<= a -3.2e+41)
     t_1
     (if (<= a 2.1e-88)
       (+ x (* y z))
       (if (<= a 4.5e-18)
         (* z (+ y (* a b)))
         (if (<= a 1.72e+24) (+ x (* t a)) t_1))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (t + (z * b));
	double tmp;
	if (a <= -3.2e+41) {
		tmp = t_1;
	} else if (a <= 2.1e-88) {
		tmp = x + (y * z);
	} else if (a <= 4.5e-18) {
		tmp = z * (y + (a * b));
	} else if (a <= 1.72e+24) {
		tmp = x + (t * a);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = a * (t + (z * b))
    if (a <= (-3.2d+41)) then
        tmp = t_1
    else if (a <= 2.1d-88) then
        tmp = x + (y * z)
    else if (a <= 4.5d-18) then
        tmp = z * (y + (a * b))
    else if (a <= 1.72d+24) then
        tmp = x + (t * a)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = a * (t + (z * b));
	double tmp;
	if (a <= -3.2e+41) {
		tmp = t_1;
	} else if (a <= 2.1e-88) {
		tmp = x + (y * z);
	} else if (a <= 4.5e-18) {
		tmp = z * (y + (a * b));
	} else if (a <= 1.72e+24) {
		tmp = x + (t * a);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = a * (t + (z * b))
	tmp = 0
	if a <= -3.2e+41:
		tmp = t_1
	elif a <= 2.1e-88:
		tmp = x + (y * z)
	elif a <= 4.5e-18:
		tmp = z * (y + (a * b))
	elif a <= 1.72e+24:
		tmp = x + (t * a)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(a * Float64(t + Float64(z * b)))
	tmp = 0.0
	if (a <= -3.2e+41)
		tmp = t_1;
	elseif (a <= 2.1e-88)
		tmp = Float64(x + Float64(y * z));
	elseif (a <= 4.5e-18)
		tmp = Float64(z * Float64(y + Float64(a * b)));
	elseif (a <= 1.72e+24)
		tmp = Float64(x + Float64(t * a));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a * (t + (z * b));
	tmp = 0.0;
	if (a <= -3.2e+41)
		tmp = t_1;
	elseif (a <= 2.1e-88)
		tmp = x + (y * z);
	elseif (a <= 4.5e-18)
		tmp = z * (y + (a * b));
	elseif (a <= 1.72e+24)
		tmp = x + (t * a);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(a * N[(t + N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -3.2e+41], t$95$1, If[LessEqual[a, 2.1e-88], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 4.5e-18], N[(z * N[(y + N[(a * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 1.72e+24], N[(x + N[(t * a), $MachinePrecision]), $MachinePrecision], t$95$1]]]]]

Derivation

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

Alternative 8: 73.9% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -6.2 \cdot 10^{+41} \lor \neg \left(a \leq 850\right):\\ \;\;\;\;a \cdot \left(t + z \cdot b\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= a -6.2e+41) (not (<= a 850.0)))
   (* a (+ t (* z b)))
   (+ x (* y z))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((a <= -6.2e+41) || !(a <= 850.0)) {
		tmp = a * (t + (z * b));
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((a <= (-6.2d+41)) .or. (.not. (a <= 850.0d0))) then
        tmp = a * (t + (z * b))
    else
        tmp = x + (y * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((a <= -6.2e+41) || !(a <= 850.0)) {
		tmp = a * (t + (z * b));
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (a <= -6.2e+41) or not (a <= 850.0):
		tmp = a * (t + (z * b))
	else:
		tmp = x + (y * z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((a <= -6.2e+41) || !(a <= 850.0))
		tmp = Float64(a * Float64(t + Float64(z * b)));
	else
		tmp = Float64(x + Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((a <= -6.2e+41) || ~((a <= 850.0)))
		tmp = a * (t + (z * b));
	else
		tmp = x + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[a, -6.2e+41], N[Not[LessEqual[a, 850.0]], $MachinePrecision]], N[(a * N[(t + N[(z * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 9: 58.8% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -7.5 \cdot 10^{+86} \lor \neg \left(a \leq 3.8 \cdot 10^{+97}\right):\\ \;\;\;\;t \cdot a\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= a -7.5e+86) (not (<= a 3.8e+97))) (* t a) (+ x (* y z))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((a <= -7.5e+86) || !(a <= 3.8e+97)) {
		tmp = t * a;
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((a <= (-7.5d+86)) .or. (.not. (a <= 3.8d+97))) then
        tmp = t * a
    else
        tmp = x + (y * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((a <= -7.5e+86) || !(a <= 3.8e+97)) {
		tmp = t * a;
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (a <= -7.5e+86) or not (a <= 3.8e+97):
		tmp = t * a
	else:
		tmp = x + (y * z)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((a <= -7.5e+86) || !(a <= 3.8e+97))
		tmp = Float64(t * a);
	else
		tmp = Float64(x + Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((a <= -7.5e+86) || ~((a <= 3.8e+97)))
		tmp = t * a;
	else
		tmp = x + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[a, -7.5e+86], N[Not[LessEqual[a, 3.8e+97]], $MachinePrecision]], N[(t * a), $MachinePrecision], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 10: 62.8% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.3 \cdot 10^{+133} \lor \neg \left(z \leq 3.2 \cdot 10^{-51}\right):\\ \;\;\;\;x + y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x + t \cdot a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (or (<= z -4.3e+133) (not (<= z 3.2e-51))) (+ x (* y z)) (+ x (* t a))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -4.3e+133) || !(z <= 3.2e-51)) {
		tmp = x + (y * z);
	} else {
		tmp = x + (t * a);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((z <= (-4.3d+133)) .or. (.not. (z <= 3.2d-51))) then
        tmp = x + (y * z)
    else
        tmp = x + (t * a)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((z <= -4.3e+133) || !(z <= 3.2e-51)) {
		tmp = x + (y * z);
	} else {
		tmp = x + (t * a);
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (z <= -4.3e+133) or not (z <= 3.2e-51):
		tmp = x + (y * z)
	else:
		tmp = x + (t * a)
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if ((z <= -4.3e+133) || !(z <= 3.2e-51))
		tmp = Float64(x + Float64(y * z));
	else
		tmp = Float64(x + Float64(t * a));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((z <= -4.3e+133) || ~((z <= 3.2e-51)))
		tmp = x + (y * z);
	else
		tmp = x + (t * a);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[Or[LessEqual[z, -4.3e+133], N[Not[LessEqual[z, 3.2e-51]], $MachinePrecision]], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision], N[(x + N[(t * a), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 11: 39.4% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.5 \cdot 10^{+75}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 1.3 \cdot 10^{+29}:\\ \;\;\;\;t \cdot a\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= x -2.5e+75) x (if (<= x 1.3e+29) (* t a) x)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (x <= -2.5e+75) {
		tmp = x;
	} else if (x <= 1.3e+29) {
		tmp = t * a;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (x <= (-2.5d+75)) then
        tmp = x
    else if (x <= 1.3d+29) then
        tmp = t * a
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (x <= -2.5e+75) {
		tmp = x;
	} else if (x <= 1.3e+29) {
		tmp = t * a;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if x <= -2.5e+75:
		tmp = x
	elif x <= 1.3e+29:
		tmp = t * a
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (x <= -2.5e+75)
		tmp = x;
	elseif (x <= 1.3e+29)
		tmp = Float64(t * a);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (x <= -2.5e+75)
		tmp = x;
	elseif (x <= 1.3e+29)
		tmp = t * a;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[x, -2.5e+75], x, If[LessEqual[x, 1.3e+29], N[(t * a), $MachinePrecision], x]]

Derivation

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

Alternative 12: 25.5% accurate, 15.0× speedup

Math

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

FPCore

(FPCore (x y z t a b) :precision binary64 x)

C

double code(double x, double y, double z, double t, double a, double b) {
	return x;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = x
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return x;
}

Python

def code(x, y, z, t, a, b):
	return x

Julia

function code(x, y, z, t, a, b)
	return x
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = x;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := x

Derivation

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

Developer target: 97.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(b \cdot a + y\right) + \left(x + t \cdot a\right)\\ \mathbf{if}\;z < -11820553527347888000:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z < 4.7589743188364287 \cdot 10^{-122}:\\ \;\;\;\;\left(b \cdot z + t\right) \cdot a + \left(z \cdot y + x\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ (* z (+ (* b a) y)) (+ x (* t a)))))
   (if (< z -11820553527347888000.0)
     t_1
     (if (< z 4.7589743188364287e-122)
       (+ (* (+ (* b z) t) a) (+ (* z y) x))
       t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z * ((b * a) + y)) + (x + (t * a));
	double tmp;
	if (z < -11820553527347888000.0) {
		tmp = t_1;
	} else if (z < 4.7589743188364287e-122) {
		tmp = (((b * z) + t) * a) + ((z * y) + x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a, b)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (z * ((b * a) + y)) + (x + (t * a))
    if (z < (-11820553527347888000.0d0)) then
        tmp = t_1
    else if (z < 4.7589743188364287d-122) then
        tmp = (((b * z) + t) * a) + ((z * y) + x)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (z * ((b * a) + y)) + (x + (t * a));
	double tmp;
	if (z < -11820553527347888000.0) {
		tmp = t_1;
	} else if (z < 4.7589743188364287e-122) {
		tmp = (((b * z) + t) * a) + ((z * y) + x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (z * ((b * a) + y)) + (x + (t * a))
	tmp = 0
	if z < -11820553527347888000.0:
		tmp = t_1
	elif z < 4.7589743188364287e-122:
		tmp = (((b * z) + t) * a) + ((z * y) + x)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(z * Float64(Float64(b * a) + y)) + Float64(x + Float64(t * a)))
	tmp = 0.0
	if (z < -11820553527347888000.0)
		tmp = t_1;
	elseif (z < 4.7589743188364287e-122)
		tmp = Float64(Float64(Float64(Float64(b * z) + t) * a) + Float64(Float64(z * y) + x));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (z * ((b * a) + y)) + (x + (t * a));
	tmp = 0.0;
	if (z < -11820553527347888000.0)
		tmp = t_1;
	elseif (z < 4.7589743188364287e-122)
		tmp = (((b * z) + t) * a) + ((z * y) + x);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(z * N[(N[(b * a), $MachinePrecision] + y), $MachinePrecision]), $MachinePrecision] + N[(x + N[(t * a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Less[z, -11820553527347888000.0], t$95$1, If[Less[z, 4.7589743188364287e-122], N[(N[(N[(N[(b * z), $MachinePrecision] + t), $MachinePrecision] * a), $MachinePrecision] + N[(N[(z * y), $MachinePrecision] + x), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Reproduce

herbie shell --seed 2023348 
(FPCore (x y z t a b)
  :name "Graphics.Rasterific.CubicBezier:cachedBezierAt from Rasterific-0.6.1"
  :precision binary64

  :herbie-target
  (if (< z -11820553527347888000.0) (+ (* z (+ (* b a) y)) (+ x (* t a))) (if (< z 4.7589743188364287e-122) (+ (* (+ (* b z) t) a) (+ (* z y) x)) (+ (* z (+ (* b a) y)) (+ x (* t a)))))

  (+ (+ (+ x (* y z)) (* t a)) (* (* a z) b)))